Communication Systems and Information Technology, Volume 4

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Author: Ming Ma

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Lecture Notes in Electrical Engineering Volume 100

Ming Ma (Ed.)

Communication Systems and Information Technology Selected Papers from the 2011 International Conference on Electric and Electronics (EEIC 2011) in Nanchang, China on June 20-22, 2011, Volume 4

ABC

Ming Ma NUS ACM Chapter 81 Victoria Street, Singapore 188065, Singapore E-mail: [email protected]

ISBN 978-3-642-21761-6

e-ISBN 978-3-642-21762-3

DOI 10.1007/978-3-642-21762-3 Lecture Notes in Electrical Engineering

ISSN 1876-1100

Library of Congress Control Number: 2011929654 c 2011 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microﬁlm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typeset & Cover Design: Scientiﬁc Publishing Services Pvt. Ltd., Chennai, India. Printed on acid-free paper 987654321 springer.com

EEIC 2011 Preface

The present book includes extended and revised versions of a set of selected papers from the International Conference on Electric and Electronics (EEIC 2011), held on June 20–22, 2011, which is jointly organized by Nanchang University, Springer, and IEEE IAS Nanchang Chapter. The goal of EEIC 2011 is to bring together the researchers from academia and industry as well as practitioners to share ideas, problems and solutions relating to the multifaceted aspects of Electric and Electronics. Being crucial for the development of Electric and Electronics, our conference encompasses a large number of research topics and applications: from Circuits and Systems to Computers and Information Technology; from Communication Systems to Signal Processing and other related topics are included in the scope of this conference. In order to ensure high-quality of our international conference, we have high-quality reviewing course, our reviewing experts are from home and abroad and low-quality papers have been refused. All accepted papers will be published by Lecture Notes in Electrical Engineering (Springer). EEIC 2011 is sponsored by Nanchang University, China. Nanchang University is a comprehensive university which characterized by "Penetration of Arts, Science, Engineering and Medicine subjects, Combination of studying, research and production". It is one of the national "211" Project key universities that jointly constructed by the People's Government of Jiangxi Province and the Ministry of Education. It is also an important base of talents cultivation, scientific researching and transferring of the researching accomplishment into practical use for both Jiangxi Province and the country. Welcome to Nanchang, China. Nanchang is a beautiful city with the Gan River, the mother river of local people, traversing through the whole city. Water is her soul or in other words water carries all her beauty. Lakes and rivers in or around Nanchang bring a special kind of charm to the city. Nanchang is honored as 'a green pearl in the southern part of China' thanks to its clear water, fresh air and great inner city virescence. Long and splendid history endows Nanchang with many cultural relics, among which the Tengwang Pavilion is the most famous. It is no exaggeration to say that Tengwang Pavilion is the pride of all the locals in Nanchang. Many men of letters left their handwritings here which tremendously enhance its classical charm. Noting can be done without the help of the program chairs, organization staff, and the members of the program committees. Thank you. EEIC 2011 will be the most comprehensive Conference focused on the various aspects of advances in Electric and Electronics. Our Conference provides a chance for academic and industry professionals to discuss recent progress in the area of Electric and Electronics. We are confident that the conference program will give you detailed insight into the new trends, and we are looking forward to meeting you at this worldclass event in Nanchang.

EEIC 2011 Organization

Honor Chairs Prof. Chin-Chen Chang Prof. Jun Wang

Feng Chia University, Taiwan Chinese University of Hong Kong, HongKong

Scholarship Committee Chairs Chin-Chen Chang Jun Wang

Feng Chia University, Taiwan Chinese University of Hong Kong, HongKong

Scholarship Committee Co- chairs Zhi Hu Min Zhu

IEEE IAS Nanchang Chapter, China IEEE IAS Nanchang Chapter, China

Organizing Co-chairs Jian Lee Wensong Hu

Hubei Normal University, China Nanchang University, China

Program Committee Chairs Honghua Tan

Wuhan Institute of Technology, China

Publication Chairs Wensong Hu Zhu Min Xiaofeng Wan Ming Ma

Nanchang University, China Nanchang University, China Nanchang University, China NUS ACM Chapter, Singapore

Contents

The Design and Implementation of DDR PHY Static Low-Power Optimization Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Ge, Mengnan Zhao, Cheng Wu, Jun He Design on Triple Bi-directional DC/DC Converter Used for Power Flow Control of Energy Storage in Wind Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanlei Zhao, Naiyong Xia, Housheng Zhang ESL Based SoC System Bandwidth Estimation Method . . . . . . . . Zhen Xie, Xinning Liu, Weiwei Shan, Wei Ge Cellular Automaton for Super-Paramagnetic Clustering of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhang Botao, Zhang Shuqiang, Yu Zhongqiu Embedded Memory Wrapper Based on IEEE 1500 Standard . . . . Maryam Songhorzadeh, Rahebeh Niaraki Asli A SVPWM Control Strategy for Neutral Point Potential Compensation in Three-Level Inverter . . . . . . . . . . . . . . . . . . . . . . . . . . Jinsong Kang, Yichuan Niu Process Mining: A Block-Structured Mining Approach . . . . . . . . . Yan-liang Qu, Tie-shi Zhao China RoHS: How the Changing Regulatory Landscape Is Aﬀecting Process Equipment Reliability . . . . . . . . . . . . . . . . . . . . . . . . Chris Muller, Henry Yu Building Process Models Based on Interval Logs . . . . . . . . . . . . . . . . Yan-liang Qu, Tie-shi Zhao

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Fostering a Management Model of Librarians at Vocational College e-Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ling-Feng Hsieh, Mu-Chen Wu, Jiung-Bin Chin

79

Extension 2D to 3D of FAN Transform in Image . . . . . . . . . . . . . . . . Fan Jing, Xuan Ying, Li Honglian

87

A Design of Low Cost Infrared Multi-touch System . . . . . . . . . . . . . Juan Wu, Yao-hui Hu, Guo-qiang Lv, Jing Yin

95

A Review of Research on Network-on-Chip Simulator . . . . . . . . . . 103 Haiyun Gu Design Methodology of Dynamically Reconﬁgurable Network-on-Chip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Haiyun Gu Numerical Simulation Study on a Flat-Plate Solar Air Collector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Wenfang Li, Shuhui Xu, Haiguang Dong, Jun You Artiﬁcial Immune for Harmful Information Filtering . . . . . . . . . . . . 125 Yan Sun, Xue guang Zhou Identiﬁcation and Pre-distortion for GaN-PA . . . . . . . . . . . . . . . . . . . 133 Yuanming Ding, Yan Wang, Akira Sano On-Line Monitoring System of Methane Reaction Generator . . . 141 Ma Baoji Security Flaws in Two RFID Lightweight Authentication Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Wang Shaohui Study on Mechanical Parameters in Finite Element Analysis of Children’s Orbital-Bone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Weiyuan Lu, Zhixiang Liu, Xiuqing Qian, Tingting Ning, Huagang Yan 3D Model Retrieval Based on Multi-View SIFT Feature . . . . . . . . 163 Shungang Hua, Qiuxin Jiang, Qing Zhong Current Issues and Future Trends in Analysis of Automotive Functional Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 Chunyang Mu, Xing Ma, Hongxing Ma, Xianlian Huang, Ling Zhang, Rong Fan Development of an Automatic Steering System for Electric Power Steering (EPS) System Using Fuzzy Control Theory . . . . . 179 Tao Hong, Xijun Zhao, Yong Zhai

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Design and Implementation of Interactive Digital Shadow Simulation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 Qian Li, Qing-yi Hua, Jun Feng, Wei Niu, Hao Wang, Jie Zhong Weighing Machine Modiﬁcations for Purblind and Sightless People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Vladimir Kasik, Martin Stankus Active RFID Based Infant Security System . . . . . . . . . . . . . . . . . . . . . 203 Lin Lin, Nan Yu, Tao Wang, Changan Zhan Modiﬁed Segmentation Prony Algorithm and Its Application in Analysis of Subsynchronous Oscillation . . . . . . . . . . . . . . . . . . . . . . 211 Yujiong Gu, Dongchao Chen, Tiezheng Jin, Zhizheng Ren Group Control Strategy of Welding Machine Based on Improved Active Set Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Chen Shujin, Zhang Junjun Simulation and Experiment Research on the Eﬀects of DC-Bias Current on the 500kV Power Transformer . . . . . . . . . . . . . 227 Feng-hua Wang, Jun Zhang, Cheng-yu Gu, Zhi-jian Jin A HID Lamp Model in Simulink Based on the Principle of Electric Arc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Xiaohan Guan, Zhongpeng Li Coordinated Control for Complex Dynamic Interconnected Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 Xin-yu Ouyang, Xue-bo Chen Study on Calibration of Transfer Character of Ultrasonic Transducer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Qiufeng Li, Quanhong Zhang, Min Zhao, Lihua Shi A Novel Non-contact Pulse Information Detection Method Based on the Infrared Sequence Images . . . . . . . . . . . . . . . . . . . . . . . . . 259 Weibin Zhou, Bin Jing, Dian Qu, Guihong Yuan, Chunyan Wang, Haiyun Li A Method of Neurons Classiﬁcation and Identiﬁcation . . . . . . . . . . 267 Xingfu Li, Donghuan Lv A Broadband Image-Rejection Sub-harmonically Pumped Mixer MMIC for Ka-band Applications . . . . . . . . . . . . . . . . . . . . . . . . 275 Fang-Yue Ma, Yang-Yang Peng, Xiao-Ying Wang, Wen-Quan Sui

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Study of Phase-Shift Laser Ranging on Travelling Crane Anti-collision System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 Bisheng Cao, Zhou Wan, Zhongguo Jing, Xin Xiong Bearing Condition Monitoring and Fault Diagnosis of a Wind Turbine Using Parameter Free Detection . . . . . . . . . . . . . . . . . . . . . . . 289 Shenggang Yang, Xiaoli Li, Ming Liang An Approach of K-Barrier Coverage of WSN for Mine . . . . . . . . . 295 Qianping Wang, Liangying Wang, Rui Zhou, Dong Jiang Design and Implementation of Intrusion Detection System . . . . . 303 Tai-ping Mo, Jian-hua Wang Self Tuning PID Controller for Main Steam Temperature in the Power Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 Amin Aeenmehr, Alireza Sina A Novel Transformerless Single-Phase Three-Level Photovoltaic Inverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Xiaoguang Yang, Youhua Wang Image Reconstruction Algorithm Based on Fixed-Point Iteration for Electrical Capacitance Tomography . . . . . . . . . . . . . . . . 325 Cuihuan Li, Xiaoguang Yang, Youhua Wang Research on Data Preprocessing in Exam Analysis System . . . . . 333 Ming-hua Zhu The Development and Application of Environmental Art Project Which Based on Semiotics in Information Age . . . . . . . . . 339 Ke Zuo Rotor Time Constant Estimation for the Vector Controlled Induction Motor Drive Based on MARS Scheme . . . . . . . . . . . . . . . 345 Hua Li, Shunyuan Zhou Small-World Request Routing System in CDNs . . . . . . . . . . . . . . . . . 353 Lan Li Experimental Study on Simulated Cerebral Edema Detection with PSSMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Gui Jin, Mingxin Qin, Chao Wang, Wanyou Guo, Lin Xu, Xu Ning, Jia Xu, Dandan Gao Medium Choice of Chinese Consumers in Obtaining Advertising Information about Minitype Automobile . . . . . . . . . . . 369 Dao-ping Chen

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The Finite Element Modeling for Mechanical Feature Analysis of Human Lumbar L4-L5 Segment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379 Juying Huang, Haiyun Li, Hao Wu Hybrid Control Using Sampling PI and Fuzzy Control Methods for Large Inertia and Time Delay System . . . . . . . . . . . . . 387 Jia Xie, Shengdun Zhao, Zhenghui Sha, Jintao Liang An Operator Controllable Authentication and Charging Solution for Application Store . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 Jianbing Xing, Zhaoxia Li, Xiongwei Jia, Zizhi Qiao A Modiﬁed Kalman Filter for Non-gaussian Measurement Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Muhmmad J. Mirza A Novel Framework for Active Detection of HTTP Based Attacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 Liang Jie, Sun Jianwei, Hu Changzhen Model Predictive Control for Single Phase Inverters . . . . . . . . . . . . 419 Yecheng Lv, Ningxiang Xie, Kai Wang Evaluation Method and Standard on Maintainability of Cockpit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427 Junwei Zhao, Lin Zhou, Haitao Zhao, Xuming Mao, Youchao Sun Smart Mobile User Adaptive System with Autonomous Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 Ondrej Krejcar, Robert Frischer Reduction of Player’s Weight by Active Playing Using Motion Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 Ondrej Krejcar, Dalibor Janckulik Use of Neural Networks Library for Material Defect Detection Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 Ondrej Krejcar Multi-sensor Measurement Fusion via Adaptive State Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455 Li-Wei Fong A Simple Automatic Outlier Regions Detection . . . . . . . . . . . . . . . . . 463 Kitti Koonsanit The Feature Parameters Algorithm of Digital Signal in Circuit Based on Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471 Xiaodong Ma, Guangyan Zhao, Yufeng Sun

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Research of Stereo Matching Based on Improved Median Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479 Huiyan Jiang, Rui Gao, Xiaojie Liu Generating Method of Three-Phase Voltage Sag in EV Charging System Performance Testing . . . . . . . . . . . . . . . . . . . . . . . . . . 487 Xiaoming Yue, Hui Fan, Jin Pan, Xiaoguang Hao, Zhimeng Zhang Improved Predictive Control of Grid-Connected PV Inverter with LCL Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497 Huijie Xue, Wei Feng, Zilong Yang, Chunsheng Wu, Honghua Xu Study on Application of Demand Prognosticating Model Based on Grey Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503 Han Qingtian, Li Lian, Zhang Yi Research on Bayes Reliability Assessment for Test Data . . . . . . . . 509 Han Qingtian, Li Lian, Cui Jia Improved Particle Swarm Optimization by Updating Constraints of PID Control for Real Time Linear Motor Positioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 Ying-Hao Li, Yi-Cheng Huang, Jen-Ai Chao Small Form-Factor Driver for Power LEDs Powered with One Element Battery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525 Garate Jose Ignacio, de Diego Jose Miguel, Araujo Jose Angel The Empirical Analysis of the Impact of Industrial Structure on Carbon Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533 Mei Liao, Tianhao Wu An Ant-Routing Algorithm for Wireless Sensor Networks . . . . . . 541 Pengliu Tan, Xiaojun Deng Transient Sensitivity Computations for Large-Scale MOSFET Circuits Using Waveform Relaxation and Adaptive Direct Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549 Chen Chun-Jung Incremental Circuit Simulation for Large-Scale MOSFET Circuits with Interconnects Using Iterated Timing Analysis . . . . 557 Chen Chun-Jung Inversion of Array Lateral-Logging Based on LSSVM . . . . . . . . . . . 563 Yu Kong, Li Zhang, Linwei Feng, Yueqin Dun

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Voice Recognition Based on the Theory of Transmission Wave and LSSVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 Yu Kong, Yue Min, Jing Xiao Design of a Two-Phase Adiabatic Content-Addressable Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577 Meng-Chou Chang, Yen-Ting Kuo Branch Importance Assessment under Cut-Oﬀ Power Flow Based on EM Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585 Feng Yuan, Wang Li-ming, Xia Li, Bu Le-ping, Shao Ying Knowledge Discovery of Energy Management System Based on Prism, FURIA and J48 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593 Feng Yuan, Xia Li, Wang Li-ming, Pu Le-ping, Shao Ying Study on the Medium-Term and Long-Term Forecast Technology of Wind Farm Power Generation . . . . . . . . . . . . . . . . . . . 601 Yang Gao, Li Liu, Guoyan Liang, Shihai Ma, Chenwei Tian Optimal Space Vector Modulation Control for Three-Phase Inverter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609 Su Xiaofang, Rao Binbin, Li Shijie, Zheng Ruilan Optimal Dead-Time Elimination for Voltage Source Inverters . . . 617 Su Xiaofang, Rao Binbin, Zeng Yongsheng Research of the Inﬂuence Factors in Single-Phase Inverter Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625 Xiaofang Su, Binbin Rao, Chong Chen, Junbo Liu The Inspiration of the Economic Restructuring in Ruhr of Germany to the Sustainable Development of Mining Cities in Henan Province of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633 Wang Liping Design of Metal Pipe Defect Detection Device Based on Electromagnetic Guided Wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639 Shuwang Chen, Deliang Li, Zhangsui Xu Implement of Explosion-Proof and Intrinsic Safe Model Mult-protocol Converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645 Ming-san Ouyang, Cheng-jie Zhu, Zhe Liang The Application in Mobile Computing of Spatial Association Rules Mining Algorithm Based on Separating Support Items . . . 651 Gang Fang

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Automatic Test Case Generation for Web Applications Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657 Lulu Sun, Junyi Li, Shenglan Liu Design of Holmium Laser Treatment Instrument Control System Based on STM32 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667 Youjie Zhou, Chunhua Xiong, Changbo Lu Research on Attribute Granular Computing and Its Fuzziﬁcation Method Based on Qualitative Mapping . . . . . . . . . . . . 673 Ru Qi Zhou, Yi Lin Wu, Yi Qun Chen A CT Image Denoise Method Using Curvelet Transform . . . . . . . 681 Junmin Deng, Haiyun Li, Hao Wu Research System Optimization of the Wireless Mesh Networks Based on WLAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 689 Jin Wen Stabilization Control of Chaotic System Based on LaSalle Invariant Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697 Chunchao Shi, Yanqiu Che, Jiang Wang, Xile Wei, Bin Deng, Chunxiao Han Parameter Estimation in a Class of Chaotic System via Adaptive Steady State Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705 Chunchao Shi, Yanqiu Che, Chunxiao Han, Jiang Wang, Bin Deng, Xile Wei Algorithms of 3D Segmentation and Reconstruction Based on Teeth CBCT Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 713 Wenjun Zhang A Calculation Method for Series Lagging Correction Based on Root Locus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 721 Li Wang The Computer Simulation and Real-Time Control for the Inverted Pendulum System Based on PID . . . . . . . . . . . . . . . . . . . . . . 729 Yong Xin, Bo Xu, Hui Xin, Jian Xu, Lingyan Hu Application of Fractal Dimension in Analysis of Soil Micro Pores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 737 Shu-hua Zhang Assessment of Loess Collapsibility with GRNN . . . . . . . . . . . . . . . . . 745 Shu-hua Zhang

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Grey Comprehensive Relational Analysis of Fighting Eﬃciency Inﬂuencing Factors of AEW Radar . . . . . . . . . . . . . . . . . . . 753 Guangdong Liang, Guangshan Lu, An Zhang, Yanbin Shi Diesel Misﬁre Fault Diagnosis Using Vibration Signal over Cylinder Head . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 761 Liu Li-tian, Liao Hong-yun, Chen Xiang-long, Feng Yong-min, Xiao Yun-kui Proof of a Conjecture about k-Graceful Graph . . . . . . . . . . . . . . . . . . 769 Li Wuzhuang, Yan Qiantai High Dynamic GPS Signal Analysis and Acquisition Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773 Xiaoming Han, Guangyu Zheng, Shusheng Peng On the Mutual Information and Capacity of Coded MIMO with Interference Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 781 Haitao Li, Haiying Yuan Interdisciplinary Education Model of Fashion Marketing and E-Commerce . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 789 Zhaoyan, Renli Research on the Image Registration Algorithm Based on Regional Feature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795 Xiaoyan Cao, Xiangxin Shao, Xinying Li, Chunying Wang, Yan Zhou Accurate Curvature Approximation of 3-Dimension Discrete Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803 Shilin Zhou, Jianping Yin, Xiaolin Yang, Junyun Wu The Research of a Symmetrical Component Method and Dynamic Reactive Power Compensation of Electric Arc Furnace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 811 Xuezhe Che, Fenglong Shen, Jianhui Wang Numerical Simulation of Electrokinetic Flow in a Nanotube with Variable Physical Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819 Davood D. Ganji, Moﬁd Gorji-Bandpy, Mehdi Mostoﬁ Physical Properties of Camellia Oleifera Heated by Micro-wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 827 Jian Zhou, Lijun Li, Yihua Hu, Ke Cheng, Zhiming Yang, Ye Xue Research on Trust-Based Dynamic Role Access Control Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 833 Dongping Hu, Guohua Cui, Aihua Yin, Liang Chen

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Design of Special Welding Machine Based on Open CNC System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 839 Haibo Lin Optimization Algorithm on the Intelligence Schedule of Pubic Traﬃc Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 845 Cui Yonghong, Wang Qingrong The Criterion of Intrinsic Safe Circuit Characteristic Based on Electric Arc Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 853 Ligong Wang Multiple View Locality Preserving Projections with Pairwise Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 859 Xuesong Yin, Qi Huang, Xiaodong Chen Analysis of E-SCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 867 Huang Hua, Peng Cong Information Sharing of Partnership in Supply Chain and Enhancement of Core Competitiveness of Enterprises . . . . . . . . . . . 875 Huang Hua, Peng Cong Novel Spatial Diversity Equalizer Suitable for 16-QAM Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 885 Rao Wei Fuzzy Logic Controller for a Pneumatic Rotary Actuator . . . . . . . 893 Logah Perumal Evolutionary Neural Network Based on Immune Continuous Ant Colony Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 903 Gao Wei Reliable and Delay-Eﬃcient Routing Algorithm in Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 911 Mohammad Ali Jabraeil Jamali, Adel Fathi, Javad Pashaei Interfacial Impedance Sensor Employing Bio-activated Microbeads and NiHCF-Coated Interdigitated Microelectrodes: A Model Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 921 Nuno M.M. Pires, Tao Dong, Zhaochu Yang, Lei Zhang An Improved Fp-Tree Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 929 Haijun Zhang, Changchang Zhang, Bo Zhang

Contents

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A Decoupling Method for Evaluating Lightning-Induced Overvoltages on One Certain Line of an Overhead Transmission Lines System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937 Dongming Li, Cheng Wang, Xiaohu Liu Power Flow Calculation in the Voltage Output Stage . . . . . . . . . . . 945 Sun Qiuye, Li Zhongxu, Ma Dazhong, Zhou Jianguo Research on Trajectory Control of Polishing Robot Based on Secondary Deceleration and Dynamic Model . . . . . . . . . . . . . . . . . . . 953 Tongying Guo, Languang Zhao, Haichen Wang Security of Industrial Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . 959 Peng Jie, Liu Li The Application of Automatic Control Theory in Software Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 965 Wensong Hu, Xingui Yang, Min Zhu, Yan Zhang Portable MIT-BIH Physiological Signal Playback System Based on Android . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 969 Wensong Hu, Li Ming, Min Zhu, Linglin Xia The Application of Network Model on Power Station . . . . . . . . . . . 975 Min Zhu, Wensong Hu, Yan Zhang The Application of Algorithm in Power Station . . . . . . . . . . . . . . . . . 981 Min Zhu, Wensong Hu, Yan Zhang The Application of IP/UDP Protocols in 51 Microcontrollers . . . 987 Wensong Hu, Yuyuan Zhu, Min Zhu, Ke Zuo A Collaborative Filtering Recommendation Algorithm Based on User Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 993 Daner Chen The Personalized Recommendation Algorithm Based on Item Semantic Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 999 Yulong Ying Application Research of Four-Branch Antenna Design by Improved Orthogonal Multi-objective Evolutionary Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005 Jincui Guo, Xiaojuan Zhao, Jinxin Zou

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Research on Aviation Forewarning Systems Based on Business Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 Liu Yongjun, Hu Qing, Ruan Wenjuan The Need for Teleradiology System in Medical Remote-Diagnosis Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1021 Mohammad H. Al-Taei, Ziyad T. Abdul-Mehdi, Subhi H. Hamdoon The Clustering Methods Based on the Most Similar Relation Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1027 Yan Yu, Wei Hong Xu, Yu Shan Bai, Min Zhu Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035

The Design and Implementation of DDR PHY Static * Low-Power Optimization Strategies Wei Ge1, Mengnan Zhao1, Cheng Wu1, and Jun He2 1 National ASIC System Engineering Research Center, Southeast University, Nanjing 210096 People's Republic of China {duiker,zmn,wcccc}@seu.edu.cn 2 Huawei Technol. Co., Ltd., Nanjing, China [email protected]

，

Abstract. The static power of DDR PHY has increasingly become the limit of the low-power application of system-on-a-chip (SoC). An optimization of static power based on "behavior" and "state" of DDR PHY static power is proposed, considering the design principle and physical properties. Experimental results show that the proposed optimization strategy can achieve the highest 59.12% reduction in work mode and only 0.723uW power consumption in sleep mode. Keywords: DDR PHY, DDR PAD, Static Power, UPF.

1 Introduction The power consumption of DDR controller has been drawn more and more attention while providing faster transfer rate and higher data bandwidth. At present, low power consumption of DDR PHY research has the following two aspects. 1) Improve the control logic to minimize the number of row opens and closes, and lower the energy consumption during read/write operations [1]. Reshape the memory traffic to coalesce short idle periods into longer ones, thus enabling existing techniques to effectively exploit idleness in the memory [2]. 2) Improve the design of DDR PHY by using the direct clock pulses to latch the data, without a need for additional pulse generator circuitry for the clock signal, which lowers the clock dynamic power consumption by factor of 2x [3]. Employ a clock branch-sharing scheme to reduce the number of clocked transistors in the design. The newly proposed design also employs conditional discharge and split-patch techniques to further reduce switching activity and short-circuit currents, respectively [4]. The research above optimized the access method and the structure design, but did not consider the DDR PHY static power of activities and non-activities. We propose innovatively low-power strategies based on "behavior" and "state" which effectively reduce the DDR PHY static power consumption of activities and nonactivities. *

This work was sponsored by the National Scientific Foundation of China (Grant No. 61006029) and Jiangsu Scientific Foundation (Grant No. BK2010165).

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 1–6. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Design Principle In order to reduce the DDR PHY static power consumption in work mode and support the ultra-low-power mode, both the DDR PAD and the DDR PHY need to be optimized according to different "behavior" and "state". 2.1 Principle of DDR PHY Low-Power Based on Behavior DDR PHY does the jobs of exchanging data with external chip, multilevel conversion, DQS phase shift, etc., and its static Power consumption is mainly comprised of Leakage Power, which does not change with frequency. Static power consumption formula is expressed as:

Psta = Vdd * I SUB

，which I

SUB

W =C V e L 2 ox th

VGS −VT nVth

(1)

From the formula (1), the static power would be close to zero by cutting off the Vdd, thus greatly reducing the static power consumption of DDR PHY.

Fig. 1. Optimization design of DDR PAD

DDR PAD shows high static power consumption under no flipping circumstances. Therefore, the control signals OutputShutDown (OSD) and InputShutDown (ISD) are added to cut off the DDR PAD output and input leakage paths respectively, as shown in figure (1). So that, the leakage current will decrease to nearly zero. 2.2 Principle of DDR PHY Low-Power Based on State The UPF language provides a new way to specify the power requirements of a design and specifies how to create a power supply network to each design element, the behavior of supply nets with respect to each other, and how the logic functionality is extended to support dynamic power switching to design elements.

The Design and Implementation of DDR PHY Static Low-Power Optimization Strategies

3

In order to switch chip from sleep mode to work mode rapidly, the DDR controller should be special designed. "Sub Power Domain" need to be controlled respectively, as shown in figure (2), DDR PHY Domain (DDRPD) and DDR Retention Domain (DDRRD) separated from the power network on chip are provided different power domain optimization methods by the controller in the Always on Domain (AOD).

Fig. 2. DDR PHY power domain design base on UPF

It makes ultra-low-power available by designing different working states for each power domain: 1. NORMAL: each power domain works normally. DDR PHY reduces power consumption through frequencies adjusting. 2. STOP: the kernel of SoC is powered down, and DDRPD power domain works normally, meanwhile the DLL of DDR PHY (Delay Locked Loop) works in Bypass Mode. 3. SLEEP: only AOD and DDRRD power domain are active, DDR PHY offers the least control signals, to ensure the data integrity of the external memory chip.

3 Implementation of Low-Power Design 3.1 DDR PAD Static Optimization Strategy Based on Behavior Based on the design of the DDR interface timing specified in JEDEC specifications, we propose various optimization strategies for different behavior (command, data) operations, which control the switch of DDR PAD static leakage path. For the unidirectional command signals, CMD_OSD always shut down the input channel, and the output path is switched by the CMD_ISD signal when the command is valid. For the bidirectional data signals, DATA_OSD and DATA_ISD make effect only in the valid data operating phase. In order to realize these functions, low-power control logic is added to generate the control signals (OSD and ISD), which make relative responses according to each DDR operation such as read, write, refresh, activate, etc. At the same time, considering the recovery time of PADs after enabling OSD and ISD, there must be enough margin to satisfy the various frequencies requirement in different working states. Figure (3) shows the HSIM simulation result of OSD and ISD under the condition of tsmc 65nm standard library (1.2v Core Vdd, 1.8v PAD Vdd, 25°C). After the validation of OSD and ISD, the PAD current between the power (vmvddq) and ground (vmvssq) changes correspondingly. When the OSD and ISD are valid, the data transfers between

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the chip external and the pad internal. On the contrary, the data path is been cut off. There is a slight current disturbance, which will not impact the function.

Fig. 3. HSIM simulation of DDR PAD

3.2 DDR PHY Power Management Based on State In NORMAL mode, the DDR PHY works normally in SoC. In STOP and SLEEP mode, the external memory chip enters the Self Refresh or Power Down mode, and the DDR PHY need guarantee the control signals (CKE, RESETB) valid to avoid data loss. The kernel of SoC is powered down in STOP mode, so the registers in the DDR PHY are used for latching the command and data signals. In SLEEP mode, except the AOD and DDRRD power domains, the logic of the DDR PHY controller and most of the IO PADs are powered down. For this reason, the signal control and isolation should be taken into consideration in the DDRRD power domain. The detail design of DDRRD is shown in Figure (4). The independent power supply network is separated by ISOLATION PAD, so the DDRRD can work without being influenced when other parts of the chip are powered down. The RETLEC PAD provides internal control signal in SLEEP mode, which automatically lapses when the chip wakes up. The CKE PAD and RESETB PAD provide the necessary control signals for the external memory chip.

Fig. 4. PAD placement of DDR Retention Domain

The Design and Implementation of DDR PHY Static Low-Power Optimization Strategies

5

4 Design Result The DDR PHY static power consumption optimization strategies, based on “behavior” and “state”, can significantly reduce power consumption in work and standby modes. By using tsmc 65nm standard library and Prime Time PX power analysis tools, we can obtain DDR PHY power consumption simulation results in Table (1). The available work modes are defined as follows: SLEEP: the power domains except the AOD and DDRRD are powered down and the chip is in “sleep” state. STOP: the kernel is powered down, only the necessary interrupt applications are reserved to wake up the system. IDLE: the kernel clock is gated, and the chip enters the standby state. NOMAL: chip works normally, typical applications of computing and display work properly. FASTHOT: chip works in high speed, most of the scientific computing, image processing and display applications occupy the memory bandwidth as much as possible. Table 1. DDR PHY power consumption comparison in defferent mode

STATE SLEEP STOP IDLE NORMAL FASTHOT

DDR_PHY_OLD(mW) NULL NULL 227.73 314.84 385.92

DDR_PHY(mW) 0.000723 3.6 92.95 218.4 312

Optimization(%) NULL NULL 59.18 30.63 19.15

Through the above comparison of power consumption simulation in different modes, we can see that the power optimization of the DDR PHY changes antilinear with increasing of the memory access load. The power optimization ratio in FASTHOT mode and IDLE mode are up to 19.15%and 59.18%. Through the low-power domain design based on the “state”, power consumption on DDR PHY just reaches 3.6mW in STOP mode, and ultra-low-power of 0.723uW in SLEEP mode.

5 Conclusions This paper proposed static low-power optimization strategies based on “behavior” and “state”, which effectively reduce the power consumption of DDR PHY in both working state and sleep state. The simulation results show that the strategies not only improve the DDR PHY power characteristic in different operation modes (19.15%~59.18%), but also provide ultra-low-power application methods for SoC (only 0.723uW in SLEEP mode).

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References 1. Dongwook, L., Sungjoo, Y., Kiyoung, C.: Entry control in network-on-chip for memory power reduction. In: International Symposium on Low Power Electronics and Design (ISLPED), pp. 171–176. ACM/IEEE (2008) 2. Hai, H., Shin, K.G., Lefurgy, C., Keller, T.: Improving energy efficiency by making DRAM less randomly accessed. In: International Symposium on Low Power Electronics and Design, pp. 393–398 (2005) 3. Devarapalli, S.V., Zarkesh-Ha, P., Suddarth, S.C.: A robust and low power dual data rate (DDR) flip-flop using c-elements. In: 11th International Symposium on Quality Electronic Design, pp. 147–150 (2010) 4. Peiyi, Z., McNeely, J., Golconda, P., Bayoumi, M.A., Barcenas, R.A., Weidong, K.: LowPower Clock Branch Sharing Double-Edge Triggered Flip-Flop. IEEE Transaction on Very Large Scale Integration (VLSI) Systems 15, 338–345 (2007)

Design on Triple Bi-directional DC/DC Converter Used for Power Flow Control of Energy Storage in Wind Power System* Yanlei Zhao, Naiyong Xia, and Housheng Zhang School of Electrical and Electronic Engineering, Shandong University of Technology Zhangdian District, Zibo, Shandong Province, China [email protected]

Abstract. A triple bi-directional DC/DC converter used for power flow control of energy storage in wind power system is designed. Firstly, the paper analyzes energy flow characteristic of wind power system containing hybrid energy storage (the battery and the supercapacitor). Secondly, working principle of the triple bi-directional DC/DC converter is explained and mathematical model is constructed. Then, based on the model, control unit consisting of voltage outer loop and current inner loop is designed. To ensure the consistency of working state of batteries in parallel, a novel current-sharing strategy is proposed based on battery SOC (state of charge). To realize energy distribution between the battery and the supercapacitor, a one-order low pass filter is used to detach the low frequency component in feedback voltage. The simulation results show that the converter can effectively regulate the load voltage and reasonably distribute the power between both energy storage elements. Keywords: wind power system; energy storage; power flow optimization; bidirectional DC/DC converter.

1 Introduction The ransom fluctuation of output power of wind power system changes grid power flow frequently in terms of direction and size, which will lead to voltage instability, frequency fluctuation and some other power quality problems, threaten the security and stability of power system. Consequently, wind power penetration level in power grid is confined. Equipping with certain amount of energy storage in wind power system can resolve the problems mentioned above. Battery energy storage system has been widely used in renewable energy sources power system, which can smooth power fluctuation of the generation system, enhance dispatching ability of the generation system, improve static and dynamic characteristic of the grid[1]. However, the battery, if solely used in wind power system, has its inherent shortcomings: Firstly, the battery charges and discharges continually, which reduces *

Project Supported by National Natural Science Foundation of China (50807034).

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 7–14. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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cycle life of the battery; secondly, the battery has small power density and low dynamic response speed, so the battery has no method to compensate many dynamic power quality problems occurring in short time span. The supercapacitor, as a new fast energy storage element, has high power density and long cycle life[2]. The supercapacitor, combinated with the traditional battery and used for power conditioning or power flow optimization in wind power system, can take full advantage of the battery’s superiority of high energy density and the supercapacitor’s superiority of high power density. As a result, energy storage unit in wind power system can be optimized in respects of its performance and cost. The combination of the supercapacitor and the battery generally has three patterns, including direct parallel connection, parallel connection through the inductor and parallel connection through the power converter (DC-DC converter)[3]. With regard to the first two hybrid energy storage types of direct parallel connection and of parallel connection through the inductive, their port voltages vary in wide range in the process of charging and discharging. More adverse is, the energy distribution between two energy storage elements can not be controlled flexibly, so that it’s difficult to fully play the both ones’ respective advantages. While the energy storage unit of parallel connection through power converter can easily control energy flow between both energy storage elements, so the capacity and the performance of energy storage unit can be flexibly optimized. Power converters used in the hybrid energy storage system can be divided into two types: the one of unilateral energy flow and the one of bidirectional energy flow. For power flow optimization and control of wind power system, the energy flow between energy storage unit (DC link) and the public power grid (AC Link) is two-way, correspondingly, the current of the DC-DC converter used in hybrid energy storage unit is also bidirectional. On the basis of the energy flow characteristic in wind power system containing power flow optimization by virtue of hybrid energy storage, the paper designs a triple bi-directional DC/DC converter used for energy flow control of the hybrid energy storage unit.

2 Energy Flow Characteristic in Wind Power System Containing Power Flow Optimization The ultimate goal of power flow optimization of wind power system is to restrain the power fluctuation, which is based on the balances of the active power as well as the reactive power. Therefore, the fundamental functions of power flow optimization system have two: one is to smooth the active power flowing into the grid in real time; another is to compensate reactive power flowing into the grid in real time. According to these basic functions, the overall layout of the wind power system containing power flow optimization is shown in Figure 1. Suppose, in the whole wind power system, output power of wind generation is denoted as PWG ; charging( or discharging) power of energy storage unit is denoted as PS , of which, charging( or discharging) power of the supercapacitor is denoted as

PC , the battery PB ; the power flowing into the grid is denoted as PG . Because of

Design on Triple Bi-directional DC/DC Converter

9

randomness and uncertainty of wind energy, PWG is obviously randomly fluctuant. If, without any compensation,

PWG is directly transported into the grid, the voltage, the

frequency and some other parameters of the local grid are will be influenced negatively. Fluctuations components in wind energy of above 1Hz can be absorbed by the inertia of generation system, while the components of 0.01Hz ~ 1Hz have the maximum impact on the grid in terms of the voltage, the frequency and other performances[4]. And the ones of below 0.01Hz directly affect the capacity reliability of generation system. To restrain the negative impact of wind power, its power flow optimization system should smooth the active power injected into the power grid. That is to say, of the output power of the wind generation system, only the components of relatively low frequency(lower than 0.01Hz) is transported into the public grid, while the higher frequency components should be absorbed by the energy storage. Apparently that the power absorbed by energy storage is PS = PWG − PG , which may be flexibly controlled by a four-quadrant DC-AC converter. Power Grid Grid Voltage

Wind Power System output current

Wind Power System

Wind Power System gridconnected Converter

Power Flow Optimization Control Strategy

Controller)

Control Strategy for Power Flow of Energy Storage

Battery State of Charge(SOC)

Storage Battery

Four-quadrant converter (Power Flow Optimization

Bi-direction DC converter Power Flow of Energy Storage

DC Voltage

Super-Capacitor

Fig. 1. The overall layout of the wind power system containing power flow optimization

Of two energy storage elements, the supercapacitor has high power density and lower energy density; while the battery has low power density, high energy density and short cycle life. Hence, in the process of power flow optimization, the charging (discharging) times of the battery should be reduced to less as far as possible. That is,

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the relatively lower frequency components of the wind power are taken by the battery, while the remaining higher frequency components are borne by the supercapacitor. The energy between the two energy storage devices is controlled by the bi-directional DC-DC converter designed by the paper.

3 Design on Triple Bi-directional DC/DC Converter 3.1 Working Principle and Small-Signal Model In the wind power system of comparatively large power, the batteries generally need to be paralleled to meet the power demand. Due to individual differences in feature, the charging (discharging) current of various sub-unit may be different, which easily lead to the problems of over-charge or under charge. Obviously, the number of directly parallel individuals is larger, the imbalance problem is more serious. To reduce the number of batteries directly in parallel and to ease the charging (discharging) imbalance problem, in this paper, the triple two-way DC-DC converter is adopted, which can decrease the number of batteries directly in parallel and flexibly control charging (discharging) currents of the three groups of batteries. At the same time, multiple combinations of the conversion circuit can increase the transmission power and reduce current ripple as well as the filter components (inductors, capacitors) parameters. The topology of the converter is shown by Fig. 2.

vo v1i

v 2i

v 3i

Fig. 2. The topology of the triple two-way DC-DC converter

The control of the converter is based on PWM. The switches S1~S6 and D1~D6 are composed of power electronics device such as IGBT and diode. If the switches S1, S3, S5 operate in PWM mode, and the switches S2, S4, S6 are always off, the converter runs in Buck chopper mode. While the switches S2, S4, S6 operate in PWM mode, and the switches S1, S3, S5 are always off, the converter runs in Boost chopper mode. When the switching state of S1, S3, S5 and the ones of S2, S4, S6 are opposite each other, and all operate in PWM mode, the converter is in the unified working mode, i.e. Buck-Boost chopper. In a switching cycle of Buck-Boost chopper, its inductor current may be two-way, which can increase response speed[5], so the third mode is chosen in the paper.

Design on Triple Bi-directional DC/DC Converter

11

Because the topology and the control method of three sub-units are identical in the triple converter, as for modeling and analysis, only an independent sub-unit is taken into account. An independent bi-directional DC-DC converter sub-unit is shown in Fig. 3.

L

vo

S1

i

C

S2

vi

R

Fig. 3. An independent bi-directional DC-DC converter ∧

L

Vo d (t ) −

+

D′ :1

+

vˆi (t )

+

∧

i (t )

Idˆ (t )

C

R

vˆo (t ) −

Fig. 4. Small signal AC equivalent circuit model of Buck-Boost

In Fig. 3, vi , i , vo and R respectively stand for the battery voltage, the battery current, the supercapacitor voltage and the equivalent load resistance. By virtue of average switch modeling method, the small signal AC linear equivalent circuit model of Buck-Boost can be obtained and shown as Fig. 4. In Fig. 4, d (t ) is the duty ratio of the bottom switch (S2), D is the value of ∧

d (t ) in quiescent operation point, and d (t ) is the perturbation of d (t ) . ∧

Based on the linear equivalent circuit, the transfer function of duty cycle

d ( s ) to

∧

output voltage

vo ( s) Gvd ( S ) =

vˆo ( S ) dˆ ( S ) vˆi ( S )=0

=

D′Vo (1 − LCS 2 +

LS ) RD′2

L S + D′2 R

(1)

3.2 Control Strategy According to the overall structure of wind power system with energy storage, the energy exchange between power flow optimization system and the public grid can be reflected in the fluctuation of the DC bus voltage. If the relatively low frequency components of PS is expected to be absorbed by the battery, the lower frequency

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components of the DC bus instantaneous voltage should be detached by a low pass filter and be stabilized to a given value. Considering response speed, an one-order low pass filter is chosen in the paper. To ensure that the battery absorbs the relatively lower frequency energy fluctuation, firstly, the voltage regulation control loop for the low frequency components of transient DC bus voltage is necessary, which ensure super-capacitor voltage is constant at the low frequency. To guarantee the charge state of three paralleled battery groups are identical, this paper proposed a current-sharing strategy based on the battery SOC (state of charge). For the battery, SOC

=

QC

QI

, where: QC is the residual capacity,

QI is the rated

capacity under a constant discharge current. In the process of current-sharing, for an independent sub-unit of the triple converter, the SOC of the corresponding battery group is higher, its average inductor current is larger, Conversely, the SOC lower, the current smaller. As a result, this can make the SOC of three battery groups tend to no difference. On the basis mentioned above, the paper proposes the control strategy of triple bidirectional DC-DC converter consisting of voltage outer loop and current inner loop, whose structure is shown as Fig. 5.

I ref

1 SOC1 I ref ∑ SOCi

LS ) RD′2 L LCS 2 + S + D′2 R D′Vo (1 −

kip s + kii s

i =1, 2,3

ki * U dc

kup s + kui s

I ref

3 I ref

SOC3

∑ SOC

i =1, 2,3

LS ) RD′2 L LCS 2 + S + D′2 R D′Vo (1 −

kip s + kii s

i

ki 1 1+τ 2s

ku

Fig. 5. The control structure of triple bi-directional DC-DC converter

According to [6], the parameter of the regulator

kip , kii , kup and kui can be

determined.

4 Simulation Results Based on Matalab / Smulink, the triple bi-directional DC converter is simulated. In the converter, the nominal voltage of the battery is 72V, the expected voltage of the supercapacitor (load voltage) is 200V, kip = 10 , kii = 60 , kup = 6

Design on Triple Bi-directional DC/DC Converter

and

13

kui = 10 , SOC1 = SOC2 = SOC3 , τ 2 = 0.01 . When the equivalent load

current (discharging current) varied in the case shown by

Fig. 6. (A) Charging current of the supercapacitor (B) Real voltage of the supercapacitor

Fig. 7. The currents of three sub-units of triple bi-directional DC-DC converter

Fig. 6 (A), under the control strategy proposed by the paper, the real-time voltage of the supercapacitor is illustrated as Fig. 6 (B), and the currents of three sub-units are shown as Fig. 7. Seen from the two figures, the relatively lower frequency components of load current (power) are undertaken by the battery, while the remaining higher frequency

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components are undertaken by the supercapacitor. The simulation results indicate that the converter can not only reasonably distribute the power between two energy storage elements but effectively regulate the load voltage.

5 Conclusions The design on a triple bi-directional DC/DC converter used for power flow control of energy storage in wind power system are discussed. The paper designs control unit of the converter with voltage outer loop and current inner loop, which includes a novel current-sharing strategy based on battery SOC and uses a one-order low pass filter to detach the low frequency component in feedback voltage. The simulation results show that the converter can effectively regulate the load voltage and reasonably distribute the power between both energy storage elements with comparatively good static and dynamic characteristic.

Acknowledgments This paper and its related research are supported by National Natural Science Foundation of China (50807034) and Support Program for Young Teachers Development of Shandong University of Technology.

References 1. Yang, Z., Shen, C., Zhang, L., et al.: Integration of a StaCom and Battery Energy System Storage. IEEE Trans on Power System 16(2), 254–260 (2001) 2. Andrew, B.: Ultracapacitors: Why, How, and Where is the Technology. Journal of Power Sources 91, 37–50 (2000) 3. Tang, X., Qi, Z.: Study on the Ultracapacitor/Battery Hhybrid System. Chinese Journal of Power Sources 30(11), 933–936 (2006) 4. Luo, C., Ooi, B.-T.: Frequency Deviation of Thermal Power Plants Due to Wind Farms. IEEE Trans. on Energy Conversion 21(3), 708–716 (2006) 5. Wang, Z., Liu, J.: Power Electronics Technology. China Machine Press (May 2009) 6. Xu, D.: PowervElectronics System Modeling and Control. China Machine Press (January 2006)

ESL Based SoC System Bandwidth Estimation Method* Zhen Xie, Xinning Liu, Weiwei Shan, and Wei Ge National ASIC System Engineering Research Center, Southeast University, Nanjing 210096 People's Republic of China {xz,xinning.liu,wwshan,duiker}@seu.edu.cn

，

Abstract. The increasing complexity of system-on-a-chip (SoC) design is challenging the design engineers to estimate the system bandwidth. A method of SoC system bandwidth estimation based on electronic-system-level (ESL) is proposed, which estimates the system bandwidth by transaction-level-modeling (TLM) and analysis depended on simulation. Compared with the traditional RTL simulation, the estimation is effective and the simulation speed is more than two orders of magnitude faster. Keywords: ESL, SoC, TLM, Bandwidth estimation.

1 Introduction Bandwidth is always one of the bottlenecks in system-on-a-chip (SoC) systems. The increasing complexity of SoC design is challenging the design engineers to estimate the bandwidth. If the bandwidth is unable to well fit the requirement, the designer can modify the architecture in the early stage of the product development cycle, thus reducing the potential risk of system re-design [1]. In [2], the authors used statistical models to calculate execution cycles for bus traffic analysis and system performance estimation. However, the main drawback of static analysis is the lack of dynamic analysis information, such as bus contention, arbitration, dynamic scheduling, etc [3]. So some designers explore architecture by dynamic simulation at register-transfer-level (RTL). While this was possible for designs that were relatively simple, exploring complex SoC designs at RTL is an intimidating prospect. The speed of RTL simulation is too slow to allow adequate coverage of the large design space. Besides, making small changes in the design will probably require considerable re-engineering effort due to the complex nature of these systems [4]. To overcome these limitations, system designers should raise the level of abstraction, which gives an early estimation of the system characteristics before committing to RTL development. In this paper, a method of SoC system bandwidth estimation based on electronicsystem-level (ESL) is proposed, which estimates the system bandwidth by transaction-level-modeling (TLM) and analysis depended on simulation. In Section 2, basic principles of ESL design methodology are introduced. In Section 3, the process that *

This work was sponsored by the National Scientific Foundation of China (Grant No. 61006029) and Jiangsu Scientific Foundation (Grant No. BK2010165)

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100,. pp. 15–21. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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using the method based on ESL to model the target system is described. In Section 4, the analysis depended on simulation and the bandwidth estimation of the target system are given. Section 5 is the conclusion.

2 Principles of ESL Design Methodology In current SoC design, the model established at the level of algorithm-and-function (ALF) is lack of timing information, and has little relationship with the system structure and implementation, which cannot be used to estimate the system performance. While the RTL model needs to focus on details of signal processing and design implementation, the speed of modeling and simulation is slow. ESL design methodology introduces a new level, transaction level, between ALF and RTL to abstract systems. Transaction-level-modeling (TLM) provides software and hardware engineers a virtual platform for the exploration of architecture and embedded software development.

Fig. 1. Comparison of the three modeling methods

ESL design methodology enables SoC designers to describe the system abstractly, with no need to prematurely be involved in the specific of implementation. Through ESL design methodology, designers can model the system in the early stage, explore the system architecture and guarantee the advancement. So that the risk of re-design due to performance cannot meet the requirement is reduced. In this paper, the architects view TLM is proposed, which has enough timing information and is mainly used for architecture exploration. Modules of the system will be abstracted as bus, memory or device to simulate system load. This method considerably reduces the workload required in the modeling, and greatly speeds up the system simulation.

ESL Based SoC System Bandwidth Estimation Method1

17

Fig. 2. Architects view TLM

3 Modeling of the Target System In this paper, ESL design methodology is used to analyze the performance, estimate the bandwidth by TLM on the ESL design platform, and optimize the architecture during the research to meet the requirement. 3.1 General Situation of the Target System The block diagram of the target system is shown at Fig. 3. 1. The system uses DDR2-800 as memory, data bus width is 32-bits, main timing parameters are CL=4, tRCD=4; 2. The interface frequency of AXI based LCDC, VPU and DDR2 is 200 MHz; 3. LCDC supports resolution up to 1920x1080 at refresh rate of 60Hz, and has three overlays, considering the worst case, bandwidth demand is about 1200 MB/s; 4. VPU accesses data every 4085ns, bandwidth demand is up to 900 MB/s, of which 700 MB/s is read access and 200 MB/s is write access. A rough estimate, LCDC and VPU consume 65% of the peak bandwidth. However, because of the complexity of access to DDR2, evaluation for the bandwidth is difficult. DDR2 storage can be divided into bank, row and column. According to the current and previous access addresses of the bank and row, access to DDR2 can be classified as continuous access and with-precharge access. Time spans of the two types of access are different. LCDC and VPU simultaneously request transaction whose required address is a random variable. On the other hand, bus transfer and arbitration will consume some bandwidth. So in fact, the bandwidth consumed by the two modules will significantly exceed 65%. To evaluate the bandwidth effectively, a correction factor must be involved.

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Fig. 3. The block diagram of the target system

However, due to the randomness of data access, evaluation of the factor is a great challenge. Using ESL design methodology, modeling the target system abstractly, and depending on the ESL design platform, the factor and the system bandwidth can be obtained accurately. 3.2 Modeling of the Target System To estimate the system bandwidth, the amount of data transfer is concerned, while data content and processing are not cared. Architects view TLM can greatly speed up the modeling, and evaluate the system bandwidth effectively. 1. Data access to DDR2 takes 38 bus cycles for data transfer in the bus. The depth of task buffer in the DDR2 controller is 16. LCDC owns higher priority than VPU. Considering the two types of access, continuous access needs 2 bus cycles to complete burst-4 operation, while with-precharge access takes 4 bus cycles. 2. LCDC will request access to DDR2 when any of the three overlay FIFOs is not full. If data of continuous access belong to the same overlay, access is continuous access. Otherwise access is with-precharge access. 3. VPU will always request access to DDR2, which is continuous access. Because LCDC and VPU request access simultaneously, access may be interrupted by each other, continuous access may be interrupted to become with-precharge access often. Thus, the access to DDR2 is similar to a random process. Taking the advantage of the ESL design platform, the actual situation of access to DDR2 can be simulated and analyzed. Thus the system bandwidth can be evaluated effectively. The system model built in the ESL design platform is shown at Fig.4.

ESL Based SoC System Bandwidth Estimation Method1

19

Fig. 4. The system model built in the ESL design platform

4 Simulation and Analysis Result The simulation result of the target system model in the ESL design platform is shown in Table 1. Because of the existence of the with-precharge access, the utilization of the system bandwidth rises significantly. When LCDC is not optimized, the utilization of bandwidth is about 83%, the system bandwidth margin is about 17%. Table 1. The contrast of the Utilization of system bandwidth between ideal estimation and actual simulation (LCDC is not optimized)

The utilization of system bandwidth

Ideal estimation

Actual simulation

62%

83%

LCDC can be optimized to effectively improve the utilization of system bandwidth by arranging the continuous access and reducing the number of with-precharge accesses. On the other hand, the more continuous accesses, the requirement of FIFO depth is higher. The depth of FIFO will affect the implementation area, so there should be a trade-off between the bandwidth and the area. The simulation results of LCDC with different transmission modes are shown at Fig.5. Through optimizing the LCDC design, the utilization of system bandwidth drops from 83% to 64%, in the worst case the margin of system bandwidth is still about 36%, which can meet the requirement.

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Fig. 5. The simulation results of LCDC with different transmission modes

Fig.5 also shows the utilization of system bandwidth and the requirement of FIFO depth drops and rises in an exponential manner. Comparing Case 4 with Case 3, the utilization of system bandwidth drops by only 2%, but the requirement of FIFO depth rises by 24. Thus it is reasonable to access data belong to the same overlay 3 times continuously. In the late stage of the development, RTL simulation results show that, when LCDC accesses data belong to the same overlay 3 times continuously, the requirement of FIFO depth is 27, and the utilization of system bandwidth is 76%. These are in agreement with ESL simulation results, indicating the efficiency of the ESL based SoC system bandwidth estimation method. The time spent on ESL simulation and RTL simulation of the target system is shown in Table 2. Compared with the traditional RTL simulation, the time is reduced from 33 hours to 9 minutes, more than two orders of magnitude shorter. Table 2. The time spent on ESL simulation and RTL simulation of the target system

The time spent on data output of 24 frames of 800x640 image

ESL simulation About 9 minutes

RTL simulation About 33 hours

5 Conclusions In this paper, a method of SoC system bandwidth estimation based on ESL is proposed, which estimates the system bandwidth by TLM and analysis depended on simulation. Compared with the traditional RTL simulation, the estimation is effective

ESL Based SoC System Bandwidth Estimation Method1

21

and the simulation speed is more than two orders of magnitude faster. Practice shows that the effective use of ESL design methodology can improve system performance and success rate of the implementation. With the improvement of complexity in SoC designs, the introduction of ESL design methodology has become an irresistible trend, and strongly promotes the development of SoC designs.

References 1.

2.

3.

4.

Ruei-Xi, C., Wei, Z., Qinyi, L., Fan, J.: Efficient H.264 Architecture Using Modular Bandwidth Estimation. In: International Conference on Embedded Software and Systems, pp. 277–282 (2008) Cho, Y.S., Choi, E.J., Cho, K.R.: Modeling and analysis of the system bus latency on the SoC platform. In: International Workshop on System-Level Interconnect Prediction, pp. 67–74. ACM, New York (2006) Zhe-Mao, H., Jen-Chieh, Y., Chuang, I.Y.: An accurate system architecture refinement methodology with mixed abstraction-level virtual platform. In: Design, Automation & Test in Europe Conference & Exhibition, pp. 568–573 (2010) Shin, C., Grun, P., Romdhane, N., Lennard, C., Madl, G., Pasricha, S., Dutt, N., Noll, M.: Enabling heterogeneous cycle-based and event-driven simulation in a design flow integrated using the SPIRIT consortium specifications. Des. Autom. Embed. Syst. 11, 119–140 (2007)

Cellular Automaton for Super-Paramagnetic Clustering of Data Zhang Botao, Zhang Shuqiang, and Yu Zhongqiu Institute of Science, Information Engineering University, 450001 Zhengzhou, China [email protected], [email protected]

Abstract. Using the basic idea of Super-paramagnetic Clustering (SPC), we propose a cellular automaton approach for data clustering: A data set is regarded as a Potts magnetic system and a short-range interaction is introduced between the neighboring spins. Let the system evolve automatically under the function of the spin-spin interaction and the thermal motion. Finally, at a proper temperature the system will reach the super-paramagnetic phase, in which the spins (data points) will form into a number of ‘magnetic domains’ (data clusters). We apply this method to some data sets with different structures and get satisfactory results. Keywords: Data clustering, Potts magnetic system, Cellular Automaton.

1 Introduction Data clustering is a basic technique in data analyzing and in a variety of scientific and engineering fields. It is widely used in the fields such as pattern recognition, artificial intelligence, computer image processing, biology, astrophysics, etc. [1]. The general definition of data clustering is as follows: partition N points given in the feature space of the data into M different groups so that two points that belong to the same group are, in some sense, more similar than two points that belong to different ones [1]. The procedure of the traditional and typical clustering methods is as follows: firstly some knowledge about the clusters’ structure is assumed (e.g. each cluster is represented by a center and some point around it), and then the similarity between data points is calculated. Finally, partition data points into different clusters according to their similarity. It should be noted that, in the above procedure a successful clustering is based on a priori knowledge and a correct assumption about the structure of data set. That is, in the traditional procedure, some external factors is imposed to the set structure no matter it is right or not. In 1996, Eytan Domany presented a new approach for clustering: Super-paramagnetic Clustering (SPC)[2-4]. The basic idea of SPC is to build clusters naturally by utilizing the physical properties of an inhomogeneous ferromagnetic model. Here a Potts spin variable is assigned to each data point and a short range-interaction is introduced. Thus, the Potts spins will evolve automatically under the function of the spin-spin interaction and the thermal motion. At a proper temperature, the system will reach the super-paramagnetic M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 23–29. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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phase. Spins with strong interaction to each other will form a magnetic domain in which all spins have a same state. This magnetic domain is thus corresponding to a data cluster. Domany explored a way to identify the magnetic domain by using Monte Carlo procedure [5]. Domany’s idea inspires us to consider a “Cellular Automaton (CA) for data clustering”. Contrast to the Monte Carlo procedure, the CA model is a dynamic model and is based on the spatial sequences [6], which is very similar to the procedure of magnetizing of a Potts system. Furthermore, CA model may be run on the special CA machine. Thus, we expect to build a CA model to realize SPC in a more useful way. In the following sections we first introduce the idea of the Super-paramagnetic Clustering (Sec2), and then describe the CA model for data clustering and corresponding algorithm (Sec3). In Sec4 we apply the method to two data sets with different typical structures. At last in Sec5 we give some conclusions and discussions.

2 The Idea of SPC A data point is a Potts spin when the data set is regarded as inhomogeneous Potts system. The spin-spin correlations (presented by the coupling function J) let the uniformity of the system but thermal motion destroys it. The magnetic domain will be formed and separated from others in the super-paramagnetic phase. This is the data clusters. Here, the coupling function J represents the similarity between data points. The closer two points are to each other, the more they ‘like’ to belong to the same class. Obviously, J is some positive decreasing function of the distance, which is chosen as follows [2]: 2

d 1 J i = exp(− i 2 ) K 2a

(1)

Where a stands for the average nearest-neighbor distance. K is the average number of neighbors per set. The thermal motion will destroy the “bonds” among spins. At the low temperature enough, all spins of the system are in the same state, Obviously, the temperature plays the role of clustering resolution here.

3 The Simulation of Cellular Automaton Clustering In this section we propose a Cellular Automaton [8] model for the clustering of data. 3.1 Model Firstly, we consider the effect of the interaction Ji and the temperature T by setting a link between the center site and its neighbor i [5]. The probability that a spin can’t link up with its neighborhood i is as follows:

Cellular Automaton for Super-Paramagnetic Clustering of Data

Pni = exp(−

25

Ji ) T

(2)

and the probability of linking-up is:

Pbi = 1 − Pni = 1 − exp(−

Ji ) T

(3)

The next state of a site is determined by the configuration of the links between the site and its neighbors. The model is described as follows: The possible states of each lattice site (spin, data point) is s (s=1, 2,... , q). In order to apply the method to different dimension data sets especially in high dimension data sets, we use the mutual K-nearest-Neighbor[9] which is defined as follows: two points, A and B, are defined as neighbors if they have a mutual neighborhood value K; that is ,if A is one of the K nearest neighbors of B and vice-versa. This definition ensures that the local interaction is symmetric; the number of bonds of any site is less than K. In general the value of K determines the range of interactions between a site and its neighbors. The rules are defined as: (i) If a site doesn’t link up with all of its neighbors, it updates its state randomly with probability Pn. otherwise it will be ‘magnetized’ by its linked neighbors with probability 1 –Pn (converts its state to the state of one of its linked neighbors), Here Pn is the probability of the configuration of a site with no links (see Fig 1 (a)): K

K

Pn = ∏ Pni = exp(−

∑J

i =1

ķ

ķ

Ĵ

Ĵ

Ĺ

Ĺ (a)

Ĺ

T

i

ķ Ĺ

(4)

)

ķ

Ĵ Ĺ

(b)

i =1

ķ

Ĵ Ĺ

(c)

（）

Ĺ

Ĵ Ĺ

(d)

Fig. 1. An example of realized configurations. a a site with no link, of s=1, c d e a site with links of s=3

（）、（）、（）

Ĺ

Ĺ (e)

（b） a site with a link

(ii) If a site has links with its neighbors with state s, it will be ‘magnetized’ to the state s with probability Ps. Suppose it has m neighbors with state s, Ps is the combined probability of the configurations the site and its neighbors with state s:

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K

m

Ps = [1 − exp( −

∑ Ji i =1

T

)] ⋅ exp( −

∑J j =m

T

j

)

(5)

Here, the configurations that a site links up with different states are forbidden, because it is impossible that a site be ‘magnetized’ by different states simultaneously. Fig 1 shows all the realized configurations of a site with three neighbors. 3.2 Algorithms At a proper range of T, the steps of the procedures of data clustering are: (i) Find out the neighbors of each site (Sec 3.1.2), and calculate K , di and Ji; (ii) Generate an initial configuration randomly; (iii) Calculate the converted probability of each site to different possible states; (iv) Update the states of all sites according to the above probabilities; (v) Repeat steps (iii) and (iv), until the system goes to stable; (vi) Class the sites (data points) with the same state into a cluster.

4 Tests 4.1 Random Numbers of the Two-Dimensional Normal Distribution This is a group of artificial data. Two groups of random numbers, which are evenly distributed in angle and normally distributed in radial, are generated by random number generator. This is a dataset of two natural kinds and two class central coincide annular, including 100 data points of average radius of 0.5 and 200 data points of average radius of 2. Clustering parameters are chosen to be: q = 15, K = 8. Figure 2 to figure 5 give four clustering results from low to high temperature (resolution). When the temperature is very low, the system is in ferromagnetic phase, all the datum being grouped into one cluster (figure 2). When the temperature increases to about 0.007, the system will experience a phase change, and the data points will be split into two clusters (figure 3). Then, when the temperature increases to about 0.1, the outer data class is further divided into two mass, and all the data is divided into three clusters (figure 4) , because there is an area of low density both above and below the outer datum. When the temperature continues to increase, the data mass continuously split, until the system turns into the paramagnetic phase (figure 5). As far as the natural class of the datum is concerned, proper clustering happens at an interval of T = 0.005 to 0.009.

Cellular Automaton for Super-Paramagnetic Clustering of Data

Fig. 2. T=0.001 into one cluster

，the data points are grouped

Fig. 4. T=0.01, all the data is divided into three clusters

Fig. 3. T=0.007 two clusters

27

，the data points are split into

，

Fig. 5. T=0.5 paramagnetic phase

4.2 Iris Data Iris data is a complete data set, including 150 samples of each Iris species (50 samples each), got by Fisher in 1936 after he collected the four features (the length and width of calyx leaves and the length and width of petals)of three different Iris genus(Iris Setosa, Iris Versicolor & Iris Virginica) . In the feature space, Iris Setosa and the samples of the other two species separate linearly, and the samples of Iris Versicolor and Iris Virginica have a little overlap. Clustering parameters are chosen to be: q = 15, K = 35. After a clustering test on this data set, we found that a roughly correct classification can be obtained at the interval of T = 0.01 to 0.1, and the clustering effect is the most stable when T≈0.018.At this time, the data were divided into three clusters, the samples of Iris Setosa being correctly divided but the samples of Iris Versicolor and Iris Virginica is more or less wrongly divided. Repeated testing results show that the number of wrong division is 5 to 25. Figure 6 shows the projection of the four-dimensional Iris number upon the first dimension and the fourth dimension. This is the best example of clustering, only 5 data

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Fig. 6. The projection of the Iris number upon the first dimension and the fourth dimension. T =0.018, five wrongly divided points are marked by rectangular square full sample.

being wrongly divided (see the five rectangular squares in Figure 6). In most tests, the number of wrong division is 13 or so. It shows in the chart that there are probably less than 50 samples in each cluster, which is the result of projection overlap of the data points.

5 Conclusions and Remarks We’ve constructed the “Cellular Automaton for data clustering” with the help of the similarities between super-paramagnetic phase formation and data clustering in the inhomogeneous Potts magnetic system. This method is based on the density of data points, which is suitable for different structure data set, and there is no need to make assumption about the data structure. In the clustering process, the rules of Cellular Automaton has played a decisive role, it provides a dynamic mechanism, letting the data points joint with each other according to their similarity. And the introduction of thermal motion functions as a kind of adjustment, letting the data point make multiple and repeated choices. The system will eventually evolve into a relatively stable configurations, and it has little to do with the initial configuration. This work is just a preliminary realization of clustering, and some questions need further research or optimization, including clustering stability, similarity and the best functional relation of the distance between data points, the best Cellular Automaton rules and the objective method to determine a candidate neighbor number K, etc.

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References 1. 2. 3. 4. 5. 6. 7. 8. 9.

Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Publishing House of Electronics Industry, BeiJing (2010) Blatt, M., Wiseman, S., Domany, E.: Super-paramagnetic Clustering of Data. Physical Review Letters 76, 3251–3254 (1996) Blatt, M., Wiseman, S., Domany, E.: Data Clustering Using a Model Granular Magnet. Neural Computation 9, 1805–1842 (1997) Domany, E.: Superparamagnetic Clustering of Data-The Definitive Solution of an Ill-Posed Problem. Physica A 263, 158–169 (1999) Wang, S., Swendsen, R.H.: Cluster Monte Carlo alg. Physica A 167, 565 (1990) Dan Mueller, D.C., Chen, K., et al.: Solving the advection-diffusion equations in biological contexts using the cellular Potts model. Physical Review E 72(4), 1–10 (2005) Wu, F.Y.: The Potts model. Reviews of Modern Physics 54(1), 235–265 (1982) Zhang, B.T., Liu, C.H.: Cluster-approximation mean field theory of a class of cellular automation models. Physical Review E 59(5), 4939–4944 (1999) Blatt, M., Wiseman, S., Domany, E.: Clustering data through an analogy to the Potts model. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Advances in Neural Information Processing Systems, vol. 8, MIT Press, Cambridge (1996)

Embedded Memory Wrapper Based on IEEE 1500 Standard Maryam Songhorzadeh and Rahebeh Niaraki Asli Guilan University Guilan, Rasht, Iran [email protected]

Abstract. IEEE Std 1500 defines a modular and scalable test interface for embedded cores of a system-on-chip (SoC) which simplify test challenges. In this paper, we present a specialized wrapper compatible with IEEE Std 1500 to implement at-speed testing for embedded memory cores. The proposed embedded memory wrapper (EMW) supports test diagnosis with reasonable area overhead which makes it suitable for memory BIST applications. All required test control signals of EMW is generated on-chip by a single centralized memory Built-In-Self Test (BIST) controller. The BIST controller can be used in a hierarchical test design and implement parallel test to handle multiple test wrappers concurrently. Simulation and synthesis results on a group of embedded memory cores confirm that the proposed wrapper has been effectively reduces the test time and area overhead. Keywords: Embedded memory testing; System-on-a-Chip (SoC); Built-in-SelfTest (BIST); IEEE Std 1500; at -speed testing.

1 Introduction Today, the fast innovation in VLSI technology enables the design of complex system on a single chip. Although system chips offer advantages such as higher performance, lower power consumption and smaller volume and weight, the recent methods of design like synchronous production of different cores and using reusable and heterogeneous IP cores, poses new test constraints to the test community [1]. On the other hand, basic features of testability like controllability and observability are hard to achieve because direct access to core ports is virtually impossible [1]-[3]. To address these problems, IEEE Std 1500 was proposed which defines a scalable structure for independent, modular test development and test application for embedded design blocks. This standard also enables testing the external logic surrounding cores [4]-[6]. One of the core types which occupy a significant amount of SOCs is memory cores. The high density of embedded memory cores make them more prone to manufacturing defects than other types of on-chip circuitries. Since direct access to memory ports is virtually impossible and at-speed testing is difficult to achieve, BIST is a more practical method to test and diagnose embedded memories [7], [8]. But a M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 31–39. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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serial BIST like BIST based on IEEE std 1500, has a long and unacceptable test and diagnosis time, because this standard can’t implement a parallel test and manage multiple cores concurrently. Considering all these issues, several BIST schemes using IEEE Std 1500 serial links has been proposed [8]-[12]. In [8], a modular test wrapper for small wide memories has been introduced. The interfaces between the memories and BIST circuit are based on IEEE std 1500. This scheme allows for at-speed test at low area overhead. In [9]-[11], a serial test interface based on IEEE 1500 for small memories has been proposed. But the area overhead of the IEEE std 1500 wrappers is high. On the other hand, testing multiple memories using a serial BIST based on IEEE std 1500 requires a long and intolerable test time. In [12], another wrapper based on IEEE std 1500 is introduced which is in complete accordance with the standard but has no modification and optimization for memory testing, so it can’t support at-speed and parallel test. In this paper, the structure of a wrapper based on IEEE Std 1500 for Built-In SelfTest of embedded memory cores is proposed. The proposed structure is capable of implementing at-speed test and also supports diagnosis which is an important requirement in repairing procedure. By using a centralized BIST controller to generate control signals of the wrapper, the proposed scheme can be imported in a hierarchical test methodology and handle multiple test wrappers concurrently. On the other hand, the area overhead of the design is improved comparing the existing schemes. The organization of this paper is as follows: An overview of the general specification of the scheme is first given in section 2. In section 3 we propose different operational modes of the wrapper and Section 4 is allocated to the results of simulation and synthesis on a group of embedded memory cores. The paper is concluded in section 5.

2 Embedde Memory Wrapper (EMW) Figure 1 shows a memory core surrounded by the proposed IEEE-1500-compliant wrapper called EMW. The structure is composed of a Wrapper Instruction Register (WIR) circuitry, a Wrapper Boundary Register (WBR), a Wrapper Bypass register

Fig. 1. The overall structure of the IEEE-1500-compliant wrapper (EMW)

Embedded Memory Wrapper Based on IEEE 1500 Standard

33

(WBY), a Wrapper Data Register (WDR) and two multiplexers. All components of EMW are specialized for memory testing while adopt with IEEE Std 1500. In the reminder of this section, we introduce each part in detail. 2.1 WIR Circuit Figure 2 shows the structure of the proposed WIR circuit. It is composed of three main parts: WIR controller, WIR register and selector. According to IEEE Std 1500, WIR circuit must produce the signals required for controlling the other components of the wrapper and this can be done through initializing the wrapper serial port and WIR register.

Fig. 2. Structure of the proposed WIR circuit

WIR Controller. WIR controller generates control signals of the wrapper. From hierarchical point of view, this controller is the last level of test hierarchy. The BIST controller constitutes the upper level controller and generates all of the wrapper serial port signals and an additional signal for parallel shift. The BIST controller initializes input signals to the WIR controller and shifts the necessary instructions to WIR register through WSI signal. When there is no instruction to shift, WSI signal is used to transfer the related data of the current mode. Generating the input signals of WIR controller through the BIST controller can help producing a hierarchical test structure in which a memory BIST controller manages all WIR controllers of the wrappers. This relation can reduce test complexity while causes higher compatibility between the two controllers. On the other hand, the test methodology which also affects the wrapper will be more flexible because the BIST controller is completely definable by designer. WIR Register. In IEEE Std 1500, WIR register receives required instructions and test data. The 3 bit register proposed in figure 3 is in complete accordance with the standard and composes of two stages: shift and update. WIR_WSI is the serial input which shifts data and instructions to WIR register. After shifting operation, the contents of shift register are loaded into the update register. WIR_controller produces WIR_shift and WIR_update control signals.

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Fig. 3. Structure of WIR Register

WIR Selector. WIR selector is another part of WIR circuit. It is used to connect WSI signal to any of the wrapper registers. Based on the IEEE Std 1500, only one register can be set between the WSI and WSO signals. The selector circuit separates the respective register and connects WSI signal to it. 2.2 Wrapper Boundary Register (WBR) WBR is an important element of IEEE Std 1500 which has the responsibility of applying test and functional stimulus to the core and receiving the core responses. Figure 4 shows the proposed structure of WBR which is specialized for embedded

Fig. 4. The proposed structure for Wrapper Boundary Register (WBR)

Embedded Memory Wrapper Based on IEEE 1500 Standard

35

Fig. 5. The proposed structure for WBR input cell

Fig. 6. The proposed structure for WBR output cell

memory core BIST applications and composed of several input and output cells, multiplexers and some glue logics. The operation of input and output cells is controlled by control signals from memory BIST controller. Figures 5 and 6 show input and output cell architectures, respectively. Each cell has two data and test input and output ports and can be set in three different operational modes. CFI and CFO ports are used to receive and shift out the functional data, while the CTI and CTO ports are allocated to receive and shift out the test data. There are three control signals for controlling the operation of the cells: WBR_s_shift, WBR_p_shift and WBR_capture. WBR_s_shift and WBR_capture signals are derived from the shiftWR and captureWR signals of WSP respectively and WBR_p_shift is resulted from an optional signal called WPP_shift which is added to WSP port for enabling parallel shift of WBR cells. Table 1 shows the value of each control signal in different operational modes. Table 1. Control signals of WBR Mode Functional Internal Test External Test

WBR_S_shift

WBR_P_shift

WBR_capture

0 0 1

0 1 0

0 1 1

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One of the most important issues in memory testing is at-speed test to detect delay faults. BIST architectures based on IEEE Std 1500 need many clock cycles to shift in test commands, update operation to the memory and capture and shift out the memory responses. In the proposed architecture, due to parallel operation of WBR cells, the test data is sent to the memory in each clock cycle, and the memory outputs is compared in real time. So by such an approach, the new test data is applied to the memory in each clock cycle. This time scheduling not only reduces test time, but also applies valid data to memory in each cycle. To realize the approach, a design like figure 4 is used. As can be seen, a 2-to-1 multiplexer is set between two input cells. One input of this multiplexer is used only for parallel test and the other comes from CTO of the previous cell. WBR_s_shift and WBR_p_shift signals control multiplexers and set WBR in appropriate configuration. 2.3 Wrapper Bypass Register (WBY) WBY is a one bit register sets between WSI and WSO signals and bypasses the wrapper in functional mode. WBY block diagram with input and output signals is shown in figure 7. WIR controller produces the only control signal of WBY which is called WBY_shift.

Fig. 7. WBY structure

2.4 Wrapper Data Register (WDR)

WDR is an optional register of IEEE std 1500 which shifts test outputs. When the memory is under test, WDR is set between WSI and WSO signals and shifts the diagnosis information out. The volume and content of this data is dependent on the structure of the designed BIST. WIR controller generates WDR_shift control signal.

Fig. 8. WDR structure

3 Operational Modes of EMW Functional mode: In this mode, the wrapper is bypassed and the memory implements the functional operation. The instruction code of this mode is 111. According to figure 3, when the reset signal is enabled, this value is loaded in the update register of WIR and

Embedded Memory Wrapper Based on IEEE 1500 Standard

37

WBY is set between WSI and WSO signals. According to figure 4, the three control signals WBR_p_shift, WBR_s_shift and WBR_capture are all set to 0 and the functional data can be shifted through the paths WFI_n into input_n and output_n into WFO_n for input and output cells respectively. Internal test mode: This mode is assigned to testing the memory and has an instruction code of 001. When the wrapper sets in this mode, the test data is shifted to the input cells through WPI parallel port and the memory responses are also shifted out from the memory through WPO parallel port. WDR is set between WSI and WSO signals and shifts the test results out. The values of the control signals are shown in table 1. External test mode: In external test mode, the logic surrounded the memory core is tested by shifting 010 to WIR. The values of the control signals change as shown in table 1. By setting WBR_s_shift and WBR_capture to 1 and WBR_p_shift to 0, WBR configures as a scan chain and sets between WSI and WSO signals. So applying the test data through WFI or receiving the response through WFO from the logics surrounding the memory, can be done by setting the WBR_capture to 0.

4 Simulation and Synthesis Results To evaluate the design, EMW has been simulated and synthesized with Xilinx 11.1 for memories with different configurations. It is synthesized using Xilinx 11.1 with 0.18 CMOS standard cell library. At first, EMW is simulated for a 16K×16 SRAM to measure test time. Table 2 shows the results of the synthesis for some march algorithms with different length. The proposed structure guarantees an at-speed test with the 147.1MHz clock frequency and it has an access time of 6.8 ns which are results from the synthesis. To evaluate the area overhead of the proposed approach, EMW is synthesized with Xilinx 11.1 and the results are compared with [12]. The wrapper structure of [12] is completely designed based on IEEE std 1500 without any modification. In this structure, WSP of the wrapper comes from a TAP controller and not from any the memory BIST controller, so the test procedure has less flexibility compared to the proposed structure. Table 3 summarizes the results on area overhead for different blocks in EMW and [12]. These wrappers are both synthesized for a 16K×16 SRAM and using 0.18 CMOS standard cell library. The results show the area overhead of the EMW is 120.79% improved compared to [12]. Also to evaluate the area overhead of the proposed wrapper for different memories, it is simulated and synthesized using Xilinx 11.1 for memories with various size and configurations. The memories are high density embedded SRAMs and EMW structure are synthesized using 0.18um CMOS standard cell library. Table 4 summarizes the synthesis results of area overhead of the proposed wrapper. In this table a N×W memory configuration denotes a memory with N words and each word has W bits. As can be seen, by increasing the size of the memory, the area overhead is significantly decreasing. Because by changing the configuration of the memory, only the number of the input and output cells of the WBR needs to be changed and the rest of the structure is the same for memories with different configuration and due to the results of table 3, WBR has the most area overhead.

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M. Songhorzadeh and R.N. Asli Table 2. Test time for three different algorithm using EMW on a 16×16K SRAM March Algorithm March RABL [13] March BDN [14] March LR [15]

Test Time 7.616 ms 4.787 ms 3.046 ms

Table 3. Wrapper area overhead compared to [12] Wrapper Components WBY WDR WBR WIR circuit Total(Wrapper 16K×16) optimization

# of gates in Proposed Wrapper 16 50 864 426 1356

# of gates in Previous Wrapper NR* NR NR NR 2994 120.79%

Table 4. Wrapper area overhead for different memory sizes Memory size

EMW Number of gates

4 K×16 16 K×16 4 K×32 16 K×32 4 K×64 16 K×64

1320 1356 1896 1932 3048 3084

EMW area (mm2) 0.0136 0.0146 0.0196 0.0200 0.0316 0.0319

Area Overhead 3.59% 0.96% 2.59% 0.66% 2.08% 0.52%

5 Conclusion The structure of an at-speed Embedded Memory Wrapper (EMW) has been proposed. The proposed EMW structure is based on IEEE Std 1500 and suitable for Built-In Self-Test applications. One of the main issues in memory testing is at-speed test to detect delay faults and in the proposed wrapper this feature is realized by using parallel ports for input and output cells of the introduced embedded memory BIST wrapper boundary (MBWBR). Also diagnosis can be supported by shifting the diagnostic information out through WDR. The results shows EMW structure also has reasonable area overhead. Also any number of wrappers in EMW structure can receives their control signals from one memory BIST controller so the presented structure has the capability of diagnosis and parallel testing of multiple wrappers concurrently. In the future, we hope to develop a MBWBR which can share between multiple identical memories to reduce the area overhead of EMW.

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References 1. Hong Fang, B.: Embedded Memory BIST for Systems-on-a-Chip. MS Thesis, McMaster University, Hamilton, Ontario,Canada (2003) 2. Larsson, A.: Test Optimization for Core-based System-on-Chip. PHD Thesis, Linköpings university (2008) 3. Naddeau-Dosite, B., Adham, M.I.S., Abbott, R.: Improved Core Isolation and Access for Hierarchical Embedded Test, vol. 26(1), pp. 18–25. IEEE Computer Society Press, Los Alamitos (2009) 4. Wang, L., et al.: Turbo1500: Toward Core-Based Design for Test and Diagnosis Using the IEEE 1500 Standard. In: International Test Conference 2008 (ITC 2008), pp. 1–9 (2008) 5. Li, J., et al.: A Hierarchical Test Methodology for Systems on Chip. IEEE Micro 22(5), 69–81 (2002) 6. IEEE Computer Society, IEEE Standard Testability Method for Embedded Core-based Integrated Circuits- IEEE Std 1500TM-2005. IEEE, New York (2005) 7. Dean Adams, R.: High Performance Memory Testing:Design Principles, Fault Modeling and Self –Test. Kluwer Academic Publishers, New York (2005) 8. Aitken, R.C.: A Modular Wrapper Enabling High Speed BIST and Repair for Small Wide Memories. In: International Test Conference (ITC 2004), pp. 997–1005 (2004) 9. Vadeau-Dostie, B., Silburt, A., Agarwal, V.K.: A serial interfacing technique for external and built-in self-testing of embedded memories. IEEE Design & Test of Computers 7(2), 5–64 (1990) 10. Jone, W.B., Huang, D.C., Wu, S.C., Lee, K.J.: An efficient bist method for small buffers. In: Proc. IEEE VLSI Test Symp (VTS), pp. 246–251 (1999) 11. Huang, D.C., Jone, W.B.: A Parallel Built-in Self-Diagnostic Method for Embedded Memory Arrays. IEEE Trans. Computer-Aided Designed of Integrated Circuits and Systems 21(4), 44–465 (2002) 12. Squillero, G., Rebaudengo, M.: Test Techniques for Systems-on-a-Chip, Politecnico di Torino (2005) 13. Benso, A., Bosio, A., Di Carlo, S., Di Natale, G., Prinetto, P.: Automatic March Tests Generations for Static Linked Faults in SRAMs. In: Proceedings Design, Automation and Test in Europe DATE 2006, pp. 1–6 (2006) 14. Bosio, A., Natale, G.D.: March Test BDN: A new March Test for Dynamic Faults. In: IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR 2008, pp. 85–89 (2008) 15. Van de Goor, A.J.: March LR: A test for realistic linked faults. In: Proc. IEEE VLSI Test Symp., pp. 272–280 (1996)

A SVPWM Control Strategy for Neutral Point Potential Compensation in Three-Level Inverter Jinsong Kang and Yichuan Niu Tongji University, College of Electronic and Information Engineering, 200092 Shanghai, China

Abstract. This paper introduces the topology of three-level inverter and analyses its operational process. According to the analysis, traditional SVPWM control strategy will cause neutral point potential shifting, thereby an improved control strategy is designed to solve the potential shifting by resynthesizing vectors in particular sectors. This control strategy reduces the neutral current to a value near zero to compensate the neutral point potential shifting. Simulation and experiment have verify the effect of this SVPWM control strategy. Keywords: three-level neutral point potential SVPWM.

1 Introduction Three-level inverter is widely applied in frequency control for high power scale, utility systems and rail transit etc. In comparison with traditional two-level inverter, three-level inverter possesses the advantages such as lower output voltage harmonics and lower switching loss, besides the electrical stress of power electronic devices is also minimized. Two dominant kinds of topology for three-level inverter are diode clamped and capacitor clamped between which the diode clamped is the most common topology in pragmatic use. Control strategies for three-level inverter could also be divided into three parts: single-pulse control method, PWM control based on carrier wave and space vector pulse width modulation (SVPWM). Because of easier implement in program and a higher DC voltage utilization, SVPWM is applied more extensively. Besides, neutral point potential balancing in three-level inverter needs to be taken into account and realized by modulating operating period of voltage vectors, thereby the mid-point voltage of capacitor can be stabilized.[1] Based on the operational principle of diode clamped three-level inverter (hereinafter referred to as the three-level inverter), this paper focuses on SVPWM control strategy and neutral point potential compensation for three-level inverter. It, starting from the voltage space vector distribution, analyzes the influence for neutral point voltage induced by voltage vector, thus a SVPWM control strategy based on vector synthesis is promoted and its effectiveness is verified by simulations.

2 Analysis of Three-Level Inverter Topology A three-phase three-level inverter topology is shown in Fig. 1. Each phase has four main switching devices, four freewheeling diodes and two clamping diodes. DC rail M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 41–48. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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voltage is divided into two levels by two series capacitors C1 and C2. When power electronic devices VTa1 and VTa2 are conducting, the output voltage of terminal A is UDC/2, while VTa2 and VTa3 are conducting, the output voltage is 0. The conduction of VTa3 and VTa4 means the output voltage has its magnitude of -UDC/2.[2]

Fig. 1. Main circuit of diode clamped three-level inverter

In Fig. 1, the capacitor mid-point O is considered to be the reference neutral point, while point O’ is the mid-point of three-phase load. The relations between phase voltage of three-phase load and output voltage of inverter are

：

⎧U a = U A − U O ' ⎪ ⎨U b = U B − U O ' ⎪U = U − U ⎩ c C O'

(1) D

On the basis of space vector theory, introduce complex operator g( g = e j120 )into these equations, and combine the three-phase voltage equations. Then the vector representation of load phase voltage could be derived as Uref : U ref =

2 (U a + gU b + g 2U c ) 3

2 = [(U A − U O '' ) + g (U B − U O '' ) + g 2 (U C − U O '' )] 3 2 = (U A + gU B + g 2U C ) = u sα + ju sβ 3

In α and β stationary coordinates system,

(2)

usα and usβ are expressed as:

2 1 1 (U A − U B − U C ) 3 2 2 1 = (U B − U C ) 3

u sα = u sβ

(3)

Therefore, the 27 switching states known as output voltage vectors in stationary coordinates for three-phase three-level inverter are shown in Fig. 2(a).

A SVPWM Control Strategy for Neutral Point Potential Compensation

43

β

β

α

α

a. Space vector diagram

b. Vector synthesis in sector I

Fig. 2. Voltage space vector of three-level inverter

There are 19 voltage space vectors valid in the total 27, the other 8 vectors are redundant vectors. According to their magnitude, all space vectors can be divided into four categories: large vectors, medium vectors, small vectors and zero vectors. The 6 large vectors with a magnitude of 2U DC / 3 are: PNN, PPN, NPN, NPP, NNP, PNP. The 6 medium vectors are: PON, PNO, ONP, NOP, NPO, OPN which possess a magnitude of 3U DC / 3 . There are 6 groups of small vectors, each group has 2 vectors with a magnitude of U DC / 3 : (PPO, OON), (POO, ONN), (POP, ONO), (OPP, NNO), (OPP, NOO), (OPO, NON). The three zero vectors are: OOO, PPP and NNN.[5]

3 Neutral Point Potential Balancing of Three-Level Inverter In the operational process of three-level inverter, the inaccuracy of output voltage is caused by the non-conformity in charging and discharging of capacitors, which due to the discrepancy between the current flows into and out of the capacitors. [3] The heterogeneous influences upon neutral point potential may vary with the different vectors. Large vectors connect the phase terminals either to the positive or the negative dc rail. They do not cause the potential shifting in neutral point. The potential in each phase of three-phase load is equal in the 3 switching states of zero vectors. Even when all the loads are connecting to neutral point simultaneously, it will not cause the potential ripple. But, in medium vectors and small vectors, there is at least one phase connected to neutral point while others connected to the dc rail. Thus, the current flows through neutral point causing the unbalancing of neutral point potential in a form of potential ripple that fluctuates around the zero potential over time in a cycle. Fig. 3. shows the analysis of medium vector PON and small vectors POO, ONN. Equivalent circuits are given in Fig. 3, in order to explicate the cause of neutral point potential. iO represents neutral point current, while it is assumed the direction that the current flows out of neutral point is positive. As it's shown in Fig. 3(a), when the medium vector PON is on, neutral point current equals to the phase B load current, that is iO＝ib. When ib>0, capacitor C1 is charging, hence its voltage UC1 increases; while capacitor C2 is discharging, its voltage UC2 decreases.

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a. PON

b. POO

c. ONN

Fig. 3. Equivalent circuit of voltage vectors β NPN

NON NPO

Ⅲ

OPN

Ⅱ

ξ

OPO

OON

PPO

NNN

NPP

OPP

NOO NOP

Ⅳ NNO

NNP

OOP

PPN

PON

Ⅰ

POO

ONN

PPP POP

Ⅵ

α

PNO

ONO

ONP

PNN

Ⅴ

OOO

PNP

Fig. 4. Voltage space vectors with UDC/4 under neutral point potential shifting

Thereby, neutral point potential UO＝(UC2-UC1)/2 will decrease. Similarly, when ib<0, capacitor C1 is discharging, thus UC1 decreases; meanwhile capacitor C2 is charging, its voltage UC2 increases causing neutral point potential UO increases. In Fig. 3(b), neutral point current equals to the sum of load current in phase B and phase C when small vector POO is turning on, that is iO＝ib+ic=-ia. Realizing that when small vector ONN is on, as it is shown in Fig. 3(c), neutral point current equals to the load current of phase A, that is iO＝ia, the conclusion can be made that this small vector has an opposite effect upon neutral point potential in comparison of the previous analysis. Thus, it is neutral point current which contribute the primary cause to the neutral point potential shifting. Moreover neutral point current depends on three-phase load. Further analysis of neutral current shows that: current caused by medium vector can lead to fluctuation around the neutral potential, and quadrature axis will derive more low-frequency ripple than direct axis. Besides, the reactive component of small vector does not affect the neutral point potential, for that its integration is null during a cycle. On the other hand, due to its nonzero integration of active component, the neutral point potential will shift. By assuming the neutral point potential has shifted, the amount of valid voltage space vectors will increase up to 24, which is shown in Fig. 4. Both large vectors and

A SVPWM Control Strategy for Neutral Point Potential Compensation

45

zero vectors remain unchanged. Although the amount of medium vectors is unchanged, the direction and magnitude will alter. They deflect a clockwise angle of ξ degree in sector II, sector IV and sector VI, while in sector I, sector III and sector V, they deflect a anticlockwise degree of ξ. The 6 increments in small vectors remain the same direction, but their magnitudes change significantly in the form of that P vectors will be halved and N vectors will increase by half.

4 An Improved SVPWM Control Strategy An improved SVPWM control strategy utilizes redundancy of vectors to synthesize the output voltage space vectors and the neutral point potential shifting can also be compensated by this strategy. The directions of output voltage vectors are unchangeable, but their magnitudes are adjustable.

a. Improved space vector coordinates

b. Vector synthesis in sector ĉ

Fig. 5. Improved space vector coordinates and vector synthesis in sector

Ⅰ

From the previous analysis, the conclusion is that the two redundant small vectors in the same group have opposite effects upon neutral point potential, thereby the neutral point potential shifting is able to compensate by regulating the operating period of each small vector. For instance, the small vector ONN leads the neutral current iO= ia, while the small vector in the same group POO causes iO=-ia, hence the synthetic small vector is: U10 = (OOP + ONN ) / 2 . If there is serious neutral point potential shifting, it can be balanced by the operating periods of OPP and ONN in this cycle. The effects made by medium vectors alone will surely cause the neutral point potential shifting, however, small vectors can be introduced in order to balance the potential by reducing the neutral point current to zero for that the load current of every phase is remaining constantly during a relatively short sampling time. Use medium vector PON as an example. It will affect the neutral point potential by bringing load current ib out of it. When the small vectors ONN and POO are added, they will cause the corresponding currents ia and ib, thus the final output voltage vector is a synthetic vector: U20 = (PON +ONN + PPO)/3 , which is able to compensate the neutral potential shifting.

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Ⅰ for improved SVPWM control strategy

Table 1. Operating period of vectors in sector Region 1

T00 [1 2msin(S / 3 T )]Ts T10 2m sin(S / 3 T )TS

Region 2 Region 3 Region 4 Region 5

T10 [2 3mcosT 3msinT]Ts

T10

[2 3m cosT 3m sinT ]Ts

T11 [2 2 3m cos T ]Ts T20

T11

T 20

[

3 m cos T 1 ] T s

[2 2 3m cosT ]Ts T21

T11 [2msinT ]Ts

T21 [3m sinT ]Ts [ 3 3 m cos T 3 m sin T 3]T s

T21 [3 3 mcosT 3 msinT]Ts T22 [ 3 mcosT 3 msinT 1]Ts 2 2 2 2

3 3 m cos T 3 m sin T ]T T [ 3 mcosT 3 msinT 1]T [ 3m cosT 1]Ts T21 [3 22 s s 2 2 2 2

Large vectors and zero vectors are not necessary to synthesize, because they have no influence on neutral point potential. Fig. 5(a) shows all the voltage space vectors of improved SVPWM control strategy in coordinates of α and β, while Fig. 5(b) is the synthesis of vectors in sector Ⅰ. Table 1 illustrates the operating period of vectors in sectorⅠ. The regions of other sectors are similar to sector Ⅰ. In region 1 of sector Ⅰ, three basic vectors are U00, U10, U11 and their operating periods are T00, T10, T11. The switching sequence can be derived (the initial small vector is negative): ONN→OON→OOO→POO→PPO→PPO→POO→OOO→OON→ONN, while their operating periods are: T10/4→T11/4→T00/2→→T10/4→T11/4→T11/4→T10/4→T00/2→T11/4→T10/4.

5 Analysis of Simulation and Experiment Simulation is constructed in MATLAB. Assume that the DC voltage is UDC=1000V, the value of capacitor in DC link is 2000uf, the parameter of three-phase load is a motor with a power scale of 20 kW. The result of simulation about motor start is presented in Fig. 6. Fig. 6(a), showing waveforms of load phase voltage and load lineto-line voltage as well as neutral point potential of capacitor, is derived by traditional SVPWM control strategy with a modulation ratio m=0.8. It can be spotted that the neutral point potential suffers a relatively serious fluctuation caused by medium vectors. The potential shifting causes the inaccuracy of output voltage vectors both in phase voltage and line-to-line voltage. Fig. 6(b) shows the waveforms of load phase voltage, load line-to-line voltage and neutral point potential in improved SVPWM

(a)Traditional SVPWM

(b)Improved SVPWM

Fig. 6. Traditional SVPWM and improved SVPWM control strategy with m=0.8

A SVPWM Control Strategy for Neutral Point Potential Compensation

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control strategy with a modulation ratio m=0.8. Obviously, the neutral point potential shifting is compensated by the control strategy and does not ripple at a large fluctuation.

Fig. 7. Neutral point potential in different control strategy with m=0.8

Fig. 7. is the waveform under different control strategies in different period. Period from 0 to 0.1 seconds uses mode 1 in traditional control strategy, while period from 0.1 to 0.3 seconds uses mode 2 in traditional control strategy. From 0.3 seconds on, the improved control strategy for compensating the neutral point potential is introduced. The waveform demonstrates the eminent effect of this control strategy.

(a) Driving signals of VTa1 and VTa3

(b) Driving signals of VTa2 and VTa4

Fig. 8. Driving signals of phase A in improved SVPWM

Fig. 9. Line-to-line voltage and current of phase A

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Fig. 8. and Fig. 9. show waveforms under experiment of improved SVPWM control strategy. Driving signals of VTa1 and VTa3 are shown in Fig. 8(a), and driving signals of VTa2 and

VTa4 are shown in Fig. 8(b). In Fig.9, line-to-line voltage possesses an outline of staircase which is familiar with the single-pulse control strategy. The phase current waveform is nearly sinusoidal, and the identical phase of voltage and current indicates the power factor is 1.

6 Conclusion This paper has researched SVPWM control strategy about compensation of neutral point potential shifting base on diode clamped three-level inverter. It is definite that the traditional control strategy has little effect about the compensation for neutral point potential shifting. Thus, the improved SVPWM control strategy based on vector synthesis is broached and is designed to reduce the potential shifting of capacitor. From the result of simulation, neutral point potential shifting is compensated and the output voltage vectors are more accurate than before. Besides, this improved control strategy can also be applied in multi-level inverters.

References 1.

2. 3.

4. 5. 6. 7.

8.

9.

Holtz, J., Oikonomou, N.: Neutral Point Potential Balancing Algorithm at Low Modulation Index for Three-Level Inverter Medium-Voltage Drives. IEEE Transactions on Industry Applications 43(3), 761–768 (2007) Kang, J.S., Wu, Y., Zhang, Y., Tao, S.G.: Carrier-PWM Control Strategies of Three-level Converters. Journal of Tongji University(Natural Science) 38(1), 124–129 (2010) Celanovic, N., Boroyevich, D.: A Comprehensive Study of Neutral-point Voltage Balancing Problem in Three-level Neutral-point-clamped Voltage Source PWM Inverters. IEEE Transaction on Power Electronics 15(2), 242–249 (2000) Wu, B.: High-Power Converters and AC Drives. Wiley-IEEE Press (2006) Kang, J.S., Zhang, Y.: Multi-level Converter Applied to Wind Power Generation System. Proceedings of the CSEE 24 (2009) Bose, B.K.: Modern Power Electronics and AC Drives. Prentice-Hall, Englewood Cliffs (2001) Song, W.X., Cao, D.P., Qiu, J.Y., Chen, C., Chen, G.C.: Study on the control strategy of three-level PWM rectifier based on SVPWM. In: IEEE 6th International Power Electronics and Motion Control Conference, IPEMC, vol. 1622 (2009) Kocalmis, A., Sunter, S.: Simulation of a Space Vector PWM Controller for a ThreeLevel Voltage-fed Inverter Motor Drive. In: 32th IEEE Indstrial Electronic Coference, pp. 1915–1920 (2006) Xia, L., Xu, G.Q., Hu, B., Kang, J.S.: Simulation Research on Clamped Topology Multilevel Converters. Mechatronics 13(1), 56–60 (2008)

Process Mining: A Block-Structured Mining Approach Yan-liang Qu* and Tie-shi Zhao College of Mechanical Engineering , Yanshan University , Qinhuangdao 066004, China [email protected]

Abstract. Constructing process models from scratch is a complicated timeconsuming task that often requires high expertise. And there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, techniques for discovering process models have been developed. Process mining just attempts to improve this by automatically generating a process model from sets of systems’ executions. In this paper, a block structured process mining approach from audit logs to support process discovery is designed. Compare with other algorithms, the result of this approach is more visible and understanding of process model. This approach is used to a widely commercial tool for the visualization and analysis of process model. Keywords: Process mining; Process logs; Process models; Block-structured Mining.

1 Introduction Today, Setting up and maintaining a process management system requires process models which prescribe how business processes should be managed by the system [1,2,3]. Typically, the user is required to provide these models. Constructing process models from scratch is a difficult and error-prone task that often requires high expertise. An alternative way of constructing process models from scratch is to extract them from event-based data captured in form of logs from running business processes. The goal of discovering models is to extract process models for business processes from such logs [4,5,6,7]. In this paper, a block-structured process mining approach from audit logs to support process modeling is designed. Process mining includes three steps: preprocessing of logs, process mining and reasonable validation of model. Our research focuses on the process mining step and its interfaces to the preceding and subsequent steps. In the first step, the logs are filtered and noise removed to provide a pure and simple log (i.e. audit logs). The second step is the core step of mining. The remainder of this paper is organized as follows: first, we introduce the related works and preliminaries (Section 2), describe in more detail of the approach (Section 3) and then give an application of securities trade (Section 4). To conclude we provide pointers to related work and give some final remarks. *

Corresponding author.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 49–56. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Related Work and Preliminaries In fact the idea of deducing the process model from logs is not new. In 1995, Pro. Jonathan. E.Cook firstly put forward the idea in software engineering area[8]. In 1998, Rakesh Agrawal firstly applied the idea in workflow modeling area[9]. However, until recent years this modeling technology is paid more attention and gradually becomes a new modeling research direction. Gudio Schimm has developed a mining tool (Process Miner) suitable for discovering hierarchically structured workflow processes[10,11]. Joachim Herbst and Dimitris Karagiann applied inductive approach and machine learning method in workflow mining[12,13]. W.M.P van der Aalst and his research team do much research work in workflow mining[14,15]. They have designed several different workflow mining algorithms, which include formal algorithms ( α , α+ , β ) and heuristic algorithm. And they firstly put forward Workflow nets (WF-nets), creatively mapping workflow management concepts onto Pertri nets. A workflow process is designed to handle similar cases. Cases are handled by executing tasks in a specific order. The workflow process model specifies which tasks need to be executed and in what order. Tasks are modeled by transitions and causal dependencies are modeled by places and arcs. Figure 1 is a working example of WF-nets.

Fig. 1. An example of WF-nets

A workflow log is a sequence of events. For reasons of simplicity we assume that there is just one workflow process. Note that this is not a limitation since the case identifiers can be used to split the workflow log into separate workflow logs for each process. Therefore, we can consider a workflow log as a set of event sequences where each event sequence is simply a sequence of task identifiers. Formally, W⊆T+ where W is a workflow log and T is the set of tasks. An example event sequence of Figure 1 is given below: A,O,E,I,J,E,I,J,E,I,P,H Definition 2.1. Let W be a workflow log over T, i.e., W⊆T+. Let a, b ∈T: – a >>W b task a followed by task b directly or indirectly – a >W b if and only if there is a trace σ = t1t2t3 . . . tn−1 and i ∈ {1, . . . , n−2} such that σ∈ W and ti = a and ti+1 = b – a →W b if and only if a >W b and b ≯W a – a W b if and only if a >W b and b >W a

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3 The Block-Structured Mining Method Because the flow of tasks is to be portrayed, control-flow mining techniques need to support the correct mining of the common blocks (e.g., control-flow constructs) that appear in process models. These blocks are: sequences, parallels, alternatives, loops, and non-free-choice, invisible tasks and duplicate tasks. In structured WF-nets, there are only four blocks: sequences, parallels, alternatives and loops. The block-structured mining method is focus on the four blocks above. Of cause, the approach is also helpful to other algorithms that face other blocks. 3.1 Route of the Mining Approach For four blocks: sequences, parallels, alternatives and loops, four mining algorithms are designed. The idea is: aim directly at logs, four mining algorithms are applied circulative in order and found blocks to make logs shrink through replace. The approach show in the below: Do while { do while{ algorithmsⅠ /* Sequence blocks mining } until can’t find new mining blocks do while{ algorithmsⅡ /* Alternative blocks mining } until can’t find new mining blocks do while{ algorithmsⅢ /* Loop blocks mining } until can’t find new mining blocks do while{ algorithmsⅣ /* Parallel blocks mining do mining sequence blocks in parallel blocks mining alternative blocks in parallel blocks } until can’t find new mining blocks } until can’t find new mining blocks Each out loop, one or a few blocks can be found. Through substitution, logs can get shrink and simplifying logs is as the basic data sets for the next loop. 3.2 Four Algorithms Algorithm I: Sequence blocks mining There are many sequence blocks in logs. Sequence blocks can be divided two kinds: explicit and implicit. Explicit sequence constructs can be shown in the logs conspicuously. Some sequence constructs is difficult to be found intuitionally due to the disturbing of other tasks (i.e., tasks in parallel). Next is the explicit sequence blocks mining algorithm. Implicit sequence blocks will be given in parallel blocks mining algorithm.

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Input: logs W, m cases, W={σ1, σ2,…,σm,}, n tasks, T={ t1,t2,t3, . . . tn } Output: a set of new tasks T’, a set of new cases W’ For each pair ∈T×T and a!=b { If a→wb //exist sequence relation between a and b { Found sequence block B=ab Replace ab(series) with B in each case Modify task set T: add B, drop tasks a,b Record B=a+b in document table } } Algorithm II: Alternative blocks mining Alternative construct has two characteristics. First, each alternative block has one start task and one end task in process model. Second, there is only one task between start task and end task. Also, alternative blocks can be divided two kinds: explicit and implicit. Explicit alternatives constructs can be shown in the logs conspicuously. Some alternatives constructs is difficult to be found intuitionally due to the disturbing of other tasks (i.e., tasks in parallel). Next is the explicit alternatives blocks mining algorithm. Implicit sequence blocks will be given in parallel blocks mining algorithm. Input: logs W, m cases, W={σ1, σ2,…,σm}, n tasks, T={ t1,t2,t3, . . . tn } Output: a set of new tasks T’, a set of new cases W’ For each pair ∈T×T and a!=b { Sign=.T. A=Φ Forσ=σ1 to σm { If exist si=a in σ =s1s2s3… { If si+2=b A=A {si+1} Else {sign=.F. Break For } If exist si=b in σ =s1s2s3… { If si-2=a A=A∪{si-1} Else {sign=.F. Break For} }

∪

If sign and |A|>1 //a is start task and b is end task in alternative block. // the elements in set P are alternative items. { B=a+A+b Replace aAb(series) with B in each case Modify T, add B, drop tasks a,b

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Record B=a+A+b in document table } } Algorithm III: Loop blocks mining After running of algorithmⅠand algorithmⅡ, loop constructs have only two kinds in Figure 2 at this time. The length of loop is 0 or 1. So, algorithmsⅢ is composed of part1 and part2.

Fig. 2. Two Kinds of Loop Constructs

Part1: mining loop construct C1 Input: logs W, m cases, W={σ1, σ2,…,σm}, n tasks, T={ t1,t2,t3, . . . tn } Output: a set of new tasks T’, a set of new cases W’ For each t∈T { Sign=.F. For σ = σ1 to σm If exist si=t in σ=s1s2s3… { If si+1=t { Sign=.T. Break For } } If sign{ B=ttt… Replace ttt… with B in each case Modify T, add B, drop task t Record B=t in document table } } Part2: mining loop construct C2 Input: logs W, m cases, W={σ1, σ2,…,σm}, n tasks, T={ t1,t2,t3, . . . tn } Output: a set of new tasks T’, a set of new cases W’ For each t∈T { Sign=.F. L=Φ Forσ=σ1 to σm If exist si=t in σ =s1s2s3… { If si+1<>t and si+2=t {

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Sign=.T. L=L si+1 }

∪

} If sign{ B=t+L Replace t+L with B in each case Modify T, add B, drop tasks t,L Record B=t+L in document table } } Algorithm IV: Parallel blocks mining Firstly, it is necessary to part the parallel tasks. The key of partition is to create the equivalence classes of parallel tasks. It assumes that A={T1,T2,…} is a equivalence classes of parallel tasks. ∀ x1,x2∈Ti, x1 wx2. The algorithm of equivalence classes of parallel tasks:

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(1) Let T’ be the set of parallel tasks and R be the set of parallel tasks relations on logs W. T’=Φ; R=Φ.

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(2) ∀ a,b∈T, if a Wb then R=R∪(a,b),T’=T’∪{a}∪{b} (3) Extended relation R until make R be a equivalence relation. It is easy to make R satisfy reflexive, symmetric and transitive. (4) Partition equivalence classes T’/R.

∀ a∈T’, set Ti=[a]R={x|x∈T,a

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w

x}

So, A=T’/R={T1,T2,…}. Ti is a equivalence class that is the set of all elements that are related to a task a. In other words, the tasks in the set of parallel tasks may be not parallel tasks. Maybe, there exist sequence or alternative relations. For example, it is in Figure 3.

Fig. 3. An example of parallel constructs.

Logs are {BCFDG, BCDFG, BDCFG}. Tasks CFD are parallel tasks. But task C never appears in the behind of task F. So there has the sequence relation between C and F. Next is the rule to find out sequence relation among parallel tasks equivalence classes of A:

∀ Ti∈A, a, b∈Ti if a>>b then the relation of a and b is sequence. Also, there is the rule to find out alternative relation among parallel tasks equivalence classes of A: ∀ Ti∈A, a, b∈Ti if a>>b & b>>a then the relation of a and b is alternative.

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Also, parallel construct have one characteristic. Each parallel block has one start task and one end task in process model. Input: logs W, m cases, W={σ1, σ2,…,σm}, n tasks, T={ t1,t2,t3, . . . tn }, A={T1,T2,…} is the equivalence classes of parallel tasks on W. Output: a set of new tasks T’, a set of new cases W’ For ∀ m, n∈T { Sign=.T. TS=Φ Forσ=σ1 to σm { If tasks(σ,m, n)<>Φ and ∃ Ti∈A,tasks(σ,m, n) ∈Ti { do case TS=Φ: TS=tasks(σ,m, n); TS<>tasks(σ,m, n): Sign=.F.; break for; Endcase } Else { Sign=.F.; Break for;} } If sign { Found parallel block B=mSn Replace mSn with B in each case Modify task set T: add B, drop tasks m, S, n Record B=m+S+n in document table } } Function Tasks(σ,a, b): caseσ= t1t2t3…tn, σ∈W. Exists a=ti,b=tj,i>j then Tasks(σ,a, b)={ti+1,ti+2,…,tj-1}

4 Conclusion In this paper we have presented an approach on discovering process models from process logs. This approach has been validated using logs of transactional information systems. It shows that the approach has an obvious advantage in getting the reasonable, security and understandable model Process mining is not restricted to creating new formal process models. Any organization's process will evolve over time, and thus their process models will need to evolve as well. Methods for discovery process may give a process engineer clues as to when and in what direction the process model should evolve, based on data from the currently executing process. While we have focused here on the use of these methods for generating process models, we also believe that they are useful in visualizing the data collected on a process. An engineer may simply be interested in a way to better understand the current process, as captured by the event data. Discovering patterns of behavior may be of help.

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References 1. van der Aalst, W.M.P., van Hee, K.M.: Workow Management: Models, Methods, and Systems. MIT press, Cambridge (2002) 2. Fischer, L. (ed.): Workow Handbook 2001, Workow Management Coalition. Future Strategies, Lighthouse Point, Florida (2001) 3. Herbst, J.: A machine learning approach to workflow management. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 183–194. Springer, Heidelberg (2000) 4. Agrawal, R., Gunopulos, D., Leymann, F.: Mining Process Models from Workflow Logs. In: Sixth International Conference on Extending Database Technology, pp. 469–483 (1998) 5. van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering 47(2), 237–267 (2003) 6. van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workow Mining: Which Processes can be Rediscovered, WP 74. BETA Working Paper Series. Eindhoven University of Technology, Eindhoven (2002) 7. Hammori, M., Herbst, J., Kleiner, N.: Interactive workflow mining. In: Desel, J., Pernici, B., Weske, M. (eds.) BPM 2004. LNCS, vol. 3080, pp. 211–226. Springer, Heidelberg (2004) 8. Cook, J.E.: Process Discoverying and Validation through Event-Data Analysis. Technical Report CU-CS-817-96, University of Colorado, Boulder, Colorado, Novermber (1996) 9. Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: Sixth International Conference on Extending Database Technology, pp. 469–483 (1998) 10. Schimm, G.: Generic linear business process modeling. In: Mayr, H.C., Liddle, S.W., Thalheim, B. (eds.) ER Workshops 2000. LNCS, vol. 1921, pp. 31–39. Springer, Heidelberg (2000) 11. Schimm, G.: Process miner - A tool for mining process schemes from event-based data. In: Flesca, S., Greco, S., Leone, N., Ianni, G. (eds.) JELIA 2002. LNCS (LNAI), vol. 2424, p. 525. Springer, Heidelberg (2002) 12. Herbst, J., Karagiannis, D.: Integrating machine learning and workflow management to support acquisition and adaptation of workflow models. International Journal of Intelligent Systems in Accounting, Finance and Management 9, 67–92 (2000) 13. Herbst, J.: Dealing with concurrency in workflow induction. In: Baake, U., Zobel, R., AlAkaidi, M. (eds.) European Concurrent Engineering Conference. Society of Computer Simulation (SCS), Europe (2000) 14. van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow Mining: Discovering Process Models from Event Logs. IEEE Transactions on Knowledge and Data Engineering (12), 369–378 (2004) 15. de Medeiros, A.K.A., van der Aalst, W.M.P., Weijters, A.J.M.M.T.: Workflow mining: Current status and future directions. In: Chung, S., Schmidt, D.C. (eds.) CoopIS 2003, DOA 2003, and ODBASE 2003. LNCS, vol. 2888, pp. 389–406. Springer, Heidelberg (2003)

China RoHS: How the Changing Regulatory Landscape Is Affecting Process Equipment Reliability Chris Muller1,* and Henry Yu2 2

1 Purafil, Inc., 2654 Weaver Way, Doraville, GA 30340 USA Purafil Asia, Room 602B, Tengda Plaza, Haidian District, Beijing 100044 China [email protected]

Abstract. In 2006, China promulgated a law entitled “Administration on the Control of Pollution Caused by Electronic Information Products.” The purpose of this law is similar to that of the European Union’s RoHS Directive (2002/95/EC, “restriction of the use of certain hazardous substances in electrical and electronic equipment”). These regulations require the elimination of lead in electronic products and manufacturers now have to comply with RoHS if they want to continue to do business in the EU and China. Corrosion-induced failures were frequent in industrial process control systems even before RoHS regulations with a typical failure mechanism being the reaction of atmospheric sulfur with exposed metals. Corrosion can occur quite rapidly in humid environments especially in the presence of small amounts of atmospheric sulfur and chlorides resulting in e.g., intermittent equipment malfunctions, unplanned shutdowns, or failure of critical systems. This paper will discuss issues related to RoHS compliance, and China RoHS in particular, and the resulting potential for corrosion-related problems. Air quality and failure analysis data will be presented from several sites in China illustrating the fact that in addition to industrial environments, corrosive environments exist in locations that would otherwise be considered benign if not for the changes in electronic equipment mandated by RoHS legislation. Keywords: China RoHS, copper corrosion, corrosion control, electronic equipment, ISA Standard 71.04-1985, process controls, reactivity monitoring, reliability, RoHS, silver corrosion.

1 Introduction In 1998, the European Union (EU) discovered that alarmingly large amounts of hazardous waste were being dumped into landfill sites. Trends also indicated that the volumes were likely to grow 3-5 times faster than average municipal waste. This highlighted a massive, and growing, source of environmental contamination. In order to address these issues, the member states of the EU decided to create the Waste Electrical and Electronics Equipment (WEEE, 2002/96/EC) directive, whose purpose was to: *

Corresponding author.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 57–69. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Improve manufacturers’ designs to reduce the creation of waste, Make manufacturers responsible for certain phases of waste management, Separate collections of electronic waste (from other types of waste), and Create systems to improve treatment, refuse, and recycling of WEEE.

The WEEE directive laid the groundwork for additional legislation and a proposal called EEE (Environment of Electrical & Electronics Equipment) was also introduced along the same lines. However, now this policy is generally referred to as the RoHS Directive and is often referred to as “Lead-Free” legislation. This is not a very accurate nickname, because it extends to other pollutants as well. The European Union (EU) directive 2002/95/EC “on the Restriction of the use of certain Hazardous Substances in electrical and electronic equipment” or RoHS was implemented in July 2006. This directive applies to electrical and electronic equipment designed for use with a voltage rating not exceeding 1,000 volts for alternating current and 1,500 volts for direct current. The requirements of this directive are applicable to the member states of the European Union. The purpose of the directive is to restrict the use of hazardous substances in electrical and electronic equipment and to contribute to the protection of human health and the environmentally sound recovery and disposal of waste electrical and electronic equipment. The EU's RoHS Directive restricts the use of six substances in electrical and electronic equipment: mercury (Hg), lead (Pb), hexavalent chromium (Cr(VI)), cadmium (Cd), polybrominated biphenyls (PBB) and polybrominated diphenyl ethers (PBDE). In order to comply with the EU ROHS legislation, all of these substances must either be removed, or must be reduced to within maximum permitted concentrations, in any products containing electrical or electronic components that will be sold within the European Union. Manufacturers have made significant investments in new processes that will eliminate these substances – especially lead. All applicable products in the EU market must now pass RoHS compliance. In short, RoHS impacts the entire electronics industry and compliance violations are costly – product quarantine, transport, rework, scrap, lost sales and man-hours, legal action, etc. Non-compliance also reflects poorly on brand and image and undercuts ongoing environmental and “due diligence” activities.

2 RoHS – The EU and Beyond Companies selling a broad range of electrical goods in the EU must now conform to WEEE and those same companies must also conform to RoHS. WEEE and RoHS rules, while laid down at the European level, are put into law at the national level. When exporting to Europe, it is essential to comply with national law in each relevant country.1 The EU law simply serves as a template for national laws, which may differ considerably. At the end of February 2006, China promulgated a law entitled “Administration on the Control of Pollution Caused by Electronic Information Products.” The purpose of 1

Croatia, Norway, and Switzerland are not part of the EU. They may nevertheless have legislation implementing EU WEEE and RoHS rules, or similar legislation.

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this law is similar to that of the European Union’s so-called RoHS Directive (2002/95/EC, “restriction of the use of certain hazardous substances in electrical and electronic equipment”). In fact, the Chinese law is simply called “China RoHS” in the industry. While there is some commonality between the RoHS requirements in the EU and those in China, there are also significant differences that must be recognized and dealt with. However, in both instances these RoHS regulations require the elimination of lead in electronic products and manufacturers have to comply with RoHS if they want to continue in to do business in the EU and China. Many consider China RoHS regulations to be considerably more restrictive than those passed in the EU. As described by a potentially-impacted customer: “Without exemptions, it is impossible to build a compliant board” [1]. Although, the regulations are different and are based on different processes, the aims are similar. Convergence of the regulations is not foreseen at present, as it would require high-level negotiations between the EU and China and changes of approach [2]. Some of the key differences between China RoHS and EU RoHS [3]:

The scope is different. The requirements are different. There are no exemptions ... yet. Labels, marks, and disclosure are required. The concept of "Put on the market" is different. The penalties are different. The responsibilities dictated by the law are different. Material testing down to the homogeneous materials in every single part you use to build your product may be required. The regulation has been in force since March 1, 2007. You will have to design labels and issue change orders in order to comply. The standards that you have to comply with just became available in finalized versions.

RoHS regulations are also either in effect or pending in many countries – including Argentina, Australia, Brazil, Japan, Korea, Taiwan, and the United States.

3 Unintended Consequences An aim shared by almost all RoHS legislation is the elimination of lead in electronic products. Thus the main issue for the electronics industry became the use of lead in the manufacture of components and circuit board assemblies. A printed circuit board, or PCB, is used to mechanically support and electrically connect electronic components using conductive pathways, or traces, laminated onto a non-conductive substrate. Alternative names are printed wiring board (PWB), and etched wiring board. A PCB populated with electronic components is a printed circuit assembly (PCA), also known as a printed circuit board assembly). All PCBs have conducting layers on their surface typically made of thin copper foil. If the copper is left unprotected, it will oxidize and deteriorate. Traditionally, any exposed copper was plated with lead (-based) solder by the hot air solder leveling (HASL) process.

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HASL has been working well for many years, is the predominant surface finish used in the industry, and is also the cheapest PCB available. Now RoHS essentially makes PCBs using the HASL process obsolete. Failure modes on other common leadfree PCB finishes such as Organic Solder Preservative (OSP) and electroless-nickel immersion gold (ENIG) make these technologies undesirable. As a result, alternatives such as immersion silver (ImmAg) and organically coated copper (OCC) are currently used as board finishes. Due to inherent processing difficulties with OCC boards, ImmAg boards are becoming the standard PCB finish in the electronics industry [4]. Immersion silver would seem to have a bright future under RoHS [5]. It is easy to apply to the boards, relatively inexpensive, and usually performs well. While ENIG presently has a larger market share, over the past 12 months more immersion silver process lines have been installed in PCB facilities than any other finish. However, some manufacturers have complained about issues with corrosion. If severe enough, this could lead to shorts and ultimate failure of the board. The Internationsl Society for Automation (ISA) Standard 71.04 [6] classifies several levels of environmental severity for electrical and electronic systems: G1, G2, G3 and GX, providing a measure of the corrosion potential of an environment (Table 1). G1 is benign and GX is open-ended and the most severe. Table 1. Classification of Reactive Environments Class G1

Severity Copper Level Reactivity2 Mild

G2 Moderate

G3

Harsh

GX

Severe

Comments

An environment sufficiently well-controlled such that corrosion is not a factor in determining equipment reliability. An environment in which the effects of corrosion are measurable <1000Å and corrosion may be a factor in determining equipment reliability. An environment is which there is a high probability that corrosive attack will occur. These harsh levels should prompt further <2000Å evaluation resulting in environmental controls or specially designed and packaged equipment. An environment in which only specially designed & packaged equipment would be expected to survive. Specifications for ≥2000Å equipment in this class are a matter of negotiation between user & supplier. <300Å

In a study performed by Rockwell Automation [7] looking at lead-free finishes, four alternate PCB finishes were subjected to an accelerated mixed flowing gas corrosion test. Important findings can be summarized as follows: 1) The immersion gold (ENIG) and immersion silver (ImmAg) surface finishes failed early in the testing. These coatings are the most susceptible to corrosion failures and are expected to be much more susceptible than traditional HASL coatings. The use of these two coatings may make the PCB the weak link with regard to the sensitivities of the electronic devices to corrosion. 2

Corrosion is reported as angstroms (Å) normalized to a 30-day exposure.

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2) None of the coatings can be considered immune from failure in an ISA Class G3 environment. 3) The gold and silver coatings could not be expected to survive a mid to high Class G2 environment based on these test results. A leading world authority on RoHS, ERA Technology, has also reported that, “Recent research has shown that printed circuit boards made using lead-free materials can be more susceptible to corrosion than their tin/lead counterparts.” Industry is working diligently to address these concerns but they cannot be addressed overnight. The Reliability and Failure Analysis group at ERA Technology has diagnosed failures in electronic devices due to interaction with low levels of gaseous sulfides – failures that caused both a financial impact to the manufacturers and safety issues with their customers. Recent work showed that corrosion could occur even with measured hydrogen sulfide levels as low as 0.2µg/m3 (0.14 ppb). Another reference describes the formation of a 200 angstrom (20 nanometers) thick layer of silver sulfide in 100 hours at a concentration of just 100µg/m3 (72 ppb) [8]. This is the equivalent of an ISA Class G3 severity level.

4 The Reality of RoHS Corrosion-induced failures have been and continue to be frequent in electronics products used in industrial environments. A typical failure mechanism of electronic systems in these environments is the reaction of atmospheric sulfur with exposed metals – particularly copper and silver. These metals are found in PCB traces, integrated circuit (IC) leads and device terminations. Copper sulfide (Cu2S) or silver corrosion products can grow and creep across surfaces such as IC packages and PCB substrates. Historically, the use of silver in electronic assemblies has been a reliability risk unless the silver is protected from the environment. Silver creep corrosion (electromigration) can occur quite readily in humid environments especially in the presence of small amounts of atmospheric sulfur and chlorides which are common in many paper mill environments. More than twenty years ago manufacturers of distributed control systems (DCS) such as Digital Equipment Corporation (DEC), ABB, and Honeywell described their concerns regarding equipment corrosion in research documents, technical papers, and site planning guides. These companies also provided guidance in terms of environmental conditions necessary to protect their computer equipment (Figure 1) [9]. Manufacturers of industrial computer equipment still specify the control of corrosive gases in their site planning guides and/or the terms and conditions necessary to maintain warranties and service contracts. Even in environments previously considered benign with regards to electronics corrosion serious problems as a direct result of RoHS compliance are being reported. One failure mechanism caused by the lead-free transition was not foreseen by the industry. It was soon discovered that lead-free products with an immersion silver (ImmAg) surface finish will creep corrode in what some electronic equipment manufacturers consider to be high sulfur environments (ISA Class G2 or higher). The

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Fig. 1. Copper corrosion rate distribution guidelines [9].

majority of creep corrosion failures occurred on hard disk drives (HDD), graphic cards, and motherboards in desktop or workstation systems (only those with ImmAg PCB finish were affected). Echoing the study by Rockwell, Hewlett Packard documented several case studies where creep corrosion of ImmAg finish caused failures of computer systems in high sulfur environments (Mazurkiewicz 2006). It was stated that sulfur-based corrosion failures increased dramatically upon introduction of ImmAg surface finish on computer products (due to ROHS requirements). Alcatel-Lucent also has experience with this issue in a paper detailing their work with mixed flowing gas testing of various lead-free surface finishes and their resistance to creep corrosion [10]. Analysis revealed the corrosion products to be copper sulfide (Cu2S) and silver sulfide (Ag2S). High amounts of Cu2S typically indicate the presence of active sulfur compounds such as elemental sulfur (S), hydrogen sulfide (H2S), or organic sulfur

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compounds such as mercaptans. Ag2S can also be formed by these contaminants but can also be formed by exposure to sulfur oxide (SOx) contamination. The most common failures are with the most common components, with the highest incidence of failures being found in capacitors, plastic encapsulated microcircuits (PEMs) and printed circuit boards (PCBs). A University of Maryland study of field failures from 70 companies revealed 30% of ALL failures were capacitors – mainly MLCCs (multilayer chip capacitors) [11].

5 RoHS and Reliability The requirement for corrosion control in industrial environments remains constant. However, more companies are now taking a much closer look at developing or updating specifications due to the changes made by controls manufacturers to comply with the RoHS restrictions on the use of lead. This includes specifying an ISA Class G1 environment for control rooms, etc., where in the past a Class G2 environment was considered acceptable. Specifications are also now showing up requiring the measurement and quantification of silver corrosion rates and the related corrosion products. With many control systems manufacturers opting to use the ImmAg process for their PCBs and other electrical components, and the fact that silver is much more sensitive to lower levels of corrosive gases, we have already seen increased concerns over equipment reliability. As the National Reliability Manager for one pulp and paper manufacturer described it, “The ISA Standard is becoming irrelevant because it does not take into account silver corrosion. All of the new equipment purchased over the last year contains silver and the failure rate for some components is now being measured in months instead of years.” 5.1 Problems with RoHS-Compliant Equipment A recycled paper mill began a project in 2006 to replace obsolete equipment that could no longer be supported (no manufacturer technical support, lack of spares, etc.) with new DCS and programmable logic controls (PLCs). The original hardware had been installed for 15 years and had proven to be reliable and robust. One contributing factor to this was the fact that many of the locations where this equipment had been installed were classified as ISA Class G1 for copper. However, the corresponding silver corrosion rates were up to 20x higher and essentially all of the silver corrosion reported from the analysis of corrosion classification coupon (CCCs) collected over several years was due to sulfur corrosion. This presented some concern based on changes to the equipment due to RoHS compliance. Before starting the replacement program, mill staff was very aware of copper corrosion and the reliability risks associated with exceeding the ISA severity levels specified by the equipment manufacturers. Routine CCC monitoring results showed that copper corrosion was significant but not extreme in many locations; however, the corresponding silver corrosion rates were routinely much higher. On average the silver corrosion rate was 10x that for the corresponding G1 copper rate [12].

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Within three months of replacing the old systems, the mill began experiencing frequent failures of the input/output (I/O) stations installed on the process fieldbus (PROFIBUS).3 A lot of work was put into making the PROFIBUS installations errorfree, and the architectures were reengineered to follow the core rules precisely (i.e., the systems were pulled back to very simple, straight-line installations). These I/O stations or nodes have a number of I/O modules attached to them and communicate the inputs and outputs over the PROFIBUS with the DCS. Since the problems were intermittent and symptoms appeared related to PROFIBUS communication or installation, a full diagnostic process was implemented to capture any and all information about each failure. This included examination of the hardware, the hardware location, its position on the bus, architectures, and software settings. Through systematic investigation and analysis, several potential causes were eliminated and root cause analysis for the remaining issues was performed. Mill staff recognized that the resistance of a choke installed on the base unit of the I/O station to eliminate signal noise was increasing and on failed units was a high or open circuit. Ironically enough this choke was only installed to meet other European legislation pertaining to radio frequency interference (RFI) emissions. The critical issue identified pertaining to this choke was that it is an electronic chip design with a very thin layer of silver on the IC legs in compliance with the lead-free regulations of RoHS. Corrosion of this silver layer destroyed the choke’s connection to the printed circuit and hence the high resistance. Consultation with the manufacturer confirmed the presence of silver on the chokes. The mill was already in the process of installing air cleaning equipment to remove corrosive gases from the air and reduce the rates of copper corrosion, however, the extremely short time to failure for these silver-containing components accelerated this effort in order to prevent additional failures. Staff is confident that these failures were purely the results of RoHS compliance and the use of silver. The lack of information and warnings by the supplier regarding potential issues related to their RoHS compliance programs is troubling considering that even when presented with this evidence, they still concluded these failures were due to “installation errors.” 5.2 Potential Problems Exist Everywhere CCC data from many different locations throughout China show the same trend of environments being classified as an ISA Class G1 for copper but having silver corrosion rates much higher. This indicates serious cause for concern for any electronic equipment using an ImmAg surface finish specifically or any silver or silver-plated components in general. Another contributing factor to concerns over the increased use of silver in industrial applications is that even with tightened control requirements for other environmental parameters (e.g., temperature and humidity) and the positive effect this has on the rate of copper corrosion, silver can still exhibit high rates of corrosion even in well-controlled environments. Closer examination of this CCC database shows that in industrial locations reported as ISA Class G1 for copper corrosion, the 3

PROFIBUS is the dominant fieldbus, with more than 4.8 million of these installed in the process industries by the end of 2008. Utilizing a single-cable “bus” structure, fieldbus eliminates hardwiring in the automation of production lines (http://www.profibus.com/).

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corresponding silver corrosion rate can be up to 10x higher (Table 2). Furthermore, every CCC analyzed shows evidence of sulfur contamination (as Ag2S). In urban locations for many commercial applications such as bank data centers, financial institutions call centers, telecommunications switch centers, and many others, the ratio of silver corrosion to copper corrosion can also be 10:1. On average, however, the amount of silver corrosion measured is 4-5x that of the copper corrosion reported (Table 3) [13]. Contaminant gases containing sulfur, such as SO2 (sulfur dioxide) and H2S, are the most common gases in paper mills, refineries, chemical plants, etc. that cause hardware corrosion and corrosion control in industrial environments is acknowledged as a requirement to assure electrical and electronic equipment reliability. One example of component failure is from sulfur gases entering a component package and attacking the silver resulting in the formation of Ag2S. The mechanical pressure created by the Ag2S formation inside the package damaged its mechanical integrity Table 2. Pulp and Paper Mill CCC Data4 [13]

Location

Area/Room

DC Drive Room Australia DC Drive Rm MCC AHU Room Austria DCS Room Electrical Room Control Room Brazil Operations Room MCC DCS Room China Wet End Control Computer Room DCS Cabinet Finland MCC Control Room Mechanical Room Holland Server Room Splicer Room Control Room Japan IPC Room DCS Room DCS Panel MCC Room Korea PLC Panel Electrical Room USA Wet End Drives Control Room 4

Cu2S 0 0 0 0 182 115 192 205 0 182 182 124 0 0 0 0 0 0 0 0 215 187 204 102 172 196 175

Copper Corrosion CuCu2O Total Unk 101 0 101 142 0 142 112 0 112 180 0 180 78 0 260 46 0 161 101 0 293 88 0 293 125 0 125 104 0 286 78 0 260 44 0 168 118 0 118 106 0 106 150 0 150 68 0 68 78 0 78 105 0 105 173 0 173 231 0 231 62 0 277 61 0 248 56 0 260 50 0 160 34 0 206 81 0 277 86 0 261

Silver Corrosion AgISA Total AgCl Ag2S Unk Class G1 0 1,259 0 1,259 G1 0 2,455 0 2,455 G1 0 2,356 0 2,356 G1 0 3,142 0 3,142 G1 0 3,491 0 3,491 G1 0 1,582 0 1,582 G1 0 1,042 0 1,042 G1 0 1,234 0 1,234 G1 0 948 0 948 G1 0 1,022 0 1,022 G1 0 1,118 0 1,118 G1 0 5,467 0 5,467 G1 0 1,636 0 1,636 G1 0 2,634 0 2,634 G1 0 2,796 0 2,796 G1 0 2,182 0 2,182 G1 0 1,964 0 1,964 G1 0 1,145 0 1,145 G1 0 1,497 0 1,497 G1 0 2,145 0 2,145 G1 0 1,038 0 1,038 G1 0 1,848 0 1,848 G1 0 1,810 0 1,810 G1 0 1,155 0 1,155 G1 35 1,315 0 1,340 G1 0 1,565 0 1,565 G1 0 5,073 0 5,073

The total corrosion measured is actually the sum of individual corrosion films: Cu2S = copper sulfide, Cu2O = copper oxide, CuUnk = copper unknowns, AgCl = silver chloride, Ag2S = silver sulfide, AgUnk = silver unknowns. All data is normailzed to a 30-day exposure.

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Location Bank Headquarters – Tianjin Bank – Beijing Financial Services Co. – Beijing Electronics Co. HQ – Shanghai Financial Services Co. – Shanghai Bank – Shanghai (1) Financial Institution – HK Banking Co. – Shanghai Bank – Shenzhen Investment Co. – Beijing Financial Services Co. – Beijing Bank – Shanghai (1) Banking Company – HK Bank – Shenzhen Industrial Co. HQ – Dong Ying Bank – Jinan Electronics Co. HQ – Zhuhai Bank – Beijing Bank HQ – Tianjin Bank – Shanghai (2)

Cu2S 0 0 110 92 70 0 143 139 159 0 248 258 230 265 299 334 0 426 415 587

Cu2O Cu-Unk Total 69 0 69 75 0 75 40 0 150 88 0 180 121 0 191 205 0 205 69 0 212 90 0 229 91 0 250 268 0 268 67 0 315 89 0 347 121 0 351 91 0 356 80 0 379 75 0 409 465 0 465 85 0 511 179 0 594 180 0 767

G1 copper coupon average G2 copper coupon average

Silver Corrosion ISA Class AgCl Ag2S Ag-Unk Total G1 0 623 0 623 G1 0 636 0 636 G1 0 1,680 0 1,680 G1 0 2,421 0 2,421 G1 0 1,253 145 1,398 G1 0 1,309 0 1,309 G1 0 1,434 0 1,434 G1 0 491 0 491 G1 0 909 0 909 G1 835 96 96 1,027 G2 0 1,666 0 1,666 G2 0 1,047 0 1,047 G2 0 1,476 0 1,476 G2 0 3,200 0 3,200 G2 0 1,603 0 1,603 G2 0 1,833 0 1,833 G2 0 1,549 0 1,549 G2 0 861 0 861 G2 0 1,122 0 1,122 G2 45 1,742 0 1,787

183 G1 silver coupon average 449 G2 silver coupon average

2,128 1,501

and caused the device to fail [14]. This and other failure mechanisms are becoming common occurrences when using RoHS-compliant electronic equipment and components produced using the ImmAg process, and to a lesser degree, the ENIG process. To maintain a high level of equipment dependability and availability, it should be understood that these are dynamic environments where many maintenance operations, infrastructure upgrades, and equipment change activities occur on a regular basis. Airborne contaminants harmful to sensitive electronic devices can be introduced into the operating environment in many ways in addition to the ventilation system. For instance, chlorine can be emitted from PVC insulation use on wires and cables if temperatures inside the DCS cabinets get too high. However, it is still the outdoor ambient air used for cooling and pressurization that remains the primary source of corrosive contaminants and it should be cleaned before its introduction into the control room environment. With the changes to process control equipment due to the RoHS directives, plant managers and operators should include an environmental contamination monitoring and control section as part of an overall site planning, risk management, mitigation, and improvement plan. Included in this plan should be considerations for the assessment of the outdoor air and indoor environment with regards to corrosion potential. ISA Standard 71.04 can be used to provide site-specific data on the types and levels of gaseous contamination and the amount of corrosion being formed. CCCs can be used as a survey tool to

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establish baseline data necessary to determine if and what type of environmental controls are needed. The second part of the plan should be the development and specification of a specific contamination control strategy. Corrosion in an indoor environment is most often caused by a short list of chemical contaminants or combinations of contaminants. The contaminants present in a specific room are highly dependant on the controls put in place to mitigate them. Most often this would involve the selection and application of the appropriate chemical filtration systems to clean both the outdoor ventilation air as well as the indoor air. The final part of the plan would be to establish a real-time environmental monitoring program based on the severity levels established in the ISA Standard. Real-time Atmospheric Corrosion Monitors (ACMs) or Continuous Corrosion Transmitters (CCTs) can provide accurate and timely data on the performance of the chemical filtration system as well as the room air quality [15]. The absence of gaseous contamination controls can be the result of a lack of knowledge and education. Often the relationship between corrosion levels and hardware failures in control rooms is overlooked or unknown. However, due to the continuing efforts of companies like ABB, Dell, Honeywell, HP, IBM, Rockwell, Siemens, and others, this knowledge gap is shrinking and successful corrosion monitoring and control programs are being developed and implemented – assuring reliable operation of process measurement and control equipment. New manufacturing techniques using much thinner conductive components than in previous generations of this equipment, the use of silver as a primary surface finish, and the lack of silver standards from ISA are all contributing factors to an increased risk for electronic process measurement and control equipment used in a paper mill environment. Further, the general lack of information from manufacturers regarding the changes made for RoHS compliance and any resulting reliability issues means that environmental monitoring and contamination control will be mandatory to assure reliable performance and profitable operations.

6 Ongoing and Future Work The amount of corrosion forming over any given period is a primary indicator of how well-controlled an environment may be. It has been proven that if an environment exhibits a reactivity rate of <300Å / 30 days for each copper (ISA Class G1) – without evidence of sulfur contamination (as Cu2S) and silver corrosion – without chlorine contamination (as AgCl) – there is little else that can be done, economically, to improve the environment. Examination of corrosion data from copper and silver CCCs makes it more and more apparent that one cannot accurately determine the true corrosive potential of an environment when following the current methodology of ISA Standard 71.04. This copper-only corrosion monitoring will not and cannot conclusively determine the presence or absence of environmental chlorine or sulfur oxides, and it only provides circumstantial evidence of whether silver corrosion is occurring or not. The fact that the ISA Standard does not take silver corrosion into account when assigning severity levels poses a serious dilemma for those charged with the operation

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and maintenance of critical process control equipment. If monitoring indicates a Class G1 environment (for copper) but there are high levels of silver corrosion, does one go about their business and assume that the environment poses no threat to their electronic equipment? Or do they use silver corrosion monitoring as evidence of an increased risk potential for those components that are now RoHS compliant? These questions are being asked and many are working to provide the answers. The authors are involved with current efforts to update the ISA Standard to include silver corrosion monitoring as a requirement in developing environmental severity levels. The first changes will most likely be to require the use of both copper and silver for environmental assessments with the silver data used both qualitatively (e.g., sulfides present/absent, chlorides present/absent) and quantitatively. Additionally, the Ag2S/Cu2S ratio can be used to indicate humidity effects. 6.1 RoHS Is Here to Stay RoHS costs have been conservatively estimated at $8B industry-wide, yet whether restricting the use of these substances yields even $8B worth of positive environmental impact is questionable [16]. However, the fact remains that there is a perceived environmental benefit to eliminating the lead in the HASL process. Manufacturers of electrical and electronic equipment have to comply if they want to continue to do business in the EU, China, and other countries, and many have taken the ImmAg route as their path to compliance. This has taken care of one issue but has presented new challenges with regards to equipment reliability. Assuring long-term equipment reliability requires working with plant personnel to quantify the corrosive potential of an environment towards the various types of electronic equipment in use, providing engineered solutions for gaseous contaminant control, and ongoing monitoring of the controlled environment to assure compliance with standards and specifications. The successful application of such a corrosion control program in pulp and paper mills, refineries, chemical plants, and many other industrial settings will protect critical process measurement and control equipment and assure profitable manufacturing operations.

References 1. Dills, J.: China RoHS Phase 2: Hard Data Reveals a Red Flag (2007), http://www.emsnow.com/npps/story.cfm?id=24880 2. Barker, C., Xing, W.: Comparative Study of European and Chinese Environmental Measures Restricting Hazardous Substances in Electrical and Electronic Equipment, EUChina Trade Project (2007), http://www.rohs-international.com/site_files/rohsinternational.com/RoHS_Comparative_Study_Final_Report_EN.pdf 3. Kirschner, M.: RohS in China. Conformity Magazine (2006), http://www.conformity.com/PDFs/A07/A07_F25.pdf 4. Mazurkiewicz, P.: Accelerated Corrosion of Printed Circuit Boards due to High Levels of Reduced Sulfur Gasses in Industrial Environments. In: Proceedings of the 32nd International Symposium for Testing and Failure Analysis, Austin, Texas, November 12-16, pp. 469–473 (2006)

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5. UYEMURA International Corporation: Printed Circuit Boards: Final Finish Options (2008), http://www.uyemura.com/library-2.htm 6. ISA: Standard ISA-71.04-1985 – Environmental Conditions for Process Measurement and Control Systems: Airborne Contaminants, International Society for Automation, Research Triangle Park, North Carolina (1985) 7. Veale, R.: Reliability of PCB Alternate Surface Finishes in a Harsh Industrial Environment. In: Proceedings of SMTA International, Rosemont (Chicago), Illinois, September 25-29, pp. 494–499 (2005) 8. Guide to the Compliance with the RoHS Directive [9] Digital Equipment Corporation, Copper Corrosion Rate Distribution Guidelines (2007), http://www.era.co.uk/index.asp 9. Reid, M., Punch, J., Ryan, C., et al.: The Corrosion of Electronic Resistors. IEEE Transactions: Components and Packaging Technologies 30(4), 666–672 (2007) 10. Center for Advanced Life Cycle Engineering, CALCE Long-Term Pb-Free Study (2008), http://www.calce.umd.edu/lead-free/longterm.htm 11. Crosley, G., Muller, C.: How the Changing Regulatory Landscape is Affecting Equipment Reliability, TAPPI EPE Conference (2009) (in print) 12. Purafil .:Corrosion Classification Coupon (CCC) Database (2009) (unpublished data) 13. Anon .: ASHRAE Datacom Series: Particulate and Gaseous Contamination in Datacom Environments (2008) (unpublished) 14. Purafil .: Environmental Monitoring & Assessment Tools (2009a), http://www.purafil.com/products/monitoring.aspx 15. EuroAsia Semiconductor, Substance Regulations, more law! (2006), http://www.euroasiasemiconductor.com/news_full.php?id=7447

Building Process Models Based on Interval Logs Yan-liang Qu* and Tie-shi Zhao College of Mechanical Engineering,Yanshan University, Qinhuangdao 066004, China [email protected]

Abstract. Contemporary process management systems are driven by explicit process models, i.e., a workflow design is required in order to enact a given workflow process. Constructing process models is a complicated and timeconsuming task that requires high expertise. Therefore, techniques for discovering process models have been developed. In this paper, we extend the execution of an activity as a time interval, and present a new method for synthesizing process models from sets of systems’ audit log. A process model graph (directed graph) generated by the method for a process captures all its executions and dependencies that are present in the audit log, and preserves existing parallel. We compare the model graphs generated by the method (interval) to non-interval methods. The observation is that the graphs are more faithful, the number of excess and missing edges is consistently smaller and it depends on the size and quality of the log. Keywords: Process discovery; Process management; Logs; Process models.

1 Introduction Today, process management systems (i.e., workflow management systems) are applied in many organizations. Setting up and maintaining a process management system requires process models which prescribe how business processes should be managed by the system [1]. Typically, the user is required to provide these models. Constructing process models from scratch is a difficult and error-prone task that often requires high expertise. An alternative way of constructing process models from scratch is to extract them from event-based data captured in form of logs from running business processes. The goal of discovering models is to extract process models for business processes from such logs [2,3,4,5]. We assume that the execution of process instances from which we have captured a log is controlled by knowledge that only the actors have in mind. This process-related knowledge is called the implicit model. In the case that a log does not cover all possible ways of executing a process, the discovering process model may be different from the implicit model due to the fact that it contains dependencies between activities which are not part of the implicit model. The obvious limitation of discovering process models is that the quality of a discovering model, with respect to *

Corresponding author.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 71–78. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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its implicit model, depends on how much the log covers the implicit model. Also, its characteristic of being the most specific can only be related to the log, not to the implicit model itself. On the other hand, many organizations that run their systems using legacy applications do not have a model of the processes within the organizations [1]. Current tools for model detection operate on the resource level only. Thus, there is a need for tools to build business process level models rather than the resource level. There are few methods for constructing a business process model from information stored in a workflow log (a collection of process execution logs). In this paper, the interval model is introduced. the idea of interval model is unlike the event-based models in which the execution of an activity is represented as a single event, we view the execution of an activity as a time interval based on its starting and ending events that are present in the workflow log. So we can recognize concurrent activities with a single process execution, since intersecting intervals in a process execution represent concurrent activities. In event based models concurrent activities cannot be recognized in a single process execution. In addition, based on the data in a log of some workflow systems we can recognize conditions that specify when activities are immediately concurrent (i.e., modeled as AND-join from some activity in the model). Given a workflow log we show that compared with the event based approach, the interval approach permit the reconstruction of a more accurate process model graph.

2 Related Studies The topic of process discovery is not new [2,3,4,5,6]. Compared to existing work, our work can be viewed as an extension to the event based approach taken by [2,8]. They use a single event to denote the execution of an activity and reconstruct a directed acyclic graph as the model. In [6] Cook and Wolf searched for software processes by investigating events in concurrent environment. They present three methods for process discovery: neural networks, a purely algorithmic approach, Markovian approach. However, they do not provide an approach to generate explicit process models. W.M.P van der Aalst and their research team do much research work in workflow mining. They have designed several different workflow mining algorithms, which include formal algorithms (α, α+ , β ) and heuristic algorithm. The limitations of the α algorithm are loops, invisible tasks and duplicate tasks. In [9] the method is based on time interval. The algorithm is complex and difficult to realize. In [8] Joachim Herbst and Dimitris Karagiann applied inductive approach and machine learning method in workflow mining. However the algorithm only deduced workflow models with sequential structure.. A comprehensive survey on process discovery techniques is presented in [3].

3 Interval Model The workflow’ log contains the recorded basic information that refers to process executions. Each execution of a process is composed of executed activities ordered on

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a time axis. Although in different systems the structure forms of the log are different,the recorded basic information nearly same. For each process instance (execution), logged records contain the following events of an activity: {ready, started, restarted, ended normally, force finished, terminated}. Each record in the log contains additional data such as: time, process name, process ID (an execution), Activity name, Activity ID, and User ID. Some other systems generate richer audit trails that contain more events per executed activity. One example is the MQWorkflow Audit trail that follows the WFMC audit standard and provides logs that contain start and stop events for each activity (in addition to other events), the details of the running business process instance, and its business process template. Life-span. The life-span of an executed activity is to be the time interval from its start event to its termination event. Extended life-span. The extended life-span of an executed activity is to be the time interval from its ready event to its termination event. Since many legal executions may not be present in the log and since parallel activities appear sequentially in an execution, some none dependent activities can be considered as dependent with respect to the log. In a given process log W over T (T is the set of activities of process) ,let a, b∈ T,we can get: a wb: Activities a and b are concurrent activities if one of the following conditions is satisfied: (3-1) ∃a,b in two executions belong W and two executions over the same activities: in one execution: a appears before b; in other execution: a appears after b (3-2) in one execution: aextended life-span∩bextended life-span≠null a→w b: activity b is not a successor of a if for every execution in W at most one of a or b is present. A consistent workflow graph of a given process log is optimal if it has the smallest number of excess and absent edges (with respect to the original model graph) over all consistent workflow graphs defined by that log.

‖

4 Interval Method for Generating a Process Model Graph The interval method is divided into two steps: Step 1: generating process execution graphs A graph is generated for each execution (execution graph). The interleaving of intervals in a single execution makes this graph non-trivial. During this step we keep information of overlap intervals. Next, a single graph is generated for all the execution graphs that have the same set of activities. Step 2: combining execution graphs Combining executions graphs is done in step2. The reason for combining first executions that run over the same set of activities stems from the observation that, in general, they were all run on a single subset of the workflow graph (i.e., the

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parameters for the business process that was used to select the participating edges were the same). Thus, the resulting graph corresponds to a flow graph. So in the end, the process model graph is generated by combining graphs that are generated from the process executions in the log.

Ⅰ

4.1 Algorithm for Step 1 Input: an execution E from a process log where activity events are sorted based on time. Output: an execution graph 1. Scan E from left to right ,find out all pairs of set :,,…, ,set Ai is continuous finished events, set Bi is continuous ready events; 2. Add to the graph each node for each different activity (not distinguish ready state and finished state) in A1,A2,…,An,B1,B2, …,Bn. For every pair (i=1,2, …,n), add edges from each node in Ai to every node in Bi . Fig. 1 is a sample of original workflow graph and following is its three executions where Ti to a ready event and Ti’ is a terminated event.

Fig. 1. An sample of original workflow graph

1. (T1,T1’,T2,T3,T4,T3’,T5,T6,T2’,T7,T4’,T7’,T8,T5’,T6’,T8’,T9,T9’) 2. (T1,T1’,T3,T2,T4,T2’,T7,T3’,T4’,T5,T6,T8,T7’,T5’,T6’,T8’,T9,T9’) 3. (T1,T1’,T3,T3’,T5,T6,T6’,T8,T8’,T5’,T9,T9’)

Ⅰ

For execution 1,we run algorithm : First we get pairs of set: <{T1},{T2,T3,T4}>,<{T3},{T5,T6}>,<{T2},{T7}>,<{T4,T7},{T8}>,<{T5,T6,T8},{T9}> then we can get:

Fig. 2. Execution graph for execution 1

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Also, algorithm applied on execution2 and 3. Fig. 3 shows the result.

Fig. 3. Execution graphs for execution 2 and 3

Because from a single execution we could not be able to find all the available parallel. And at this stage, there may have redundant edges that may be removed when combining the graph with other execution graphs on the same set of activities. 4.2 Step2: Combining Execution Graphs First we combine execution graphs that have the same set of activities. We refer to the new graph as the reconstructed flow graph. In the step 1 we also generated a set of pairs that comprise the “forbidden” edges. If two activities overlap then there is a pair (forbidden edge) in this set. For example, in execution 1 we can get forbidden edges: (T2,T3),(T2,T4), (T2,T5), (T2,T6), (T3,T4), (T4,T5),(T4,T6), (T4,T7), (T5,T6),(T5,T7) ,(T5,T8) ,(T6,T7) ,(T6,T8); In execution 2:(T3,T2),(T3,T4), (T3,T7), (T2,T4), (T4,T7), (T7,T5),(T7,T6), (T7,T8), (T5,T6),(T5,T8) ,(T6,T8) ; In execution 3:(T5,T6),(T5,T8) ; So, in the example, the resulting flow graphs of executions 1 and 2 are given in Fig. 4. We can see that the edges (T7,T8) (T4,T5) (T4,T6) are removed because they are forbidden edges of executions 1 and 2.

Fig. 4. The resulting flow graph of executions 1 and 2

Now we merge all flow graphs to get the merged flow graph. Fig. 5 is the result of the sample. Edge (T3, T8) is a excess edge. At this merge stage, some of the nodes are changed to have an OR semantics. The merge is carried out in a similar way to the previous step but the forbidden set is now empty. As a result there may exist strongly connected cycles in the merged flow graph. So in the last step, it is necessary to break

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Fig. 5. The resulting flow graph of executions 1,2 and 3

the strongly connected components in the merged flow graph and get the finial workflow graph.

Ⅱ

Algorithm for Breaking Strongly Connected Components Given a merged flow graph G = (V, E). N(G): the set of nodes in G; A(G): the set of G’s edges( included in or out edges). Gscc={C1,C2,…,Ck} is the set of strongly connected components in G. For each C∈Gscc , N(C)=Sc Bc Ic Oc satisfied Sc Bc Ic Oc=null The set of nodes Sc is defined as :

∪ ∪∪ ∩ ∩∩ S ={v | v∈N(C)∧A(v) ⊆A(C)} c

The set of nodes Bc is defined as : Bc={v | v∈N(C)

∧ ∃ s,t∈{N(G)-N(C)}∧<s, v>∈A(C)∧∈A(C)}

The set of nodes Sc is defined as :

{

Ic={v | v∈ N(C)-N(Bc)}

∧ ∃ s∈{N(G)-N(C)}∧<s, v>∈A(v)}

The set of nodes Sc is defined as :

∧ ∃ t∈{N(G)-N(C)}∧∈A(v)}

Oc={v | v∈{N(C)-N(Bc)} do

The edges in A(Bc), the edges going from N(Bc) to N(Ic) and the edges going from N(Sc) to N(Ic) are removed ( The reason of moved edges is that there have no depended relations between them.). So the time complexity of the algorithm is O(e·V2),where e is the number of execution and V is the number of different activities in the log.

5 Experiment Results For comparing different algorithms, a process graph generator was designed and used it to generate a process graph and a set of legal flow subgraphs. Executions were made from the flow graphs to serve as input to the algorithms. We tested two algorithms. The interval algorithm (Interval Sorted) and the Non-Interval algorithm. We show the average percentage of missing and excess edges for each of the two algorithms as function of the size of the log.

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In Fig. 6 each input graph has 15 nodes.

Fig. 6. Comparing the algorithm on logs from graph with 15 nodes

The average missing edges percentage of each of the two algorithms is relatively stable and is independent with the log size. The Interval algorithm gives the best results (between 10% and 9%). The average excess edges percentage is relatively stable for the Non-Interval algorithm. the interval algorithms are more sensitive to the size of the logs. For more experiments and details see [11,12]. One issue of concern is the percentage of missing edges. All two algorithms miss some portion of the original edges. This portion does not depend on the number of executions. Interval algorithms are more accurate (in resemblance to the original model) than the Non-Interval.

6 Conclusion In this paper an approach on discovering process models from time-based logs is introduced .This approach has been validated using logs of transactional information systems such as MQWorkflow. The process model graph generated is more faithful in the sense that the number of excess and missing edges is consistently small and is depends on the size and quality of the logs. Process discovery is not restricted to creating new formal process models. Any organization's process will evolve over time, and thus their process models will need to evolve as well. Methods for discovery process may give a process engineer clues as to when and in what direction the process model should evolve, based on data from the currently executing process. Whilst we have focused here on the use of these methods for generating graphical process models, we also believe that they are useful in visualizing the data collected on a process. An engineer may simply be interested in a way to better understand the current process, as captured by the event data. Discovering patterns of behavior may be of help.

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References 1. van der Aalst, W.M.P., van Hee, K.M.: Workow Management: Models, Methods, and Systems. MIT press, Cambridge (2002) 2. Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 469–483. Springer, Heidelberg (1998) 3. van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow Mining: A Survey of Issues and Approaches. Data and Knowledge Engineering 47(2), 237–267 (2003) 4. van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workow Mining: Which Processes can be Rediscovered? BETA Working Paper Series, WP, vol. 74. Eindhoven University of Technology, Eindhoven (2002) 5. Hammori, M., Herbst, J., Kleiner, N.: Interactive workflow mining. In: Desel, J., Pernici, B., Weske, M. (eds.) BPM 2004. LNCS, vol. 3080, pp. 211–226. Springer, Heidelberg (2004) 6. Cook, J.E., Wolf, A.L.: Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology 7(3), 215–249 (1998) 7. van der Aalst, W.M.P., van Dongen, B.F.: Discovering workflow performance models from timed logs. In: Han, Y., Tai, S., Wikarski, D. (eds.) EDCIS 2002. LNCS, vol. 2480, pp. 45–63. Springer, Heidelberg (2002) 8. Herbst, J., Karagiannis, D.: An inductive approach to the acquisition and adaptation of workflow models. In: Proceedings of the IJCAI 1999 Workshop on intelligent Workflow and Process Management: The new frontier for AI in Business, Stockholm, Sweden, pp. 52–57 (1999) 9. Golani, M., Pinter, S.S.: Generating a process model from a process audit log. In: van der Aalst, W.M.P., ter Hofstede, A.H.M., Weske, M. (eds.) BPM 2003. LNCS, vol. 2678, pp. 136–151. Springer, Heidelberg (2003) 10. Măruşter, L., Weijters, A.J.M.M.T., van der Aalst, W.M.P., van den Bosch, A.: Process mining: Discovering direct successors in process logs. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 364–373. Springer, Heidelberg (2002) 11. Feng, C.: Workflow Mining Algorithm Research on Time-based Log.Master thesis, Shandong University (2006) 12. Cui, Y-s.: Workflow Management with Openness and Flexibility. Master thesis,Shandong University (2004)

Fostering a Management Model of Librarians at Vocational College e-Libraries Ling-Feng Hsieh1, Mu-Chen Wu2, and Jiung-Bin Chin1,3 1

Department of Transportation Technology and Logistics Management, Chung Hua University 2 Department of Technology Management, Chung Hua University Director of Library and Department of Health Business Administration, Hungkuang University 3 Department of Hospitality Management, Hungkuang University, No.34, Chung-Chie Rd., Shalu, Taichung County, 433, Taiwan [email protected]

Abstract. The most serious difficulty in human resources confronted by the vocational college e-libraries of today in Taiwan are resulted from extreme shortage in number of librarians of various libraries, room for improvement in librarian quality and the lack of performance fostering system for librarians. The paper tries to combine innovative human resource practices and systems of business circle in USA and a concept of competency introduced by the business circle of USA, UK and Japan consecutively, conducts in-depth discussion in terms of the effect of human resources at vocational college e-libraries in Taiwan; and plans to propose human resources management strategies that are beneficial to vocational college e-libraries in Taiwan, so as to achieve a purpose of fostering the performance of library affairs to expect to create new opportunities for future development of vocational college e-libraries. Keywords: Innovative Human Resource Practices and Systems, Competency, Vocational College e-Library.

1 Introduction The vocational colleges in Taiwan nowadays face acute competition and are in an era of rapid change in technology, an innovative thinking and reform is inevitably necessary to cater the trend in order to face various harsh challenges. The library on the one hand plays a role to support class activity and academic research in campus, it on the other hand, plays an extremely important “service” unit in various schools; undertaking great task of “information services” required by teachers and students of entire school. In order to cater information technology that changes with each passing day and strong pressure of competition from libraries of same kind, the vocational college e-libraries of today have to apply many management concepts of business circle to foster the performance of library affairs, in which the authors’ more than a decade experience in vocational college e-libraries has enabled a profound understanding to them that human resource related issues are the biggest problems for M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 79–86. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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such kind of library to manage. The paper tries to further discuss the innovative human resource practices and systems of business circle in USA from various perspectives, with various concepts of competency added on to conduct a discussion. Lastly, it plans to propose human resource management strategies that are beneficial to vocational college e-libraries in Taiwan by referring to authors’ 10 years experience in actual participation in vocational college e-library affairs, so as to achieve a purpose of fostering the performance of library affairs to expect to create new opportunities for future development of vocational college e-libraries.

2 Literature Review 2.1 Innovative Human Resource Practices and Systems Perspective Ichniowski et al. (1996) has summed up the meaning of innovative human resource management is to look for higher level of freedom to work, enhance level and scope of employee participation in decision making and foster employee-employer collaboration within an organization. These systems include workplace innovation, profit sharing to employees and job security [1]. According to Huselid (1995), those create High Performance Work Practices are named as Innovative Human Resource Practices and Systems, and the elaboration of so-called High Performance Work Practices includes comprehensive personnel recruitment, selection and training process; official platform for information sharing, attitude towards work evaluation, position design, complaint handling process and employee management participation plan; performance evaluation, promotion & incentive remuneration system policies and processes to reward employees who report outstanding performance [2]. According to Ichniowski et al. (1995), Innovative Human Resource Practices and Systems extensively refer to various non-conventional work practices applied by the business circle of USA, which are all non-conventional work practices that differ from “conventional work practices such as narrow job descriptions that are in direct proportion with salary level, serious cut between worker and supervisor responsibilities, decision right only limited to management level, official channel is required to achieve communication and solve conflict” [3]. Hsu Lien-en, Kang Shuo-fu (1998) pointed that, most of the outcomes of empirical studies have led to a fact that the facilitation of employee’s participation in decision making, enhancement of employee’s capability or creation of high performance working environments all support the theory of Innovative Human Resource Practices and Systems comparatively, where contributions in reduction in staff turnover, enhancement of business productivity and increase in financial performance are recognized especially. However, it will subject to the effects of factors such as system uncertainty, undefined system combination relationship, organization inertia, distrustful employee-employer relationship, features of industrial technology and industry competition environment while implementing Innovative Human Resource Practices and Systems [4,5]. Pfeffer (1994) has once proposed that the Innovative Human Resource Practices and Systems have many positive effects on the business management including : 1.

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Cautious election of employees, 2. Long-term employment, 3. Relative high salary, 4. Employee participation, 5. Training and development, 6. Job rotation, 7. Bonus and reward, 8. Information sharing, 9. Gap shortening, 10. Regular measurement [6]. 2.2 Competency Perspective A general understanding on the competency studied by McClelland (1973) since 1970s is action characteristics of high achiever in terms of job function. The high achievers here refer to people who are steady and always report high performance. The most significant definition of competency in USA refers to personal characteristics that are highly associated with excellent and sustainable job performance [7]. The concept of competency proposed by Mirable (1997) covers knowledge, skills, ability and other characteristics, which are the so-called KSAOs [8]. Lee You-ting and Wu Wei-wen (2003) have consolidated a trend of development for competency as follows: [9]Generally speaking, competency is originated from McBer Consultancy Company founded by McClelland under the commission of United States Department of State in 1970s and began a study on the performance difference of diplomats. There are applications and promotions of competency devoted by McLagan after the mid-1980s, which were respectively applied to manpower recruitment and competency development of human resource management field. Besides, having a lasting achievement, Spencer has developed a competency dictionary with standard scale, which helps evaluate level of competency. A truly growing stage of competency comes after the mid-1990s, with important catalyst comes from the “Competence at work: Model for superior performance” announced by Mr. & Mrs. Spencer (1993), in which a wave of fever in competency adoption has raised among the enterprises [10]. To sum up impartially, it should be feasible for enterprises to apply a concept of competency to recruitment or selection, education training or competency development, human resource appraisal by looking from the experiences of UK, USA and Japan, while it is more difficult to apply the concept to salary or remuneration. Schoonover, Stephen C. (1998) has proposed that the human resource competency required for future success can be divided into: core competencies, level-specific competencies and role-specific competencies according to a “Human Resource Competencies for the Year 2000: The Wake-up Call” [11].

3 The Applications of Two Perspectives in Vocational College e-Libraries According to various discussions mentioned above, it is known that “Innovative Human Resource Practices and Systems” and competency concepts were highly attended to and practically adopted by the business circle, however, the budget amount the librarian number of vocational college e-library are far less than general university libraries, providing service quality and reader demand that have no difference to general university libraries in reality. Therefore, an introduction of novel and effective human resource practices and systems and concepts to vocational

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college e-libraries in Taiwan will be extremely urgent and important issue. Being part of the members who actually works for the vocational college, the authors shall consider how to apply the Innovative Human Resource Practices and Systems and competency concepts to the human resource practices of vocational college e-libraries effectively and practically, and try to discuss and propose feasible suggestions from below few directions: 3.1 An Angle of Management Philosophy The librarian in the library is one of the key factors that affects human resource management, they have to develop unique management philosophy upon specific targets set upon his/her own departments, which are described briefly as below: 1. Abandon authority of leaders and supervisors. 2. Empower staff with greater rights and responsibilities. 3. Empower staff with higher level of freedom to work. 4. Share a chance of leadership with staff. 5. Provide channels to communicate and solve conflicts. 3.2 Features of Department Culture The management of vocational college e-libraries shall establish specific department culture of their own, the essence of this culture should cover employee’s employment security, sharing of employee’s welfare, complete employment system for the employees, diverse training systems for existing and new employees, staff incentive remuneration system, staff job rotation system, incessant innovation of workplace, immediate problem solving ability, establishment of problem solving team, suggestions hearing from employees, mission related systems establishment by role and responsibility, a concept of library is a “work team”. The library will be attended to and recognized more if vocational college e-libraries possess the culture characteristics of this department to provide professional, swift and correct service quality through kind, proactive and active service attitudes. 3.3 Division of Job and the Innovation The vocational college e-libraries in Taiwan are extremely short of manpower in overall, especially the large-scale book procurement, book inventory checking, book numbering, book processing, data input, book on the shelf, shelf location adjustment and other bulky and trivial tasks. The author has realized that a fence between departments shall be broken and shall subject to no original department structure and workplace limitation as long as it involves the whole library, massive and complicated business. An innovative way of temporary mission grouping and project team establishment shall be fully adopted so as to jointly accomplish the tasks of entire library as there is certain timeline for project team establishment normally, therefore the library should have a managerial and forming system to keep the original organization structure exist as remained to sustain high work efficiency. 3.4 Work Team Learning The vocational college e-librarians are unable to deal with the challenges of rapid change in external and internal environments individually, they need to rely on a

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complete work team, and a concept of teamwork, division of job and innovation adopted and encouraged by Innovative Human Resource Practices and Systems will all foster the performance of library. Therefore, a learning-based organization is required for the vocational college e-libraries in terms of establishing an effective and innovative work team, the librarians shall try the five core disciplines of learningbased organization, to proceed from change of mental model, how to learn anytime anywhere, fostering self competency, develop systematic thinking in a novel way and learn self-breakthrough. 3.5 Cautious Selection and Employment of Librarian The guidelines to select and employ the librarians of vocational college e-library are complied with the Innovative Human Resource Practices and Systems and competency concepts. First of all, the library shall conduct “work analysis” upon the job function of each librarian, i.e. requirements of academic and work experiences, level of job title, authority, expertise & competencies and function by responsibility are criteria of human resource planning to the library, which is also a benchmark for the library to select librarians. Moreover, more and more new librarians selected by vocational college e-libraries nowadays are the so-called “new generation”. This fresh new force within the library refers to people born after 1970s or even after 1980s. According to the study by Wu Mei-lien (2005), the new generation reports low stability in work moods comparatively, a weak sense of connection to the organizations or institutions, emphasizes on egoism, reports lower level of commitment to the organization, is short of thinking and reports stronger subjectivity. Therefore, the libraries have to effectively utilize the characteristics of this new generation, with sufficient freedom and flexibility, compliments and encouragement and in-depth understanding rendered through education training, respect & care, to turn their disadvantages into advantages, which will definitely better expert the manpower of vocational college e-libraries [12]. 3.6 Job Rotation of Librarians The librarians of vocational college e-libraries are universally insufficient; therefore, the library should simplify its business and introduce job rotation system. The job rotation of librarians helps reinforce absorption of diverse experience of librarians among various departments of libraries, and enables understanding between departments and experience sharing; while a faster speed of rotation to work under other departments that causes inability to explore a full view of various job functions should be avoided. 3.7 Training and Development of Librarians Another point to highlight the application of Innovative Human Resource Practices and Systems to the vocational college e-libraries is the continuing education of librarians, which can be considered in terms of following directions:

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1. Design the tour training to enable a comprehensive and clear understanding to the new joined librarians who just enter a professional workplace. 2. Establish a monitoring system; minimize the scope of gap between theory and practice through experience sharing from senior librarians. 3. Accomplish the framework of professional discussion; establish an issue that could enable parallel conversation individually, public discussion on the subject with jointly interest, as well as a professional conversation system that stimulates mutual practices. 4. Reinforce management competencies, for example: human resource management, communication and negotiation skills, use of budgets &funds and capability in decision making and analysis etc. 5. Establish an information update system to precisely take control of expertise and development and movement of information technologies. 6. Acquire information skills, according to Huang Cheng-chun (2000), the librarians of vocational college e-libraries should reinforce network transmission know-how, computer software application and computer hardware maintenance and other information related skills [13]. 3.8 Incentive System of Librarians The vocational college e-library is intrinsically a non-profit organization, in which the incentives to motivate the librarians should combine the goal of library with the goal of individual in one upon following methods: 1. Suggest the school to provide special reward to librarians who report outstanding performance. 2. Provide librarians a better quality, comfortable and pleasant working environment. 3. Provide librarians a fair and smooth promotion procedure. 4. Establish a set of comprehensive further study and learning system for librarians. 5. The school should give more respect, consideration and recognition to the librarians. 3.9 Performance Evaluation of Librarians Performance enhancement of employee and organization is one of the purposes of Innovative Human Resource Practices and Systems and competency concepts. Although the knowledge, competency, skill, performance and level of effort of individual librarian differ from one another, a proper measure for performance evaluation is required to enable more understanding of librarians to the management, and to award them and provide suggestion to their advantages and disadvantages in terms of work, so as to enable good interaction to each other, in which this outcome of performance evaluation can be regarded as an indicator to staff promotion, job transfer, disposal, discharge, training and development; however, they should pay attention to and work hard to establish a set of subjective performance evaluation criteria that comply with the times and shall respect personal differences and encourage exceptional outstanding performance of each individual in order to achieve the purpose of performance evaluation.

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3.10 Problem Solving Skill There are many new ways for vocational college e-libraries to execute the missions; including work team, job rotation, problem solving team, employee’s suggestions and quality related missions, the vocational college e-libraries nowadays should introduce training through a manner of “case study” under a circumstance of extreme manpower shortage, have the librarians developed a deep thought on the questions, propose solutions to the questions, choose solutions and analyze the outcomes of decision makings to foster the quality of librarian and adapt-ability for immediate problem solving.

4 Conclusions There will be tougher challenges for various organizations to face when stepping into the 21st century, in which the human resource will be the most valuable asset to various organizations and there has no exception to the library unit of course. The vocational college e-libraries in Taiwan are now facing challenges such as social environment that varies from minute to minute, a more acute competition between schools, rapid change in information technology and increasing reader demand and a difficulty in manpower shortage generally. Therefore, various libraries should effectively adopt Innovative Human Resource Practices and Systems and competency concepts, and introduce related effective strategies that fully implement to daily library affairs. To sum up, a difficulty in manpower shortage will certainly be solved if vocational college e-libraries fully implement the viewpoints and methods of Innovative Human Resource Practices and Systems and competency concepts, where the library staff will be responsible for the tasks that suit themselves and will be able to create action features and competencies with high performance for the library. The library staff will eventually exert the human resource function of vocational college e-libraries to a level of perfection and create opportunities for the development of vocational college e-libraries!

References 1. Ichniowski, C., Kochan, T.A., Levine, D., Olson, S., Strauss, G.: What Works at Work: Overview and Assessment. Industrial Relations 35(3), 299–333 (1996) 2. Huselid, M.A.: The Impact of Human Resource Management Practices on Turnover, Productivity, and Corporate Financial Performance. Academy of Management Journal 38(3), 635–672 (1995) 3. Ichniowski, C., Shaw, K., Crandall, R.W.: Old Dogs and New Tricks: Determinants of the Adoption of Productivity-Enhancing Work Practices. Brookings Papers on Economic Activity, 1–65 (1995) 4. Hsu, L.A., Kang, S.F.: Innovative Human Resource Practices and Systems (Part I). Employee-Employer Relationship Monthly 17(7), 432–438 (1998) 5. Hsu, L.A., Kang, S.F.: Innovative Human Resource Practices and Systems (Part II). Employee-Employer Relationship Monthly 17(7), 491–495 (1998)

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6. Pfeffer, J.: Competitive Advantage through People: Unleashing the Power of the Work Force. Harvard Business School Press, Boston (1994) 7. McClelland, D.C.: Testing for Competence Rather Than for Intelligence. American Psychologist 28(1), 1–24 (1973) 8. Mirable, R.: Everything You Wanted to Know about Competency Modeling. Training and Development 51(8), 73–77 (1997) 9. Lee, Y.T., Wu, W.W.: A Competency Driven Human Resource Management. Princeton, Taipei (2003) 10. Spencer, L.M., Spencer, S.M.: Competence at Work: Model for Superior Performance. John Wiley & Sons, New York (1993) 11. Schoonover, S.C.: Human Resource Competencies for the Year 2000: The Wake-up Call. Alexandria. Society of Human Resource Management, VA (1998) 12. Wu, M.L.: Human Resource Management: Theories and Practices. Best-Wise, Taipei (2005) 13. Huang, C.C.: A Pushing Hand of Human Resource Revolution: E-Learning. Management Magazine 315, 78–79 (2005)

Extension 2D to 3D of FAN Transform in Image Fan Jing, Xuan Ying, and Li Honglian Optoelectronics Information and Communication Engineering College Beijing Information Science and Technology University [email protected]

Abstract. FAN Transform-set was introduced in paper[1], however, Arnold transform only becomes a special case, the encrypted ability was enhanced since the types of transform was expanded. The factor in FAN Transform is generally significant increase than that is in Arnold, so FAN Transform has a better effect which reduces the computation load. On the basis of paper [1], we extended FAN Transform from 2D to 3D, even to ND. The extension not only complements the theory of FAN Transform, but also extends the application of FAN Transform. In the digital watermarking technology, we adopt FAN Transform to Scrambling the image, since the type of transformation is infinite, we can choose one type to scramble at will, this will enhance the ability of encryption, improve the security. This paper mainly explains the principles of multi-dimensional FAN, and tested the practicability of multi-dimensional FAN by computer. Keywords: Image Scrambling, FAN Transform, N-dimensional.

1 Introduction As an encryption technology, image scrambling has become one of the important means of digital image transmission and security storage. The so-called scrambling make use of the character that the digital image has number arrays, disorganizing the location of image pixels or color to make it into disorder, which can not be identified as the original one. As the image watermarking represents the watermarking of copyright information, scrambling can remove the correlation of watermark image pixel and scattering the distribution of bit errors during the watermark pre-processing and post-processing stage, further, enhance the robustness of the watermark. In image encryption technology, the scrambling technique mainly concerned the strength of the encryption or decryption. However the watermarking technology put emphasizes on: image scrambling has the best results as well as possible. Cost (time and computation) of image scrambling and recovery is as low as possible. Nowadays, people often use Arnold transform, magic square transform, fractal Hilbert curve, Gray code conversion, chaotic sequence and so on. Arnold transform algorithm is easy to achieve, applying in the digital watermarking fields now. But Arnold transform gives only one form, which reduces their security, we can resume the image if we do Arnold Transform step by step. Paper [1] analyzes the principles of FAN Transform expanded the types of Arnold transform, improving the strength of encryption. We introduce multi-dimensional FAN Transform on the basis of FAN Transform in this paper,

①

②

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 87–94. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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extending FAN Transform from 2D to 3D, even to ND. The extension not only complements the theory of FAN Transform, but also extends the application of FAN Transform.

2

Dimensional Extension of FAN Transform

The definition of normal 2D FAN Transform-sets: ' ⎛ x⎞ ⎛t00 t01⎞⎛ x ⎞ ⎛ N⎞ ⎟⎟⎜⎜ ' ⎟⎟ + K⎜⎜ ⎟⎟ mod N ⎜⎜ ⎟⎟ = ⎜⎜ y t t ⎝ ⎠ ⎝ 10 11⎠⎝ y ⎠ ⎝ N⎠

(1)

K = Max [ABS (t 00 ), ABS (t 01 ), ABS (t10 ), ABS (t11 )]

(2)

Which,

t00,t01,t10,t11 are integer x’, y’ is the pixel coordinates of the original image, x, y is the transformed pixel coordinates of the new image, N is the side of rectangular image(the amount of pixels in one line)

t 00 × t11 − t 01 × t10 = ± 1

and

(3)

formula (3) is the necessary and sufficient condition of FAN Transform. The Inverse FAN Transform below:

⎛ x ' ⎞ ⎛ r00 ⎜ ⎟=⎜ ⎜ y' ⎟ ⎜r ⎝ ⎠ ⎝ 10 Which,

r01 ⎞⎛ x ⎞ ⎛N⎞ ⎟⎟⎜⎜ ⎟⎟ + K ⎜⎜ ⎟⎟ r11 ⎠⎝ y ⎠ ⎝N⎠

mod N

K = Max [ ABS (r00 ), ABS (r01 ), ABS (r10 ), ABS (r11 )]

r00 =

t11　 t 00 × t11 − t 01 × t10

r10 =

− t10 t00 × t11 − t01 × t10

，，

r01 =

− t 01 t 00 × t11 − t 01 × t10

r11 =

t00 t00 × t11 − t01 × t10

(4)

， (5)

(6)

The principle of 2D FAN Transform also applicable to 3D and even ND. The definition of three-dimensional image of FAN Transform below: ' ⎛ x ⎞ ⎛ t 00 t 01 t 02 ⎞⎛⎜ x ⎞⎟ ⎛N⎞ ⎜ ⎟ ⎜ ⎟ ' ⎜ ⎟ ⎜ y ⎟ = ⎜ t10 t11 t12 ⎟⎜ y ⎟ + K ⎜ N ⎟ mod N ⎜ z ⎟ ⎜t ⎟⎜ ⎟ ⎜N⎟ ⎝ ⎠ ⎝ 20 t 21 t 22 ⎠⎜⎝ z' ⎟⎠ ⎝ ⎠

(7)

Extension 2D to 3D of FAN Transform in Image

89

Which, K is a positive integer, it makes the coordinate value to be a positive value when do Mod operation. t00,t01, t02,t10,t11,t12,t20,t21,t22 are integers. x’, y’, z’ is the pixel coordinates of the original image, x, y, z is the transformed pixel coordinates of the new image, N is the side of square 3D image(the amount of pixels of one side).And

t 00 t11t 22 + t 01t12t 20 + t10 t 21t 02 − t 01t10 t 22 − t 00t12 t 21 − t 02 t11t 20 = ± 1

(8)

In formula (8),the expression on the left of the equal sign is the value of the

t 00

t 01

t 02

t10 t 20

t11 t 21

t12 t 22

determinant: recorded as |T|. The formula above can be recorded as:

，

(9)

|T| = ±1 Now, the inverse FAN Transform below: ⎛ x ' ⎞ ⎛ r00 ⎜ ⎟ ⎜ ⎜ y ' ⎟ = ⎜ r10 ⎜ ⎟ ⎜ ⎜ z' ⎟ ⎝ r20 ⎝ ⎠

r01 r02 ⎞⎛ x ⎞ ⎛N⎞ ⎟⎜ ⎟ ⎜ ⎟ r11 r12 ⎟⎜ y ⎟ + K ⎜ N ⎟ ⎜N⎟ r21 r22 ⎟⎠⎜⎝ z ⎟⎠ ⎝ ⎠

mod N

(10)

Which, K is a positive integer, it makes the coordinate value to be a positive value when do Mod operation.

t t −t t r00 = 11　22 12 21 |T | r10 =

t12　t 20 − t10t 22 |T |

r20 =

t10　 t 21 − t11t 20 |T |

，，，

r01 =

t 02t 21 − t01t 22 |T |

r11 =

t 00t 22 − t02t 20 |T |

r21 =

t 01t 20 − t 00t 21 |T |

，，，

r02 =

t01t12 − t02t11 |T |

(11)

r12 =

t10t 02 − t 00t12 |T |

(12)

r22 =

t00t11 − t01t10 |T |

(13)

if and only if |T| = ±1,the factor of each inverse transform matrix is integer. So |T| = ±1

(14)

Formula (14) is the necessary and sufficient condition of all of the R array factors are integers. According to the definition above, we can give four 3D FAN Transform examples easily:

90

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F. Jing, X. Ying, and L. Honglian ⎛1 ⎜ T = ⎜1 ⎜1 ⎝

．

1⎞ ⎟ 2⎟ 3 ⎟⎠

1 2 2

⎛ 2 ⎜ R = ⎜− 1 ⎜ 0 ⎝

− 1

0 ⎞ ⎟ − 1⎟ 1 ⎟⎠

2 − 1

This is an Arnold Transform expanded to 3D and its inverse transform matrix.

2

3

4

．

．．

⎛1 ⎜ T = ⎜2 ⎜2 ⎝

1⎞ ⎟ 1⎟ 1 ⎟⎠

1 2 1

⎛ 1 ⎜ R = ⎜ 0 ⎜− 2 ⎝

⎛3 ⎜ T = ⎜2 ⎜1 ⎝

2 2 1

1⎞ ⎟ 1⎟ 1 ⎟⎠

⎛ 1 ⎜ R = ⎜−1 ⎜ 0 ⎝

⎛1 ⎜ T = ⎜1 ⎜1 ⎝

1 2 1

1⎞ ⎟ 1⎟ 2 ⎟⎠

⎛ 3 ⎜ R = ⎜−1 ⎜−1 ⎝

No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

t00 1 1 3 1 3 2 2 2 1 2 1 1 1 2 1 1 1 2 2 1 1 2 2 1

t01 1 1 2 2 2 2 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1 1

t02 1 1 1 3 2 3 2 2 1 1 2 1 1 1 2 1 1 1 2 2 1 1 2 2

− 1⎞ ⎟ 1 ⎟ 0 ⎟⎠

0 −1 1

−1

0 ⎞ ⎟ − 1⎟ 2 ⎟⎠

2 −1 −1 1 0

− 1⎞ ⎟ 0 ⎟ 1 ⎟⎠ T10 1 2 2 1 2 1 1 2 1 1 1 1 2 1 1 1 2 1 1 1 2 1 1 1

t11 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 2 2 2 2

t12 2 1 1 2 1 2 2 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1

T20 1 3 1 1 2 2 2 3 2 1 1 1 2 1 1 1 2 2 1 1 2 2 1 1

t21 2 2 1 1 1 1 2 2 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 2

t22 3 1 1 1 2 2 3 2 1 1 1 2 1 1 1 2 2 1 1 2 2 1 1 2

The table below gave some examples of 3D FAN Transform matrixes: As to be requested that |T| = ±1,so if we find an appropriate transform matrix T, then its transport matrix S = TT meets the conditions of |S| = ±1, while the transport matrix

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and the re-transposing matrix of S also meet the conditions of FAN Transform, all of them can be the transform matrixes of FAN Transform. This is to say, if we just find a 3D FAN Transform matrix that is equals to finding four different FAN Transform matrixes. Taking a look at the table above again, in fact, it consists of six kinds of transform matrixes and their eighteen kinds of transport matrixes. Expanding to ND Arnold Transform further: ⎡ x1 ⎤ ⎡1 ⎢ x ⎥ ⎢1 ⎢ 2⎥ ⎢ ⎢. ⎥ = ⎢1 ⎢ ⎥ ⎢ ⎢. ⎥ ⎢. ⎢⎣ xn ⎥⎦ ⎣⎢1

1 1 . 1⎤ 2 2 . 2⎥ ⎥ 2 3 . 3⎥ ⎥ . . . .⎥ 2 3 . n⎦⎥

⎡ x1' ⎤ ⎡N ⎤ ⎢ ' ⎥ ⎢N ⎥ ⎢ x' 2 ⎥ ⎢ ⎥ ⎢. ⎥ + K ⎢. ⎥ mod N ⎢ ⎥ ⎢ ⎥ ⎢. ⎥ ⎢. ⎥ ⎢x' ⎥ ⎢⎣ N ⎥⎦ ⎣ 'n ⎦

(13)

And there are FAN Transform-sets which expanded to ND: ⎡x1 ⎤ ⎡t11 t12 ⎢x ⎥ ⎢t t ⎢ 2 ⎥ ⎢ 21 22 ⎢. ⎥ = ⎢ . . ⎢ ⎥ ⎢ ⎢. ⎥ ⎢ . . ⎢⎣xn ⎥⎦ ⎢⎣tn1 tn2

. . . . .

' . t1n ⎤ ⎡x1 ⎤ ⎡N⎤ ⎥ ⎢ . t2n ⎥ ⎢x''2 ⎥ ⎢N⎥ ⎢ ⎥ ⎥ . . ⎥ ⎢. ⎥ + K⎢. ⎥ mod N ⎥⎢ ⎥ ⎢ ⎥ . . ⎥ ⎢. ⎥ ⎢. ⎥ . tnn ⎥⎦ ⎢x''n ⎥ ⎢⎣N⎥⎦ ⎣ ⎦

(14)

Which, K is a positive integer, it makes the coordinate value to be a positive value when do Mod operation. The ND FAN Transform can be set up only if the determinant made up by matrix T meets the condition of |T| = ±1. The Inverse FAN Transform below:

⎡x'1 ⎤ ⎡r11 ⎢'⎥ ⎢ ⎢x 2 ⎥ ⎢r21 ⎢. ⎥ = ⎢ . ⎢ ⎥ ⎢ ⎢. ⎥ ⎢ . ⎢x' ⎥ ⎢r ⎣ n ⎦ ⎣ n1

r12 . . r1n ⎤ r22 . . r2n ⎥ ⎥ . . . .⎥ ⎥ . . . .⎥ rn2 . . rnn⎥⎦

⎡x1 ⎤ ⎡N⎤ ⎢ ⎥ ⎢ ⎥ ⎢x'2 ⎥ ⎢N⎥ ⎢. ⎥ + K⎢. ⎥ mod N ⎢ ⎥ ⎢ ⎥ ⎢. ⎥ ⎢. ⎥ ⎢x ⎥ ⎢N⎥ ⎣ 'n ⎦ ⎣ ⎦

(16)

Which, matrix R is the inverse matrix of matrix T, and all the elements of matrix T and matrix R are integers.

3

The Simulated Result of 3D FAN Transform

An example of a 16 long side gradient color square below:

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Fig. 1.

Do FAN Transform once with the 3D Transform matrix (1, 1, 1, 1, 2, 2, 1, 2, 3), the result below:

Fig. 2.

Then, do three times FAN Transform, the result is:

Fig. 3.

An example of a 32 long side gradient color square below:

Fig. 4.

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Do FAN Transform once with the 3D Transform matrix (1, 1, 1, 1, 2, 2, 1, 2, 3), the result below:

Fig .5.

Then, do twice FAN transform, the result is:

Fig. 6.

As we can seen from the scramble result, it is very difficult to indentify the original image.

4 Summary In this paper, we discuss the FAN scrambling transform-sets which contain infinite kinds of transform matrixes based on dimensional expanding, 3D FAN Transform can be applied to 3D image scrambling encryption, and simulated on microcomputer. It is illustrated that the FAN Transform expanding from 2D to multi- dimensional is rational, and analyzed the property of 3D Transform matrix. The restoration of spontaneous circulation (ROSC) frequency of multi- dimensional FAN Transform equals to the frequency of 2D FAN Transform and Arnold Transform, there is no analytical expressions at present, it is remained to be studied in the further.

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Aknowledgement This research is supported by National Special Important Science & Technology Research Program (No.2009ZX05042-002-04) and Supported by Human Resources Plan of Beijing Municipal Education Commission (Grant No. PHR200907219)

References 1. Jing, F., Honglian, L.: Image Scrambling By FAN Transform. In: The 2nd International Conference on Image Analysis and Signal Processing, April 9-11 (2010) 2. Wang, X., et al.: Information Hiding Techniques and Applications, pp. 85–109. Mechanical Industry Press, Beijing (2001) 3. Xiong, C., Zou, J.: Digital Image Scrambling Sampling Techniques and Analysis of Results. North China University Journal 14(3), 5–12 (2002) 4. Qiuqi, R.: Digital Image Processing. Publishing House of Electronics Industry, Beijing (2001) 5. Jia, S., Huang, R., Wen, X., Ye, W.: Scrambling and chaos based encryption of digital image watermarking technology research. Journal of Beijing Normal University 41(2), 146–149 (2005) 6. Qi, D., Li, T., Zhang, Z.: A new image scrambling transformation and its application in image information hiding China science(E), vol. 30(5), pp. 440–447 (2000) 7. Ding, W., Yan, W., Qi, D.: Scrambling and integration based on digital image hiding technology and applications. Chinese Journal of Image and Graphics 5A(8), 644–649 (2000) 8. Sun, X., Lu, L.: Arnold Transform in Digital Image Watermarking in applied research. Information Technology (October 2006) 9. Zou, J., Tie, X.: The two dimensional digital image transformation and its cyclical Arnold. Journal of North China University 12 (March 2000) 10. Kong, T., Zhang, Q.: New algorithm of Arnold inverse transform. Software Journal 15(10), 1558–1564 (2004) 11. Tan, Y., Liang, X., Zhang, J., Liu, K.: Scrambling Technology Based on Arnold Transform. Microcomputer Information 22(12-3), 74–76 (2006)

A Design of Low Cost Infrared Multi-touch System Juan Wu1,2,3, Yao-hui Hu1,3, Guo-qiang Lv1,3, and Jing Yin1,2,3 1

Key Laboratory of Special Display Technology, Ministry of Education, Hefei Anhui 230009, China 2 School of Instrument Science and Opto-electronic Engineering, Hefei University of Technology, Hefei Anhui 230009, China 3 Academe of Opto-electronic Technology, Hefei University of Technology, Hefei Anhui 230009, China [email protected], [email protected], [email protected], [email protected]

Abstract. This paper discusses a implementing scheme of infrared multi-touch system. In this scheme, we first employ a preprocessing which combined meanmedian filter with weighted recursive filter. And then the algorithm of mass center is adopted to get the precise position where users touched. Afterwards, we discuss a gesture identification method for multi-touch which is suitable for infrared touch panels. At last, we set up a experiment platform based on C8051F340 to implement and test this algorithm. The result shows that, a infrared multi-touch system is achieved which has the advantage of simple, accurate coordinates, and smooth moving. Keywords: infrared touch panel; filtering; multi-touch; C8051F340.

1 Introduction In recent years, multi-touch as a nature Human Computer Interaction device, with its convenience and nature gesture gets more and more attention. At present, the common used multi-touch can be falled into two broad categories based on the method of recognition and interpreting multiple simultaneous touches[1, 2]. One is sensor based, and the other is based on Computer-Vision. Sensor based touch screen can be divided into resistive touch screen, capacitive touch screen, surface acoustic wave and infrared touch screen [3, 4, 5]. In the mode of realization, computer-vision based system, because of containing a camera, costs high and has a complicate structure[6, 7]. Infrared touch panel, compared with the other sensor based systems, has the advantage of high translucent, long working life for not afraid of scratching, has cost performance especially in large-size display [8]. This article discusses a implementing method of infrared touch pannel, which has the feature of simple structure, high anti-interference, move smooth. In this paper, first, we analysis the touch signal, study preprocessing algorithms for touch signal and technique for implementing multi-touch. Then, we employ a low-cost MCU C8051F340 to build an experimental platform for realizing with this method. Finally, the experimental results of this system are analyzed and discussed. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 95–101. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Algorithm for Implementation Infrared touch panel arranges transmitters and receivers in parallel forming a infrared array, and the receivers sense infrared light from transimitters reflected by a finger. Whenever a finger on the screen, infrared light in certain area is not able to receive the light from the tansmitters, and it will each generate a maximum value on the x-axis and y-axis see Fig.1. Based on this, following coordinate resolution, we display the touched points on screen. In system realization, touch panel exists unstable coordinates, whose influence is not obvious in single touch, however, when used for multi-touch, especially for fast moving fingers, leading to gesture recognition errors. We believe that there are two main causes:

Fig. 1. Theory of infrared touch panel

1. Infrared light of the receivers is sensitive to the fingers movement, so it may swing with a subtle change of the fingers, especially serious when fingers just go on or will soon leave. Therefore, the sampling of touch information has sudden impulse disturbance and periodic interference. Particularly for touch panel must have quick reasponse to user’s actions, so the sampling frequence is high, and coordinates vibrate. 2. Physical coordinates are deside by the number of sensors and the extender position in the corresponding sensor. However, single touch coordinates only judged by the number of sensors, touched or untouched which has low resolution. When the number of sensors is low, mouse jump phenomenon is serious. Hence, even if the sensors closely packed, the display resolution cannot meet the requirements, for that the width of a sensor is 5mm, 8.6mm high, and 940nm wave length. 2.1 Signal Preprocessing we have to increase sampling frequency to satisfy rapidly changing fingers, as exsiting impulse and periodic interference at the same time, a suitable preprocessing algorithm is demanded. The preprocessing presented in this paper, first is to return all the sampling value to zero, that means each sensor subtract its DC element. Then adopt a filter method which combined mean-median filter with weighted recursive filter, to eliminate signal interference, compensate mouse drift. After pretreatment, we employ mass center theory to compute coordinates in order to process the final touch events, such as gesture arithmetic.

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Mean-median filter is a neutralized filtering method which mixed median filter and arithmetic mean, plays a good effect to sudden impluse disturbance. Its main idea is ordering the sampling data X1, X2, …, Xn by size, X1< X2<… <Xn. Remove the maximum and minimum, and we compute arithmetic mean with the rest data. With function(1) we are able to get the result.

x = ( x2 + x3 + ... + xN −1 ) /( N − 2)

(1)

Weigted recursive filter is improvement of recursive filter. It gives different weight to continuous sampling data of each sensor, the later sampling data is, the bigger weight given. Filter algorithm for N-sampling can be seen in function (2). x=C1x1+ C2x2+ …+ CNxN

，

(2)

Where C0 C1,… , Cn are constant, denote the weight of sampling data Xi, the bigger Ci is, the more contribution to the process. Due to cutting down rounding errors and increasing mouse send times, we choose four times for mean-media filter and three for recursive filter, where the constant we select C0=1/4 C1=1/4 C2=1/2. Here we get the balance of frequence of coordinates sending and data wave, according to the experiment data.. This algorithm combines the advantage of median filter and recursive filter, benefits to sudden pulse disturbance and periodic interference.

，

，

2.2 Coordinate Resolving Carefully observe the characteristics of sampled data, we found one finger may block up to four sensors at one time, and varied to each sensor. In this paper, we separate mapping process into quantization and mass center calculate. Quantization means that touch signal is divided by the correspoding sensor data when completely blocked, the percentage of full touched is got. Mass center is an illusion point where quality converge. First, on the basis of many experiment, we select a threshold to determine the position of touched sensor k. And then calculate the quantization of the sensor k, k-1, k-2, k-3 respectively. We can achieve the precise location of touched point from function (3), if we see the width of each sensor as 1.

x = k − 1 + ( data[k ] − data[k − 1] + data[k + 1] + data[k + 2]) / 2

(3)

Where k indicates the kth sensor is touched, and data[k] is its quatization value. Due to the linear relationship of sensor and resolution, we use the touched center x from function (3) to obtain coordinates as function(4). With the same method, Y coordinates are achieved. X=xRes*x/N

(4)

Where xRes is the resolution of X-axis on the display, and N is the sensor number on horizontal axis. 2.3 Multi-touch Gesture Recognition Gesture can be classified into single touch and multi-touch[10]. In single touch, which only has one touched point, we can realize click, double click, and drag. Multi-touch

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in this paper is multi-touch gesture, we control the graphical interface including move, zoom, rotation and scroll using two fingers. However, when there are two fingers touched, each axis has two touched points, which make up four points see Fig.2, and we can not deside which two are the exact touch points, leading to gesture recognition error. Some system cite time sequence to distinguish, believe that one finger always after the other[9], yet there is always the situation that touching at the same time, here we only judge by guessing. In this paper, we recognize gestures by movements of the coordinates in the up left corner and down right corner in a touch circle see Fig.3, which is the average time that fingers stay on the touch panal according to peoples normal habit. Next, we will introduce the judge process and parameter in detail.

Fig. 2. Coordinates exsiting false touched points

Fig. 3. Gesture recogniton

A: Zoom When the two touched points move closer or further to each other, and the rate of its Euclidean distance change exceeds our threshold, we see it as zoom. We use the change trend of maximum and minimum coordinates in a touch cycle, mentioned above, to calculate the zoom ratio. If the Euclidean distance becomes bigger, we zoom in the picture, otherwise zoom out. The width and height after transform can be obtain by function (6,7). Fig.4(a) clearly shows the disposition of zoom in, where A(xa ya), B(xb yb), is the coordinates before judgement, and C(xc yc), D(xd yd) are the latest coordinates.

，

，

，

，

Width = WidthPr e + ( xc − xa ) + ( xb − xd )

(6)

Height = Heigh Pr e + ( y c − y a ) + ( y b − y d )

(7)

B: Rotation When one touch anchors as focus position, while the other rotating around the focus point is detected, as Euclidean distance invariant, we see it rotation. However rotation a picture is not as obvious as it might seem at first because of the two false touched point. It not only contains the judge of rotation, but also rotation angle compute. We use curve fitting error and Euclidean distance to identify. In order to obtain the accurate rotation angle, we adopt trigonomrtric function (8) as Fig.4(b) shows. Follow this, we can rotate a picture in any angle with 0-360o.

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（）Zoom in (b) Rotation

Fig. 4. Multi-touch gesture recognition a

cosA=(a2+b2+c2)/2ab Where a, b are the Euclidean distance before rotate and after rotate, and rotation angle.

(3) A is the

C: Movement or scolling It will be move or scroll, when all touched points close to each other and move as a whole on the panel. The dragging distance is equal to the movement of touched points.

3 Experimental Platform In order to verify the feasibility of this scheme, in this section, we choose low cost MCU C8051F340 to set up a test platform. The main parameters of the MCU: 8051compatible microcontroller core, up to 48 MIPS; true 10-Bit 200Ksps ADC; 1KB USB FIFO RAM, support full speed transfer of 12Mbps; 64KB FLASH, 4KB RAM; precision internal calibrated 12 MHz oscillator and 4x clock multiplier. We designed a multi-touch system to apply to 15-inch LCD(4:3). Touch panel parameters: 63 sets of infrared LEDs on horizontal axis, 51 sets on longitudinal axis. Horizontal and vertical infrared LEDs form an orthogonal array in front of the display. We employ the algorithm including preprocessing, coordinates resolving introduced above to deal with the touch signal after A/D conversion. The results are sending to the host, which is Pentium 4 processor, 3.0GHZ, and 256MB memory. In the host computer, we develop a test software using VC for receiving and displaying the data from MCU, showing the function of multi-touch.

4 Result and Discussion In the test software, we give the same width to each sensor on x-axis, and draw sampling data on y-axis, received from MCU. Fig.5 shows the wave when one finger touched. The first half wave in x-axis shows the sampling value of horizontal, and the last is the data on vertical axis. Through the original data Fig.5(a), we see that the

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Fig. 5. Sample value one finger touched (a) original data (b) data after preprocessing

value of each sensor has great difference to each other, touch signal vibrates and it can not fully express touch information. After returning to zero and filter, we can see the data wave shows in Fig 5(b). There are only minor changes, untouched sensor close to zero, and the peaks can be fully show the touch information. We join all the touched coordinates in VC, with a staight line see Fig.6, after coordinates mapping. Among them, Fig.5(a) is the coordinates result that with the original data, existing obvious mapping errors. After preprocess without mass center theory, we get the touch path as Fig.5(b). Here serious mistakes disappeared, yet the movement error is still big, can not say touch path accurately. Fig5(c) is obtained follow the program discussed in this paper. In this picture, we can see the program not only remedy jitter, but also make move much more smooth. Success rate of coordinates sending can respectively be 95 times/sec and 89 times/sec for without filter and after preprocesing.

Fig. 6. Touch path (a) without filter (b) without mass center algorithm (c)result in this paper

When used for multi-touch, gesture recognition errors caused by mistake coordinates is obvious, and low-resolution add the opportunity of recognition error. Experiments show that the pretreatment and coordinates resolution method discussed in this paper, enhanced the application of infrared touch panel in multi-touch.

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5 Conclusion This article discussed a implementation program for infrared multi-touch. In the program, we preprocessed the signal data of sensor first of all, which including return to zero, mean-median fiter and weighted-recursive filter. Then centroid principle is adopted to compute touch center. Afterwards, from gesture recognition, we achieved click/double click/drag in single touch, and zoom/rotation/move in multi-touch. Finally, low-cost C8051F340 as the experimental platform, built a infrared multitouch system for 15-inch LCD, and developed a test, observe software using VC in host computer to verify the feasibility of this program. Experimental results show that the program implements a cost-effective system, which has the ability that simple, accurate coordinates, and smooth moving, successful coordinates delivery rate up to 89 times/sec. Besides that it can response to touch command quickly and accurately in multi-touch.

References 1. Buxton, B.: Multi-Touch systems that i have known and loved. J. Microsoft Research (2009) 2. Chang, R., Wang, F., You, P.: A Survey on the Development of Multi-touch Technology. In: J. Asia-Pacific Conference on Wearable Computing Systems, pp. 363–366 (2010) 3. Rekimoto, J.: Smartskin: an infrastructure for freehand manipulation on interactive surfaces. In: J. Proceedings of the SIGCHI conference on Human factors in computing systems: Changing our world, changing ourselves. CHI 2002, pp. 113–120. ACM Press, New York (2002) 4. Dietz, P., Leigh, D.: Diamondtouch: a multi-user touch technology. In: Proceedings of the 14th annual ACM symposium on User interface software and technology, UIST 2001, Orlando, Florida, November 11 - 14, pp. 219–226. ACM Press, New York (2001) 5. Apple iPhone, http://www.apple.com/iphone/technology/ 6. Han, J.Y.: Low-cost multi-touch sensing through Frustrated Total Internal Reflection. In: Proceedings of the 18th Annual ACM Symposium on User Interface Software and Technology, pp. 115–118. ACM Press, New York (2005) 7. Morrison, G.D.: A camera-based input device for large interactive displays. J. Computer Graphics and Applications 25(4), 52–57 (2005) 8. Lee, B., Hong, I., Uhm, Y., Park, S.: The Multi-Touch System with High Applicability using Tri-axial Coordinate Infrared LEDs. J. IEEE Transactions on Consumer Electronics. 55(4), 2416–2424 (2009) 9. Bérard, F., Laurillau, Y.: Single user multitouch on the DiamondTouch from 2 x 1D to 2D. In: J. Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces ITS 2009, pp. 1–8. ACM, New York (2009) 10. Kim, S.-G., Kim, J.-W., Bae, K.-T., Lee, C.-W.: Multi-touch interaction for table-top display. In: Pan, Z., Cheok, D.A.D., Haller, M., Lau, R., Saito, H., Liang, R. (eds.) ICAT 2006. LNCS, vol. 4282, pp. 1273–1282. Springer, Heidelberg (2006)

A Review of Research on Network-on-Chip Simulator Haiyun Gu College of Information Engineering, Shanghai Maritime University, Shanghai 200135, China [email protected]

Abstract. The huge design space of Network-on-Chip raise the need of simulators for NoC to efficiently evaluate the performance and select the optimal architecture. This paper presents a review of research on NoC simulators, differentiates the current approaches into three categories: regular network simulators, dedicated NoC simulators, and full-system simulators. We compares the current NoC simulators according to topologies, routing and switching algorithms, traffic models, and discuss the open issues and developing trends in this field. Keywords: NoC, simulator, traffic model.

1 Introduction The amount of hardware resources on a single chip continues to increase. SoCs (System-on-Chips) will integrate hundreds of heterogenous IPs. It is not an easy task to connect all those IPs together and meet the high communication demands. Moreover, the interconnet’s area and power become more significant compared to the transistor’s. The major challenge to SoC designers shifts from computation to communication. NoC (Network-on-Chip), borrowing the communication technologies from the wide area computer networks, is a promising approach to providing a high performance interconnection architecture on a chip. Previous works proposed many different forms of NoC, such as mesh, torus, bipolar tree, application-specific toplogy, etc., along with different routing algorithms and strategies. So it is difficult to select the right form of NoC for a particular design. This raise the need of simulators for NoC to efficiently evaluate the performance and select the optimal interconnection network.. Currently we can mainly differentiate three categories of NoC simulators. The first category is regular network simulators, borrowing from the computer and communication network, such as NS-2[1,2], opnet[3], and OMNET++[4], utilizing the similarities that exist between general networks and NoC. The second category includes several dedicated NoC simulator, such as Nostrum[5], Noxim[6], Nirgam[7], NoCSEP[8], and a JAVA NoC simulator[9]. Most of them implement a high-level representation of NoC, modeling data at message level, providing a set of architectures and protocols, and evaluating the network with the chosen traffic pattern in terms of latency and throughput. Besides, Orion[10] and Ocin_tsim[11] concentrate on the power and area simulation of NoC. DRAGOON[12] proposed a simulator for M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 103–110. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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dynamically reconfigurable NoC. The third category integrates a NoC model into a full-system simulator. GARNET[13] is a NoC model integrated into GEMS, which is a simulation platform for CMPs. All three categories of NoC simulators have their advantages and limits. This paper presents a review of current research on NoC simulators. In section 2, 3, and 4, we compare the existing three categories NoC simulators, and discuss several open issues organized on three aspects: the hardware architecture, the routing protocol, and the traffic pattern. Finally we conclude the current research state of NoC simulator, and suggest the trends in this field.

2 Regular Network Simulators The primary difference between NoCs and general networks is in the size of the two. Because of small dimensions, NoC don't have enough resources for NIC or buffer, but it do have large bandwidth for communication compared to general networks. It means that easier protocols need to be designed for NoCs which require minimum logic and memory resources. ï NS-2, OPNET and OMNET++ are open source, object-oriented and discrete event driven network simulators written in C++. They are very common and widely used tool to simulate computer and communication networks. There are some previous works using these network simulators to evaluate NoC. Because it has been shown that on-chip traffic can exhibit self-similarity, which is also referred as LRD (long-range dependence) [14], [1] used a long-range dependent traffic model to simulate a 8*8 mesh NoC with NS-2. It is convenient for NS-2 to generate arbitrary kind of traffic. The used routing algorithm is Dijkstra’s SPF (Shortest Path First). The simulation results just showed the common relations among loss rate, buffersize and delay. [2] considered about the permanent or transient failures of the communication links under the deep submicron technology, modified the NS-2 simulator to assess their fault tolerant routing protocol, which included a low cost, efficient and fast packet retransmission mechanism to deal with transient faults in NoCs, and a dynamic routing protocol suitable for NoCs in order to deal with permanent faults occuring on-chip. [3] used OPNET to analyse the latency and power of a 4*4 mesh NoC with XY routing, uniform traffic model, and multiple topologies. [4] simulated a 2D torus NoC by OMNET++ choosing wormhole switching and exponential traffic model. Regular network simulators usually have a huge variety of protocols and various topologies can be created with little effort, but they cann’t deal with the logic gates. Although it is simple to simulate a NoC at behaviour level, but it is impossible to obtain a structual or physical design view of NoC by using the regular network simulators.

3 Dedicated NoC simulators Regular network simulators are targeted towards the simulation of large networks with complicated protocols, and the traffic models in them are not representative for

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on-chip traffic. Researchers in NoC field are motivated to develop several dedicated NoC simulators. Nostrum[5], Noxim[6], and Nirgam[7] are open code simulators written in SystemC. They provided configuration commands to define the architecture of NoC (mesh, torus, tree, ring), the routing algorithm (XY routing, source routing, adaptive routing), flow control (wormhole, deflection), and traffic model (possion, burst, on/off, CBR, and input trace). The evaluation standards include the latency, the throughput and the link utilization. Figure 1 shows a typical evaluation flow of NoC simulator.

Fig. 1. A typical evaluation flow of NoC simulator

NoCSEP[8] means the “NoC centric System Exploration Platform”. The special feature of this work is to introduce a quasi-formal description language called “Parallel Application Communication Mechanism Description Language” (PACMDL) to describe the task-graph features of any parallel application. PACMDL was used to drive the application traffic generator which was represented by thread modeling in Fig. 2.

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Fig. 2. Application modeling of NoCSEP

So, NoCSEP can use the application-driven traffic model to simulate the parameterized NoC. Apparently that is very useful to optimize the design of application specific NoCs. [9] proposed a dedicated NoC simulator written in Java, which processes the messages at flit level, implements routing policies and flow control, and collects measurement data. Fig. 3 shows the block diagram of this Java NoC simulator. The paper gave the simulation results of mesh topopogy NoC, with wormhole routing and uniform traffic. It was claimed that Java brought the advantage of portability to this NoC simulator. The dedicated NoC simulators parameterize the NoC at a high level and provide the evaluation of features such as different topologies, variable buffer size, link utilization, routing stratgies, and traffic models. Besides all these dedicated NoC simulators we discussed above, there are several special NoC simulators which respectively enhanced these abstract simulators above in different aspects: ORION[10] intoduced a power and area models for NoC, which has been widely used for early-stage NoC power estimation in literature and industry. Ocin_tsim[11] proposed a DVFS (dynamic voltage frequency scaling) aware NoC simulator with an ORION instance for each node to compute the per-node power consumption. GARNET[13] also incorporates ORION 2.0 power models for power estimation.

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Fig. 3. Block diagram of Java NoC simulator

Fig. 4. Conception flow of DRAGOON

DRAGOON[12] is a dynamically reconfigurable architecture compliant generator and simulator of network. It was used to generate and simulate the DRAFT, a fat-tree topology NoC which can be dynamically reconfigurable depending on the partial reconfiguration capacity of its underlying Xillinx Virtex 5 FPGA. Fig. 4 shows the flow of DRAGOON.

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4 Full-System Simulator A major challenge when designing a NoC simulator is to achieve the right balance between the level of abstraction, and the accuracy and the speed of simulation. Most dedicated NoC simulators used stand-alone models to evaluate the performance of the interconnect architecture, not the performance of the full system. [13] proposed GARNET, a detailed cycle-accurate interconnection network model, to enhance the GEMS full-system simulation platform for CMPs. GARNET model is highly parameterizable and flexible. Various network properties, such as number of VCs, buffer size, number of pipeline stages, flit size, etc., can be passed as parameters. The topology of GARNET is extracted from the GEMS, and can be arbitrary. With the integration of ORION into GARNET, GEMS with GARNET can report full-system power and performance results. Apparently full-system simulator can improve the simulation accuracy and efficiency, but GEMS is just used for CMPs. Similar design tools need to be developed for application specific MPSoCs.

Fig. 5. GEMS (with GARNET incorporated) overview

5 Conclusion To complete our investigation work on NoC simulators, a survey of some reseach and design methods for NoC simulators is presented in Table 1. In this paper, we classify the current research results on NoC simulators into three categories. Comparison of these simulators is done according to topologies, routing and switching algorithms, traffic models. The regular network simulators are flexible and complexed. The dedicated NoC simulators are specific, but the performance need to be improved. The full-system simulator is just for CMPs, similar development tools need to be invented for MPSoC. We should note that most simulations of NoC used

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Table 1. Survey of research of NoC simulators Category

Regular network simulators

Research

Language

topology

Routing, switching

Traffic model

NS-2 with LRD traffic model[1]

C++ and Otcl

mesh

Dijkstra’s SPF

LRD (long range dependence

NS-2 with fault tolerant routing[2]

C++ and Otcl

mesh

2D, 3D mesh, torus, ring, shared-bus OMNET++[4] C++ 2D torus SystemC and Mesh, torus, Nostrum[5] Python tree, ring

OPNEC-Sim[3]

Dedicated NoC simulators

Full-system simulator

C++

Dynamic routing Constant rate XY, adaptive Odd-Even

Uniform

Wormhole

Exponential

XY,VC

Constant rate

Noxim[6]

SystemC

mesh

XY, adaptive

Nirgam[7]

SystemC

Mesh, torus

Source, XY, adaptive, wormhole

NoCSEP[8]

SystemC

Mesh, ring, binary tree, multistage

XY, SPF, dedicated

Java NoC simulator[9]

Java

any

wormhole

DRAGOON [12]

SystemC, VHDL

Fat-tree

Turn-back algorithm

GARNET[13]

C++

any

XY, VC

Possion, burst, on/off Constant rate, burst, input trace Real application driven Uniform Uniform, normal, pareto on/off Uniform random, tornado

the traditional traffic model (uniform, normal, on/off). But NoC is a new class of networks. A traffic model which can reflect the behaviour of on-chip traffic is needed.

Acknowledgement This work is supported by the science & technology program of Shanghai Maritime University. The author is grateful for the anonymous reviewers who made constructive comments.

References 1. kos Hegedus, A., Maggio, G.M., Kocarev, L.: A ns-2 Simulator Utilizing Chaotic Maps for Network-on-Chip Traffic Analysis. ISCAS (4), 3375–3378 (2005) 2. Ali, M., Welzl, M., Hessler, S.: A Fault tolerant mechanism for handling Permanent and Transient Failures in a Network on Chip. In: ITNG 2007, pp. 1027–1032 (2007)

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3. Cai, J., Huang, G., Wang, S., et al.: OPNEC-Sim: An Efficient Simulation Tool for Network-on-Chip Communication and Energy Performance Analysis. In: ICSICT 2010, vol. 2, pp. 1892–1894 (2010) 4. Al-Badi, R., Al-Riyami, M., Alzeid, N.: A Parameterized NoC Simulator Using OMNet++. In: ICUMT 2009, pp. 1–7 (2009) 5. Lu, Z., Thid, R., Millberg, M., et al.: NNSE: Nostrum Network-on-Chip Simulation Environment. In: DATE 2005 University Booth Tool Demonstration (March 2005) 6. http://noxim.sourceforge.net/ 7. http://nirgam.ecs.soton.ac.uk/ 8. Chang, C.-F., Hsu, Y.: A System Exploration Platform for Network-on-Chip. In: ISPA 2010, pp. 359–366 (2010) 9. Grecu, C., Ivanov, A., Saleh, R., et al.: A Flexible Network-on-Chip Simulator for Early Design Space Exploration. In: MNRC 2008, pp. 33–36 (2008) 10. Kahng, A.B., Li, B., Peh, L.-S., Samadi, K.: ORION 2.0: A Power-Area Simulator for Interconnection Networks. In: DATE 2009 (2009) 11. Prabhu, S., Grot, B., Gratz, P.V., Hu, J.: Ocin tsim - DVFS Aware Simulator for NoCs. In: Proc. SAW 2010 (January 2010) 12. Devaux, L., Sassi, S.B., et al.: Flexible Interconnection Network for Dynamically and Partially Reconfigurable Architectures. International Journal of Reconfigurable Computing (2010) 13. Kumar, A., Agarwal, N., Peh, L.-S., Jha, N.K.: A System-Level Perspective for Efficient NoC Design. In: IPDPS 2008 (2008) 14. Varatkar, G., Marculescu, R.: Traffic analysis for on-chip networks design of multimedia applications. In: Proc. of DAC, New Orleans, Louisiana (June 2002)

Design Methodology of Dynamically Reconfigurable Network-on-Chip Haiyun Gu College of Information Engineering, Shanghai Maritime University, Shanghai 200135, China [email protected]

Abstract. NoC (Network-on-Chip) is inherently suitable to the dynamic partial reconfiguration. DRNoC, combining these two promising technologies, will be a desirable platform for the next generation portable eletronic consuming products. Taking the irregular 2D mesh NoC as a study case, this paper discusses the design methodology of DRNoC, including the topology, router, mapping algorithm, routing algorithm, implementation and simulation of DRNoC. Keywords: NoC, dynamica reconfiguration, Xilinx FPGA

1 Introduction Semiconductor technology entered into the era of deep submicron. Interconnect design issues become more important than computation issues in the field of MPSoC and CMP. The performance, area, and power of on-chip communications deserve great attention. A number of research studies have demonstrated the feasibility and advantages of NoC over traditional bus-based architectures. As a packet switched on-chip network for connecting different kinds of IPs, NoC is inherently compatible with dynamical reconfiguration technology, which can greatly improve the system performance by implementing only the needed tasks intermittently. Because NoC is a layered modular network, which can provide a unified interface to different IPs. No direct comunication between modules, especially between dynamic modules and static modules, can satisfy the requirments of Xilinx DPR design methodology. As far as we know, Xilinx FPGAs (especially the Virtex 2 Pro, Virtex 4, Virtex 5, and Virtex 6) are the only ones that both provide sufficient programmable resources to implement complex applications and support the DPR paradigm[1]. In this paper, we will discuss the design issues of DRNoC, including network topology, router architecture, mapping algorithm, routing protocol, implementation and the simulation technology. Section 2 is a brief review of related work. Section 3 is about design issues of DRNoC, we will take our research case of 2D irrgular mesh DRNoC as an example. Section 4 is about the implementation and simulation of DRNoC. We will give the discussion and conclusion in the last section. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 111–116. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Related Work The realization of all DPR approaches strongly depends on the characteristics of the underlying hardware. The architecture of DRNoC is also constrained to the features of target FPGA. [2] presented DyNoC Dynamic Network on Chip , in which larger modules can be implemented on a set of neighbor PEs in a mesh topology. The routers inside the boundary of a module are useless. CoNoChi (Configurable Network-on-Chip)[3] propsed to divide the FPGA into rectangular subareas which can be configured as a switch, as a communication line or as part of a hardware module. Modules are connected to a switch through the interface. A switch can be dynamically inserted or deleted. So CoNoChi is a full customed communication architecture. DRAFT (Dynamic Reconfiguration Adapted Fat-Tree)[1] was implemented as a central column into the FPGA, in order to match the architecture of Xilinx Virtex 5 FPGA in which the center column contains I/Os and DPR ports (ICAP). Actually DRAFT itself was static, it can be seen as an independent core of network or even as an IP block connecting each part of the application. CuNoC (Communication Unit Network-on-Chip)[4] was composed of two types of CU: the classic CU and to-give-away CU (Cugw). Each CU was connected at least to one CU of different type as a back-up. Modules replaced one or several CUs according to its size. The CU does not have input/output buffers, but it has one for all 4 inputs.

（

）

2 Design Issues of DRNoC Previous works proposed many different forms of NoC, such as mesh, torus, bipolar tree, application-specific toplogy, etc., along with different routing algorithms and strategies. To CMPs, it is reasonable to choose a regular topology NoC, such as mesh, torus, tree etc. But in DRNoC, hundreds of heterogenous IPs which may be vary significantly in size, will be integrated on one chip. So regular topology may suffer from area overhead. On the other hand, full-customed topology may cause much code rewriting work of simulator for the lack of flexibility. We try to get a tradeoff by choose an irregular 2D mesh topology, shown in Fig. 1. In [5], we presented the mathmetic model and mapping algorithm in details. Fig. 1 is a mapping result when using a hybrid objective function, compromising between the communication and area cost by a coefficient. Fig. 2 is a typical architecture of router in NoC. Because of small dimensions, NoC don't have enough resources for Network Interface Card or buffer, but it do have large bandwidth for communication compared to general networks. It means that easier protocols need to be designed for NoCs which require minimum logic and memory resources. Xilinx DPR design flow states that the communications between dynamic and static modules should be performed through a interface called Bus Macro, which uses eight 3-state buffers (TBUFs) hooked up in an arrangement that allows one bit of information to travel between static and dynamic modules[6]. DyNoC defined that the routers can be available as hard macro on the programmable chip, apparently the routers in DyNoC integrated the function of Bus Macros in Xilinx architectures.

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Fig. 1. Irregular 2D mesh NoC

Fig. 2. Router architecture in NoC

CoNoChi used a FIFO capable of storing a complete packet for input ports of the switch. The router in DRAFT connected the DPR modules through the Bus Macros provided by Xilinx. We used Dijkstra routing algorithm to find the shortest path between each pair of source and destination node in NoC. As shown in Fig. 3, each node stands for a router, each edge for the communication channel, and the weight of edge for

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∞ ∞

∞ ∞

∞

∞

∞

Fig. 3. Dijkstra SPF routing

communication cost. Speaking to the irregular 2D mesh, when some edges are covered by the resource module, just set the weights of these edges as infinity. Dynamic reconfiguration means a change of traffic at run-time. The path between source and destincation modules should be calculated at run-time with the consideration of network situations. So simple routing algorithm is more suitable to DRNoC than complexed ones. The congestion control and flow control are open issues that needs more attention. Most of current researches on DYNoC choosed adpative XY routing algorithm.

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4 Implementation and Simulation of DRNoC With the embedded hard and soft processor cores, current FPGAs can provide enough hardware resourecs for designers to implement a complete reconfigurable SoC. Xilinx suggested two flow of partial reconfiguration: module-based and difference based. The first approach is broadly used in DRNoC research. But till now no work really implements or simulates the administration of the whole dynamic reconfiguration process. One reason is that current DRNoC approaches are strong restricted to the characteristics of the underlying hardware (Xilinx FPGA). Another reason is the lack of automative development tools. Recently, [1] proposed DRAGOON to generate and simulate the DRAFT, a static fat-tree topology NoC which was claimed to be dynamically reconfigurable depending on the partial reconfiguration capacity of its underlying Xillinx Virtex 5 FPGA. A simulator of DRNoC which will be not stricted to the underlying hardware is needed. It has been shown that on-chip traffic can exhibit self-similarity, which is also referred as LRD (long-range dependence) [7], but most present simulation framework of NoC used the traditional traffic model (uniform, normal, on/off). Especially, there are three distinct types of communication in DRNoC: administration data, application data and configuration data. Obviously a traffic model which can reflect the behaviour of on-chip traffic of DRNoC is needed.

5 Conclusion In this paper, we discussed the design issues of DRNoC, its implementation and simulation. The irrgular 2D mesh topology is used as a study case. Comparing to the traditional computer or communication network, DRNoC has two distinct different characters: the lack of enough hardware resource, and the strong dependence on the underlying FPGA architectures. Definitely, DRNoC is a very potential technology for portable multemedia applications. More work needs to be done to increase the design efficiency of DRNoC. Acknowledgments. This work is supported by the science & technology program of Shanghai Maritime University. The author is grateful for the anonymous reviewers who made constructive comments.

References 1. Devaux, L., Sassi, S.B., et al.: Flexible Interconnection Network for Dynamically and Partially Reconfigurable Architectures. International Journal of Reconfigurable Computing (2010) 2. Bobda, C., Ahmadinia, A., Majer, M., Teich, J., Fekete, S., Veen, J.v.d.: DyNoC: a dynamic infrastructure for communicationin dynamically reconfigurable devices. In: Proceedingsof the International Conference on Field Programmable Logicand Applications (FPL 2005), Tampere,Finland, pp. 153–158 (August 2005)

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3. Pionteck, T., Koch, R., Albrecht, C.: Applying partial reconfiguration to networks-onchips. In: Proceedings of the International Conference on Field Programmable Logic and Applications (FPL 06), pp. 155–160, Madrid, Spain (August 2006) 4. Jovanovic, S., Tanougast, C., et al.: CuNoC: A dynamic scalable communication structure for dynamically reconfigurable FPGAs. Microprocessors and Microsystems 33, 24–36 (2009) 5. Gu, H., Li, C., Sunshu: Research on Mapping Algorithm of irregular mesh NoC for Portable Multimedia Appliances. In: CCWMSN 2007 (2007) 6. Xilinx: Difference-Based Partial Reconfiguration, Application Note XAPP290 (2007) 7. Varatkar, G., Marculescu, R.: Traffic analysis for on-chip networks design of multimedia applications. In: Proc. of DAC, New Orleans, Louisiana (June 2002)

Numerical Simulation Study on a Flat-Plate Solar Air Collector Wenfang Li, Shuhui Xu, Haiguang Dong, and Jun You Beijing University of Civil Engineering and Architecture, 100044 Beijing, China [email protected]

Abstract. Solar air collector is a solar energy utilization device suitable for heating; a new solar air collector with cylindrical fin in the holes arranged in heat collecting plate was designed and studied. Three different structure of the new solar air collector were simulated. Flow patterns, temperature distribution, solar energy collecting efficiency were analyzed. Based on the simulation, solar air collector with optimal structure parameter was manufactured. The simulated results indicated that heat collecting plate with staggered holes arrangement is superior to that with lined holes arrangement, and collector with segment baffle to make the air flow in zigzag flow improved heat collecting efficiency. Heat collecting efficiency of the new designed collector is about 90%. Keywords: solar energy, air collector, numerical simulation, efficiency.

1 Introduction The major population of China lives in rural area, and the main energy consumption of rural house especially in northern China is heating house in winter. On the other hand, the solar resource in northern China is about 5 MKJ/m2·a, and sun shine hour is about 2000 hrs a year. To use solar energy for heating in northern China rural house is a good way to solve energy and environment problem, and the solar air collector is a simple way to use the solar energy. Solar air collector was studied both theoretically and experimentally. YAO Tao studied air collector efficiency with different material experimentally. The air collector includes vacuum glass, polycarbonate, single glass and double glass, and vacuum glass air collector has higher heat collecting efficiency [1]. ZHANG Liping designed a inclined solar air collector to improve the efficiency [2]. LUO Zanji etc al developed an air collector with vacuum glass tube, and the efficiency improved 30% [3]. N. N. Wadaskar also compared the efficiency of plate air collector and vacuum glass tube air collector, and the efficiency of vacuum glass tube air collector is higher 16.12% [4]. Suleyman Karsli compared the different structure of air collector of tube with fin, plate air collector and heat tube collector, and find the structure and solar radiation influence the efficiency [5]. Murat Caner etc al compared the curve and plate air collector experimentally, and simulated the heating characteristics of the air collector with artificial network model and Matlab [6]. B.M. Ramani studied the M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 117–123. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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porous media in air flow channel to improve the efficiency 20% ~ 25% theoretically and experimentally [7]. A simple low cost plate solar air collector was designed and simulated in this paper. Three different structure models of the new solar air collector were simulated. Flow patterns, temperature distribution, solar energy collecting efficiency were analyzed.

2 Model Establishment 2.1 Physical Model The size of the solar air collector is 1600 mm × 250 mm × 80 mm, it contains glass plate, heat plate, back plate and heat isolation walls, it was shown as Figure 1. Both sides of the heat plate was painted with selective absorb coating. The heat plate is made of aluminum, and holes were arranged in the plate, and a fin was made in every hole.

outlet double openings

equally distributed openings double openings inlet

(a)

(b)

Fig. 1. The structure of the solar air collector. (a) solar air collector; (b) heat plate, d1 = 10 mm, d2 = 15 mm.

Fig. 2. The heat plate holes with cylindrical fin.

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The cylindrical fin was made in every hole arranged in heat plate. The height of the fin is 10 mm, and all plate and fin was coated with selective absorb material, which was shown as Figure 2. 2.2 Mathematical Model For making the calculation convenient, some assumptions and simplification are based on the discussion of the air solar-collector system. A. air flow in constant temperature, low velocity, and uncompressible, satisfying the ideal gaseous state equations; B. air in room meets the Boussinesq assumptions; C. natural convection, forced convection and radiation heat transfer occurred simultaneously. k-ε two-equation turbulent model was used for simulation in the paper. Considering the air as incompressible turbulent flow, it could be described by Reynolds time-average equation and continuity equations, which are as follows in the Descartes rectangular coordinate system. Continuity equation

∂ρU i =0. ∂xi

(1)

⎡ ∂U i ∂U j ⎤ + ) ⎥ + ρβ gi (Tref − T ) . ⎢( μ + μt ) ( ∂x j ∂xi ⎥⎦ ⎢⎣

(2)

Momentum conservation equation

∂ρU i ∂ρU iU j ∂p ∂ + =− + ∂t ∂x j ∂xi ∂x j Energy conservation equation

∂ρ h ∂ρ hU j ∂ ⎡ λ μt ∂h ⎤ + = ) ⎢( + ⎥ + SH . ∂t ∂x j ∂x j ⎣⎢ c p Prt ∂x j ⎦⎥

(3)

where ui is velocity of xi, m/s; uj is velocity of xj, m/s; i = 1, 2, 3 are in the rectangular coordinate; ρ is the density of air, kg/m3; p is the pressure of air, Pa; μ and μt are laminar flow viscosity and turbulence viscosity, kg/(m⋅s); β is thermal expansion coefficient of air, 1/K; Tref is reference temperature, K; T is temperature of air, K; gi is acceleration of gravity, m/s2; h is specific enthalpy of air, J/kg; SH is heat source, W/m3; λ is thermal conductivity of air, W/(m⋅K); cp is specific heat at constant pressure, J/(kg⋅K); Prt is turbulence Prandtl number. k equation

ρ

∂k ∂k ∂ + ρU j = ∂t ∂x j ∂x j

⎡⎛ μt ⎢⎜ μ + σ k ⎣⎢⎝

∂U j ⎛ ∂U j ∂U i ⎞ ∂k ⎤ + ⎜ ⎥ + μt ⎟ ∂xi ⎜⎝ ∂xi ∂x j ⎠ ∂x j ⎦⎥

⎞ − ρε . ⎟⎟ ⎠

(4)

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ε equation ρ

∂ε ∂ε ∂ + ρU j = ∂t ∂x j ∂x j

⎡⎛ μt ⎞ ∂ε ⎤ C1 ∂U j ⎢⎜ μ + ⎥+ μ ⎟ σ ε ⎠ ∂x j ⎥⎦ k t ∂xi ⎢⎣⎝

⎛ ∂U j ∂U i + ⎜⎜ ∂x ∂x j ⎝ i

⎞ ε2 . (5) ⎟⎟ − C2 ρ k ⎠

Turbulence viscosity

μt = ρ Cμ

k2

ε

.

(6)

where Cμ = 0.09, C1 = 1.44, C2 = 1.92, σk = 1.0, σε = 1.3. 2.3 Boundary Condition

The simulation was conducted by solar radiation model with software Fluent 6.0. The average environment temperature is 268K, that is the average winter temperature in Beijing area. The glass plate is 3 mm thickness, density of the glass is ρ = 2220 kg/m3, cp = 830 kJ/(kg·K), λ = 1.15 W/(m·K).

3 Simulation Results Analysis In Figure 3 to Figure 5, it is can be found that when the air collector without baffles, the temperature of the heat collecting plate is high, but the temperature of the air in both channels are relatively low. When segment baffles are installed in the channel, the heat transfer of the plate and air was enhanced, and the air temperature increased. The optimal structure of the collector is the flat-plate arranged staggered holes and baffles in the air channel to enhance the solar radiation absorbing and heat transfer with air. There are two methods to evaluate the performance of the solar air collector. One of them is based on the temperature of air in inlet and outlet, Qd c p mΔt c p m (tout − tin ) . (7) = = Qs I⋅A I⋅A where Qd is the heat air obtained, Qs is the heat of solar radiation, m is the mass flow rate, kg/s; cp is specific heat of air, kJ/(kg·K); I is the solar radiation, W/m2; A is the area of the heat collector, tin is the temperature of air in the inlet, and tout is the temperature of air in the outlet. The second way of the air collector evaluation is based on the total heat flow rate of the air,

η1 =

η2 =

Qa .out − Qa .in . I⋅A

(8)

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(a)

(b)

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(c)

bution of the heat plate; (b) Temperature distribution of the side Fig. 3. (a) Temperature distrib wall; (c) Velocity distribution inside of the air collector.

The solar air collector designed d was simulated. Temperature distribution, velocity distribution was shown in Figure F 3 and Figure 4.

Fig. 4. Temperature distributiing on the solar air collector without baffle. line-1: heat colllect plate; line-2: front air channel;; line-3: back air channel.

Fig. 5. Temperature distributing on the solar air collector with segment baffle. line-1: hheat c line-3: back air channel. collect plate; line-2: front air channel;

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Fig. 6. Temperature distributing on the solar air collector with segment baffle. line-1: hheat c line-3: back air channel. collect plate; line-2: front air channel;

The efficiency and param meters of different structure are calculated in table 1. Table 1. The perfformance of three different types of solar air collector Mass Type flow kg/s

T. of inlet K

T. off outleet K

Heat flow Heat flow Solar of inlet of outlet radiation W W W/m2

Plate Solar area radiation m2 W

η %

1

0.03

283.2

288.7 74

-451.16

-261.57

592.2

0.4

236.9

0.800

2

0.03

283.2

289.0 05

-451.16

-248.53

592.2

0.4

236.9

0.866

3

0.03

283.2

290.9 94

-451.16

-235.92

592.2

0.4

236.9

0.899

Note: type 1 is the plate of holes aligned arrangement and without baffle; type 2 is a arrangement and with baffle; type 3 is holes w with holes with cylindrical fin aligned cylindrical fin staggered arrrangement and with baffle.

4 Conclusion Solar air collector with flatt heat collect plate arranged holes, it’s efficiency is abbout 80%. A better arrangemen nt holes increases 4% of the efficiency. Baffles in the air channel also enhance the heat h transfer of plate and air and increase the efficiency about 6%. The heat collect efficiency of the new designed flat plate is about 90%. w was supported in Science Research Project of Beijjing Acknowledgments. This work Municipal Education Com mmission (051100502), Funding Project for Academ mic Human Resources Develo opment in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality (PHR201007127), Beijing Characteriistic B Environment and Facilities Engineering and the Specialty Construction of Building Key Laboratory of Heating,, Ventilation and Air Conditioning in Beijing.

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References 1. Yao, T.: Study on the Solar Collector with the Novel Transparent Material. D. Tianjin University Master Thesis (2005) (in Chinese) 2. Zhang, L.P., Duan, Q.B.: Numerical Simulation on Solar Air Collector with Tilt TIM. J. Energy Conservation Technology 25, 128–129, 189 (2007) (in Chinese) 3. Luo, Z.J., Cheng, X.Y.: Solar Tubular Air Collector Combined with the Construction. In: China Solar Thermal Utilization Annual Meeting (in Chinese) 4. Wadaskar, N.N.: Experimental Investigation of Vacuum Tube Solar Collector. In: Second International Conference on Emerging Trends in Engineering and Technology, ICETET (2009) 5. Karsli, S.: Performance Analysis of New-design Solar Air Collectors for Drying Applications. J. Renewable Energy 32, 1645–1660 (2007) 6. Caner, M.: Investigation on Thermal Performance Calculation of Two Type Solar Air Collectors Using Artificial Neural Network. Expert Systems with Applications 38, 1668–1674 (2011) 7. Ramani, B.M.: Performance of a Double Pass Solar Air Collector. J. Solar Energy 84, 1929–1937 (2010)

Artificial Immune for Harmful Information Filtering Yan Sun and Xue guang Zhou College of Electronic Engineering, Naval University of Engineering, Wuhan, Hubei 430033, China [email protected], [email protected]

Abstract. Biologically-inspired methods such as evolutionary algorithms and neural networks are well suited to dynamic problems. Artificial immune systems are proving useful in the field of information filtering. We tackle this dynamic problem with IHIF, a harmful information filtering model inspired by the immune system. It is based on a self-organising antibody network that reacts to dynamic evolvement in order to define and preserve the features of harmful Web pages. The experiment results demonstrate IHIF’s ability to filter harmful information. Keywords: information security; artificial immune; harmful information; IHIF.

1 Introduction With the proliferation of harmful Internet content such as pornography, violence, and hate messages, network content security presents great challenges to our evolving information society and economy. Effective content-filtering systems are essential. Many Web-filtering systems are commercially available, and potential users can download trial versions from the Internet. However, the ever changing nature of the Web calls for new techniques designed to classify and filter Web sites and URLs automatically. Bio-inspired approaches are well suited to dynamic problems. Artificial immune systems which could discriminate between the host organism’s own molecules (“self”) and external pathogenic molecules (‘‘non-self’’) have applied to computer virus detection [1], spam email filtering [2-3], intrusion detection [4-5], collaborative filtering [6-7], etc., widely. In this paper, we investigate the problem of pornographic Web site classification and filtering. We propose using artificial immune to classify harmful Web pages during filtering. Unlike most commercial filtering products, which are mainly based on indicative keywords detection or manually collected black list checking, our solution, immune-based harmful information filtering (IHIF), is an automatic machine learning-based pornographic Web site classification and filtering system [8]. IHIF would first be trained to learn a class of harmful patterns. Next, use the trained IHIF to filter those which are similar to previous harmful examples. The rest of this paper is organized as follows. Section 2 investigates related work on harmful information filtering. Section 3 proposes the IHIF model. Section 4 describes key techniques of IHIF. Section 5 presents the experimental results. Finally, section 6 concludes this work. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 125–131. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Related Work Four approaches can be quote for harmful information filtering. The first one is the Platform for Internet Content Selection(PICS). The PICS is a set of specification for content-rating systems which is supported by Microsoft Internet Explorer, Netscape Navigator and other several Web filtering systems. PICS can only be used as supplementary means for Web content filtering because it is a voluntary self-labelling system freely rated to content provider [9]. The second approach is white-black list approach. White-black list approach allows Web sites on a white list access and block Web sites on a black list access. This approach is hard to construct and maintain the list timely. The third approach is keywords-based approach. If a page contains a certain number of forbidden keywords, it is considered harmful page. This approach is easy to block inoffensive Web pages which fight against violence or pornography [10]. The fourth approach is intelligent content analysis approach [11]. Intelligent content analysis filtering relies on machine learning. Semantic analysis, image processing and other technique can be applied in. Immune-based harmful information filtering we proposed belongs to intelligent content analysis filtering.

3 IHIF Model Design Fig.1 shows the architecture of our IHIF. It entails two processes: antibodies creation and harmful information filtering. In the antibodies generation process, a large sample of Web pages is used to generate mature cell. The harmful information filtering process then uses antibodies to identify harmful information.

Fig. 1. Immune-based harmful information filtering architecture

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Artificial immune systems are not meant to be accurate models of the biological immune system, but use relevant processes and concepts. In our IHIF, antigens are the word bags of Web pages including harmful and non-harmful Web pages. Antibodies consist of URL, title, keywords, update count, update time, abstract, opinion orientation degree, harmful degree and age of Web pages. Self consists of harmful Web pages while non-self consists of non-harmful Web pages. The main processes of IHIF are described as follows. It consists of 8 stages. (1) Choose certain harmful information dictionary according to requirement. (2) Generate the antigen according to the dictionary. (3) Compute the parameters of WebPages. (4) Generate the mature antigen by the strength threshold. (5) Compute affinity between antibody and antigen. (6) Filter the Webpage which include the certain harmful information by matching threshold. (7) Evolve the antigen according to the harmful information Webpage and memorize the characteristic of certain Webpage. (8) Dynamic updating the harmful information dictionary.

4 IHIF Description Three important processes are described in this section, including affinity computing, self-non-self discrimination and dynamic evolvement of antibodies. 4.1 Affinity Computing Given a current cell, ab, and a Webpage processed into the form of an antigen, ag, the affinity between ab and ag is computed as shown in equation (1) It should be noted that in IHIF we use r-continuous bits matching algorithm. ⎧ 1 iff ∃i, j ag k = ab.keyword k ∧ k = i," , j ∧ j − i ≥ r f m (ag , ab) = ⎨ ⎩0 otherwise

(1)

Where 1 denotes antibody matches antigen successfully and 0 denotes antibody matches antigen unsuccessfully. ag k denotes the number character in antigen ag .

ab.keywordk denotes the number character of keyword in antibody ab . 8 ≤ r ≤ l . l denotes the total number of characters of antigen ag . 4.2 Self-non-self Discrimination The ability for self–non-self discrimination is what makes the immune system a particularly appealing metaphor. In IHIF, memory cells will identify antigen firstly, if identify unsuccessfully, mature cells will identify it again. Pseudocode of elf-non-self discrimination algorithm is presented as follows.

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program Self-non-self discrimination algorithm (output) Begin Present ag and form Ag; Initialize α,β; Flag=0; //recognized by abmemory For each ag in Ag For each abmeomory in Abmemory If(Match(ag,abmemory)> β) {Output abmeomory.url, abmeomory.abstract; abmeomory.count++ Flag=1;} If(0= =Flag;) { For each abmature in Abmature If(Match(ag,abmature)> β)] {Output abmature.url, abmature.abstract; abmature.count++; Flag=1; If(abmature.count>α) Move abmature from Abmature to Abmemory;} } If(0= =Flag) Output “Can’t be found.” End. Self-non-self discrimination process is a machine learning process. It can enhance the affinity of immune cells and expand the scope of immune colony. The optimum antibody is typically selected in the form of immune memory. 4.3 Dynamic Evolvement of Antibodies Since Web pages are timely, antibodies in IHIF should be evolved dynamically to adapt Web pages. Dynamic evolvement process is presented as follows:

t =0 ⎧Φ Tb (t ) = ⎨ ⎩Tb (t − 1) ∪ Tnew (t ) − T dead (t ) t ≥ 1

(2)

t=0 ⎧Φ M b (t ) = ⎨ ⎩ M b (t − 1) ∪ M activation (t ) − M dead (t ) t ≥ 1

(3)

M activation (t ) = {x x ∈ Tb (t ) ∧ x.Sfrequency ≥ α }

(4)

Equation (2) denotes the dynamic evolvement process of mature cells and Equation (3) denotes the dynamic evolvement process of memory cells. Tnew denotes the new mature cells. M activation denotes the activated mature cells. T dead and M dead denote the death of mature cells and memory cells respectively.

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The dynamic repertoire of IHIF adapts to the diversification of Web pages. It can preserve the excellent antibodies and remove the inferior antibodies, which will make IHIF be more maturity and self-non-self discrimination ability be stronger.

5 Experimental Results In order to validate the IHIF, we carried out a series of experiments. The quality of IHIF is evaluated using precise rate. The creation of a database requires a large number of texts. 600 pornographic and 600 nonpornographic Web pages are added to the database. We use the keyword-based harmful information filtering method to compare. Fig.2 shows the precise rate between IHIF and keyword-base method. In our case, strength threshold is ten, namely α = 10 , matching threshold is 0.8, namely β = 0.8 , and evolvement cycle is 1h, namely T = 1h .

Fig. 2. The relation between precise rate and filtering times

We can see from Fig.2, the precise rate of IHIF is fluctuant in different matching times. It is decided by the randomicity of learning time and choosing mechanism. However, the precise rate of IHIF is higher than of keyword-based method every time. It shows the filtering effectiveness of IHIF. In the experiments, we find that the strength threshold and matching threshold can influence the convergence speed. Fig.3 shows the relation between strength threshold, matching threshold and convergence speed. The convergence speed decreases with strength threshold increasing. This is because while strength threshold increases, the process of generating mature antibodies will slow down. Similarly, the convergence speed decreases with matching threshold increasing. However, the filtering precise rate increases with matching threshold increasing. Furthermore, the influence on convergence speed caused by strength threshold is more significant than matching strength. Therefore, we can deploy the strength threshold and matching threshold to get the best filtering effect.

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Fig. 3. The relation between strength threshold, matching threshold and convergence speed

6 Conclusion Artificial immune system provides a way to filter harmful Web pages. In this paper we have presented IHIF, an immune-inspired model for adaptive, content-based harmful information filtering. Experiments results show the effectiveness of the IHIF. However, many further work directions can be considered. Web pages consist of text, pictures and multimedia. We have processed only text in IHIF. Harmful images filtering and multimedia filtering can be the directions of our future work. Acknowledgments. The authors thank the Natural Science Foundation of Naval University of Engineering of China under grant No. HGDYDJJ10008.

References 1. Tao, L.: Immune based Computer virus detection. Science in China 39(4), 422–430 (2009) (in Chinese) 2. Bezerra, G.B., Barra, T.V., Ferreira, H.M., et al.: An immunological filter for spam. In: International Conference on Artificial Immune System, pp. 446–458 (2006) 3. Wang, F., You, Z.S., Man, L.C.: Immune-based peer-to-peer model for anti-spam. In: International Conference on Artificial Immune System, pp. 660–671 (2006) 4. Gong, X., Li, T., Wang, T.F., et al.: An immune mobile agent based grid intrusion detection model. In: International Conference on Simulated Evolution and Learning, pp. 660–671 (2006) 5. Boukerche, A., Regina, K., Juc, L., et al.: An artificial immune based intrusion detection model for computer and telecommunication. In: Parallel Computing, vol. 30, pp. 629–646 (2004) 6. Nanas, N., De Roeck, A., Uren, V.S.: Immune-inspired adaptive information filtering. In: Bersini, H., Carneiro, J. (eds.) ICARIS 2006. LNCS, vol. 4163, pp. 418–431. Springer, Heidelberg (2006) 7. Secker, A., Freitas, A.A., Timmis, J.: AISIID: An artificial immune system for interesting information discovery on the web. Applied soft computing 8, 885–905 (2008) 8. Chao, D.L., Forrest, S.: Information immune systems. Genetic programming and evolvable machines 4, 31–331 (2003)

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9. Hammami, M., Chahir, Y., Chen, L.M.: WebGuard: A Web filtering engine combining textual, structural, and visual content-based analysis. IEEE transaction on knowledge and data engineering 18(2), 272–284 (2006) 10. Guermazi, R., Hammami, M.: Using a semi-automatic keyword dictionary for improving violent Web site filtering. In: Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, pp. 337-344 (2008) 11. Lee, P.Y., Hui, S.C., Fong, A.C.M.: Neural Networks for Web Content Filtering. IEEE Intelligent Systems, 48–57 (2002)

Identification and Pre-distortion for GaN-PA Yuanming Ding1,2, Yan Wang2, and Akira Sano3 1

Liaoning Key Laboratory of Communication Networks and Information Processing Department of Information Engineering, Dalian University, Dalian 116622, China 3 Department of System Design Engineering, KeioUniversity, Yokohama 223-8522, Japan [email protected] 2

Abstract. A memory polynomial model (MPM) is adopted to describe the characteristics of GaN power amplifiers (GaN-PA) in orthogonal frequency division multiplexing (OFDM) communication systems, and the RLS algorithm is used to estimate the MPM parameters in on-line manner. The MPM with odd-even orders is considered in choosing models. Furthermore, the orders and memory length of MPM are decided by the final prediction error (FPE). Finally, an adaptive pre-distorter is designed besed on the achieved MPM, and its effectiveness is proved by numerical simulation. Keywords: GaN Power Amplifier, Pre-Distortion, Memory Polynomial Model.

1 Introduction OFDM signal itself has high peak to average power ratio and frequently generates distortion, includes the linear and nonlinear distortions from the inherent non-memory saturation characteristic and the memory of high power amplifiers (HPA), due to the influence of HPA [1, 2]. So distortion compensation is significant in OFDM systems for raising the system efficiency and communications quality. The model-based digital adaptive pre-distortion is a compensation method to realize the linearization of HPA [3]. We can design a Pre-Distorter (PD) for HPA in the base of estimating the uncertain characteristics of HPA. In the previous researches, we proposed the adaptive PD design based on the memory polynomial model (MPM) and Volterra series model (VSM). Comparing with VSM, MPM has fewer parameters, so the parameter estimated that becomes easy. Therefore, this paper mainly discusses an on-line manner about identification of the MPM of the GaN-PA. The recursion least squares (RLS) algorithm is also used to estimate the overall characteristic parameters of the GaN-PA, and the order and memory length of MPM are chosen by FPE [4].

2 Experiment System for Modeling GaN-PA The input and output signals can be got from the experiment system as shown in Fig. 1. The base-band OFDM signal is generated by IFFT [5] and the GaN-PA includes pre-amplifier and high frequency attenuator (ATT).. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 133–140. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Fig. 1. Experiment system for modeling GaN-PA.

As shown in Fig.2, the memory polynomial model is adopted to describe the unknown characteristics of GaN-PA in base-band, and it can be expressed as: Lm

Lq

zˆ (n) = Qˆ (u (n)) = ∑∑ qˆ l ,m (n) | u (n − m) |l −1 u (n − m)

(1)

m =0 l =1

Equation (1) can be changed into the LS form:

zˆ(n ) = ϕ qH (n )θˆq (n )

(2)

Q (•)

u ( n)

z ( n)

+ e (n ) −

Qˆ (•)

zˆ( n)

Q (•)

Fig. 2. GaN-PA modeling system.

The complex vector θˆq ( n) can be identified on-line by the RLS algorithm when the input and output data renew gradually [6].

θˆq (n) = θˆq (n −1) + Γ(n −1)ϕq (n −1) Γ(n) =

z(n) −ϕqH (n −1)θˆq (n −1)

(3)

1+ϕ (n −1)Γ(n −1)ϕq (n −1) H q

λ2 (n)Γ(n −1)ϕq (n −1)ϕqH (n −1)Γ(n −1) ⎤ 1 ⎡ ⎢Γ(n −1) − ⎥ λ1(n) ⎢⎣ λ1(n) + λ2 (n)ϕqH (n −1)Γ(n −1)ϕq (n −1) ⎥⎦

Here, 0 < λ1 (n) ≤ 1 , λ2 (n) = 1 , Γ(0) = Γ H (0) = ϒ 4 I > 0 .

(4)

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3 Determining Method of GaN-PA Model Order 3.1 The Final Prediction Error

The memory length m and model order l in Equation (1) can be decided by the final prediction error (FPE).

FPE =

N + N1 ⎧ 1 ⎨ N − N1 ⎩ N

N

∑ ( z(n) − zˆ(n)) n =1

2

⎫ ⎬ ⎭

(5)

Here, N is the total number of the measured data, N1 is the number of parameter. When the GaN-PA drain voltage v = 50V , the FPE has been got by numerical simulation (as is shown in Table1). The memory-less nonlinearity of HPA can be expressed by an odd-order polynomial [7, 8]. But the FPE changes smaller when we consider the even-order l = 2 for the identification of the GaN-PA. Moreover, the FPE reduces largely when we change m = 0 to m = 1 . However, there is no obvious change of FPE even though we change m = 1 to m = 2 and m = 3 . The less identification parameters are, the better modeling will be. Hence, the memory length m = 1 is the most suitable. Table1 shows that, the FPE of the model with even-order l=2 is smaller than that only with odd-order. Table 1. FPE Corresponding different model order and memory length

m=0 m=1 m=2 m=3

l=1,3,5,7 3.514e-5 8.155e-6 7.332e-6 7.086e-6

l=1,3,5,7,9 3.296e-5 6.002e-6 5.129e-6 4.870e-6

l=1,2,3,5 3.253e-5 6.558e-6 4.684e-6 4.416e-6

3.2 Convergence of Estimation Paramter

The gain characteristic and the phase characteristic of PA change with the temperature and the time. The parameter must be updated gradually when the model is identified online. Therefore, when the parameter of the model is estimated by RLS algorithm, convergence of parameter is good. It is important that it won’t produce lots of trouble if the parameter is greatly changed. The behaviour of the real part of the estimated parameters based on RLS algorithm shows in Fig.3 (a)and (b). We can see from the figures that any model with drain voltage v=50V starts with a slight change and begins to converge after one frame data. On the other hand, the parameter convergence of the MPM with even-order is better than that only with odd-order when the drain voltage v=50V.

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HPA coefficients real parts

3

3

2

2

1

1

0

0

-1

-1

-2

-2

-3

-3

-4 0

500

1000 1500 2000 Carrier Number

2500

-4 0

3000

500

(a) l=1,2,3,5 and m=0,1

1000 1500 2000 Carrier Number

2500

3000

(b) l=1,3,5,7 and m=0,1

Fig. 3. The behaviour of the real part of the estimated parameters.

4 Adaptive Pre-distortion for GaN-PA 4.1 Adaptive Pre-distortion Structure for GaN-PA

The concept of the pre-distortion methods is to distort the input signals to the PA with an inverse function of the PA nonlinearity and generate linearly amplified signals at the PA output. As shown in Fig.4, the identification-based pre-distortion apply the inverse system of the PA to indirectly construct the Pre-Distorter of the PA, and the PA output z(n)= γs(n) is achieved. The GaN-PA inverse model (pre-distorter model) is expressed as:

uˆ( n ) = Rˆ ( z ( n )) =

Lm

Lr

∑ ∑ rˆ

m = 0 l =1

l ,m

( n ) | z ( n − m ) |l −1 z ( n − m )

(6)

Equation (6) can be changed into the LS form:

uˆ( n ) = ϕ rH ( n )θˆr ( n )

(7)

Here, ϕ rH (n ) and θˆr ( n ) are defined as:

ϕ rH ( n ) = [ z ( n ), ...,| z ( n ) |L

r

−1

z ( n ),

z ( n − 1), ..., | z ( n − 1) |Lr −1 z ( n − 1), … , z ( n − Lm ), ...,| z ( n − Lm ) |Lr −1 z ( n − Lm )]

θˆr (n) = [rˆ1,0 ,..., rˆL ,0 , rˆ1,1,..., rˆL ,1,..., rˆ1,L , ..., rˆL ,L ]T r

r

m

r

m

(8) (9)

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The parameter vector θˆr ( n ) can be identified on-line by the RLS algorithm:

θˆr (n) = θˆr (n −1) + Γ(n −1)ϕr (n −1) Γ(n) = Γ(n −1) −

s (n )

γ

u(n) − ϕrH (n −1)θˆr (n −1) 1 + ϕrH (n −1)Γ(n −1)ϕr (n −1)

Γ(n −1)ϕr (n −1)ϕrH (n −1)Γ(n −1) 1 + ϕrH (n −1)Γ(n −1)ϕr (n −1)

x ( n)

u ( n)

(10)

(11)

z ( n)

Rˆ (•) Q(•)

e( n )

+ −

uˆ(n)

Rˆ (•)

Fig. 4. The pre-distortion structure of the GaN amplifier.

4.2 Simulation Conditions

The setup and conditions for the simulation are summarized as follows: (1) 64QAM-OFDM modulation symbol: ck = a k + jbk ( a k , bk ∈{±1,±3,±5,±7} ). (2) FFT size: 2048. (3) Frame number: 5. (4) Guard interval: 1024. (5) Carrier number: 400. (6) Total OFDM signal number: (2048 + 1024)×5. (7) Pre-distorter model: l=1,2,3,5, m=0,1 in equation (6). 4.3 Simulation Results 4.3.1 Parameters Convergence Figure 5 illustrates the parameters of the pre-distorter fully convergent in one frame data.

Y. Ding, Y. Wang, and A. Sano

1

Imaginary of parameters

Real of parameters

138

0 -1 -2

2000

4000

6000 8000 10000 12000 14000 Carrier number

2000

4000

6000 8000 10000 12000 14000 Carrier number

1 0.5 0 -0.5

Fig. 5. Convergence of the estimated pre-distorter coefficients. 4.3.2 Pre-distortion Compensation Performance

In Fig.6, ‘.’ represents the actual output of demodulated symbols and ‘◦’ symbols represent the ideal output demodulated symbols. Figure 6 (a) shows distortions of the amplifier and phase nonlinearity. The accuracy of signal transmission is very low. Figure 6 (b) shows that the distortion has been compensated. The accuracy of signal transmission increased. Compensated signal 10

5

5

Imag

Imag

No Compensated signal 10

0

-5

0

-5

-10

-10

-10

-5

0

5

10

-10

-5

Real

0

5

10

Real

(a)

(b)

Fig. 6. The constellation diagram of the output demodulated signal.

Figure 7 (a) illustrates the power spectrum of input OFDM signals. Figure 7 (b) illustrates the power spectrum of the PA output without any compensation. The spectral gaps between in-band and out-of-band are small due to out-of-band leakage of OFDM signal power, and in-band spectra are not flat due to memory effects of PA, so the PA output will have many distortions in amplitude and phase. On the other hand, Figure7 (c) shows that the power spectrum of PA output compensated by the adaptive pre-distorter. The spectral gaps can be improved to 57dB.

Identification and Pre-distortion for GaN-PA No Compensated PA Output Spectrum

Input Spectrum

0

0

-10

-20

Magnitude [dB]

Magnitude [dB]

-10

-30 -40

-20 -30 -40

-50

-50

-60

-60

-70 -1500

139

-1000

-500

0 500 Carrier number

1000

1500

-70 -1500

-1000

-500

0 500 Carrier number

(a)

1000

1500

(b) Compensated PA Output Spectrum

0

Magnitude [dB]

-10 -20 -30 -40 -50 -60 -70 -1500

-1000

-500

0 500 Carrier number

1000

1500

(c) The power spectrum of the compensated output signals.

Fig. 7. Power spectrum of input and output signals.

5 Conclusion This paper mainly discusses the online identification of GaN-PA in the OFDM transmission system. The model order and memory length are decided by FPE. There are conclusion as follows: (1) The MPM with model order l=1,2,3,5 and memory length m=0,1 is suitable for GaN-PA modeling. (2) By using the MPM with m=0,1 and l=1,2,3,5, can construct an adaptive predistorter and realize the linearization of the GaN-PA. (3) The numerical simulation results show that the adaptive pre-distorter works well. Acknowledgments. This work is supported in part by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (20080890), and the NSFC under Grant No. 60871089.

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References 1. Vuolevi, J.H.K., Rahkonen, T., Manninen, J.P.: Measurement technique for characterizing memory effects in RF power amplifiers. IEEE trans. Microwave Theory and Techniques 49, 1383–1389 (2001) 2. Dardari, D., Tralli, V.: Analytical evaluation of total degradation in OFDM systems with TWTA or SSPA. IEICE Trans. Commun., E85-B, 845–848 (2002) 3. Eun, C.S., Powers, E.J.: A new Volterra predistorter based on the indirect learning architecture. IEEE Trans. Signal Process 45, 223–227 (1997) 4. Vassilakis, B., Cova, A.: Comparative analysis of GaAs/LDMOS/GaN high power transistors in a degital predistortion amplifier system. In: Proc. IEEE Microwave Conference, IEEE Press, New York (2005) 5. Ding, Y., Wang, J.: Identification and pre-distortion for high power amplifiers with memory. In: Proc. 2008 IET Conference on ICWMMN, pp. 728–732 (2008) 6. Ding, Y., Sun, L., Sano, A.: Adaptive nonlinearity predistortion schemes with applicationto OFDM system. In: Proc. 2003 IEEE Conference on Control Applications, pp. 1130–1135 (2003) 7. Shen, Q., Wang, A., Ding, Y.: Linearization technology of adaptive pre-distortion for power amplifiers in OFDM systems. The Journa of Liaoning University of Petroleum & Chemical Technology 26, 87–90 (2006) 8. Lazzarin, G., Pupolin, S., Sarti, A.: Nonlinearity compensation in digital radio systems. IEEE Trans. Commun 42, 988–999 (1994)

On-Line Monitoring System of Methane Reaction Generator Ma Baoji Industrial Training Center, Xi’an Technological University, Xi’an, Shaanxi, China [email protected]

Abstract. Due to the advantages of strong ability of treating waste water, high efficiency of biogas generating, less land occupied and energy saving. Internal Circulation(IC) anaerobic process is extensively used in the fields of wastewater treatment, methane generating and more fields. But this technology applied to pig farm wastewater treatment is just underway. Knowledge about the process control, influence facts of the biogas quantity and quality, relationship between the control parameter and the producing efficiency and so on are much less to know. A multi-parameter monitoring system used to monitor the react state in the internal circulation anaerobic process was developed. This system was applied to a pig farm wastewater treatment factory located at Yan Liang China. According to customer and the special environment of IC reactor, data acquisition circuit and the hardware and software of the system were designed. By means of this system the main parameters for reacting process control such as the temperature, pH value (pH), REDOX potentials (Eh) and liquid flow in the internal circulation system can be measurement on-line. Keywords: IC reactor; On-line monitoring; Single-chip microcomputer; Data acquisition.

1 Introduction Today, energy and environmental problems have become the focus of global attentions. Although oil, coal and natural gas still are the main source of organic raw materials and fuel, with exhausting of fossil fuels and growing environmental problems, developing clean and renewable energy has become a pressing issue. In this situation, biogas come from wastewater treatment are get more attention all over the world. Because powerful ability in the processing and potential prospect in application, Anaerobic biological technology of waste treatment is a significant technology in the waster water treatment fields[1]. From 1980 to 1990 years, the anaerobic biological treatment for wastewater has become a mature process. With the development of industrial production, requirements on finance investment, energy consumption, soil occupancy are more strict. There is a necessary effort to find a more efficient and more economic technique for wastewater treatment. Especially how to deal with the M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 141–148. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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highly concentration organic wastewater makes this effort to become more necessary. In this background, a new type of reactor, internal circulation anaerobic reactor, has been developed[2]. Internal Circulation (IC) anaerobic process is a hot technology which is currently adopted in the fields of sewage treatment, methane generated and more fields [3]. But this technology adopted by pig farm wastewater treatment is just underway[4]. This paper, which is about a pig farm’s internal circulation anaerobic process of methane cycle engineering in Yan Liang provides a multi-parametric monitoring system applied to monitoring the methane generators. This system is used to on-line measurement the temperature of methane, pH value (pH), REDOX potentials (Eh) values and internal circulation system flow. It will provide accurate and timely methane production field data and make people to know much about the regular pattern of anaerobic reaction in order to optimize internal circulation anaerobic process, reduce methane production cost and increase methane production efficiency.

，

2 The Basic Composition of The System As shown in figure 1, The monitoring system is composed of a PC, a piece of AT89S52 SCM, temperature sensor, PH sensor, REDOX potentiometer and flowmeter. In the system PC performed as host computer, AT89S52 SCM performed as the data acquisition card . The data acquisition card is used to collect the internal parameter of the whole methane reaction generator to be measured and convert sensor output parameters (temperature, pH, REDOX potential, flow within the circulatory system, etc.) into digital signals and temporarily save these data. The data acquisition card via the USB communication interface will send the data to the PC When the PC sends the read command to the data acquisition card. The function of PC mainly completes data storage, data display, data management, data query etc.

Fig. 1. On-line monitoring system diagram

Sensor Selection The pig farm wastewater in the methane reaction generator produces the methane by complicated chemical reaction. These reactions need appropriate environmental conditions. The main conditions include that the reaction temperature, pH, REDOX potentials (Eh)of the liquid in methane generator and the flow of internal circulation system .According to variation range of above four parameters in reaction and practice experience, chosen sensors were shown in table 1 [5].

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Table 1. Selected sensors

` temperature Sensor model

PH value

potential

PH7000PH/ORP

PH7000PH/ORP

Transmitter

PHG-1101

PHG-2101

0~14pH

-1999

±0.02pH

±1 mV

SWZ-Cu50

℃～100℃

Range

0

Precision

±0.1%

～1999mV

flow

FFT-0050150-1 0.5~10m/s 0.5%

3 Data Acquisition System Hardware Design The main function of the data acquisition system is to convert the received four 4 ~ 20mA analogy signal from sensor to digital signal through A/D converter. Then the digital signal will be sent to the PC via the USB communication interface control chip. Hardware block diagram is shown in Figure 2. Through the analog signal input interface, four channel analog current signals which come from the transmitter is converted to analog voltage signals. Then analog voltage signals were converted into the digital signal through the A/D converter after the collecting. According the USB protocol the SCM changes the parallel data into serial and sends to the PC via a USB control chip microcontroller (PDIUSBD12). In order to implement the firmware program download on line, which can get rid of the readwrite device to facilitate debugging, a MAX232 chip was employed to realize the serial communication between the host Fig. 2. Hardware block diagram computer and the lower computer. MCU and Peripheral Circuitry Among the many SCM products the series of MCS - 51 SCM is used widespread. Furthermore plenty of technical date and compatible peripheral chips can be used in application design. Especially the AT89S52 SCM of ATMEL has higher ratio of performance to price. So it was adopted as the main control unit of this monitoring system [6]. At the beginning to work the SCM need be reset in order to keep the same defined initial status with other parts and from this status began to work. In this system the design of the reset circuit adopts the power-on reset circuit shown in Figure 3. When the system is power-on the SCM will be reset. The SCM can not be reset if the system is running and only through cutting the switch again to reset the system when the program or system appears problems.

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The internal oscillator of the AT89S52 SCM is adopted as the clock source of the MCU. It also can make use of the external oscillator which produces oscillating signal that will be sent to the oscillating signal input channel, as the clock source of the MCU. This system employs the former. A crystal oscillator was connected to pin XTAL1 and pin XTAL2 of the AT89S52 SCM and then two small capacitances in parallel to ground were designed, as shown in Figure 4.

Fig. 3. Reset circuit

Fig. 4. Clock circuit

Data Acquisition Circuit A piece of TLC2543 was adopted as A/D converter, which is a 12- bit switchedcapacitor successive approximation digital analog converter manufactured by TI Company. It has 11 moulding channels, with automatic sampling protection, conversion time of 10μs. There are three input control terminal: chip select (CS), serial input /output clock (I / O CLOCK) and serial data input DATAINPUT port, along with a three-state serial data output DATAOUT. Don't need other components and can be directly connected to SCM, the interface circuit of the TLC2543 is very simple [7]. The Serial clock input and output signal (I / O CLK) of the TLC2543 is provided by the Pl.3 pin of AT89S5, chip select signal (CS) is provided by the P1.0, and DATAINPUT is provided by the P1.2. DATAOUT is connected to P1.1 and The reference voltage (REF), which need + 5V voltage, is provided by the TL431 regulator diode. As shown in figure 5.

Fig. 5. Data acquisition circuit

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USB Interface Communication Circuit Considering that the controller’s function, price, accessible and difficulty to develop and other factors, this design uses a PDIUSBD12 as USB interface components of data acquisition system [8]. Design of the peripheral circuit of PDIUSBD12 was shown in fig. 6. The PCB of the data acquisition system is shown in figure 7.

Fig. 6. PDIUSBD12 peripheral circuit

Fig. 7. PCB of the data acquisition system

4 Software Design of the Data Acquisition System The software designs employ the uVision2 of the Windows integrated development environment as software development platform, high-level C51 of Single Chip Micyoco program language as the developing tool and Keil company’s V5.0 version as compiler respectively[9]. Modular programming technique was adopted to make the program structure is clear, simple to read and modify.

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Main Program Design and USB Communication Module The firmware of this system main perform the following operations: control the A/D converter, which collects the measured parameters of the analog variable from the sensors, and converts into digital quantity, then sends the data to host via the D12. This system mainly includes the following several modules: Major module, USB communication module, TLC2543 control module and serial interface communication module. Main program flowchart was shown in figure 8. Program block structure of the USB communication module was shown in Figure 9. start System

Delay 20s, waiting for each sensor probe

The main loop: send USB request, processing USB bus events and user function processing, etc Standard equipment

Interrupt service PDIUSBD12 command interface

Date

PC

The parameter to PC

Fig. 8. Main program flow chart

Fig. 9. USB communication module

USB Interrupt Service ISR The USB interrupt service processing is generated by the D12. It will get the data, which comes from the FIFO in the internal D12, and sent to MCU memory saving. Then establish the correct event signs to inform the main loop program for processing. The process was shown in figure 10. The Main Circulation Processing Program MAINLOOP The main loop examines the event marks, and enters the corresponding procedure for further processing. In the main loop, the MCU will make its port, memory, timer and interrupt service routine initialize, firstly. Then the MCU will reconnect the USB, and include the Soft-Connect that is set to ON. The process is very important. It ensures that the D12 do not operate until the MCU has been ready the service for the D12. After initialization, the MAINLOOP will enter the circulation status to check the various statues. When the setup packet has been checked, it needs knowing whether or not the setup flag has already been set "ON" by the interrupt service routine. If the setup flag has been set, a signal will be send to the protocol layering in order to request processing. This working process is shown in figure 11.

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The main loop

Interrupt service

Interrupt entrance

Initialize the I/O port, timer and interrupt, reconnecting to the USB bus

Read D12 interrupt registers

Yes

Bus

No Setting bus reset logo

Suspend

Yes

Loop Setting suspend change logo

No Yes Control

Control endpoint Sending data processing

No Yes

Control endpoint

The timer event marks?

Control A/D operation

No Yes

receive data processing

Control

Yes

Bus reset?

No Yes

Send data disposal

Endpoint 1

No suspend change?

Bus reset processing

Yes Suspend change processing

No

No Endpoint1o

Yes

Receiving data disposal

Yes

Send data disposal

Yes

Receiving data disposal

Establish

No Endpoin2

Yes

Call protocol processing

No

No Endpoin2o

No Interrupt service ended

Fig. 10. Interrupt service process

Fig. 11. Main circulation flow char

5 Conclusion The internal circulation anaerobic process, with high efficiency of decontamination, less land occupation, energy saving and high gas production rate, has been become the development direction of wastewater treatment for medium and small-sized pig farms. According to customer requirements and the special environment of IC reactor located at a pig farm in YanLiang China, A multi-parameter monitoring system used to monitor the react state in the internal circulation anaerobic process was developed. The experience has been demonstrated that this system can properly measure the

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temperature, pH value (pH), REDOX potentials (Eh) and liquid flow in the internal circulation system on-line. This information is helpful to make people to know much about the regular pattern of anaerobic reaction and optimize internal circulation anaerobic process, reduce methane production cost and increase methane production efficiency.

References 1. Zhou, L.: Anaerobic fermentation of organic waste research, Northeast Agricultural University, Harbin (2003) 2. Zhou, M., Zhang, R., Li, J.L.: Methane practical technology, pp. 34– 38. Chemical Industry Press, Beijing (2004) 3. Zhou, M., Hu, J., Zuo, J.: Anaerobic wastewater biological treatment theory and technology, pp. 121–128. China architecture &building press, Beijing (2003) 4. Wu, M., Sun, K., Yan, L.: Different reaction temperature of urban living garbage anaerobic fermentation. Chemical and biological engineering research, 28–30 (2005) 5. Huang, X., Zheng, X.: Sensor principles and applications. Electronic Science and Technology University Press, Beijing (2004) 6. Yu, Y.: ATMEL89 series SCM application technology. Beijing Aerospace University Press, Beijing (2006) 7. TLC2543Datasheet[DB/OL]., http://www.21icsearch.com/searchpdf/ti/tlc2543.pdf 8. Wang, S., Li, G.: PDIUSBD12 interface application design. SCM and embedded application 244 (2002) 9. Chen, C., Wang, H.: with KeilC51 Developing Embedded Program. Computer Application 11 (2003)

Security Flaws in Two RFID Lightweight Authentication Protocols Wang Shaohui College of Computer, Nanjing University of Post and Telecommunication, Nanjing 210046, China [email protected]

Abstract. The design of lightweight authentication protocols that conform to low-cost devices is imperative. This paper analyses two recently proposed authentication protocols[11,18]. We show the protocol in [11], which is the modification version of HB+ protocol, still can not resist Man-in-the-Middle attack, and the protocol in [18] can not resist passive attack, and after eavesdropping about 20 consecutive authentications, the adversary can deduce all the secrets stored in the tag. Keywords: RFID; Man-in-the-Middle Attack; Passive Attack; Authentication.

1 Introduction Radio Frequency IDentification (RFID) is a contactless technology used to identify and/or authenticate remote objects or persons, through a radio frequency channel using RFID Rreaders and RFID Tags (or transponders), the latters embedded into the items. RFID is becoming more and more widespread in daily-life applications, from library management or pet identification, to anti counterfeiting, access control or biometric passports. The wide and fast deployment of RFID is mainly due to the diminution of the RFID tags price while their capacities steadily increase. Moreover, the ubiquity of RFID raises new concerns about privacy. For instance in public transportation, a customer holding an RFID ticket might not want anybody else to be able to track his movings. One option to preempt such a worry is to build secure RFID authentication protocols, in order to ensure privacy for RFID users. Thus, an RFID system should provide anonymity (the identity of the tag should be kept secret) and untraceability (it should not be possible to link two different tag communications) for a user. However RFID tag constraints in processing power and memory make them tougher to deal with in security. These kinds of constraints dictate a paradigm shift in security provision for RFIDs which is known as lightweight cryptography, and lightweight authentication protocol is a subset of lightweight cryptography. One family of such protocols is based on the problem of learning parity with noise(LPN)[1], which is NP hard. Hopper and Blum designed the first protocol of this kind, called HB[2], that is shown to be secure against passive adversaries. Juels and Weis introduced HB+ at Crypto 2005[3]. The protocol is a multi-round authentication protocol where each round consists of three communications between the reader and M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 149–156. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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the tag. On the tag, HB+ is computationally lightweight since it requires only simple bit-wise operations. Furthermore, the protocol is of secure against an active attacker in detection-based model[3]. In this model adversaries can interrogate a tag in any way they wish, but not readers, before attempting to pass an identification session posing as a tag. But HB+ is insecure against a stronger active adversary who can perform man-in-the-middle attack(MIM)[4]. Since then, many protocols[5,6,7,8], attempting to resist MIM attack, were proposed, but all of them have been broken[9,10]. Because of lightweight computation cost, HB-like authentication protocols still gain much attention. Recently, Piramuthu[11] (Piramuthu Protocol) published a paper to evaluate a few lightweight protocols, and gave a modification of HB-like authentication protocol which is claimed to resist MIM attack. Another family of RFID lightweight authentication protocols are designed only using simple bitwise operations like XOR, AND, OR, etc. In 2006, Peris et al. proposed a family of Ultralightweight Mutual Authentication Protocols (henceforth referred to as the UMAP family of protocols). Chronologically, M2AP [12] was the first proposal, followed by EMAP [13] and LMAP [14]. Since the publication of the UMAP family of protocols, their security has been analyzed in depth by the research community. In [15, 16] a desynchronization attack and a full disclosure attack are presented. These require an active attacker and several incomplete run executions of the protocol to disclose the secret information on the tag. Later, Barasz et al.[17] showed how a passive attacker (an attack model that may be, in certain scenarios, much more realistic) can find out the static identifier and particular secrets shared by the reader and the tag after eavesdropping on a few consecutive protocol rounds. In 2009, David and Prasad[18](DP Protocol) presented a new ultralightweight authentication of this kind to provide a strong authentication without compromising security. In this paper, we give a MIM attack on the Piramuthu Protocol, and a passive attack on DP Protocol . The attacks show that Piramuthu Protocol still can not resist MIM active attack. And after eavesdropping about 20 authentication processes, we can deduce all the secrets in DP Protocol. The rest of the paper is organized as follows. In section 2, we review the Piramuthu Protocol and DP Protocol. The MIM attack and passive attack against these two protocols are presented in section 3 and section 4 respectively. we state our conclusion in section 5.

2 Piramuthu Protocol and DP Protocol In this section, we will introduce the two recently proposed protocols briefly. 2.1 Piramuthu’s HB-Like Protocol HB-kind authentication protocols all depend for their security on the conjectured hardness of the Learning Parity with Noise (LPN) problem, which is one of the wellknown problems in cryptography. Let a be a random known k -bit vector, x be a secret k -bit vector, let η ∈ (0,0.5) be a noise parameter, and let v be a bit noise which satisfies Pr ob (v = 1) = η . Then it is hard to recover

x from the result

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z = a ⋅ x ⊕ v , where a ⋅ x means scalar product of binary vectors a and x, and ⊕ is the Exclusive-OR (XOR). Supposed the reader and the tag shared two secrets ( x and y ), the HB+ authentication protocol requires r rounds and each round consists of the following steps. If the non-match rounds number exceeds a threshold t , the tag is rejected and otherwise it will be accepted. Step 1. The tag chooses at random a k -bit binary vector b , and sends it to the reader. Step 2. The reader generates at random a challenge a and send it to the tag. Step 3. The tag computes z = a ⋅ x ⊕ b ⋅ y ⊕ v , and sends z. Step 4. The reader checks whether z = a ⋅ x ⊕ b ⋅ y . The HB+ protocol can not resist active MIM attack. In [11], Piramuthu surveyed a plethora of new authentication protocols that promise to alleviate security and privacy problems associated with the use of RFID tags, and gave some modifications to some existing protocols including the HB-like protocol. He modified the HB+ protocol as the figure 1 shows. The protocol is more realistic, since the reader, as opposed to the tag, initiates the authentication process. The Reader and the Tag do not send the random values a and b explicitely, but blind them using the secrets x and y ; In addition, the computation is much more complicated than the original HB+ to resist the MIM attack.

Reader (secret x, y)

Tag (secret x, y)

v ∈ {0,1 | prob (v = 1) = η )

⊕ x⊕ y b ∈ {0,1}k ⎯b⎯ ⎯→ z = ( a ⋅b )*( x ⋅ y ) ⊕ ( a ⋅( y ⊕ a ))*b⋅( x ⊕ b )) ⊕ v ; a ⊕ x ⊕ y k ←⎯ ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ ⎯⎯ Chooses a ∈ {0,1} Checks z = ( a ⋅ b) * ( x ⋅ y ) ⊕ ( a ⋅ ( y ⊕ a )) * (b ⋅ ( x ⊕ b))

Chooses

Fig. 1. Round ot modified HB+ protocol

2.2 David and Prasad’s Protocol The protocol proposed by David and Prasad is very efficient for it only use the simple operands exclusive OR ( ⊕ ), bitwise AND ( ∧ ), and bitwise NOT ( x ). Here we only focus on the communication messages between the Reader and the Tag. The tag and the server share four values: the old and the new pseudonym PID1 and PID2 respectively to avoid the desynchronization attack, and two secret keys

K 1 and K 2 .

And the tag stores a static identifier ID which facilitates its unequivocal identification. The communications between the Reader and the Tag are as follows:

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1. The reader sends a request message to the tag, which replies with its pseudonym

PID2 . 2. If there is a matched pseudonym

PID2 , the following computations use PID2 , or the old

PID1 is used.

3. The reader generates two random values messages

n1 and n2 . It computes and sends the

{ A, B, D} to the tag:

A = ( PID2 ∧ K 1 ∧ K 2 ) ⊕ n1

(1)

B = ( PID2 ∧ K 1 ∧ K 2 ) ⊕ n 2

(2)

D = ( K 1 ∧ n 2 ) ⊕ ( K 2 ∧ n1 )

(3)

n1 and n2 from messages A, B , and verifies the authenticity of the Reader via the message D . After a successful authentication, the tag computes and sends messages {E , F } to the reader, and updates the two pseudonyms values: 4: The tag deduces

E = ( K1 ⊕ n1 ⊕ ID) ⊕ ( K 2 ∧ n2 )

(4)

F = ( K 1 ∧ n1 ) ⊕ ( K 2 ∧ n 2 )

(5)

PID1 = PID2

(6)

PID2 = PID2 ⊕ n1 ⊕ n2

(7)

5. The Reader will authenticate the tag using the message F , deduce the tag’s identifier ID using message E , and update the two pseudonyms values as the (6) and (7) shows in the database.

3 MIM Attack on Piramuthu’s Protocol In the Man-In-the-Middle attack, the adversary is capable of manipulating challenges sent by a legitimate reader to a legitimate tag during the authentication exchanges, and to check whether this manipulation results (or not) in an authentication failure. It is easy to see, in Piramuthu’s protocol, if scalar product of two secrets x and y is equal to 0, then the value send by the tag is ( a ⋅ ( y ⊕ a )) * (b ⋅ ( x ⊕ b)) ⊕ v . The MIM attack on Piramuthu’s protocol is based on the following observation:

x = ( x1 , x2 ,..., xk ) and a = (a1 , a2 ,..., ak ) , the value a ⋅ ( x ⊕ a ) = ( a1 x1 ⊕ a2 x2 ⊕ ... ⊕ ak x x ) ⊕ ( a1 ⊕ a2 ⊕ ... ⊕ a k ) . So, if Observation: for k -bit vectors

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x1 = 0 , when we change the first bit of a (i.e. a1 from 1 to 0, or from 0 to 1), the value of a ⋅ ( x ⊕ a ) will change with the probability 1; but if x1 = 1 , when we change the first bit of a , the value of a ⋅ ( x ⊕ a ) will maintain the same one, for at this time a1 x1 ⊕ a1 = a1 ⊕ a1 = 0 . We show how to identify the first bit of x under the condition that x ⋅ y = 0 . Different from the method used in [4], here we do not use constant k -bit vector δ = (1,0,...,0,...0) to XOR to each authentication challenge for each of the r rounds of authentication, but make the last bit of the challenge b maintain the same value, through letting the last bit of b ⊕ x ⊕ y of the r authentication rounds be the same value. From the Observation, we can find, if the last bit of x is 1, the change does not disturb the authentication, i.e. authentication process is alwayse successful. But if the last bit of x is 0, b1 participates in the calsulation of (a ⋅ ( y ⊕ a )) * (b ⋅ ( x ⊕ b))

(a ⋅ ( y ⊕ a)) * b1 , and from the ramdomness of a and b , we can suppose Pr ob((a ⋅ ( y ⊕ a)) * b1 = 1) = 0.25 . Through the MIM attack, the last bit b1 of b keeps the same value, the ablove probablity will change:

is

⎧Pr ob((a ⋅ ( y ⊕ a )) * b1 = 1) = 0, b1 = 0 ⎨ ⎩Pr ob((a ⋅ ( y ⊕ a )) * b1 = 1) = 0.5, b1 = 1 which will break the authentication process and make the reader reject. So, in our MIM attack, if the authentication process is successful, then we must have that x1 = 1 with overwhelming probability. If authentication doesn't succeed then x1 = 0 with overwhelming probability. We can retrieve the other bit of the secret x using the same way.

4 Passive Attack on David and Prasad’s Protocol In David and Prasad’s Protocol, they only use some simple operands, which do not indece any bit diffusion. We will show a passive attack to reduce the secret through eavesdropping some consecutive authentication process. It is easy to see for any bit a and b , a ∧ b = a * b , rewrite the equation (1~7) as follows:

a = 1 ⊕ a . So, we can

A = ( PID2 * K 1 * K 2 ) ⊕ n1

(1)

B = (1 ⊕ PID2 ) * K1 * K 2 ) ⊕ n2

(2)

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D = ( K 1 * n2 ) ⊕ ( K 2 * n1 )

(3)

E = ( K1 ⊕ n1 ⊕ ID) ⊕ ( K 2 * n2 )

(4)

F = ( K 1 * n1 ) ⊕ ( K 2 * n 2 )

(5)

PIDnew = PID2 ⊕ n1 ⊕ n2

(7)

Because of the distributive law of operands * and ⊕ , from the equation (1’) and

A ⊕ B = ( K 1 * K 2 ) ⊕ n1 ⊕ n2 , and from (3’) and (5’), we can obtain D ⊕ F = ( K 1 ⊕ K 2 ) * ( n1 ⊕ n2 ) . Using the equation (7’), it is easy to see K 1 * K 2 = A ⊕ B ⊕ PID2 ⊕ PIDnew , thus randomly chosen values n1 and n 2

(2’), we can obtian

can be deduced via equations (1’) and (2’). After eavesdropping m consecutive authentication process, the adversary can get all the k -bit randomly chosen numbers

n1 and n 2 . As to the position i ∈ {1,..., k} ,

the probability that the i bit of n1 = 0 and n 2 = 1 is 0.25, so m need not be too large(about 20 is enough), for every position, we can find a couple of random (i)

(i )

= 0 and n 2 = 1 , and we can get the i -th position bit of K 2 via (5’). We can get K 1 as the same way, and at last the constant identifier ID

number satisfying n1

(i)

(i )

can be obtained using (4’).

5 Conclusions In this paper we have considered two recently proposed RFID lightweight authentication protocol, one based on HB-like authenticaiton, and the other based on UMAP protocols which use simple operands. These two protocols can not resist MIM attack and passive attack respectively. Through the attack, we can see good variants to HB-like authentication are very hard to find, and we must consider carefully when designing protocol using only simple operands like exclusive OR, bitwise AND and bitwise OR. It is an interesting challenge to find a successful protocol with good security and efficiency to the problem of low-cost tag authentication.

Acknowlegement This work was supported by National Natural Science Funds (Grant No.60903181) and Nanjing University of Post and Telecommunication Funds (Grant No.NY208072).

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References 1. Blum, A., Kalai, A., Wasserman, H.: Noise-tolerant learning, the parity problem, and the statistical query model. J. ACM 50(4), 506–519 (2003) 2. Hopper, N.J., Blum, M.: Secure human identification protocols. In: Boyd, C. (ed.) ASIACRYPT 2001. LNCS, vol. 2248, pp. 52–66. Springer, Heidelberg (2001) 3. Juels, A., Weis, S.A.: Authenticating pervasive devices with human protocols. In: Shoup, V. (ed.) CRYPTO 2005. LNCS, vol. 3621, pp. 293–308. Springer, Heidelberg (2005) 4. Gilbert, H., Robshaw, M.J.B., Sibert, H.: An Active Attack Against HB+: A Provably Secure Lightweight Authentication Protocol. IEE Electronics Letters 41(21), 1169–1170 (2005) 5. Bringer, J., Chabanne, H., Dottax, E.: HB++: a lightweight authentication protocol secure against some attacks. In: IEEE International Conference on Pervasive Services, Workshop on Security, Privacy and Trust in pervasive and Ubiquitous Computing SecPerU (2006) 6. Munilla, J., Peinado, A.: HB-MP: A further step in the HB-family of lightweight authentication protocols. Computer Networks 51, 2262–2267 (2007) 7. Gilbert, H., Robshaw, M.J.B., Seurin, Y.: HB#: Increasing the security and efficiency of HB+. In: Smart, N.P. (ed.) EUROCRYPT 2008. LNCS, vol. 4965, pp. 361–378. Springer, Heidelberg (2008) 8. Bringer, J., Chabanne, H.: Trusted-HB: A low-cost version of HB + secure against man-inthe-middle attacks. IEEE Transactions on Information Theory 54(9), 4339–4342 (2008) 9. Gilbert, H., Robshaw, M.J.B., Seurin, Y.: Good variants of HB+ are hard to find. In: Tsudik, G. (ed.) FC 2008. LNCS, vol. 5143, pp. 156–170. Springer, Heidelberg (2008) 10. Frumkin, D., Shamir, A.: Untrusted-HB: Security vulnerabilities of Trusted-HB. Cryptology ePrint Archive, Report 2009/044 (2009), http://eprint.iacr.org 11. Piramuthu: Lightweight cryptographic authentication in passive RFID-tagged systems. IEEE Transactions on systems, man and cybernetics – part C: Applications and Reviews 38(3), 360–376 (2008) 12. Peris-Lopez, P., Hernandez-Castro, J.C., Estevez-Tapiador, J.M., Ribagorda, A.: M2AP: A minimalist mutual-authentication protocol for low-cost RFID tags. In: Ma, J., Jin, H., Yang, L.T., Tsai, J.J.-P. (eds.) UIC 2006. LNCS, vol. 4159, pp. 912–923. Springer, Heidelberg (2006) 13. Peris-Lopez, P., Hernandez-Castro, J.C., Estevez-Tapiador, J.M., Ribagorda, A.: EMAP: An efficient mutual-authentication protocol for low-cost RFID tags. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM 2006 Workshops. LNCS, vol. 4277, pp. 352–361. Springer, Heidelberg (2006) 14. Peris-Lopez, P., Castro, J.C.H., Estevez-Tapiador, J.M., Ribagorda, A.: LMAP: A real lightweight mutual authentication protocol for low-cost RFID tags. In: Proceedings of the 2nd Workshop on RFID Security (2006), http://events.iaik.tugraz.at/RFIDSec06/Program/papers/ 013-LightweightMutualAuthentication.pdf 15. Li, T., Wang, G.: Security analysis of two ultra-lightweight RFID authentication protocols. In: IFIP SEC 2007, Sandton, Gauteng, South Africa, pp. 14–16 (May 2007)

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16. Li, T., Deng, R.: Vulnerability analysis of EMAP – an efficient RFID mutual authentication protocol. ares. In: The Second International Conference on Availability, Reliability and Security (ARES 2007), pp. 238–245 (2007) 17. Barasz, M., Boros, B., Ligeti, P., et al.: Passive attack against the M2AP mutual authentication protocol for RFID tags. In: Proc. of First International EURASIP Workshop on FID Technology, pp. 76–83 (2007) 18. David, M., Prasad, N.R.: Providing strong security and high privacy in low-cost RFID networks. In: Schmidt, A.U., Lian, S. (eds.) MobiSec 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 17, pp. 172–179. Springer, Heidelberg (2009)

Study on Mechanical Parameters in Finite Element Analysis of Children’s Orbital-Bone* Weiyuan Lu**, Zhixiang Liu, Xiuqing Qian, Tingting Ning, and Huagang Yan*** School of Biological medicine engineering, Capital Medical University, Beijing, 100069

Abstract. Children who have an disproportionally small eye or lack one eyeball are likely to suffer from facial asymmetry. In order to gain insight into the pathogenesis and seek effective intervention strategies to it, we studied the biomechanical factors that may affect the development of children’ orbital-bone by establishing a model based on CT images so that biomechanical analyses on two-year-old children’s orbital-bone can be performed. As the preparation for later biomedical analysis, the modeling is performed using software Mimics. Although there have been several reports for orbital-bone until now, most of them are concerned about adults instead of children, especially two-year-old, who are in period of blooming growth,. Keywords: Children’s orbital; mechanical parameters; modeling.

1 Introduction Nowadays, it frequently occurs in the clinics of ophthalmology that a child becomes malformed in his/her face because of the smallness in one eye or the lack of one eyeball. This malformation severely affects the appearance of the subject. It would be beneficial to the patients, their families and the society if an effective early intervention could be applied to them when they are at the developmental stage. In order to investigate the pathogenesis and explore effective intervention strategies to it, it is necessary to study the biomechanical factors affecting the development of children’ orbital-bone from the perspective of biomechanics. For this purpose, we use *

**

***

This work is supported by National Natural Science Foundation of China(Grant No.: 30900288), Funding Program for Academic Human Resources Development in Institutions of Advanced Education of Beijing (Grant No.PHR20100898), and Basic-Clinical Scientific Collaboration Fund of Capital Medical University(Grant No.: 2009JL06). Author profile: Weiyuan Lu(12/1959), female, associate professor, interest of research: biophysical technology. Correspondence author: Huagang Yan(10/1973), male, associate professor, interest of research: biophysical technology..

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 157–162. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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software Mimics to build a model based on CT images, aiming at the orbital–bone of 2-year-old healthy children, and use finite element analysis(FEA) package ANSYS to do some biomechanical analysis. Presently, there are several reports on orbital modeling and mechanical FEA, most of them, however, are pertaining to adults instead of children, especially 2-year-old, who are in period of rapid growth.

2 Modeling Mimics is an interactive medical image control system developed by Materialise. It is a highly integrated and easy-to-use 3D image generating and editing software, which can import various scanning data(CT, MRI , build and edit 3D models, and export them in generic CAD(Computer Aided Design), FEA(Finite Element Analysis) or RP(Rapid Prototyping) formats. With Mimics, large scale of data conversion can be made on a PC.[1]

）

A. Material CT images of a two year old child that contain the orbital part, provided by TongRen Hospital. This data comes from the CT scan of the subject head with a Philips Brilliance 64 CT scanner. The acquisition setup is as follows: tube voltage 140 kV, MAS 256 mA·s- 1, slice thickness 0. 313 mm, window width 4 000 Hu, window center 700 Hu. In total, 515 coronal images, 515 sagittal images and 150 axial images are acquired. The resolution is 512×512. The data output is stored in DICOM (Digital Imaging and Communications in Medicine) format and recorded in CD-Rom. The Mimics software is purchased by School of Biological medicine engineering, Capital Medical University. B. Modeling The following rules have been observed in the modeling: Accuracy: the model matches the actual physiological structure. Simplicity: some minor structures that do not pose significant influence on the analysis is abandoned for the convenience of subsequent mechanical analysis. Intactness: no holes should exist in the model, thus,the sclerotin in the ethmoidal plate of the orbital wall is thickened a little bit in the modeling. The reason is that the output from Mimics should be a complete closed surface, but the sclerotin in the ethmoidal plate of the orbital wall is extremely thin and lots of holes, which is difficult to process, may emerge when reconstructed. Therefore, this sclerotin of this part is slightly thickened to avoid the generation of holes in the process of smoothing. Proper thresholds (the default lower and upper thresholds for bone are 226 and 2796, respectively) are selected. Data are imported after noise is eliminated using region growth. The 3D model is built.[2,3].

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Fig. 1. The final model built

3 Settings of the Mechanical Parameters in ANSYS Analysis The ANSYS package is a multi-purpose computer design program for finite element analysis, which can be used for the problems of structure, fluid, electricity, electromagnetic field and collision. The package provides interface to most CAD software and data communication & exchange is easy to accomplish. The first thing to use ANSYS package to perform biomechanical analysis for the two-year-old healthy child’s orbital part after the modeling with Mimics is to set proper mechanical parameters. After surveying, we found that there was no available relevant mechanical parameter of analysis for this particular type of subject. It is necessary to do iterative trials to find out the settings step by step. The details are provided below. Settings of mechanical parameters 1.Create a block in ANSYS whose length, width, and height are 3, 3, 1, respectively. After query and exploration, we finally set the elastic modulus to 1.37*1010Pa, and Poisson’s ratio to 0.35. Constraints for all directions is applied to the underside of the

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Fig. 2. Graph of stress and strain

block, and a 10 Pa pressure is applied from the above. The result shown in Fig. 2 is obtained after entering the solver. Since the objective of this study is to propose a remedy based on FEA, and the focus of interest in the FEA of orbital part is the inner wall, i.e., the surface upon which pressure is applied, the points on the surface of the block are selected for the calculation of stress and strain. It can be seen from the figure that the surface stress of the block is 9.354-10.522 and the strain is 0.840×10-9-0.107×10-8. 2. Double the height of the model, and then calculate the stress and strain on the surface points of the block. 3. Decrease the elastic modulus and observe the resulting stress and strain. 4. Set different Poisson’s ratio, and observe the stress on the surface points of the block.

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5. Input different pressure, and observe the stress and strain on the surface points of the block. Fig.3 shows the stress graph and the strain graph when the pressure is 9.

Fig. 3. The stress graph and the strain graph when the pressure is 9

It can be seen that the surface stress and strain drop significantly. [4-6] B. Conclusion of the trials After the iterative tests and trials described above, it proves that the remedy can be made by decreasing Poisson’s ratio or pressure. Furthermore, the experiments show that when the Poisson’s ratio is 0.2, if the stress and strain of the surface points are roughly consistent with those if the height is halved, then the solution is applicable as a remedy,

4 Discussions and Analysis The materials of investigation indicated that the normal development of the maxillofacial region has strong positive correlation with the development of the orbital

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region.[9]. Anson Chau et. al. pointed out that the size of the eyeball affects the development of the orbital volume, and the effect is far more significant in children in development stage than in adults[9]. Besides, more investigations showed that the development of orbital volume also relates to the age. The study by Robert et. al. showed that the orbital volume of 0-15 year old child and adolescent increases with the age, and the speed attain its maximum during ages 0-5.[7-9] Therefore, effective intervention should applied when the children’s orbital part is at its peak development. However, for this purpose, consideration solely from the perspective of medicine is in sufficient in that quantification is difficult to achieve. According to all works carried out so far, it is feasible to find a best mechanical stimulating point in the developing Children‘s orbital region based on biomechanical analysis including Mimics modeling and ANSYS analysis. Among these works, the study on the modeling and the setting of mechanical parameters is evidently important. Currently, there are reports on the modeling and mechanical FEA of adults orbital region based on CT images, but no reports on children are available. The modeling of orbital region for children in the early stage of development is difficult because the sclerotin of some parts is extremely thin according to the structure of the orbital region, and no relevant mechanical parameters is available for reference. However, based on the iterative trials and exploration described above, it proves that they could be determined after the modeling with Mimics, which paves the way for later study.

References 1. Chen, Z.B., Wan, L.: Modeling Approaches For Medical Finite Elements. Journal Of Clinicalrehabilitative Tissue Engineering Research 11(31) (August 5, 2007) 2. Uchio, E., Watanabe, Y., Kadonosono, K.: Simulation of airbag impact on eyes after photo refractive keratecto-my by finite element analysis method. Graefe’s Arch. Clin. Exp. Ophthalmol. 241(6), 497–504 (2003) 3. Al-Sukhun, J., Lindqvist, C., Kontio, R.: Modeling of orbital deformation using fnite element analysis. J. R. Soc. Interface 3(7), 252–262 (2006) 4. Zeng, P.: Fundamentals of Finite Element Analysis. Tsinghua University Press, Beijing (2007) 5. Escott, E., Rubinstein, D.: Free DICOM image viewing and processing software for your desktop computer:what’s available and what it can do for you. Radiographics:A Review. Publication of the Radiological Society of North America,Inc (2003) 6. Al-Sukhun, J., Lindqvist, C., Kontio, R.: Modeling of orbital deformation using fnite element analysis. J. R. Soc. Interface 3(7), 252–262 (2006) 7. Guo-dong, Z., Tao, S.-x., Mao, W.-y., Chen, J.-q., Luan, X.-g., Zheng, X.-h., Liao, W.-j.: Measurement of bone density based on three-dimensional reconstruction and finite element analysis. Journal of Clinical Rehabilitative Tissue Engineering Research 14(9) (February 26, 2010) 8. Li, G., et al.: Three2Dimensional Finite Element Analysis of the Subtalar joint by Mimics and ANSYS. Journal of Mathematical Medicine 23(2) (2010) 9. Uchio, E., Ohno, S., Kudoh, K., Kadonosono, K., Andoh, K., KisielewiczL, T.: Simulation of airbag impact on post-radial keratotomy eye using finite element analysis. J. Cataract Refract. Surg. 27(11), 1847–1853 (2001)

3D Model Retrieval Based on Multi-View SIFT Feature Shungang Hua, Qiuxin Jiang, and Qing Zhong School of Mechanical Engineering, Dalian University of Technology, Dalian, China [email protected]

Abstract. We research a 3D model retrieval algorithm on the basis of multiview SIFT features in this paper. By projecting the 3D model from multiple viewpoints, omnidirectional 2D depth images are obtained and from which SIFT features are extracted. Based on k-means clustering algorithm, we establish the codebook according to the proportion of SIFT features number of various shape types and whole SIFT features respectively. As a result, the former is much faster, so it is utilized to build the codebook in our paper. All the SIFT features associated with a model are clustered to generate a simplified vector by a histogram manner. For the similarity matching, Kullback-Leibler divergence is used to calculate distance between simplified vectors. Experiments show the algorithm based on multi-view SIFT feature can gain a satisfactory retrieval result for multiple shape benchmarks. Keywords: 3D Model Retrieval; Multiple View; SIFT Feature; KullbackLeibler divergence.

1 Introduction 3D model is intuitive for human visual perception and contains plentiful visual information, therefore it is widely used and abundant existence in various fields [1]. We usually wish to search required ones from existent 3D models to achieve model reuse. Nowadays, the number of 3D models is more and more, how to gain the required one from the mass of models quickly and accurately has become a research hotspot in the fields of computer visual and computer graphics. Existing 3D model retrieval methods can be classified roughly into four categories: graph-based, transform-based, histogram-based and view-based [2]. In these methods, view-based 3D model retrieval has many advantages, such as highly discriminative, effective for shape matching, invariance to noises and shape degeneracies, beneficial for 2D sketch-based and 2D image-based queries and so on. So this method is widely used for 3D model retrieval. Up to now, various algorithms have been proposed in this kind of methods. Pu et al. [3] proposed a method for representing a 3D model by a series of slices composed of 2D polygon sets along certain directions. Mohamed et al. [4] used 20 depth images to represent a 3D model. Each depth image is associated to a set of depth lines which will be transformed into sequences. And then the dynamic programming distance is used to compute the similarity of the models using the depth line descriptors. Napoleon et al. [5] proposed a method utilizing Continuous Principal Component Analysis for pose normalization and gaining a set of 2D M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 163–169. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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multi-views of the model. A multi-scale contour representation is introduced for each multi-view, which describes the convexities and concavities of the silhouettes at different scale levels. In this paper, we research a 3D model retrieval algorithm based on multi-view, by extracting Scale Invariant Feature Transform (SIFT) [6,7] from all the 2D depth images projected from 3D model and use Kullback-Leibler divergence (KLD) to compute the similarity of models to realize the 3D model retrieval efficiently.

2 Algorithm Description The 3D model retrieval algorithm based on multi-view SIFT feature (MV-SIFT) describes a 3D model using a set of local, multi-scale and visual features. Firstly, we obtain a series of 2D depth images of a model from multiple view directions, and then extract local features using the SIFT algorithm which is proposed by David G Lowe. The essence of SIFT algorithm is to find out the key points in different scale spaces and describe them with a set of vectors. Due to invariance to image scale and rotation as well as the change of viewpoint and illumination, the SIFT feature is widely used in the field of computer visual for the image matching, pattern recognizing and so on [6,7,8,9]. Extracted SIFT features associated with a 3D model may be up to a few thousands, so comparing calculation between these features is expensive. Therefore, the k-means clustering method is used to gain the codebook for quantizing these features. For a 3D model, quantized features are accumulated into a single histogram to become a feature vector to realize the reduction of comparing calculation. The flowchart of the feature vector generation is shown in Fig.1.

Fig. 1. SIFT feature vector generation.

The steps of the 3D model retrieval algorithm in this paper are as follows: (1) Model Preprocessing Because the randomicity for a 3D model in initial spatial location, size and direction, firstly, our algorithm performs preprocessing so that the 3D model can be compared in the same scale and regulation. We utilize position regularization to update all the coordinates of points on the model surface. The centroid of the model is calculated by the formula as follows:

xc =

1 N

N

∑ xi , y c = i =1

1 N

N

∑ yi , z c = i =1

1 N

N

∑z i =1

i

.

(1)

3D Model Retrieval Based on Multi-View SIFT Feature

Where N is the number of points on the model surface, coordinates of each point,

165

(xi , yi , zi ) is the original

(xc , yc , z c ) is the centroid coordinates of the model. Then

we translate the centroid to the origin of coordinates. Size regularization is used for normalizing the coordinates of points with the radius of the minimum sphere surrounding the model. We treat the centroid as the centre of sphere.

z i (2 × rmax ) . xi ' = ~ x i (2 × rmax ) , y i ' = ~ y i (2 × rmax ) , z i ' = ~ Where

(2)

(~xi , ~y i , ~z i ) and (xi ' , y i ' , z i ') are the coordinates of each point after the

position

regularization

and

the

size

regularization

respectively,

rmax = max ~ x +~ y +~ z i is the radius of the minimum sphere. 1≤ i ≤ N

2 i

2 i

2

(2) Multi-view Projection and Depth Image We use the similarity of multi-view depth images associated with 3D model to judge the similarity between models. Considering the integrity of expression and the complexity of calculation, 42 vertexes of 80-face polyhedron are treated as viewpoints to project the model to get the depth images. The size of depth images in this paper is defined as 256×256. (3) SIFT Feature Extraction and Clustering We extract SIFT feature, a 128 dimensional vector, from all the depth images of a 3D model. In our experiment, we use three shape benchmarks for SIFT feature extraction, McGill university Shape Benchmark database (MSB) [10], Princeton Shape Benchmark (PSB) [11] and Purdue Engineering Shape Benchmark (ESB) [12]. As a result, there are about 1500 SIFT features extracted for each model in MSB, 1000 for PSB, 554 for ESB. A SIFT feature is encoded into a codeword according to the codebook of the shape benchmark. For a shape benchmark, its codebook is built via k-means clustering. In k-means clustering, k is the number of cluster center. We set the value of k by two manners: 1) Individual-k-means For a shape benchmark which contains n shape categories, we assume the SIFT feature number of a shape category is Ni (i=1,2,…,n) and can calculate the number of clustering center ki utilizing formula

⎛ ki = ⎜ N i ⎝

n

∑N i =1

i

⎞ ⎟ × k . For ki, we gain individual codebook for each category ⎠

via k-means clustering, and gather individual codebooks of all categories together to obtain the codebook for the shape benchmark. 2) Whole-k-means Executing k-means clustering once by whole SIFT features extracted from a shape benchmark to gain the codebook for the shape benchmark. After establishing the codebook, each SIFT feature for 3D models is encoded into a codeword in the codebook. With vector quantization, code words associated with a 3D model are accumulated into a histogram having k bins. It becomes a k-dimensional feature vector. Thus, we express a 3D model with a k-dimensional vector.

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(4) Similarity Calculation The similarity between models is calculated by Kullback-Leibler divergence (KLD) that describes the difference between two probability distributions. We use an improved version of KLD formula to measure the similarity between two feature vectors [13]: k

D(U ,V ) = ∑ (vi − ui )ln i =1

Where

vi . ui

(3)

U = (u i ) V = (v i ) are feature vectors of two 3D models respectively, k is

the dimension of the vector.

3 Experiment and Analysis The algorithm is implemented on a desktop PC with Core 2 Duo 2.66 GHz CPU. We use three shape benchmarks for our retrieval experiment, (1)MSB, 427 models selected from original MSB and divided into 17 categories, while all the models are articulated; (2)PSB, 1814 rigid models separated into a training set with 907 models in 90 categories and a testing set with 907 models in 92 categories; (3)ESB, 866 engineering models classified into 45 categories. 3.1 Codebook Establishment The experiments show that the 3D model retrieval can be realized effectively if the codebook dimension k is within 1000 to 1500[13], so we set k=1000 and build the codebook utilizing Individual-k-means and Whole-k-means respectively. With Individual-k-means, we implement the k-means algorithm. Table 1 shows SIFT feature number of various types and assigned ki for MSB. Table 1. SIFT features number and ki value for MSB. Model Type SIFT Features Number (Ni) Clustering Center Number (ki)

ant 42879 102

crab 44447 106

hand 14781 35

human 28987 69

cuttlefish 37785 90

… … …

For Whole-k-means, we utilize whole SIFT features associated with models in a shape benchmark to execute k-means algorithm, and obtain the codebook of the shape benchmark. Using two methods above, we build the codebook for the MSB, PSB and ESB, and their time-cost is illustrated in Table 2. It demonstrates that the time-cost with two manners is quite different. The time-cost by Individual-k-means is reduced significantly, while experiments show the retrieval effect by two manners is almost equal. So we establish the codebook with Individual-k-means in our paper.

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Table 2. Time-cost comparing for codebook establishment using two methods. Shape Benchmark MSB PSB ESB

Individual-k-means/hour 0.72 0.50 0.34

Whole-k-means/hour 10.75 30.66 15.63

3.2 Performance Comparison for 3D Model Retrieval Algorithm We compare retrieval performance of four algorithms, MV-SIFT, D2 Shape Distribution algorithm (D2) [14], Multiple Orientations Depth Fourier Descriptor algorithm (MODF) [15] and Absolute Angle Distance histogram algorithm (AAD) [16]. D2 algorithm proposed by Robert Osada describes a 3D model as a histogram of Euclidean distances between random points on the surface. Using orthogonal depth projection of the 3D model from 42 directions, MODFD calculates the general Fourier descriptor to construct the feature vector. ADD algorithm is a kind method of 3D model feature extraction based on the outline. Transforming the 3D model contained surface information into directed points set model, all the distance of the point pair are calculated to organize 2D histogram. For the MSB, PSB and ESB, we carry out experiments with algorithms above, and obtain the precision-recall curve respectively.

(a)

(b)

(c)

Fig. 2. Recall-precision curve for MSB (a), PSB (b), ESB (c).

Fig. 2 shows the retrieval result with four algorithms. For articulated models in MSB, the R-precision with our algorithm MV-SIFT is 89% while D2, MODFD and AAD is 55%, 67% and 68% respectively; for the rigid models in PSB, the values are 67%, 30%, 37% and 32% respectively; for the ESB, the values are 86%, 42%, 49% and 47% respectively. Experiments show that, the retrieval algorithm based on SIFT feature can gain a satisfactory retrieval result for various shapes. Fig. 3 illustrates an interface of our 3D model retrieval system based on MV-SIFT algorithm. Quarry model lies on the top-left corner. 3D model retrieval is accomplished and returns 20 most similar models. The pliers model is the original model user inputs and the right models are listed as the most similar models that sorted from NO.1 to NO.20 according to similarity calculated with KLD.

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Fig. 3. Interface of our 3D model retrieval system.

4 Conclusion Based on multi-view SIFT feature, we research a 3D model retrieval algorithm. Our algorithm simplifies the complex 3D model feature extraction by projecting it into 2D depth image comprehensively. Using the MV-SIFT algorithm, the local feature of 2D image can be extracted and clustered to generate a feature vector describing the original 3D model reasonably. Our algorithm reduces the computational complexity while preserving the integrity of the expression. We discuss two kinds of manners for the establishment of the codebook. Comparing with the Whole-k-means, the Individual-kmeans obtains the same retrieval effect but the computation speed is much faster. To different shape benchmark, a series of experiments were carried on by MV-SIFT and other three kinds of 3D model retrieval algorithms, and the result shows that our algorithm is satisfactory to articulated shapes, rigid shapes and mechanical shapes. In the further work, the 3D model retrieval algorithm based on the content can be combined with text to obtain an intuitive retrieval effect. At the same time, we can introduce the biomimetic imaginal thinking to improve the feature extraction of the 3D model with a high precision.

References 1. Yang, Y.B., Lin, H., Zhu, Q.: Content-Based 3D Model Retrieval: A Survey. Chinese Journal of Computers 27, 1297–1310 (2004) (in Chinese) 2. Daras, P., Axenopoulos, A.: A 3D shape retrieval framework supporting multimodal queries. IJCV 89, 229–247 (2010)

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3. Pu, J.T., Liu, Y., Xin, G.Y.: A 3D Model Retrieval Method Based on the Similarity Between 2D Polygon Sets. Journal of Image and Graphics 9, 1437–1442 (2004) (in Chinese) 4. Chaouch, M., Verroust-Blondet, A.: A new descriptor for 2D depth image indexing and 3D model retrieval. In: Proc. ICIP 2007, vol. 6, pp. 373–376 (2007) 5. Napoleon, T., Adamek, T., Schmitt, F., O’Connor, N.E.: SHREC 2008 entry: Multi-view 3D retrieval using multi-scale contour representation. 2008 IEEE International Conference on Shape Modeling and Applications (2008) 6. Lowe, D.G.: Object recognition from local scale-invariant features. In: 7th IEEE International Conference on Computer Vision, Kerkyra, Greece, pp. 1150–1157 (1999) 7. Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004) 8. Wang, R.R., Ma, J.W., Chen, X.: SIFT Algorithm Based on Visual Matching Window for Registration Between Multi-sensor Imagery. Geomatics and Information Science of Wuhan University 36, 163–166 (2011) (in Chinese) 9. Zeng, L., Zhai, Y.: A SIFT matching algorithm based on pin-hole camera model. In: 2010 3rd International Conference on Computational Intelligence and Industrial Application (PACIIA), pp. 272–276 (2010) (in Chinese) 10. McGill 3D shape benchmark, http://www.cim.mcgill.ca/shape/benchMark/ 11. Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The Princeton Shape Benchmark. In: Proc. SMI 2004, pp. 167–178 (2004), http://shape.cs.princeton.edu/benchmark/ 12. Jayanti, S., Kalyanaraman, Y., Iyer, N., Ramani, K.: Developing an Engineering Shape Benchmark for CAD Models. In: Computer-Aided Design, vol. 38, pp. 939–953 (2006) 13. Ohbuchi, R., Osada, K., Furuya, T., Banno, T.: Salient Local Visual Features for Shape Based 3D Model Retrieval. In: Proc. IEEE Shape Modeling International (SMI), June 4-6, Brook, Stony (2008) 14. Osada, R., Funkhouser, T., Chazelle, B., Dobkin, D.: Shape Distributions. ACM Transactions on Graphics 21, 807–832 (2002) 15. Ohbuchi, R., Nakazawa, M., Takei, T.: Retrieving 3D Shapes Based On Their Appearance. In: 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, Berkeley, Californica, USA, pp. 39–45 (2003) 16. Ohbuchi, R., Minamitani, T., Takei, T.: Shape-Similarity Search of 3D Models by using Enhanced Shape Functions. International Journal of Computer Applications in Technology (IJCAT) 23, 70–85 (2005)

Current Issues and Future Trends in Analysis of Automotive Functional Safety Chunyang Mu, Xing Ma, Hongxing Ma, Xianlian Huang, Ling Zhang, and Rong Fan College of Electrical and Information Engineering, Beifang University of Nationalities, China {muchunyang,maxingsky,cherishma,hxlfzq, zhangling,fanrong11}@126.com

Abstract. The complexity of automotive electric and electronic systems is rapidly growing with increasingly new functions. Safety is one of key issues of future automotive development and is becoming more and more important. In this paper, we discuss the influence factors of automotive functional safety from three aspects during the development process, which are international standards, automotive E/E architecture and Model-Based Engineering approaches. And then we present related work such as modeling languages and tools, integration of mixed criticality applications and methods for functional safety analysis. According to the above discussion we propose the trends of analysis of automotive functional safety and give some advices for future research. Keywords: Automotive Functional Safety, Integrated E/E Architecture, ISO 26262, Model-Based Engineering, Electronic Control Unit.

1 Introduction Since 1960’s electronic systems were introduced into the automotive industry, electronic devices within a vehicle has increased rapidly and today electronics, especially software has become the main sources of automotive innovation [1]. To satisfy requirements of cost, safety, reliability and comfort from customers, the new functions of a car is still growing. Thus the complexity is rapidly increasing, which has become one of main challenges in design and development of a car. On the other hand, safety is one of key issues of future automotive development and is becoming more and more important. In the near future active safety and x-bywire technologies should be considered in the design of a car, especially for new energy source or electric vehicle. There will be a large increase in the number of safety-critical functions with increasing vehicle control authority [2]. The car designers need deal with the fail-safe design and face the big challenge of integration between safety critical and non-safety critical applications across different domains. It is expected to be resolved by using the methods and technologies of functional safety. The definition of functional safety given by ISO is “absence of unreasonable risk due to hazards caused by malfunctioning behavior of E/E systems” [3]. It has become the big challenge for automotive industry how to control the growing complexity of automobile E/E systems and improve the development efficiency. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 171–178. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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In this paper, we shall firstly discuss the influence factors of automotive functional safety during the development process. Then we shall review current situation of functional safety methods based on MBE approaches and give the trends of analysis of automotive functional safety and advises the future work on this topic. Finally, we will summarize this paper.

2 Automotive Functional Safety: Influence Factors There are many influence factors of automotive functional safety, e.g. development process, production and service processes. Here we will focus on the factors during the development process. When analyzing functional safety of automotive systems, there are three main aspects should be considered, they are: 1) international standards concerned with automotive functional safety; 2) tendency of automotive E/E architecture; 3) methods and technologies for ECU development. 2.1 Functional Safety Standards Now there are two standards available for the development of automotive electronic control systems, they are IEC 61508 and ISO/DIS 26262. Before ISO/DIS 26262, the development of automotive functional safety was carried out mainly referred to IEC 61508 which is designated by IEC as a generic standard of “functional safety of electrical, electronic and programmable electronic safety-related systems” for whole industry sectors. So there are too many contents to be mastered by car designers. And moreover, it will appear some issues if directly used in automotive industry [3]. Therefore the particular functional safety standard for road vehicle – ISO 26262 emerges as the times require which is an adaptation of IEC 61508 with needs specific to the application of automotive E/E systems. The latest release is ISO/DIS 26262 and the final standard is expected to be published with simply name ISO 26262 in the mid of 2011. To avoid various risks which might be induced during development process, ISO 26262 includes guidance by providing appropriate requirements and processes, e.g. requirements for validation and confirmation measures, definition of automotive safety lifecycle and approach for determining ASIL (automotive safety integrity level). The OEM should have the obligation to produce safe cars. However, in practice there is no legal requirement for certification of the compliance with certain standard. But in the near future, ISO26262 will be the compulsory standard that should be complied by automotive industry with the release of the final version. 2.2 Automotive E/E Architecture There are two classes of systems for distributed applications, namely federated and integrated systems [4]. Today automotive complete vehicle E/E systems typically consist of ECU (including software), sensors and actuators. Thus one car has become a complex distributed embedded system which provides both safety critical and nonsafety critical functions and therefore its architecture also can be divided into the two classes, i.e. federated architecture and integrated architecture. Comparisons with the two classes of automotive E/E architectures are given in table 1.

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Table 1. Comparisons between the federated and integrated architecture Federated architecture The E/E systems integrated within a car are designed and developed separately. The functions of ECUs are relatively independent, which will facilitate faultisolation and complexity management [4]. The communication among ECUs is realized by exchanging messages on eventtriggered bus with limited bandwidth. Thus there exists time non-deterministic e.g. latency and jitter and it is not suitable for the application of x-by-wire with high reliability requirement. Generally the design scheme with federated architecture diagram is decided before the development of systems and will be used for several years due to the causes of costs and techniques. So it is difficult of modularity and lacks of flexibility. ECUs’ messages are from a single source in general case and the functions between them are easy to be integrated, verified and validated through verification of timing constraints on the communication messages simply. Typically ECU is designed as one function one box [5] and with increasing of new functions the number of ECUs will be increased. This will result in uneven computation load among ECUs. Some reach their design limits while others might have low CPU utilization. And it also will bring some demerits, e.g. higher costs and weights (ECU hardware, wires and connectors), lower reliability of vehicle functions and fewer options in the layout of subsystems. In automotive product supply chains, the federated architecture diagram can make the integration costs of OEMs increase because that many electronic control systems are provided by Tier-1 suppliers who monopolize sources and technologies and are responsible for the development of them separately.

Integrated architecture The vehicle E/E systems are designed with comprehensive considerations from the view of system-level and become an organic whole. But it need face problems of faultisolation and complexity management. According to the domain (e.g. powertrain and chassis) concept, the ECUs turn into an interconnected distributed network by using time-triggered bus such as FlexRay. The communication bandwidth is increased significantly and can meet requirements of xby-wire technology (10-9 failures per hour). Typically the design scheme is based on international standard and PBD (platformbased design) methodology. It can effectively decouple designs of software from ones of hardware platform and improve extensibility and flexibility of systems. ECUs’ messages can be obtained from several sources because the distribution and layout of vehicle functions is considered under system-level. Although difficulty of integration and test of system, the ability of redundancy design can be provided. The architecture usually is divided into several levels of abstraction and then abstract models are setup correspondingly. It can effectively avoid the imbalance of the ECUs’ computation load by distributing system functions reasonably and optimizing design scheme of vehicle architecture. The number of ECUs can be evidently decreased by one box more function method. And thus costs and weights can be controlled and reliability of system also can be increased. The business model is changed in this architecture. The monopoly will be broken up and competition will be introduced because that the products from Tier-1 suppliers could be no more separate systems but ECUs’ components. Thus the OEMs have more opportunities to control the integration of E/E systems and to decrease the costs.

As shown in table 1, the two classes of architectures have their merits and demerits and fit for different period respectively. State of the art the federated E/E architecture has been widely applied, but has been found more and more difficult to meet the

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needs of modern automotive systems, e.g. complexity, extensibility, flexibility, reliability and costs; and the integrated E/E architecture is expected to overcome those problems and substitute for the former but still in development with its standards, methods and tools [4-7]. So when researching on automotive functional safety, the integrated E/E architecture should be considered as its design framework. 2.3 Model-Based Engineering Code-centric approaches have been used in software development of automotive E/E systems for a long time. And so far, more and more practices have shown that codecentric approaches are very difficult for dealing with the development of increasingly more complex, high dependable, real-time and embedded systems and are reaching their technical limits. We need new methods to achieve the following goals: 1) to cope with increasingly complexity of safety lifecycle and make all activities in them connected seamlessly through certain artifacts described with a mathematical formalism; 2) to do the verification and validation activities of functional safety in earlier development stages in order to reduce the costs of test and debug; 3) to increase the efficiency of development of functional safety via some automated or semi-automated tools. In contrast with code-centric approaches, model-centric approaches (i.e. modelbased engineering, MBE or model-driven development, MDD) that are expected to overcome those technical limits faced by code-centric approaches have made great progress (e.g. meta-modeling, practical model transformation and auto-code techniques) and may turn into the mainstream design approaches for embedded systems in future [8,9]. In the light of rapid progress of MBE and its advantages, ISO have taken model-centric approaches into account when drafting out the ISO 26262 standard and have given the suggestions and guides of methods using MBE approaches for functional safety analysis although still lacking of practical cases [3]. So for when design automotive E/E systems in future, MBE approaches should be considered adequately during the process of automotive functional safety analysis.

3 Methods of Functional Safety: The Current Situation When researching on the methods of automotive functional safety analysis with MBE approaches, three aspects should be considered: 1) the selection of modeling languages and tools and the connectivity with safety lifecycle specified by standard; 2) the integration of functional safety and non-functional safety SWCs (software components); 3) the methods for automotive functional safety analysis. 3.1 Modeling Languages and Tools By now there are several influential modeling languages and tools can be used for the analysis of automotive functional safety, e.g. AADL, SysML, MARTE, EAST-ADL2, AUTOSAR and HiP-HOPS. AADL is a standard from SAE and is a formal modeling language used as an architecture description language [10]. Recently AADL also can be applied for the system performance simulation and the modeling and analysis of control system [11,12].

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SysML and MARTE are both an extended profile derived from UML 2.0 of OMG. SysML can be used for system modeling with the abilities of specification, analysis, verification and validation [13,14]. And MARTE is expected for the modeling and analysis of a real-time system and can provide the formal semantics for the specification of timing requirements and properties [15,16]. EAST-ADL2 is an architecture description language developed in the ATESST project (further refining of EAST-ADL in ITEA EAST-EEA project) and can well support the development of embedded system; besides design of software framework, it also can be used for modeling of requirement engineering and traceability, variability, timing and safety analysis [17,18]. The AUTOSAR specification that puts emphasis on more logic and static expressions than behavior simulation of system did not agree on a formal model of computation [19]. Therefore EAST-ADL2 and MARTE are introduced into the AUTOSAR modeling system by AUTOSAR partnership in order to enhance the abilities to describe requirements, features, variability and timing of automotive control systems [20-22]. In addition, the current release of AUTOSAR (release 4.0) has provided the support for functional safety which is still under development [23]. HiP-HOPS is a scalable automated dependability analysis and optimization tool which focus on failure modeling of system and support multi-objective design optimization [24,25]. So it has been used with a number of different tools and modeling languages, e.g. Matlab/Simulink and EAST-ADL2 [26]. As presented, each modeling language or tools is for specific domain. When researching on automotive functional safety with MBE approaches, three factors are suggested to be considered: 1) the openness, extendibility of specification of modeling language, 2) the connectivity with methods and tools used in automotive standards, 3) the ability to explore new methods of functional safety analysis. 3.2 Integration of Mixed Criticality SWCs In order to analyze automotive functional safety under the integrated E/E architecture, one of key problems is to deal with the integration of mixed criticality SWCs. Now there have been some significant research results. A systematic resource allocation approach is presented for the consolidated mapping of safety critical and nonsafety critical applications onto a distributed platform [27]. The impacts of ISO 26262 to the development of automotive E/E architecture are discussed and methods for the allocation of safety requirements during early phases are presented in [28]. [29] illustrates how to perform an integrated safety process without lost in the details of safety lifecycle according to ISO 26262 by using a practical design of lane assistance functions, but the process is more suitable for code-centric approaches. Generally the software development processes in automotive industry are based on the V model. For non-safety critical SWCs there already exists two V processes, i.e. “V+v” model which is corresponding to the development of basic software platform and application respectively [30]. If the safety lifecycle process was considered, the dimensions of V model would be enlarged to three, which would make the solution space of E/E architecture more complex. So it is necessary to find efficient algorithms to handle the integration of mix criticality SWCs in order to reduce the dimension.

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3.3 Methods for Functional Safety Analysis: Advance with MBE Approaches Methods currently used for the analysis of automotive functional safety have Review, PHA, FMEA and FTA, which combining with MBE approaches has been becoming a new research focus. A systematic approach to PHA with the EAST-ADL2 language is presented according to the ISO/DIS 26262 so as to setup precise modeling of requirements, user functionalities and the derived safety mechanisms [31]. A modeling language translation between EAST-ADL2 and HiP-HOPS is realized by using model transformations and by leveraging the advantages of different model transformations techniques [32]. To achieve the optimization of designs with respect to dependability and cost from the early stages, a design process supported by HiP-HOPS is presented which integrates system modeling, automated dependability analysis and evolutionary optimization techniques [33]. A parallel genetic algorithm, named AHHPGA, which uses HiPHOPS to overcoming the limitations imposed by reliability block diagrams, is proposed to solve reliability optimization of series-parallel systems [34].

4 Trends and Open Issues In summary, work on the analysis of automotive functional safety should comply with following principles: a) analyzing with the integrated E/E architecture approaches; b) using MBE approaches with modeling language (e.g. AUTOSAR meta-model, UML Profile and HiP-HOPS) that can provide formal notation; c) and considering the impact of international standard such as ISO 26262. There are still some questions need to be settled. One key issue is that the semantic extension of modeling language based on existing one in order to support for modeling requirements of integrated E/E architecture and of automotive functional safety. Another one is to address efficient algorithms for finding the solution space of E/E architecture during handling with the integration of mix criticality SWCs. For the methods of functional safety analysis, it needs more practical cases in light of the safety lifecycle and methods recommended by ISO 26262.

5 Conclusion Automotive functional safety is a key issue of future automotive development. In this paper, we argued the influence factors from three main aspects which are international standards, E/E architecture and MBE approaches and reviewed current research results of them. In the end we proposed the trends of analysis of automotive functional safety and gave some suggestions for future research.

References 1. Mössinger, J.: Software in Automotive Systems. J 3(4), 92–94 (2010) 2. Di Natale, M., Wei, Z., et al.: Using system-level timing analysis for the evaluation and synthesis of automotive architectures. In: Ramesh, S., Sampath, P. (eds.) Next Generation Design and Verification Methodologies for Distributed Embedded Control System, pp. 99–113. Springer, Heidelberg (2007)

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3. ISO.: Road vehicles — Functional safety —Part 1-10. ISO/DIS 26262 (2011) 4. Obermaisser, R., Peti, P., Tagliabo, F.: An integrated architecture for future car generations. J. Real-Time Syst. 36, 101–133 (2007) 5. Di Natale, M., Sangiovanni-Vincentelli, A.L.: Moving From Federated to Integrated Architectures in Automotive: The Role of Standards, Methods and Tools. J. Proc. of the IEEE 98(4), 603–620 (2010) 6. Obermaisser, R., El Salloum, C., Huber, B., Kopetz, H.: From a Federated to an Integrated Automotive Architecture. J. IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems 28(7), 956–965 (2009) 7. Islam, S., Suri, N., Balogh, A., Csertán, G., et al.: An optimization based design for integrated dependable real-time embedded systems. J. Des Autom Embed Syst. 13, 245–285 (2009) 8. Mellor, S.J., Clark, A.N., Futagami, T.: Model-driven development: Guest editor’s introduction. J. IEEE Software 20(5), 14–18 (2003) 9. Gérard, S., Espinoza, H., Terrier, F., Selic, B.: 6 modeling languages for real-time and embedded systems. In: Giese, H., Karsai, G., Lee, E., Rumpe, B., Schätz, B. (eds.) ModelBased Engineering of Embedded Real-Time Systems. LNCS, vol. 6100, pp. 129–154. Springer, Heidelberg (2010) 10. Feiler, P.H., Gluch, D.P., Hudak, J.J.: The Architecture Analysis & Design Language (AADL): An Introduction. Technical Note, CMU/SEI-2006-TN-011 (2006) 11. Shenglin, G., Lei, L., Yun, L., Wang, L.: Formal schedulability analysis and simulation for AADL. In: The 2008 International Conference on Embedded Software and Systems, pp. 429–435. IEEE Press, Los Alamitos (2008) 12. Shiraishi, S.: An AADL-based approach to variability modeling of automotive control systems. In: Petriu, D.C., Rouquette, N., Haugen, Ø. (eds.) MODELS 2010. LNCS, vol. 6394, pp. 346–360. Springer, Heidelberg (2010) 13. OMG.: OMG Systems Modeling Language (OMG SysMLTM), Ver. 1.2. OMG Document Number: formal/2010-06-01(2010) 14. Hause, M., Stuart, A., Richards, D., Hol, J.: Testing Safety Critical Systems with SysMLUML. In: 15th IEEE International Conference on Engineering of Complex Computer Systems, pp. 325–330. IEEE Computer Society Press, Los Alamitos (2010) 15. OMG.: UML profile for MARTE: Modeling and Analysis of Real-Time Embedded Systems, ver. 1.0. OMG document number: formal/2009-11-02 (2009) 16. André, C., DeAntoni, J., Mallet, F., de Simone, R.: The Time Model of Logical Clocks Available in the OMG MARTE Profile. In: Shukla, S.K., Talpin, J.-P. (eds.) Synthesis of Embedded Software: Frameworks and Methodologies for Correctness by Construction. LLC, pp. 201–227. Springer, Heidelberg (2010), doi:10.1007/978-1-4419-6400-7_7 17. Debruyne, V., Simonot-Lion, F., Trinquet, Y.: EAST-ADL—an Architecture Description Language. In: IFIP International Federation for Information Processing, Architecture Description Languages, vol. 176, pp. 181–195 (2005) 18. Cuenot, P., Frey, P., Johansson, R., Lönn, H., Papadopoulos, Y., Reiser, M.-O., Sandberg, A., Servat, D., Tavakoli Kolagari, R., Törngren, M., Weber, M.: 11 the EAST-ADL architecture description language for automotive embedded software. In: Giese, H., Karsai, G., Lee, E., Rumpe, B., Schätz, B. (eds.) Model-Based Engineering of Embedded Real-Time Systems. LNCS, vol. 6100, pp. 297–307. Springer, Heidelberg (2010) 19. Fürst, S., Mössinger, J., Bunzel, S., et al.: AUTOSAR – A Worldwide Standard is on the Road. In: 14th International Congress Electronic Systems for Vehicles (2009)

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20. Cuenot, P., Frey, P., Johansson, R., Lonn, H., Reiser, M.-O., Servat, D., Tavakoli Kolagari, R., Chen, D.J.: Developing Automotive Products Using the EASTADL2, an AUTOSAR Compliant Architecture Description Language. In: 4th European Congress ERTS Embedded Real Time Software, Toulouse, France (2008) 21. Johansson, R., Bunzel, S., Graniou, M., et al.: A road-map for enabling system analysis of AUTOSAR-based systems. In: CARS 2010 Proceedings of the 1st Workshop on Critical Automotive applications: Robustness & Safety, ACM, New York (2010) 22. Espinoza, H., Gérard, S., Lönn, H., Kolagari, R.T.: Harmonizing MARTE, EASTADL2, and AUTOSAR to Improve the Modelling of Automotive Systems. In: The Workshop STANDRT, AUTOSAR (2009) 23. Espinoza, H., Gérard, S., Lönn, H., Kolagari, R.T.: Harmonizing MARTE, EASTADL2, and AUTOSAR to Improve the Modelling of Automotive Systems. In: The Workshop STANDRT, AUTOSAR (2009) 24. AUTOSAR.: AUTOSAR Specifications Release 4.0, http://www.autosar.org 25. Papadopoulosa, Y., McDermida, J., Sasseb, R., Heiner, G.: Analysis and synthesis of the behaviour of complex programmable electronic systems in conditions of failure. J. Reliability Engineering and System Safety 71, 229–247 (2001) 26. ATESST2.: Report name State of practice and State of the art. Version number 1.0, The ATESST2 Consortium (2008) 27. Islam, S., Lindström, R., Suri, N.: Dependability Driven Integration of Mixed Criticality SW Components. In: 9th IEEE International Symposium on Object and ComponentOriented Real-Time Distributed Computing. IEEE, Los Alamitos (2006) 28. Hillenbrand, M., Heinz, M., et al.: An Approach for Rapidly Adapting the Demands of ISO/DIS 26262 to Electric/Electronic Architecture Modeling. In: 21st IEEE International Symposium on Rapid System Prototyping, IEEE, Los Alamitos (2010) 29. Dittel, T., Aryus, H.-J.: How to “Survive” a safety case according to ISO 26262. In: Schoitsch, E. (ed.) SAFECOMP 2010. LNCS, vol. 6351, pp. 97–111. Springer, Heidelberg (2010) 30. Mu, C., Sun, L., Du, Z., Chen, Y.: Method Based on OSEK/VDX Platform using Modelbased and Autocode Technology for Diesel ECU Software Development. In: 31st Annual IEEE Computer Software and Applications Conference, pp. 629–634. IEEE, Los Alamitos (2007) 31. Sandberg, A., Chen, D., Lönn, H., Johansson, R., Feng, L., Törngren, M., Torchiaro, S., Tavakoli-Kolagari, R., Abele, A.: Model-based safety engineering of interdependent functions in automotive vehicles using EAST-ADL2. In: Schoitsch, E. (ed.) SAFECOMP 2010. LNCS, vol. 6351, pp. 332–346. Springer, Heidelberg (2010) 32. Biehl, M., DeJiu, C., et al.: Integrating Safety Analysis into the Model-based Development Tool Chain of Automotive Embedded Systems. In: ACM/LCTES 2010 (2010) 33. Adachi, M., Papadopoulos, Y., et al.: An approach to optimization of faul tolerant architectures using HiP-HOPS. J. Softw. Pract. Exper (2011) 34. Zeng, W.-h., Papadopoulos, Y., Parker, D.: Reliability Optimization of Series- Parallel Systems Using Asynchronous Heterogeneous Hierarchical Parallel Genetic Algorithm. J. Mind and Computation. 1(4), 403–412 (2007)

Development of an Automatic Steering System for Electric Power Steering (EPS) System Using Fuzzy Control Theory Tao Hong*, Xijun Zhao, and Yong Zhai Intelligent Vehicle Research Center, Beijing Institute of Technology, Beijing 100081, China [email protected]

Abstract. Automatic steering wheel is a basic and crucial part of a driverless vehicle which is highly recognized as the finally trends of vehicle. In comparison with hydraulically power system, the electric power steering (EPS) system is a new power steering system which could provide power when the driver steers. The electronic properties of the EPS are quite desirable for automatic control. A novel automatic steering controller for mass-produced vehicle equipped with EPS system is presented in this paper. This controller steers the wheel by simulate the torque signals which are generated by human drivers. Considering the nonlinearity and uncertainty of steering systems, fuzzy theories are implemented for this system instead of classic control theories such as a PID controller. This system has been tested on real road for its effectiveness. Keywords: Automatic steering control, Electric power steering (EPS), Fuzzy Controller.

1 Introduction Automatic steering system is not only one of crucial parts of Intelligent Vehicles (IV) but also a core technique in driving assisting systems such as automatic parking systems. Due to huge marketing and research potential of automatic steering systems, worldwide researchers focus on study, implementation and validation of this kind of systems [1]. Some prospective studies in the Defense Advanced Research Projects Agency (DARPA)[2] Grand Challenge and Urban Challenge, California Partners for Advanced Transit and Highways(PATH) Research Programs and the Autopia Program presented lots of excellent theories on lateral control of Intelligent Vehicles such as sliding mode control[3], optimal preview control[4], model reference adaptive control[5]. However, because of the limitation of traditional hydraulically powered steering systems, automatic steering systems had to include additional components such as motor to move the steering system[2]. As a new power steering technology, Electric power steering (EPS) system replaces the hydraulic parts by electronic signals to *

This work was supported in part by the National Natural Science Foundation of China. (Grant No. 90920304).

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control the wheel. As a result, the EPS offers a new architecture of automatic steering system which requires no additional servo components. In this paper, we present a novel EPS-based automatic steering system. An electronic controller is designed to control the EPS system on the test vehicle—a CHERY A3. Given the difficulties in establishing an accurate model of steering system, fuzzy theory, which requires no explicit mathematical model, is implemented in the controller instead of classic PID controller[6]. The effectiveness of the method and controlling rules is verified using on-road experimental results. This paper organized as the following: first, an overview of the system is given; the fuzzy control rules used in the controller are derived in section 2, and is followed by the analysis of experimental results of on-road testing, finally, conclusions are presented.

2 Controller Architecture The full architecture of the proposed automatic steering system is show in fig.1. The vehicle’s position with respect to the reference trajectory is calculated by decision making computers in every 50ms. The target steering angle is generated every 50ms based on the automatic steering algorithm and the vehicle model. The target steering angle, as well as the steering angle change rate read from the CAN bus, are inputted to the automatic steering controller at a rate of 100hz. The automatic steering controller outputs adequate signals to drive the EPS system for steering.

Fig. 1. Architecture of system

EPS system is composed of a torque sensor which measures drivers’ steering torque, a controller receives the torque signal from torque sensor and velocity from CAN bus in vehicle and generates PWM signals to drive a DC motor in EPS system[2] to drive front wheel moving. According to the structure of EPS, we constructed our automatic steering controller which could generate torque signals similar with humanity to drive EPS system as showed in fig. 2. Considering outputs of torque sensor locating in EPS system is two differential voltage signals, outputs of automatic steering controller should be the same with it, so that the controller of EPS would mistake outputs of automatic steering controller for the outputs of the torque sensor. For the reasons stated above, main goals of the automatic steering controller presented in the paper are generating a torque signal which should be the same or at least similar with output of torque sensor when human operated the steering system in every turning situation.

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Fig. 2. EPS automatic steering controller

The reason to employ fuzzy theory in the system includes: First, it is hardly to establish accurate mathematical model for the packaged EPS system; Second, fuzzy theory works better in an environment with nonlinearity and uncertainty than the classical PID controller[6].

3 Fuzzy Controller The aim of this work is to simulate the human driver’s behavior in steering. Fuzzy controller quantified qualitative control rules summarized from artificial experience according to fuzzy mathematics. Typical fuzzy control strategies including fuzzy up the input and output variables by means of defining the variable linguistic labels and membership functions, fuzzy rules specified and control variables defuzzification. 3.1 Fuzzification To generate the membership functions, trial-and-error, or export knowledge[1] is widely used. However, how to represent human experience is one of the hardest problems in fuzzy logic without unified, valid solutions. In the paper, human-operating steering experiments are done on different roads at different velocity. In these experiments, we record the data of torque signals measured by torque sensor in EPS, steering angle and its change rate from CAN bus in vehicles when human operating the car. Fig. 3 shows the data of human driving, and the goal of our work is to build a controller with similar curve as shown in fig. 3. These human operation data then become the resource and foundation when we design fuzzy controller. The shape of all membership functions used in the paper is triangular. Since the shape is generally less important than the number of curves and their placement[6], we design these curves carefully based on these experiments which could represent human’s habit or experience when they are driving.

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In this controller, target steering angle E and steering angle change rate EC are considered as two inputs of the system, torque signal U is the output. Since span of E is (500, +500), 7 fuzzy sets—(NB, NM, NS, Z0, PS, PM, PB)—are defined in the range of (-5, +5). Then, membership functions could be generated as are shown in fig.4. Meanwhile, variation range of EC whose value is restricted to the interval (0, 300), and construct 7 fuzzy sets: (NB, NM, NS, Z0, PS, PM, PB) at a range of (0, 10) whose membership functions could be seen in fig. 5. Finally, membership functions of U whose physical range is (1.5V, 3.5V) and (NB, NM, NS, Z0, PS, PM, PB) at a range of (1.5, 3.5) are appeared in fig. 6.

Fig. 3. Human driving experience

Fig. 4. Membership function of target angle

Fig. 5. Membership function of angle change rate.

Fig. 6. Membership function of torque signal.

Note that in these membership figures, NB means target angle is negative big and needs to turn to left; NM means negative medium which needs to turn to left; NS means negative small and needs to turn to left; Z0 means zero; PS means positive small and needs to turn to right; PM means positive medium and needs to turn to right; PB means positive big and need to turn to right.

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3.2 Fuzzy Logic Human experience is included in fuzzy controllers when establishing fuzzy logic. Since every input has 7 linguistic values, 49 control rules are needed to constitute as show in table 1. The main ideas of procedural rule to manage a car’s steering are: IF target angle left THEN steering left; IF target angle right THEN steering right; IF steering angle change rate large THEN steering slow; IF steering angle change rate small THEN steering fast. In order to steering the vehicle smoothly, more rules should be included except the basic rules illustrated above. For example, although the system is at a rate of 100Hz, which in most situations is efficient enough, in some extreme small target angle cases, it would appear too slow since the steering wheels are in a high steering change rate. Thus, it is necessary to set the rules to avoid these extreme kinds of phenomenon— reduce the torque signal in advance. The whole rules used in Fuzzy EPS automatic steering controller presented in this paper are demonstrated in the following table. Table 1. Control rules of fuzzy controller

NB NM NS Z0 PS PM PB

NB NB NB NB NB NB NB NB

NM NS NB NM NM NM NB NB

NS NS NS Z0 Z0 Z0 Z0 Z0

Z0 Z0 Z0 Z0 Z0 Z0 Z0 Z0

PS PS PS Z0 Z0 Z0 Z0 Z0

PM PB PB PM PM PM PS PS

PB PB PB PB PB PB PB PB

3.3 Defuzzifucation Defuzzification is the last step in designing a fuzzy controller where the output linguistic labels are transformed into crisp values. In this paper, Mamdani method which is more suitable for real-time control is employed and MATLAB/Simulink software is used to calculate the values as shown in table2. Table 2. Results of defuzzification

1 2 3 4 5 6 7 8 9 10

-5 1.56 1.56 1.56 1.56 1.56 1.56 1.56 1.56 1.56 1.56

-4 1.57 1.57 1.76 1.76 1.76 1.76 1.96 2.03 2.04 2.04

-3 1.99 1.99 2.05 2.05 2.05 2.03 2.11 2.29 2.3 2.29

-2 2.07 2.07 2.11 2.11 2.11 2.13 2.11 2.34 2.33 2.34

-1 2.31 2.31 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5

0 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5

1 2.69 2.69 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5

2 2.93 2.93 2.89 2.89 2.89 2.87 2.89 2.66 2.67 2.66

3 3.01 3.01 2.95 2.95 2.95 2.97 2.89 2.71 2.7 2.71

4 3.43 3.43 3.24 3.24 3.24 3.24 3.04 2.96 2.96 2.96

5 3.44 3.44 3.43 3.43 3.43 3.44 3.43 3.44 3.43 3.44

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Fig. 7. Control surface of fuzzy controller

4 Experiments and Results Experiments were conducted to verify the effectiveness of this controller. This system was installed on a CHERY A3 with EPS system . Experiments are divided into two parts: one test is observing the response of system to step angular signals, the other is real tests on campus road. Fuzzy logic used in our controller has been demonstrated in both step signal and real road experiments. In the first experiment, different step angular signals—35 Deg, 130 Deg, 290 Deg and 450 Deg which represent small, medium and large steering angle sequentially are tested on road. As is seen in fig. 8 to fig. 11, all four situations could finally obtain the target angle with angle error less than 5 degree and less than 2 seconds delay. The less the target angle is, the faster the system would get the goal but the easier to overshoot. Since transmission ratio from steering wheel to front wheel of vehicle is 16, the largest overshoot of our controller would lead to slip angle error of front wheel less than 0.5 Deg which could fully meet the requirement of our system.

Fig. 8. Response curve of 35 Deg step angular

Fig. 9. Response curve of 130 Deg step angular signal

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Fig. 10. Response curve of 290 Deg step angular signal

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Fig. 11. Response curve of 450 Deg step angular signal

If target angle could be delivered with slight change, this system could follow the target steering angle better as show in fig.12 comparing to step signal of the same angle which is shown in fig. 11.

Fig. 12. Response curve when target angle changes slightly

Fig. 13. Response curve when target angle changes relatively sharply

Fig. 13 shows a relatively sharp change of target angle compared to Fig. 12, which also has a good performance on following target steering angle. On road experiments have done to demonstrate the effectiveness of this controller. We detect and calculate the target angle by combining GPS, lidars with cameras. The results show the effectiveness of the controller as seen in fig. 14. In most cases, our automatic steering system could follow the target route well. When the turnings of road are extremely shape, 1-2 seconds delay would be produced. However, these delays do not affect vehicle successfully passing the turnings.

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Fig. 14. Real road test

5 Conclusion A new automatic steering system based on EPS system has been put forward in this paper. This system could move the steering system of CHERY A3 which has been equipped EPS as its steering system to a target angle calculated by computer after received route information with a moderate velocity. The controller has two inputs: target angle and steering angle change rate, one output—torque signal for EPS which is similar to the torque signal produced by torque sensor when human driving in the same situation. Fuzzy control theories are adept to this design to accomplish this function when considering the convenience of fuzzy theories dealing with nonlinearity and uncertainty of environment. Experiments on real road have been conducted to verify the effectiveness of this EPS fuzzy automatic steering controller. Results and analysis have been presented above, on the same time, proved this controller could follow target route correctly and efficiently.

References 1. onieva, E.: Automatic lateral control for unmanned vehicle via genetic algotithm. Applied Soft Computing, 1303–1309 (2010) 2. Naranjo, J.E.: Hybrid steering control for intelligent vehicle. In: ICRA 2007 Workshop: Planning Perception and Navigation for intelligent vehicle (2007) 3. Ackermann, J., Guldner, J.: Linear and nonlinear controller design for robust automatic steering. IEEE Transactions on Control Systems Technology 3, 132–143 (1995) 4. Peng, H., Tomizuka, M.: Opital preview control for vehicle lateral Guidance. ASME J. DynSyst., Meas, Control 115, 679–686 (1991) 5. Panfilov, D.U., Tkachev, S.B.: ‘tracking of reference trajectory for wheeled mobile robot. In: Proceedings of the 14th International Conference on Process Control, Strbske Pleso, High Tatras, Slovakia (2003) Paper No.095 CD-ROM 6. Takashi Shigematu, J.: Development of Automatic driving system—Automatic steering control by fuzzy algorithm. In: Intelligent Vehicles 1992 Symposium, Detroit, MI, pp. 154–159 (1992)

Design and Implementation of Interactive Digital Shadow Simulation System Qian Li*, Qing-yi Hua, Jun Feng, Wei Niu, Hao Wang, and Jie Zhong Dept. Of Information Science and Technology Northwest University Xi’an, China 710127 [email protected]

Abstract. The research of Digital Shadow is focused in recent years, to combine the traditional Shadow puppets with digital technology for manufacture and performance plays a tremendous role in promoting its heritage. However, current research are mostly simulation-based, and ignore the role of the user in the system. This article starts from the users’ point of view, presents a user’s experience-oriented, interactive design and performance model. The IDSS systerm, designed and completement on the basis of this, allows users to manipulate and control the digital Shadow props directly, meanwhile, with the rendering of visual and auditory , to coincide with the real Shadow play relatively and effectively solve the problems of lacking of interaction in current Digital Shadow system. Keywords: Digital Shadow; Interactive; user experience.

1 Introduction Shadow is a very ancient folk performing arts in China, it has a very high artistic value all over the world. However, due to the complex manufacture and performance of Shadow, difficulty to store the finished Shadow and simple communication channels, the development of this ancient art is not optimistic. Therefore, the Shadow’s digital dissemination has become an inevitable trend. The technology of Digital Shadow has become a research hotspot in recent years, but current researches mostly focus on the simulation model and the Shadow puppet figures control, the layout of opera and the simulation of performances form are still rare. Some so-called Digitalize is similar to animation manufacture and lacks of interaction and performance. Simply digitalize the result and lacking of interaction and performance make ordinary users not really understand the creation process and performance of Shadow, which not really promote the spreading and development of Shadow play. This paper, for the purpose of stimulating the fun of creation and user’s interactive experiences , designs and develops a Digital Shadow platform with the figures of user friendly , and employ the interactive experience with the innovative design for the *

Corresponding author.

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ordinary users. In order to stimulate the fun of user's creation and attract more young people to spread the Shadow. Which can provides new ideas for the inheritance and development of Shadow. IDSS separates the application and display. In the interaction scenarios, the user directly manipulate the visual objects which relevant to its mandate , including the selection, move, delete and animation control. This interactive approach gives user a direct impression, as if he is involved in the operation of the individual application of semantic objects, rather than through systematic dialogue [14]. The user's main interactive task includes opera figures selection, scene layout and action design.

2 Related Research on Digital Shadow The current studies of Digital Shadow focus on the simulation and control of Shadow model, Professor Shen Yi-fan from Fudan University using numerical simulation technology to develop a Shadow model simulation software [10]. Shadow puppets’ digital protection and performances also made some achievements. The works of digital art - Digital Shadow displayed in Shanghai international Convention Center exhibition halls [9] are mainly through the manufacture and processing of the computer that are combined with long and short shots and montage of the film , which made the traditional Shadow puppet into digital animation. Between the year of 2004 and 2005, our country has successively protected the Dun Huang heritage and Dao Qing Shadow in Huan County through digital technology [6]. Shadow Story system explored by Institute of Software, Chinese Academy of Sciences, is mainly relatively simple to simulate the Shadow puppet’s manufacturing and performing process for children. Current research on Digital Shadow is still in the primary stage. Simulation of the Shadow figures models require higher technology and equipment support. The system lacks of interactive and neglects the interaction design of the "usability goals "and"user experience goals " [11], which results that ordinary users can not really understand the Shadow of the creation and performance process [4]. To solve these problems that the Digital Shadow faces now, we need a platform to cover a full graphic design, audio manufactureion, animation, rendering, script compiler, network transmission, and interactive animation features etc., which no software can solve currently. The aim of this paper , to preliminary study and implement the layout of the opera scene and interactive performances in the context of current simulation technology, is relatively mature. Through this platform, users can manipulate and control of Digital Shadow props directly, by using multi-channel audio and video output feedback, and can coincide with the real Shadow play. Our studies will present new ideas for the future development of Digital Shadow.

3 System Design This System uses the B / S architecture, simplifying the client software. It can provide an interactive environment for users from different locations designing a Shadow play through the Web server, which can increases the user’s experience and inspired the user’s creation. The system offers the following modules: the role of selection, scene layout , action design and animation performing of four parts.

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3.1 Interaction Design Interaction Design is the key to the system, which aims to introduce "availability" and "the concept of “user experience" in the design process [11]. Good interaction not only can increase the fun and experience , but more importantly, can make the operation as a process to increase awareness of the operation for the operator. During the operation the user's cognitive development and emotional factors will be impacted. Our system is based on this purpose. IDSS can be applied to a interactive experience platform for Shadow art galleries with the purpose of Shadow play spread. The ordinary people, as the system’s main user, is difficult to grasp the complexity related technologies. They usually require little or no training as people already get used to interacting with the browsers., so the proposed system must be designed user-friendly and easy to use to reduce the users’ cognitive load [3]. IDSS provides a computing framework which supports the dynamic generation of interactive plot, management and conflict resolution, and allows users to act as a drama protagonist to determine their current behavior and action. Use interactive mode of WYSIWYC (What you see is what you control) [12] to make users fell natural, convenience and comfortable during the operation. The main tasks of the user interaction are as followings: (1) Stage of Scene design: the user select figures ,props and background which are used in opera scenes directly, then drag to layout, while figures and props in the scene can be transformed (with scales, rotations, location etc.).

Select the role

Layout the scene (Including background, props and music)

Design action

Save and play the animation

Fig. 1. The flow of user interaction task

(2) Stage of action design: The user manipulate the object figures, control the action of target figures, including body posture, tilt angle , location and direction of movement directly. For example, people can draw the line with the mouse and let the figures walk to the trajectory according to the user. Also, he can control the

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movement continuing or stoping immediately . In action design process , the system automatically records the time node, and saves records, which allows user to view the animation designed by themselves. (3) Stage of play: User can save the Shadow animation designed by themselves after the completion of the animation manufacture ,then replay the Shadow animation interactively in the page. (The flow of user interaction task is shown in Fig. 1.) 3.2 Architecture Design The IDSS is based on B / S network structure (Shown in Fig.2). The system is composed of three parts including browser interaction module, browser and server communication module, web server and database server communication module. The main function of browser is to provide a human-computer interaction interface; the browser and server communication module sends the user's request to the web server through the Hypertext Transfer Protocol (HTTP), web server responds and deals with the results feedback to the browser; The web server and database server communication module is that a web server submits user’s request to the database server, which feedbacks the relevant information to the web server. MySQL database is used in IDSS to store graphics and scene materials of Shadow play.

Fig. 2. Network structure of IDSS

This structure makes the system extremely flexible and dynamically adjusts to the scene configuration, figures and action, background music playback and so on., which achieves to moduling the function, reducing the coupling of software modules. The B / S structure simplifies the client software, systems development and maintenance is concentrated in the server side, while users just need to access it with the dynamic downloading, and can realize multi-user, distributed manufacture, greatly to meet the needs from users.

4 Related Technologies IDSS in this paper is based on JSP which is widely used in Internet. IDSS separates Shadow manufacture and scene displaying from the application logic of graphics transformation and animation generation, which is helpful to protect the code while ensuring the Web browser fully supported.

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The interfaces' layout uses CSS (Cascading Style Sheets Cascading Style Sheets). CSS is part of the DHTML technology, which can control the HTML to markup its simple syntax, and process the page's content and CSS separately so as to improve the visual effect through a variety of CSS styles [6]. Java Applet is embedded in HTML so the IDSS allows the users to manufacture and control the Shadow by themselves completely, in order to meet the need of interaction. And Java Applet works with JavaScript so that the client page can communicate with server appliance. The communication between JavaScript and Java Applet introduce both languages' advantages, so as to develop more friendly and dynamic Web applications. We must emphasis that Java Applet also has a good security mechanism and cross-platform performance. Java provides a rich library for multimedia API, the multimedia data (such as text, graphics, static image, animation audio etc.) can be processed and displayed easily. And we use this library to transform graphics and generate animation.

5 Implementation of Major Functions 5.1 Shadow Figures Modeling Shadow figure is similar to the natural human’s body structure, composed of eleven components, including the head, chest, abdomen, left arm, Left forearm, right thigh, and right calf etc. Therefore, we use sub-components method to modeling. The whole figure Composed through the connection point between components. Specifically, Create the class Part as the parent of all other component classes . The major variable members of Part including the URL of graphics resources of components, the coordinates of connection point, coordinates of rotation axis and rotation angle etc.

Part

Role

Male lead

…

Clown

Head

...

Leg

Fig. 3. Class graphs for components

In traditional Shadow Play, Shadow characters mainly divided into male lead 、 female character type 、 character with painted facial make-up 、 middle-age male character、clown categories, so we create the class Role as the parent of other

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Shadow figures’ classes. The major variable members of Role including the eleven objects of components and their location coordinates etc. （The Class Graphs shown in Fig.3）The graphics data of Shadow figures are stored in the database, such as url of graphics resources , the node coordinates etc. In the process of modeling, data are read from the database to initialize the objects and the complete figures are displayed in the user interface. 5.2 Motion Control of Shadow Figure We use sub-components method to modeling the Shadow figures, which simulate the natural bones and joints. We know that the reality of the Shadow play is controlled by the main components movement, other associated parts are drived in motion, which are similar to the Human skeleton movement, so when design the Digital Shadow figures’ action, we used forward kinematics to solve the problem of linkage between the components. Firstly, we determine the level of relations between the various components, that means, which one is parent object, and which one is child [Fig.3]. In Interactive process, the system acquires the user's operation information, access the property of primary node (the parent), through real-time control of a single bone point, the child inherited the motion(such as rotation ,scaling or transformations ) from the parent level. In this way , the parent’s movement will drive the child and form a complete action.

Chest

Left arm

Left forearm

Abdomenen

Left thign

Left calf

Head

Right thign

Right arm

Right foream

Left calf

Fig. 4. The lever of components (chest as the parent node)

5.3 The Technique of Double-Buffering in Shadow Animation When designing Shadow action , each group of animated image files owns a large amount of data, so the speed of the system drawing up on the screen slowed down every time, which results the screen flicker. To solve this problem, the system uses Double-buffering technology. When the image files which is displayed on the

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screen is large and more , the use of Double-buffering technology will create a virtual Standby screen outside of the screen , that is, between the time of the user’s interaction and the animation generated by backstage program , first create two graphics buffers, one for the display of front browser, and the other for the backstage graphics , in the stage of displaying (drawing) graphics, the two graphics buffers update the data synchronized , when the data in front display area needs to be restored , you can use backstage graphics backups to restore graphics backups, specifically ,override paint () and update () method.

a: Action of walking

b: Action of speaking

Fig. 5. The scene of action design

6 Conclusion This paper studies the current research in Digital Shadow, discusses the major problem of interaction and usability encountered. We propose a interactive mode for Shadow’s manufacture and performance. The system IDSS is designed and implemented with flexibility, interactivity, and real-time. (Shown in Fig.5) However, there are still some limitations and shortcomings on the system web-based, which only supports multi-user operation asynchronous currently. To reach the goal of different users manufacture and performance a Shadow play online immediately also need the efforts of the next step, which is a work must be completed for the development of Digital Shadow.

References 1. Pina, A., Cerezo, E., SeroHn, F.J.: Computer animation: from avatars tounrestricted autonomous actors (A survey on replication and modeling mechanisms). Computers & Graphics 24, 297–311 (2000) 2. Lau, H.Y.K., Mak, K.L., Lu, M.T.H.: A virtual design platform for interactive manufacturedesign and visualization. Journal of Materials Processing Technology 139, 402–407 (2003)

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3. Holzinger, A., Ebner, M.: Interaction and Usability of Simulations & Animations:A case study of the Flash Technology. IFIP, pp. 777–7804. IOS Press, Amsterdam (2003) 4. Zhao, L.-M.: Research and Application of Shadow Model and Control Technology.Master’s thesis (2009) (in Chinese) 5. Shen, Y.-f.: Scientific Computing Visualization.Introduction of Digital ShadowSimulation. Key Laboratory of Intelligent Information Processing in Shanghai, 1–2 (2006) (in Chinese) 6. Holmberg, N., Wunsche, B., Tempero, E.: A Framework for Interactive web-Based Visualization. In: Seventh Australasian User Interface Conference (AUIC 2006), Conferences in Research and Practice in Information Technology(CRPIT), Hobart,Australia, vol. 50 (2006) 7. Zeleznik, R.C., Conner, D.B., Wloka, M.M., et al.: An Object-OrientedFramework for the Integration of Interactive Animation Techniques. In: Proceeding of the ACM SIGGRAPH, Computer Graphics, vol. 25, pp. 105–111 ( July 1991) 8. http://www.univs.cn/newweb/univs/hfut/2004-07-11/301123.html 9. Garca, R.R., Quiros, J.S., Santos, R.G.: Interactive multimediaanimation with Macromedia Flash in Descriptive Geometry teaching. Computers &Education 49, 615–639 (2007) 10. Preece, J., Rogers, Y., Sharp, H.: Interaction Design Beyond Human-Computer Interaction ; ISBN 0-417-49278-7 11. Hua, Q.-Y., Liu, Q.-F., Yu, D., Wang, X.-W.: Perceptual-Control-BasedAgent Architecture Model. Journal of Software 20(suppl.), 76–83 (2009) (in Chinese)

Weighing Machine Modifications for Purblind and Sightless People Vladimir Kasik and Martin Stankus VSB - Technical University of Ostrava, FEECS, DMC, 17. listopadu 15, 708 33 Ostrava-Poruba, Czech Republic {vladimir.kasik,martin.stankus}@vsb.cz

Abstract. The paper discusses the possibilities of the weighing machine modifications in relation to use by purblind and sightless people. The systems use several commercial weighing machines with common digital displays. There are requested two main functions of adjusted weighing machines. One is the weight measurement and the second is a value reproduction in spoken words. These are the four methods presented in detail to solve with their advantages and disadvantages. Keywords: weighing machine, purblind, sightless, LCD, display.

1 Introduction Measuring of self body weight is easy part for healthy man when using the weighing machine. However the same task is formidable or impossible for purblind and sightless people, when the weight value is optically displayed. This paper is engaged in weight measurement system design aspects with weighing machines for purblind and sightless people. The systems under consideration exploit the commercial weighing machines with LED or LCD displays. The designed system should generally fulfill two basic functions, see Fig. 1: weight value scanning from common weighing machine and speech synthesis of scanned

weight value sensor

speech synthesis

weighing machine Fig. 1. Weighing machine system for purblind and sightless people M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 195–201. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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values. The second function – speech synthesis – has been solved and published in other applications sufficiently, therefore only weight value scanning problem is discussed in the following text. It is assumed, that the weighing machine uses an electric weight sensor and the weight value is displayed on LED or LCD display.

2 Weight Value Sensor There were four methods and corresponding sensor types proposed for weight value scanning. In all cases the scanning is performed by single digits. These methods together with their functionality evaluation are proposed in the following chapters. 2.1 Contacting to the Display (Case A) The absolutely simplest and cheapest solution for automated weight value scanning from the weighing machine is a direct contacting to display. The 3,5-digit 7-segment LCD and LED displays are the most frequent optoelectronic devices for that purpose.

to weighing machine logic

to weight value coder

Fig. 2. Contacting to LCD display

There are signals connected to individual segments of the display digits, eventually including the decimal points (26 signals for example). A 7-seg/BCD coder performs interpretation of the digits composed from separate segments (Fig. 2). We commonly use the devices with inverse function in electronic circuits - BCD/7-seg decoders. The 7-seg/BCD coder [1] used in the project is generally more complicated than the BCD/7-seg decoder due to extension by 3 columns in the truth table. It could be difficult to determine the result for illegal combinations of input values in that sparse truth table, see Fig. 3. However, it is reasonable to solve those combinations until the case of sensor B in the next chapter. It’s also necessary to deal with the LED and LCD displays differently, because LCD display segments are driven with rectangular or pulse shaped signals. Hence some shaping circuit should be inserted in the scanning line from the LCD segments.

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} BCD code

Fig. 3. 7-segments/BCD encoder

2.2 Phototransistor Pattern in Segment Positions (Case B) Binary information from separate display segments is transferred by optoelectronic devices – phototransistors – to logic signals. The electronic circuit includes phototransistors, passive and/or active electronic devices. Phototransistors

........

High luminous LEDs

Fig. 4. Phototransistor pattern in segment positions

A signal gain from the phototransistors primarily depends upon display type and size, distance between phototransistor and display, light conditions and phototransistor electrical characteristics. Higher signal gain is available with LED displays than LCD displays, at which an external light source is needed – the high luminous LEDs, for example. Also a narrow scanning angle is needed at phototransistors, typically not wider than 20° [2]. There is one fact among the disadvantages, that there is a need of different phototransistor patterns arrangement for different weighing machine (display) types. The requirements for 7-seg/BCD encoder are the same as in the previous case A. 2.3 Phototransistor Matrix (Case C) Phototransistor matrix is a solution applicable for several weighing machine types. The requirements for photoelectric sensor elements are similar to the previous solution. However it is necessary to include an adjustable threshold for input signals due to variety of usable displays.

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........ Phototransistor matrix (20x9)

High luminous LEDs

Fig. 5. Phototransistor matrix

The basic change in this case is quite different circuit for digit determination (recognition). An appropriate candidate for the circuit is an artificial neural network, implemented in programmable FPGA device for example. The number of the matrix elements and phototransistor sizes also influence the results. Success of the solution depends on the next two processes. The first one is a learning process with the back-propagation method for example. Thus the network setting will depend upon display type, especially upon segments size and shapes. The second task is a picture segmenting process for pictures obtained from the phototransistor matrix. It’s reasonable especially when we suppose, that the matrix doesn’t need to be in the fixed position.

a)

b)

... .....

.....

segmenting

recognition

1 0 2

Fig. 6. Weight value recognition. a) value on display, b) pattern in the matrix

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We can notice two disadvantages among others: slightly higher price depending directly on matrix sizes and formal uncertainity of the results outgoing from the use of neural network. 2.4 Display Scanning with a Camera (Case D) The most complex and currently the most expensive solution of the described problem is based on the use of a camera for the weight value scanning. The solution outgoes from the previous method with scanning matrix, but the number of sensing points in the matrix is significantly greater. In this case we can talk about a computing speed or performance of the system. However, there is a simplification relate to working only on static pictures. The possibility of reading and reproduction not only the weight value on weighing machine, but any kind of text scanned by camera, could be a significant advantage. That task wholly falls into the area of artificial intelligence. The result in this case influence also light conditions, text layout in camera view (camera position, slope, rotation), character font, etc. [3], [4], [7]. The main disadvantage of that solution is meaningfully higher price of the sensor and also of subsequent circuits for videosignal fitting and processing.

Fig. 7. Display scanning with CCD camera

3 Conclusions Choosing of appropriate scanning method must respect many factors, especially the function facilities requirements [4], [5], [9] and economic point of view. These requirements are contradictory as they are summarized in the following table.

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A -

B +

C +

D +

-

-

+

+

+++ +++ $5

++ + -$15

+ + --$50

+ + + ---$500

For weight value reading only from the weighing machine the case A – contacting to display seems to be the optimal choice, even if it causes the guarantee loss. This fact is compensated by low realization cost [11], [12]. When we have higher requests on function facilities of the system, the camera system (case D) will be an attractive solution, which is open for many other extensive applications. The B and C sensors are certain compromises, which can be probably applied in special cases only. Acknowledgments. The work and the contribution were supported by the project: Ministry of Education of the Czech Republic under Project 1M0567 “Centre of applied cybernetics”, student grant agency SV 4501141 “Biomedical engineering systems VII” and TACR TA01010632 “SCADA system for control and measurement of process in real time”. Also supported by project MSM6198910027 Consuming Computer Simulation and Optimization. [6], [8].

References 1. Haberle, H.: Prumyslova elektronika a informacni technologie, Europa-Sobotales cz. s.r.o., Praha, Czech Republic (2003) ISBN 80-86706-04-4 2. GES, http://www.ges.cz 3. Augustynek, M., Penhaker, M., Korpas, D.: Controlling Peacemakers by Accelerometers. In: 2010 The 2nd International Conference on Telecom Technology and Applications, ICTTA 2010, Bali Island, Indonesia, March 19-21, vol. 2, pp. 161–163. IEEE Conference Publishing Services, NJ (2010), doi:10.1109/ICCEA.2010.288, ISBN 978-0-7695-3982-9 4. Pindor, J., Penhaker, M., Augustynek, M., Korpas, D.: Detection of ECG Significant Waves for Biventricular Pacing Treatment. In: 2010 The 2nd International Conference on Telecom Technology and Applications, ICTTA 2010, Bali Island, Indonesia, March 19-21, vol. 2, pp. 164–167. IEEE Conference Publishing Services, NJ (2010), doi:10.1109/ICCEA.2010.186, ISBN 978-0-7695-3982-9 5. Krejcar, O.: Large Multimedia Artifacts Prebuffering in Mobile Information Systems as Location Context Awareness. In: 4th International Symposium on Wireless Pervasive Computing, ISWPC 2009, Melbourne, Australia, February 11-13, pp. 216–220 (2009), doi:10.1109/ISWPC.2009.4800591, ISBN 978-1-4244-4299-7

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6. Labza, Z., Penhaker, M., Augustynek, M., Korpas, D.: Verification of Set Up DualChamber Pacemaker Electrical Parameters. In: 2010 The 2nd International Conference on Telecom Technology and Applications, ICTTA 2010, March 19-21, vol. 2, pp. 168–172. IEEE Conference Publishing Services, NJ (2010), doi:10.1109/ICCEA.2010.187, ISBN 978-0-7695-3982-9 7. Krawiec, J., Penhaker, M., Krejcar, O., Novak, V., Bridzik, R.: Web System for Electrophysiological Data Management. In: Proceedings of 2010 Second International Conference on Computer Engineering and Application,s ICCEA 2010, Bali Island, Indonesia, March 19- 21, vol. 1, pp. 404–407. IEEE Conference Publishing Services, NJ (2010), doi:10.1109/ICCEA.2010.85 , ISBN 978-0-7695-3982-9 8. Stankus, M., Penhaker, M., Kijonka, J., Grygarek, P.: Design and Application of Mobile Embedded Systems for Home Care Applications. In: Proceedings of 2010 Second International Conference on Computer Engineering and Application,s ICCEA 2010, Bali Island, Indonesia, March 19- 21, vol. 1, pp. 412–416. IEEE Conference Publishing Services, NJ (2010), doi:10.1109/ICCEA.2010.86, ISBN 978-0-7695-3982-9 9. Penhaker, M., Rosulek, M.: Electrodes For Biotelemetry And Home Care Applications. In: Proceedings of 3th International Conference on Systems, ICONS 2008, Cancun, Mexico, April 13-18, pp. 179–183. IEEE Conference Publishing Services, NJ (2008) ISBN 978-07695-3105-2 10. Penhaker, M., Rosulek, M., Cerny, M., Martinák, L.: Design and Implementation of Textile Sensors for Biotelemetry Applications. In: IFBME Proceedings of the 14th Nordic – Baltic Conference on Biomedical Engineering and Medical Physics (IFMBE 2008), Riga, Latvia, June 20, vol. 20, pp. 405–408. Springer, Heilderberg (2008), ISBN 978-3540-69366-6, ISSN 1680-0737 11. Krejcar, O., Janckulik, D., Motalova, L., Musil, K., Penhaker, M.: Real Time ECG Measurement and Visualization on Mobile Monitoring Stations of Biotelemetric System. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds.) ACIIDS 2010. LNCS, vol. 5990, pp. 465–474. Springer, Heidelberg (2010), doi:10.1007/978-3-642-12090-9_6, ISBN 9783-642-12089-3, ISSN 1860-949X (Print) 1860-9503 (Online) 12. Krejcar, O., Cernohorsky, J., Fojcik, P., Janckulik, D., Martinovic, J.: Smartphone, PDA and mobile Embedded device clients of Biotelemetric System for monitoring of life functions. In: 5th International Conference on Innovations in Information Technology, Innovations 2008, Al Ain, United Arab Emirates, December 16-18, pp. 145–149 (2008), doi:10.1109/INNOVATIONS.2008.4781761, ISBN 978-1-4244-3396-4 13. Krejcar, O., Janckulik, D., Motalova, L.: Complex Biomedical System with Biotelemetric Monitoring of Life Functions. In: Proceedings of the IEEE Eurocon 2009, St. Petersburg, Russia, May 18-23, pp. 138–141 (2009), doi:10.1109/EURCON.2009.5167618, ISBN 978-1-4244-3861-7

Active RFID Based Infant Security System Lin Lin1, Nan Yu1,2, Tao Wang1,*, and Changan Zhan1,* 1

School of Biomedical Engineering, Southern Medical University, Guangzhou Dadao Bei 1838, Guangzhou 510515 China 2 Syris Corporation Limited, Jingxi Zhonglu 20-511, Guangzhou 510515 China {tatwang,changan.zhan}@gmail.com

Abstract. Although infant abduction in pediatric area of hospitals happens at a low probability normally overlooked, its impact can be devastating for families and healthcare staff. To improve infant security in hospitals, we have developed an active RFID based system that tracks the body temporature of new-borns. The system can measure body temporature through the near-infrared sensor attached to baby’s wrist and estimate the location of the subject through the signal strengthen received by RFID readers network. It documents security records for the patients in a database and reports abnormal situation with text, warning lamp signal or alarm. We design the database in a in a way compatiable with the general hospital information system. Keywords: Radio Frequency Identification (RFID), Body Temperature, Database, Infant Security.

1 Introduction Currently the most common approach to identifying newborn in hospitals relies on the information band attached to baby’s wrist. It provides an economic and convenient way to pair babies and parents, but nothing helpful in preventing babies from abduction. The potential security problem imposes significant psychological stress for parents and medical staff. Even in developed countries, infant abduction does still happen. According to statistics from the National Center for Missing and Exploited Children (NCMEC) [1], there were 252 cases of abduction occurred in United Statesd during 25 years from 1983 to 2008, indicating the necessity of improving infant security in hospitals. RFID is an identification technology based on information coded in radio frequency signals. The core components include RFID tags and readers [2]. Tag carries and emits ID information of subjects to which the tag is attached. The powered tag, also called as active RFID tag, can measure and code context information (e.g. temperatures of subjects / environment, etc.) and take data from RFID reader. RFID reader can accept information from multiple tags, and provide mechanisms to communicate with computers. With the development of VLSI circuits, digital communication and information security, RFID technologies have found their ways to vast commercial applications. Compared to barcode, RFID system provides higher capacity in terms of *

Corresponding authors.

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information volume, long distance communication and fast multiple objects identification and tracking. To effectively prevent babies from abduction in pediatric areas in hospitals, companies around the world have been developing different surveillance system. Among others, Xmark’s Hugs system has been successfully implemented in many hospitals in North America [3]. It can read and track the infant information in the monitoring areas and reports any susceptible behaviors. However, Hugs is costly for developing countries such as China and does not measure body temperature of the subject. Based on the commercial solutions currently accessible to us [2, 4-6] and the market needs in China and developing countries in general, we have developed body temperature based active RFID infant security system. The system itself can work as a local area security network, and can be integrated into including the general hospital information infrastructure (HIS). It will provide an economical approach to digital hospitals, improving infant security and hospital management in general.

2 The Design of Active RFID Infant Security System After the initial medical examination and preparation, the newborn is registered to the RFID security database. Basic information is stored in the RFID tag and the tag is attached to the baby’s wrist. Each tag has a unique ID that is bound to one baby. By reading the ID and temperature signals from the tag at pre-set time intervals, the infant security system can monitor the location and health situation of the subject. Once any abnormal situation (e.g. temperature is out of normal range, or the baby is not in the safe areas) happens, the system can proving warning signal or alarm, according to the level of risks. Special situations such as known leave for bathing time can be taken care of by disabling the monitoring of this specific ID on the operation console in the nurse station.

Fig. 1. Topological Structure of the Infant Security System

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2.1 Topological Structure Figure 1 depicts the topological structure of the infant security system. Each infant in the hospital wears an RFID tag, which can measure and send the temperature information to the RFID readers. The readers are adjusted in antenna length and direction to cover specific areas in the pediatric department. All the readers are connected to the LAN hub through the standard network interface RJ-45. System console accesses all the data from readers in real time and save the information into the database. Console identifies any abnormality based on the temperature and signal strengths reaching different readers, and provides warning and alarm accordingly. Surveillance video will be activated in all the monitored areas.

Fig. 2. System Integration

2.2 System Integration Infant security system has two main components: birth registration and surveillance. Their basic elements are shown in Figure 2. Shortly after a baby is born, registration is completed by inputting the necessary information to the database and binding the RFID tag to this specific subject. All the information is archived into the database. When registration step is done, the system enters the surveillance stage until the patient is discharged from the hospital. During surveillance period, the tag measures the infant’s

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body temperature and sends the information to the readers. The console at the nurse station can visualize the temperature measurements over time and judge the status of the subject. There are two types of abnormality that suggest the probability of infant abduction: the tag is detached from the subject; the tag cannot be tracked by any reader. In the first situation, the temperature information may not be in the normal range of body temperature; under the second scenario, the tag signal is lost. Either situation happens, the console add the abnormal events into the database and initiate the alarm system. 2.3 System Functionality The detailed functions of the infant security system include: 1) Infant information input: basic information of baby (name, sex, date of birth, room number and bed number etc.) and information about parents (names of mother and father, room number, bed number, date of registration into the hospital, contact information etc.) and RFID tag number. RFID tag number is the key information in the database and uniquely binding the baby and parents. 2) Infant temperature based tracking: the reader takes the temperature measurement at preset time intervals and the computer console in the nurse station visualizes the real time data. According to the temperature, the console can tell whether the tag is worn properly and baby is in good health. 3) Indication of tag status: beside the body temperature, signal strength from the RFID tag can be used to localize the place of the infant. RFID tag also sends the battery volume and warns the console when it is about out of power so that technician can change battery timely. 4) Abnormality judgment: newborn’s body temperature measured at the wrist is normally in a range between 35°C and 37°C. When the actual number is not in the range, it is possible that the tag is not worn properly or detached from the subject. In any case, the system reports the abnormality. Another abnormality is that no signal can be received from a specific tag. Loss of signal may indicate that the subject is not in the safe areas. The module for abnormality judgment is one of the key elements fo the system. 5) Alarming function: according to the level of risks, different warning and alarm signals are designated. Details (name of the newborn, bed number, parent contact information) are shown on the large LED screen; warning lamp, alarm and surveillance video can be initiated when necessary. 6) RFID device management: information about RFID readers and tags need to be managed properly so that they can be bound to locations and subjects. For the readers, IP addresses and locations are specified and stored in the database. The ID of working tag is specifically attached to the infant and traced over the whole period until it is unregistered. 7) All the data stored in the database, which can be integrated into the general HIS, are part of the electronic medical record.

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3 System Implementation 3.1 Hardware As mentioned before in Figure 1, the core hardware in our system includes standard PCs and file servers, general purpose hubs for local area network, and RFID tags and readers. To obtain the reliability for long distance (>10 meters) communication and subject localization, we choose the active RFID system from SYRIS Corporation [7]. 1) Active RFID tag (model: SYTAG245-NW): This tag weighs around 10 grams, supports two-way communication at frequency of 2.45GHz. It also provides high precision temperature measurement through embedded infrared sensors, and detection of vibration. 2) RFID reader (model: SYRD245-1N): It supports two-way communication at frequency of 2.45GHz with multiple tags simultaneously. All the data can be accessed through the RS232/RS485 data interface or RJ-45 ethernet interface.

Fig. 3. Data Tables for the Infant Security System

3.2 Data Structure We developed the system under Microsoft Visual Studio 2010 platform using the .NET framework in C# language. We choose the SQL Server 2008 platform to develop the database. Figure 3 illustrates the three main data tables in the database: 1) data for the infant, including name, sex, date of birth, status, tag ID, department, bed number, etc.; 2) data for mother, including name, contact information, date of admission to the hospital, home address, emergency contact, etc.; 3) data for the RFID tag, including tag ID, signal strength, events, body temperature readings, environment temperature readings, reader IP address, time of communication. The primary key in these tables is the RFID tag ID.

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3.3 Software The software is installed on the computer in the nurse station, which receives and stores the all the data from the RFID tags by accessing RFID readers. The software visualizes the real time status of patients (location and body temperature). It also provides assessment of the safety and initiates alarming system when the patients are subject to potential risks. Figure 4 illustrates the functions of the software.

Fig. 4. Diagram of the Software

Fig. 5. The Local Area Network of Infant Security Systems

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3.4 Networking of the RFID Readers For reliable coverage and real time localization of the patients, it is critical to properly network multiple RFID readers in the areas under surveillance. As demonstrated in Figure 5, we use one RFID reader for each room and adjust its antenna direction and length so that it is good coverage of the room without too much overlapping with readers in other rooms. At the main door of the department, we designate three specific readers to monitor the directions of movement of tags (leaving or entering the department).

4 Summary We have developed the active RFID based infant security system and tested in two hospitals. The results indicate that this system satisfies all the planed functionalities with several advantages: 1) Automatically tracks the status of infant (location and health situation) and initiates the multiple level alarm system when necessary. 2) Measures the body temperature in real time. 3) Simultaneously monitors multiple tags for efficiency. 4) The use of information technologies significantly improves the level of digitization in the hospital. 5) The system provides a cost-effective solution to hospitals in developing countries. Our future efforts would be improving the system mainly in two aspects: 1) the accuracy of abnormality report – reducing the false alarm rate while minimizing miss error; 2) optimizing the battery life in the RFID tag and sampling rate of body temperature.

References 1. 2. 3.

4. 5. 6.

7.

National Center for Missing and Exploited Children, http://www.missingkids.com Ni, L.M., Liu, Y., Lau, Y.C., Patil, A.P.: LANDMARC: Indoor Location Sensing Using Active RFID. Wireless Networks 10, 701–710 (2004) Wyld, D.C.: Preventing the Worst Case Scenario: An Analysis of RFID Technology and Infant Protection in Hospitals. The Internet Journal of Healthcare Administration 7(1) (2010), http://www.ispub.com/journal/the_internet_journal_of_healthc are_administration/volume_7_number_1_37/article/ preventing-the-worst-case-scenario-an-analysis-of-rfidtechnology-and-infant-protection-in-hospitals.html Guo, Z., Xu, M., Zhang, L.: Application of Radio Frequency Technology in Infant Protection System. China Digital Medicine 5(6), 60–62 (2010) (in Chinese) Cao, S., Zhao, F.: Research and Realization on the Model of Neonates Surveillance System Based on RFID. Microcomputer Applications 29(9), 89–94 (2008) (in Chinese) Luo, J., Li, R., Zeng, J., Yang, Y.: Research and Design on Blood Management & Tracking System Based on RFID Technology. Computer Engineering and Applications 23, 168–172 (2006) (in Chinese) SYRIS Technology Corp, http://www.syris.com/

Modified Segmentation Prony Algorithm and Its Application in Analysis of Subsynchronous Oscillation Yujiong Gu, Dongchao Chen, Tiezheng Jin, and Zhizheng Ren Key Laboratory of Condition Monitoring and Control for Power Plant Equipment of Ministry of Education, North China Electric Power University, Changping District, Beijing 102206, China [email protected]

Abstract. Based on traditional Prony algorithm (TPA) and segmentation PA (SPA), a modified SPA (MSPA) is proposed in this paper. By dividing the whole observation window into several segments according to certain rules and then utilizing data in all of the segments, the overall sample function matrix is constructed and the average error effect of the whole observation window is taken into account. Meanwhile, a new approach to extracting oscillation modes is presented by means of Kaiser window FFT analysis and a concept of energy class. The MSPA and time domain simulation method are integrated and used in analysis of subsynchronous oscillation (SSO). Their results can be confirmed and complemented by each other. Simulation results show that the MSPA not only enhances SPA’s computational efficiency and credibility, but also avoids the lost of oscillation modes existing in modified PA (MPA) which uses singular value decomposition to determine the rank of Prony model. Keywords: subsynchronous oscillation, segmentation Prony algorithm, time domain simulation method, oscillation modes.

1 Introduction The technologies of high voltage direct current (HVDC) transmission and series capacitor compensation have been widely used in power grid, which will make the subsynchronous oscillation (SSO) occur more often in interconnected power systems, and threaten the safety and economy of power grid. Prony algorithm (PA), which can be used to analyze simulation data as well as measured data and obtain the characteristic parameters of sampling signal easily, is applicable for SSO analysis. Most of the exiting PAs [1-5] can only be applied to part of sampling signal every time, so the results are random and inaccurate. An improved PA [6-7], which is called segmentation PA (SPA) in this paper, enhances the accuracy of the identified results by dividing the whole observation window into several segments and comparing the results of each segment obtained by PA to determine the optimal results, but it reduces the computational efficiency. More important, it is difficult to deal with and choose the results among different segments. Reference [8] reports a moving window PA (MWPA) which has stronger adaptability to noise and can be applied to long time range signals. However, M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 211–217. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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it is raised on the basis of the modified PA (MPA) [1] which uses singular value decomposition (SVD) to determine the rank of Prony model. Experimental results of this paper and reference [9] demonstrate that the order of the model acquired by means of SVD is low, so some concerned oscillation modes sometimes can not be obtained. In view of the problems mentioned above, a modified SPA (MSPA) based on traditional PA (TPA) and SPA is proposed in the paper. First, the derivation process of the proposed methodology and calculation method are described in detail. Then a new approach to extracting oscillation modes is presented by means of Kaiser window FFT (KW-FFT) analysis [4] and a concept of energy class. Finally the MSPA and time domain simulation method are integrated and used in analysis of SSO. The presented method is tested on the IEEE First Benchmark Model (FBM) for computer simulation of SSO [10].

2 MSPA PA is a method fitting a linear combination of exponential terms to a signal (N≥2p) [2]. Supposing that a signal is composed by p exponential functions, the mathematical model in discrete time function form is p

xn = ∑Ak e jθk en(σk + j2πfk )Δt

(1)

k =1

where n=0,1,…,N-1; k=1,2,…,p; p is the order; Δt is the sampling time-interval; Ak, fk, σk and θk are magnitude, frequency, attenuation factor and primary phase. Meanwhile, the expressions of residues and pole are defined as follows. bk = Ak e jθ k

z k = e (σ k + j 2 πf k ) Δ t

(2)

MWPA provides some usage for reference for the method proposed in the paper. Unlike MWPA, MSPA put forward here is based on the TPA [2-3] which is not dependent on SVD to determine the order. At the same time, MSPA has meaningful improvements on how to determine the order and extract oscillation modes. Assuming that N is the number of data points in the observation window, while the number of sampling data within each segment is same as m, and the identification order of each segment is same as p (p is great enough to ensure the identification accuracy for each segment).To ensure that it is completely applicable to effective signal, there must be several repetitive data points whose number is m-p between the adjacent sections. The serial number of each segment is i and the total number of the segments is j which can be calculated by the following formula. j = [( N − m ) / p ] − + 1

(3)

where the operator [ ]- means negative round number. For the ith segment similar with the derivation process of TPA [2-3], the forward differential equation can be obtained and expressed as p

∑a x h=0

i i h n+ h

=0

(4)

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where xni are the data points in the ith segment, i=1,2,…,j; n=0,1,…,m-p-1; api=1. Sum the two sides of equations shown as the above equation in all of the segments, and then the following formula can be obtained. j

p

∑∑a i =1 h = 0

i h

x ni + h = 0

(5)

Because ahi (i=1,2,…,j; h=0,1,…,p) should remain unchanged for each segment, let them be equal to ah. Equation (5) can be transformed into equation (6). p

j

∑a ∑x h

h=0

i =1

i n+ h

=0

(6)

The following formula can be constructed by equation (6). X(1) ⎡ X(p) ⎤ ⎡ X(0) ⎢X(p +1)⎥ ⎢ X(1) X (2) ⎥ =⎢ −⎢ ⎢ # ⎥ ⎢ # # ⎥ ⎢ ⎢ X m X m p X m ( 1 ) ( 1 ) ( − − − p) − ⎦ ⎣ ⎣

" X(p −1)⎤ ⎡ a0 ⎤ ⎢ ⎥ " X(p) ⎥⎥ ⎢ a`1 ⎥ # # ⎥⎢ # ⎥ ⎥⎢ ⎥ " X(m−2)⎦ ⎣⎢ap−1⎦⎥

(7)

where X(n)= xn1+xn2+…+xnj; n=0,1,2,…,m-1. As shown by equation (7), the overall sample function matrix is constructed and the average error effect of the whole observation window is taken into account in the MSPA, which are different from the exiting ones. Using singular value decomposition and total least square method (SVD-TLS) [1-3] to solve the coefficients ah, zk can be obtained by solving the characteristic polynomial which is presented as equation (8). Z

p

+ a p −1 Z

p −1

+ " + a1 Z + a 0 = 0

(8)

The MSPA residues can be computed by SVD-TLS according to the following formula. ⎡ 1 ⎢ z ⎢ 1 ⎢ # ⎢ m −1 ⎣⎢ z1

1 z2

"

#

#

1 ⎤ ⎡ b11 ⎤ ⎢ ⎥ z p ⎥⎥ ⎢ b21 ⎥ = # ⎥⎢ # ⎥ ⎥⎢ ⎥ z mp −1 ⎦⎥ ⎣⎢b1p ⎦⎥

"

z 2m −1 "

⎡ x 10 ⎤ ⎢ 1 ⎥ ⎢ x1 ⎥ ⎢ # ⎥ ⎢ 1 ⎥ ⎣⎢ xm −1 ⎦⎥

(9)

In term of zk and bk1 (residues in the first segment), magnitude, primary phase, damping coefficient and frequency can be obtained on basis of the following formula. 1 ⎧ Ak = bk ⎪ θ = arctan Im(b 1 ) / Re(b 1 ) ⎪ k k k ⎨ z t σ = ln / Δ k ⎪ k arctan[Im(z k ) / Re(z k )] ⎩⎪ fk = 2πΔt

[

]

(10)

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3 Selecting Parameters and Extracting Oscillation Modes According to the sampling theorem, the sampling frequency 1000 Hz is adopted in the analysis of SSO. The time length of each segment is set to 2 seconds. It is not necessary to make the time length of the observation window be too long. Here, the time length of the window 4~5 s is adopted and the failure period should be avoided. With regard to the rank p, a big value is assigned firstly, and the oscillation modes will be extracted later. By doing so, not only the complex process of determining the order is avoided, but also the identification accuracy is guaranteed. In practice, identification accuracy and efficiency should be taken into account comprehensively so as to determine an appropriate order. The rank 500 is temporarily adopted here because the simulations are offline and the demand of real-time is not high. Lots of interference components will emerge when p is assigned to a big value directly. The sampling signal is analyzed by KW-FFT method [4] in order to get the frequencies (FL, L=1,2,…) of the peak points, then the oscillation modes can be preliminary screened by equation (11). f k − FL ≤ C

(11)

where C is a constant, which could be 0.5~2 Hz according to practical needs. By means of making changes to the energy class defined in literature [5], the energy class of SSO modes is defined as m

E k = Ak2 ∑ ( e σ k sΔt ) 2 = s =1

Ak2 ( e 2 mΔtσ k − 1) 1 − e − 2 Δ tσ k

(12)

Both magnitude and attenuation factor are taken into account in formula (12). Near each FL, a group of fk can be obtained on basis of equation (11). Their energy class can be calculated by equation (12). And then, the mode whose energy class is the biggest among the group can be found out. It is just one of the oscillation modes.

4 Simulations and Results The study system is the FBM, whose wiring diagram and parameters are given in [10]. The system exhibits five torsional modes at frequencies 15.71 Hz, 20.21 Hz, 25.55 Hz, 32.28 Hz, and 47.75 Hz. The loading condition was: generator output of Pe=0.9 p.u., power factor of PF=0.9 lagging, terminal voltage of Vt=1.0 p.u., and XC=0.371 p.u.. It is assumed that the system is experienced by an instantaneous three phase short-circuit in the right of series compensation from 1.5 s to 1.5075 s. The simulation lasts 10 s and the time range of the observation window is from 2 s to 6 s. Using generator rotor speed deviation as sampling data, the analysis results by SPA (analyze each segment by TPA after segmenting), MPA, and MSPA are shown in the following tables (in which ‘—’ means that the mode is not obtained). The results in all of the tables show that the oscillation frequencies are calculated correctly. Results based on SPA are shown in Table 1, Table 2, and the front half part of Table 3. It should be noted that the results acquired by analyzing different segments are not very close, even the difference is quite big. The attenuation factor of

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the fourth segment (4~6 s) is 0.195, which is quite different with the others (about 0.07) and will impact the precision of the final results. Meanwhile, considering the subjective factors existing in the process of conjoint analysis, the credibility of the results obtained by SPA is not very high. Table 1. The results of the first and second segments based on TPA

The first segment (2~4 s) Mode 1 Mode 2 Mode 3 Mode 4 15.92 20.28 25.52 32.29 0.068 0.486 0.015 -0.004

F/Hz

σ

The second segment (2.5~4.5 s) Mode 1 Mode 2 Mode 3 Mode 4 15.92 20.28 25.52 32.28 0.074 0.485 0.011 -0.001

Table 2. The results of the third and fourth segments based on TPA

The third segment (3~5 s) Mode 1 Mode 2 Mode 3 Mode 4 15.92 20.28 25.52 32.28 0.074 0.487 0.013 -0.004

F/Hz

σ

The fourth segment (3.5~5.5 s) Mode 1 Mode 2 Mode 3 Mode 4 15.92 20.28 25.52 32.28 0.077 0.487 0.012 -0.003

Table 3. The results of the fifth segment (4~6 s) based on TPA and MPA

The results based on TPA The results based on MPA Mode 1 Mode 2 Mode 3 Mode 4 Mode 1 Mode 2 Mode 3 15.92 20.28 25.52 32.28 15.94 20.28 — 0.195 0.485 0.011 -0.005 0.112 0.485 —

F/Hz

σ

Mode 4 — —

Table 4. The results of the observation window (2~6 s) based on MSPA

F/Hz

σ

Mode 1 15.92 0.079

Mode 2 20.28 0.489

Mode 3 25.53 0.016

Mode4 32.27 -0.001

The results calculated by MPA are presented in the latter part of Table 3. Compared with the results acquired by TPA, it is obvious that the last two modes are not obtained. Table 4 shows the results computed by MSPA. It is clearly indicated that the mode at 20.3 Hz has the positive maximum attenuation factor, so it is the most dangerous mode, next are the modes at 15.9 Hz and 25.5 Hz. The absolute value of the attenuation factor of the mode at 32.3 Hz is only 0.0012, which is close to the critical damping. Obviously, the MSPA enhances SPA’s computational efficiency by reducing the TPA analysis number of times and avoiding the the process of conjoint analysis. The results in different tables show that the MSPA results accord with the general trend of TPA results in all of the segments. In other words, the credibility of the final results is improved. What’s more, unlike MPA, all of the concerned oscillation modes are obtained by using MSPA.

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Amplitude(p.u.)

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Fig. 2. The torque between LPA and LPB response curves in oscillation modes.

Mechanical and electric parameters containing SSO information such as speed deviation and torque could be obtained by using PSCAD/EMTDC. And then, the time-domain waveforms in different oscillation modes can be acquired. Due to space limitations, only the generator rotor speed deviation and the torque between LPA and LPB response curves in oscillation modes are given here (as shown in Fig. 1 and Fig. 2). It should be noted that the first three curves in each figure are divergent. The curves corresponding to the mode at 20.3 Hz diverge fastest, and then there are the modes at 15.9 Hz and 25.5 Hz. The amplitude of the curves corresponding to the mode at 32.3 Hz in each figure is nearly a constant. It is shown clearly in Fig. 1 and Fig. 2 that the results in Table 4 are correct and credible.

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5 Conclusions In this paper, a MSPA based on TPA and SPA is proposed to solve the problems of the exiting PAs. It is worth noting that the MSPA and domain simulation method are integrated and used in analysis of SSO, whose results can be confirmed and complemented by each other. The main conclusions are got as follows: (1) Because the overall sample function matrix is constructed and the average error effect of the whole observation window is taken into account, the MSPA enhances SPA’s computational efficiency and the credibility of results. (2) The MPA, the order of which is determined by use of SVD, can not obtain all of the concerned modes. With a new method for determining the order and extracting oscillation modes, this problem is solved by the MSPA. (3) Simulation results based on the FBM show that the method presented here is suitable for the analysis of SSO and superior to the traditional ones to some extent. (4) Being proposed on basis of TPA, MSPA is very sensitive to noise. How to remove noise from measured data efficiently needs to be researched later. Acknowledgments. This research is supported by the Program for New Century Excellent Talents in Univercity (NCET-08-0769) and the National Natural Science Foundation of China (No. 51075145).

References 1. Xiong, J., Xing, W., Wan, Q.: Identification of control modes in low frequency oscillation analysis by Prony method. Journal of Southeast University (Natural Science Edition) 38, 64–68 (2008) 2. Zhang, X.: Modern Signal Processing. Tsinghua University Press, Beijing (2002) 3. Sun, X., Gao, M., Liu, D., Hou, X., Xuan, R.: Analysis and Processing of Electrical Fault Signals With Modified Adaptive Prony Method. Proceedings of the CSEE 30, 80–87 (2010) 4. Ma, C., Wang, H., Meng, Y., Xu, Y., Xie, T.: Research on Integrated Monitoring Algorithm of Low Frequency Oscillation. Power System Technology 32, 31–35 (2008) 5. Deng, J., Tu, J., Chen, W.: Identification of Critical Low Frequency Oscillation Mode in Large Disturbances. Power System Technology 31, 36–41 (2007) 6. Dong, H., Liu, D., Zou, J.: Analysis of Power System Low Frequency Oscillation Based on Prony Algorithm. High Voltage Engineering 32, 97–100 (2006) 7. Wu, C., Lu, C., Han, Y., Wu, X., Liu, Y.: Comparison of applicability in low frequency oscillation mode identification between Prony and ARMA. Electric Power Automation Equipment 30, 30–34 (2010) 8. Ding, L., Xue, A., Li, J., Wang, J., Han, F., Wang, M.: A Moving-window Prony Algorithm for Power System Low Frequency Oscillation Identification. Automation of Electric Power Systems 34, 24–28 (2010) 9. Barone, P., Massaro, E., Polichetti, A.: The segmented Prony method for the analysis of non-stationary time series. Astronomy and Astrophysics 209, 435–444 (1989) 10. IEEE Subsynchronous Resonance Task Force: First Benchmark Model for Computer Simulation of Subsynchronous Resonance. IEEE Transactions on Power Apparatus and Systems 96, 1565–1572 (1977)

Group Control Strategy of Welding Machine Based on Improved Active Set Algorithm Chen Shujin and Zhang Junjun Provincial key Lab of Advanced Welding Technology, Jiangsu University of Science and Technology, Zhenjiang, China, 212003 {chenshujin7120,zjj8874}@126.com

Abstract. In order to solve the group optimal control problem on welding machine of shipyard, this paper established the optimization model of operating parameters. Optimization object includes minimum the average waiting time of welding, the largest number of online welding machine and minimum transformer energy consumption. Group control system has nonlinear, multiconstraint characteristics, but the inequality constraints in this study are linear. When quadratic programming problem was solved by improved active set algorithm, the overall control strategy was obtained, which can achieve objective optimization by priority scheduling, multi-step optimization strategy, guaranteeing the production quality at the same time. This research provides a theoretical basis and foundation technology for the stable operation of welding machine group. Keywords: Welding machine, Group control, Active set algorithm.

1 Introduction Due to disorder of all welding facilities in block assembly shop of ship-building, power system is greatly attacked, which consequently influences welding quality to a large degree. So it is necessary to control welding machine group so as to stabilize power system and guarantee welding quality. At present, there is no report about the study on group-control over welding machines in yard. However, some study on group-control over spot welding machines is valuable for reference. For example, Cui built a group-control system for resistance welding machines based on serial communication techniques[1]; Cheng studied a networking control system for resistance welding machines based on CAN-bus[2]; Wang set a simple group-control system for resistance welding machines[3]. Among all of the above studies, defect is focused on that they centered on realization of system hardware and simple integration of facilities, few of which studied group-control algorithm. As in welding process parameters of welding machines and power system is unknown, former research just set welding machines of limited power rigidly according to rated power, carrying out tolerance control. Group-control system for welding machines aims to solving complicated, nonlinear and multiple constraints optimization[4]. Therefore, to design a reasonable M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 219–226. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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group-control algorithm is the key problem in control system. To solve this problem, intelligent methodology such as artificial neural network, adaptive control techniques is used successively[5-6]. But artificial neural network has the problem of convergence and generalization; the constraints of system affects the implementation of adaptive algorithms[7]. To design group-control algorithm, this paper creatively applies active set optimization theory in group-control model according to production reality in welding shops of yard and puts forwards detail control strategies. 1.1 The Structure of Welding Machine Group Control System The structure of welding machine group control system is shown in Figure 1.

Master controller

CAN-Field bus

Welder

Welder

Welder

Welder

Welder

Welder

…

Fig. 1. Schematic diagram of welding machine group control system

In the group control system, a large number of welding machine and the master controller were connected through the CAN bus, the master controller is responsible for communication with each node, collecting information of parameters and status of all nodes in the network. After the state judgment of whole network, the master respond to the operation request of each welding machine. Because CAN bus has strong anti-interference, high reliability, it is suitable for large scale welding machine group control field, and can meet the requirements of long distance communication.

2 Group Control Model The welding equipments in the system have same three-phase welding power supply, and the three-phase power is balanced when the system is not powered. The system has large number of high power welding machine, welding specifications for each welding machine are not identical, welding nodes online are working with asynchronous, so the three-phase power supply will be unstable and adversely affected by voltage fluctuations, which may affect the welding quality in severe cases. Therefore, the group control system must ensure that the power fluctuations within a certain range in order to avoid affecting the quality of welding,

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To simplify the difficulty of modeling, make the following assumptions: specification of the welding machines and welding parameters of welding machine itself are known; assuming there is no welding fault interruptions; allow welding power over the total as a percentage of the total capacity of the transformer, overload rate is set to 10%. There are three main parameters: the number of online welding machine, the average waiting time of welding machine, the transformer energy consumption. The input parameters of master controller include network parameters, welding specifications, parameters of machine own, number and distribution of welding machine and so on. Welding specifications which mainly refers to the effective welding current and welding time, network parameters mainly refers to the power capacity, apparent power, welding parameters mainly refers to their apparent power welder, welding machine efficiency. Output parameter is the online number of welding machine and the power factor compensation capacitors. 2.1 The Probability of Synchronous Online of Welding Equipments The more the number of online welding machine means the higher efficiency. If not take into account other external factors, at any time k, the number of online welding nodes is a random event, its distribution meet the binomial law, the probability of synchronous online is the following:

PNn = CNn F n (1 − F ) N − n =

N! F n (1 − F ) N − n ( N − n )! n !

(1)

2.2 The Average Waiting Time of Welding Machine

AWT (Average Waiting Time) refers to the average time spent in waiting allow welding instruction for all welding machine, it is an important indicator to evaluate the performance of group control algorithm. Let n is number of online welding machine, the AWT is calculated as: n

∑ WT ( k ) AWT =

(2)

k =1

n

WT ( k ) refer to the application time one machine takes, including Welding cycle T, the network delay time. When the application exceeds the 10% of total power capacity, WT ( k ) can be expressed as: WT ( k ) = TPN = n

TN ! ( N − n )! n !

F (1 − F ) n

N −n

(3)

where F is the duty cycle for the welding machine, N is total number of welding machine.

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When the application phase load to meet the application requirements, WT ( k ) is:

WT ( k ) = Timed

(4)

Where Timed is communication delay time of network. 2.3 Rate of Transformer Capacity Utilization

To avoid serious overload or no load of transformer, for simplicity, assume that all the welding application can get response, and then the total welding power is the following: n

W = ∑ w( k )η ( k )

(5)

k =1

Where w( k ) and η ( k ) are the power consumption and efficiency respectly. Establish Quadratic objective optimal function as the following: J = uT Eu + qu

(6)

Where u = [ u1, u 2, u 3] = [1 n , AWT ,1 W ] , q = E [ c1, c 2, c 3] ,q is Weighting coefficient column, E is unit Matrix. When the system is running, the maximum number of line welder, the waiting time for online application, total welding power all have constraints: 1 N ≤ u1 ≤ 1 , T

T

u 2 ≥ 1 NT , u 3 ≥ (10 / 11) Wmax , these constraints can be written in the following

formula: u≥R

(7)

where R = [1 N ,1 NT , (10 / 11) W ] = [ r1, r 2, r 3] T

T

max

Obviously, formula (6) and (7) formed a quadratic programming problem.

3 Optimization Strategy Considering quadratic programming problem which is consist of formula (8):

⎧J= ⎨ ⎩s.t.

f ( u ) = u Eu + qu T

u ≥ ri

i ∈ IC

(8)

Where I C is the limited index set of inequality constraints. The inequality constraints in this study is linear, so the active set algorithm is fit for solving, the active set algorithm has a strong ability to search along with constraint border. During the solving process, we should estimates the constraint index step by step, after the amendment, the original question is equivalent to the corresponding

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equation constraint problems. So we can obtain the solution by solving a series of equality constraints. During each iteration process, we can get search direction by solving an equation constraint of the problem. Because of the linear search process, those non-functional inequality constraints can ensure the feasibility of iterative sequence. We introduce the Lagrange operator λi , and then the Lagrange function is the following: L ( u, λ ) = u Eu + q u − T

T

∑ λ (u − r ) i

i

(9)

i∈I C

We define the active set U (u * ) when u * is the optimize solution. U ( u ) = {i ∈ I C u ≥ r} *

(10)

From formula above, we know that E is half positive definition matrix, so quadratic programming is convex, that is any partial optimal solution is the overall optimal solution, and all the iterative points are feasible. So the solution of original problem is also the solution of the below formula:

⎧ min ⎨ ⎩s.t.

u Eu + q u T

T

u = ri

(11)

i ∈ U (u ) *

We define g (u) = ∇f (u) = Eu + q , iteration direction is p k = u − u k .Then the expression of quadratic programming problem is the following:

⎧ min ⎨ ⎩s.t.

p Ep + q p T

T

p=0

(12)

∀i ∈ wk

When the solution u * is obtained, Lagrange operator λ* is satisfied the below relation: Eu + q = *

∑λ

*

(13)

i

i∈ I C

If p k = 0 and λ* ≥ 0 , u k is the Kuhn-Tucker point. If p k ≠ 0 , but the solution is not feasible, then u k +1 = β k p k + u k

(14)

Where β k is step parameter, β = min {β ,1} . k

max

⎧r − u ⎩ p

β max = min ⎨

i

k

k

⎫ ⎭

pk < 0, i ∉ wk ⎬

(15)

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Based on the analysis above all, the main steps of solving the problem are the following: Step 1. Give the initial point u 0 , define the active set of system U (u * ) , ε > 0 k =1. Step 2. Solving the problem (12), if p k < ε , turn to step 3, else turn to step 4.

，

Step 3. Calculate λ* which satisfied formula (13), choose λ*i = min{λi i ∈ wκ }. If

λ*i ≥ 0 , ∀i ∈ wk ∩ U (u ) , then u k is approximate optimized solution. If λ*i < 0 , delete the relevant constraint from wk , turn to step 6. Step 4. Calculate β k , then u k +1 = β k p k + u k . Step 5. If α k < 1 , add the relevant constraint into wk , then turn to step 6. Step 6. k = k + 1 , turn to Step 2.

4 Experimental Results Ship welding workshop is equipped with SZ9-1600/10 transformer, welding equipment such as single-phase, three-phase CO2 welding machine, are connected to power line by the triangle method. Then the group control experiments on 120 sets of welding equipment were carried out. In order to achieve reactive power compensation, and avoid fluctuations of power factor, voltage and current, welding system on the two-phase power were compensated by the use of three-phase delta connection imbalance compensation system. After the operation application of each welding machine, the number of online welding machine is calculated by the master controller according to the optimization algorithm, power factor compensation is also realized based on the upload parameters of each welding machine. 440

1 0.95 3

400

1

PF

voltage(V )

420

0.9

380 0.85

360 2 340

1

5

9

13 17 time(h)

21 24

Fig. 2. Voltage curve of 24 hours power supply

0.8 1

4 5

9

13 17 time(h)

21 24

Fig. 3. Power factor curve of 24 hours

As shown in Figure 2, curve 1 and 2 is the voltage curve of different times in a one day, respectively, curve 1 represents the voltage curve using the optimized algorithm,

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curve 2 represents the voltage curve when optimization algorithm is not used. Figure 2 also shows that, the average supply voltage workshop was increased 1.1% and reached 394V after group optimization controlling.There are two troughs on the curve: 10-11 points in the morning and afternoon between 3-4 points, the maximum voltage drop of two stages appeared at the same time in these two stage. But the voltage return to the maximum in at about midnight, this indicating that welding work had stopped, we can call this stage idle time. The power factor curve of different times in a day is shown in Figure 3. Curves 3 and 4, respectively, reprensent using the optimized algorithm and optimization algorithm is not used.through controlling the number of on-line capacitance and welding machine, dynamic power factor compensation is achieved, the average power factor increased to 90%, 95% transient, about 4% higher than curve 4.

5 Conclusion In this paper, we have successfully controlled the number of online welding machine under the condition of constrained input and output of network group control system. The optimization model considered n and AWT, experiment show it is simple and effective. The results of the experiment also indicate that CAN-Fieldbus technology is fit for welding group controlling, the perfect performance of this group control system was realized by the active set algorithm, the entire control process followed the principle of continuous and smooth. The active set algorithm is fit for solving the inequality constraints problem in this text; it has a strong ability to search along with constraint border. During each iteration process, the original question is equivalent to the corresponding equation constraint problems. Because of the linear search process, those non-functional inequality constraints can ensure the feasibility of iterative sequence. Finally, the results of the experiment indicate that this method is simple and feasible. If this method is combined with the other control technology, it will reduce the dependence of electricity demand of the production, reduce energy consumption and increase energy efficiency.

References 1. 2.

3. 4.

Cui, y., Zhao, j.: Computer group control system for resistance welding. Welding Technology 35(5), 50–51 (2006) (in Chinese) Cheng, H., Yu, X., Guo, J., Yin, G.: Development of Networking System and GroupControl System for Resistance Welding Machines. Modern Electronics Technique 7, 148–150 (2008) (in Chinese) Wang, R.: The resistance welding machine control system based on microcontroller. Welding Technology 39(12), 41–43 (2010) (in Chinese) Lin, T., Chen, H.B., Li, W.H., et al.: Intelligent methodology for sensing, modeling, and control of weld penetration in robotic welding system. Industrial Robot 36(6), 585–593 (2009)

226 5.

6. 7.

C. Shujin and Z. Junjun Xin, L., Xu, Z., Zhao, M., et al.: Analysis and research on mismatch in tailored blank laser welding for multi-group. In: Proceedings of the 2008 IEEE International Conference on Information and Automation, ICIA 2008, pp. 1034–1039 (2008) (in Chinese) Sagues, P.: Adaptive control techniques advance automatic welding. Welding Journal 89(8), 26–28 (2010) Wylie, N., Wylie, S.R., Cullen, J.D., et al.: NDE system for the quality control of spot welding in the automotive industry. In: Proceedings IEEE Sensors Applications Symposium, SAS 2010, pp. 73–78 (2010)

Simulation and Experiment Research on the Effects of DC-Bias Current on the 500kV Power Transformer Feng-hua Wang1, Jun Zhang1, Cheng-yu Gu2, and Zhi-jian Jin1 1

Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Department of Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China {fhwang7723,junzhang,zjjin}@sjtu.edu.cn 2 East China Electric Power Test & Research Institue, Shanghai 200437, China [email protected]

Abstract. In the paper, the effects of DC-bias current on the 500kV power transformer with three-phase five-limb core structure are investigated by calculation and experiment. First, the coupling field-circuit model is applied to simulate the DC-bias characteristics of power transformer, where the traditional magnetic circuit models are replaced by Maxwell's equations in the magnetic circuit. Then the no-load DC-bias experiment is made to investigate the endure ability of 500kV power transformer. It is seen that the calculated results are agreed well with the experimental results. The no-load currents of the 500kV power transformer are distorted and the THD increases under DC-bias. The even harmonic is increased first and then decreased with the increasing of DC injected current. The obtained results are significant for the manufacturer of power transformer and operation staff of power system. Keywords: power transformer, DC bias, magnetizing current, harmonic.

1 Introduction When the monopolar operation with ground return is adopted or the bipolar operation with unbalanced currents is occurred in the High Voltage Direct Current (HVDC) systems or the phenomenon of magnetic storm is occurred, DC currents or the geomagnetic induced currents (GICs) which are essentially direct currents due to its extremely low frequency are found to enter and leave the directly earthed neutrals of high-voltage star-connected windings, causing a DC-bias in the magnetizing current of the transformer. Those DC currents cause the transformer core to saturate during the half cycle in which the bias current is in the same direction as the magnetizing current. Consequently, some undesirable effects such as increased noises, excessive core vibrations and overheating etc are brought out, which poses a potential threat for the integrity and longevity of the transformers [1]. In the past several years, many researchers have paid more attentions to analysis the performance of power transformer influenced by DC-bias through the simulation studies such as the coupled field-circuit method [2] where the electric circuit equations and magnetic circuit equations are solved together and the finite element M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 227–234. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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method [3] or through the experimental studies [4] and some important conclusions have been obtained. However, three-phase five-limb power transformer is always adopted in the high voltage and large capacity power transmission. Due to the large volume and high cost, it is difficult to make the DC-bias experiment in the 500kV power transformer. Moreover, the foundation of magnetic circuit model is a hard work because of the complex magnetic structure and few work has been done. In the paper, the coupled field-circuit method is applied to simulate the DC-bias characteristics of a 500kV power transformer, where the traditional magnetic circuit models are replaced by Maxwell's equations in the magnetic circuit. The no-load DCbias experiment is made to investigate the endure ability of 500kV power transformer. The results are significant for the manufacturer of power transformer and operation staff of power system.

2 Description of the Calculation Model of 500kV Power Transformer under DC-Bias The DC current is flowed into the AC system through the neural point of the two AC power transformer with star-grounded connection. Fig.1 is the schematic diagram of the coupling model of electric and magnetic circuits of a three-phase five-limb power transformer under dc current inrush. In the figure, us1, us2 and us3 are the three-phase power supply, B1, B2 and B3 are the magnetic induction intensity of three-phase core column, B4 and B5 are the magnetic induction intensity of upper return yoke, B6 and B7 are the magnetic induction intensity of low return yoke, i1, i2 and i3 are the primary current of three-phase winding, ZL1, ZL2 and ZL3 are the three-phase load reactance, e1(t), e2(t) and e3(t) are the electromotive force of three-phase winding to the neutral line, U0 is the DC voltage. 2.1 Electric Circuit Equations According to the electrical circuit model shown in Fig.1, the following equations are satisfied with the loop constituted by each of the three phases and the neutral line:

usj (t ) + U 0 − R j i j (t ) − L j

di j (t ) dt

= e j (t ) = − N

d φ j (t ) dt

j = 1, 2,3

(1)

where, φ j ( j = 1, 2,3) are the magnetic flux of three-phase core column separately, R j ( j = 1, 2,3) are the linear resistances of the transmission line and the three-phase windings and R1=R2=R3, L j ( j = 1, 2,3) are the three-phase winding inductance and L1=L2=L3, N is turn of the primary winding. Equation (1) could also be represented in matrix form with the rearrangement. [Q ] = R[ I ] + L[ I ][di / dt ] + N [ I ][dφ / di ]

(2)

where, [Q] is the summer of the AC power supply and DC voltage, [I] is the identity matrix.

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i1

i3

i2 us 2

us1

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us 3

e2 (t )

e1 (t )

e3 (t ) U0

B1

B3

B2

e1 (t ) B5

B4

B6

B7

ZL2

Z L1 Z L3

Fig. 1. Coupling model of the electric circuit and magnetic circuit for a three-phase five-limb power transformer under DC-bias

2.2 Magnetic Circuit Equations

Maxwell's integral equations, which are suitable for any situation in electromagnetic fields, are represented as follows: G G G G G ∂D G (3) v∫ l H ⋅ dl = ∫ J ⋅ dS + ∫ ∂t ⋅ dS G

G

v∫ B ⋅ dS = 0

(4)

The schematic diagram of magnetic circuit of three-phase five-limb power transformer is shown in Fig.2. Here, H1, H2 and H3 are the magnetic field intensity of three-phase core column, H4 and H5 are the magnetic field intensity of upper return yoke, H6 and H7 are the magnetic field intensity of low return yoke. For the low frequencies in the core, the displacement current is ignored, so the second term on the right side of Eq.(3) is ignored. By applied the Eq.(3) to the magnetic circuit loop , , and marked in Fig.2, the following equation can be obtained.

①

②③ ④

g j ( H1 , H 2 , H 3 , H 4 , H 5 , H 6 , H 7 , i1 , i2 , i3 ) = 0

j = 1, 2,3, 4

(5)

where, g is the function of magnetic field intensity, winding current and the length of magnetic circuit.

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H6

H7

H4

H5 H1

H2

H3

Fig. 2. Magnetic circuit of three-phase five-limb power transformer

Similarly, by applied the Eq.(4) to the three independent magnetic circuit node in Fig.2, then we have h j ( B1 , B2 , B3 , B4 , B5 , B6 , B7 ) = 0

j = 1, 2,3

(6)

where, h is the function of magnetic induction intensity and section area of the core. The single-value curve used for expressing the nonlinearity of each power transformer core [5] is represented as follows. B = f (H )

(7)

Substituting Eq.(7) into variable B of Eq.(6), the seven nonlinear equations are formed with Eq.(5). Finally, the three nonlinear equation including three variable H1, H2 and H3 are obtained. g j ( H1 , H 2 , H 3 , i1 , i2 , i3 ) = 0

j = 1, 2,3

(8)

Through programming with the iterative algorithm, the waveforms of no-load current and winding current of three phases are obtained. Then, the magnetic field intensity and flux density under DC-bias are found using Eq.(5) and Eq.(6).

3 DC-Bias Experiment of 500kV Power Transformer The DC-bias experiment are made in two 500kV power transformer connected in parallel form and the connection diagram is figured in Fig.3. The type of test transformer is YSFP-31500/220. In the figure, Tr1 and Tr2 is the two test power transformer with same structure, G is the generator. The DC source is composed of DC source and M&C system, where the M&C system is the measurement and control system to measurement the magnetic current of power transformer through Hall current sensor and to control the output of direct current of DC source [4]. The direct current output from the M&C system is applied to simulate the DC component in the earth flowed into the operated transformer. By adjusted the values of direct current in the M&C system, the distortion degree of magnetizing current and the noise of test transformer can be observed and consequently to estimate the ability of test

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transformer to endure the DC-bias. Meanwhile, the sound intensity of test transformer is measured through the sound level meter which is placed near the test transformer to investigate the noise variations of test transformer.

Fig. 3. Connection diagram of DC-bias experiment of 500kV power transformer.

4 Results and Discussions 4.1 Calculation Results

Fig.4 and Fig.5 is the calculated results of the no-load current and magnetic flux under different DC voltage for 500kV power transformer based on the coupling fieldcircuit method. It is seen that the three-phase no-load current are distorted and their peak values become larger with increasing DC voltage. The magnetic flux move forward with increasing voltage which is implied the DC component in the power transformer. Using the FFT, the harmonic distributions of three-phase exciting currents with different DC bias are shown in Tab.1, Tab.2 and Tab.3. It is seen that the even harmonic and odd harmonic and THD all increase with DC voltage.

(a) U0=0

(b) U0=100

Fig. 4. Waveforms of excitation current of 500kV transformer under different DC voltage.

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(b) U0=100

(a) U0=0

Fig. 5. Waveforms of magnetic flux of 500kV transformer under different DC voltage. Table 1. Harmonic characteristic of excitation current of A phase under different DC voltage. U0(V) 0 50 100 200

First 0.4551 0.4655 0.4919 0.6023

Second 0.0048 0.0416 0.0838 0.2065

Third 0.0476 0.0522 0.0670 0.1494

Fourth 0.0028 0.0263 0.0532 0.1372

Fifth 0.0461 0.0509 0.0636 0.1233

Sixth 0.0005 0.0055 0.0162 0.0679

Seventh THD(%) 0.0122 14.8581 0.0145 19.2292 0.0214 28.2089 0.0575 54.7439

Table 2. Harmonic characteristic of excitation current of B phase under different DC voltage. U0(V) 0 50 100 200

First 0.4781 0.4908 0.5162 0.5690

Second 0.0098 0.0572 0.0986 0.1565

Third 0.0281 0.0293 0.0294 0.0172

Fourth 0.0048 0.0299 0.0574 0.1037

Fifth 0.0485 0.0522 0.0601 0.0749

Sixth 0.0013 0.0044 0.0025 0.0146

Seventh THD(%) 0.0129 12.2590 0.0165 18.2980 0.0219 26.0107 0.0324 36.2536

Table 3. Harmonic characteristic of excitation current of C phase under different DC voltage. U0(V) 0 50 100 200

First 0.4564 0.4675 0.4980 0.2027

Second 0.0098 0.0440 0.0901 0.6263

Third 0.0490 0.0545 0.0731 0.2292

Fourth 0.0063 0.0281 0.0580 0.1708

Fifth 0.0466 0.0518 0.0666 0.1551

Sixth 0.0020 0.0066 0.0193 0.1377

Seventh THD(%) 0.0125 15.2951 0.0151 19.9317 0.0235 30.0605 0.0800 59.3634

4.2 Experimental Results

Fig.6 is the experimental results of the no-load current under different DC current for 500kV power transformer. It is seen that the no-load current are distorted clearly and their peak values become larger with increasing DC current. The harmonic distributions of exciting currents are shown in Tab.4. It is shown that the THD increases with DC current. The odd harmonic increases with DC current, while the

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even harmonic increases firstly and then decreases, which may imply the deep saturation of the core. At the same time, the sound intensity is 59dB measured by the sound level meter with no DC current. When the injected DC current is 4A, the sound intensity is 82dB.

(a) Idc=3A

(b) Idc=4A

Fig. 6. Waveforms of excitation current of 500kV transformer under different DC currents. Table 4. Harmonic characteristic of excitation current of A phase under different DC current. Idc(A)

First

Second

Third

Fourth

Fifth

1 2 3 4

1 1 1 1

0.0050 0.0128 0.0137 0.0011

0.046 0.059 0.093 0.761

0.001 0.013 0.026 0.003

0.0006 0.029 0.028 0.325

Sixth 0.0001 0.0044 0.016 0.003

Seventh THD(% ) 0.0007 4.58 0.0084 6.883 0.020 10.946 0.0242 83.947

5 Conclusion The coupling field-circuit model is applied to simulate the DC-bias characteristics of a 500kV power transformer, where the traditional magnetic circuit models are replaced by Maxwell's equations in the magnetic circuit. The DC-bias experiments are made in the 500kV power transformer with the developed DC controlled current source to simulate the DC component in the earth and to the power transformer experimentally. It is seen that the calculated results are agreed well with the experimental results. The no-load current of the three-phase five-limb transformer is distorted clearly and the THD increases under DC-bias. The variation of even harmonic is increasing firstly and then decreasing, which implies the variation of B-H curve. Therefore, it is necessary to model the B-H curve carefully including the linear segment and saturation segment to investigate the DC endure ability of 500kV power transformer by simulation. This is our next works.

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References 1.

2.

3. 4. 5.

Cao, L.: Study on the Influence of DC biasing Current on Power Transformer Magnetization Characteristics. Ph. D. thesis, Department of Electrical Engineering. Tsinghua University, Beijing (2007) Cao, L., Zhao, J., He, J.L.: Research on the Withstand Performance of Three-phase Threelimb Power Transformer under DC Current Biasing. High Voltage Engineering 33, 71–75 (2007) Tong, L.: Research of DC Magnetic Bias on 500kV Auto-transformer. Master Thesis, Department of Electrical Engineering. Shanghai Jiaotong University, Shanghai (2007) Wang, F.H., Zhang, J., Jin, Z.J.: Experimental Study of the Effects of DC-bias Current on the Power Transformer. In: APPEEC 2010, pp. 1–4. IEEE Press, New York (2010) Pedra, J., Sainz, L., Corcoles, F.: PSPICE Computer Model of a Nonlinear Three-phase Three-legged Transformer. IEEE Trans. Power Del. 19, 200–207 (2004)

A HID Lamp Model in Simulink Based on the Principle of Electric Arc Xiaohan Guan* and Zhongpeng Li College of Information Engineering, North China University of Technology 5#, Jinyuanzhuang Road, Shijingshan District, Beijing, China, 100144 [email protected]

Abstract. A dynamic conductance model of HID lamp, based on the classical principle of electric arc, will be established in this paper. Followed by the model and corresponding results, this type of HID lamp model will be simulated at low and high frequency in Simulink. The V-I characteristic, the relationship of current and voltage output, also the conductance of HID lamp model will be measured in this paper. It shows that the proposed model faithfully emulates external electrical properties of HID lamp at low and high frequency. Keywords: HID lamp; model; Simulink; electric arc

1 Introduction With the development of Computer-Aided Design, simulation tools are more significant in the power electronic systems currently. In the design process of electronic ballasts, the electronic ballast circuit would be seriously considered, and then simulated in order to test the feasibility and optimization of the circuit. Therefore, an equivalent model of HID lamp must be provided to facilitate the simulation. Matlab/Simulink is the high-performance numerical software of Mathworks Company in United States in the mid-80s in 20th century. After developing within three decades, Matlab has become a basic mathematical tool of mathematical statistics, automatic control theory, dynamic system simulation and many other courses. In fact, there are many articles on HID lamp model in Pspice. However, the model of HID lamp in Simulink is still less. Therefore, in the second section of this paper, a new dynamic conductance model of HID lamp based on the principle of electric arc will be established. The model combined the mathematical model proposed by Cassie and Mayr. This model will be simulated at low and high frequency in Simulink. Simulation results show that the model in this paper can simulate the external electrical characteristics of HID lamp and verify the *

This work is supported by Beijing Education Committee Technology Development Plan Project (KM200810009011) and Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality PHR201008185.

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applicability of the model. The model of this paper can provide numerous guidance and reference value for electronic ballasts.

2 Establishment of the New Dynamic Conductance Model Before a model of HID lamp is established, it is extremely significant to understand some basic knowledge of the HID lamp’s electrical properties, such as different electronic properties [1] of HID lamp at low and high frequency. In the other word, when the low frequency sinusoidal current is treated as input, such as tens of Hertz, lamp voltage will cause the phenomenon of re-ignition. From another view, when the sinusoidal current is changing around zero, the voltage will increase suddenly. This phenomenon is similar as beginning ignition. And with the current increase, the voltage reaches maximum value dramatically and followed by decreasing slowly to the normal level. This phenomenon occurs periodically as the sinusoidal current. So its V-I characteristics show a classic hysteresis phenomenon. When the input current is a high-frequency sine wave, for instance, tens of kHz, current and voltage of lamp are sinusoidal and at the same phase. The V-I characteristics at this time is a linear relationship, however, the slope is not fixed. As it can be seen, HID lamp model should be designed to simulate these two characteristics simultaneously. Since discharge process of HID lamp is an electric arc, researchers had carried out much study of electric arc followed by lots of results. The study of HID lamp arc can be referenced from the research of switch arc mathematical model. The classical Cassie mathematical model established in 1939 and Mayr mathematical model proposed in 1943, based on simplifications of principal power-loss mechanisms and energy storage in the arc column, have been recognized for many years. In the Cassie model, Cassie assumed that the current density in an electric arc model is a constant, so the cross-section of the arc varied directly with the arc current. The resistivity and stored energy per unit volume are constants. Based on these basic assumptions, the famous Cassie arc model can be obtained, showed in equation (1). This model has a drawback that the modeled arc cannot be ceased. It describes the behavior of the arc when the current is strong; however it is not suitable for the description of arc characteristics when the arc-current is near to zero. θ

d ln G v 2 = 2 −1 dt EO

(1)

In Mayr’s model, Mayr assumed that the heat loss occurs from the outer arc only. Also the conductance of the arc changed with the energy stored in it. The Mayr mathematical equation is

θ

d ln G i2 = −1 dt PO G

(2)

This equation does allow the arc to cease. With the decrease of conductance G, i2/POG can be still more than unity. Hence, dlnG/dt is positive and the conductance

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continues to decrease until the arc is extinguished. The fitting of model results to measured data is achieved by means of a proper selection of arc parameters like the time constant and the current-dependent cooling power, which are normally taken as a function of arc current and voltage. The modified Mayr model is used in this article, shown as equation (3).

1 dG d ln G 1 vi = = ( − 1) θ P( PO + Ci i ) G dt dt

(3)

G is the instantaneous arc conductance; θ is the arc time constant; EO is the constant steady-state arc voltage; v is the arc voltage; i is the arc current; PO is the constant power loss of temporarily stable; P is the fill pressure of the circuit breaker; Ci is the current constant. Obviously, the Cassie model and modified Mayr model are not applicable in all cases. But two models are complementary with each other. As the modified Mayr model is more feasible for zero and low arc current region, Cassie model is more suitable for high arc current region. If these two models can be combined reasonably, a more applicable mathematical arc model can be obtained. Therefore, the hypothesis is presented as follow: 1. The time constant in these two models is the same. 2. A complete arc discharge process can be described by the combination of the Cassie model and modified Mayr model. But the conversion between two models lacks a transition point. It can be assumed that the transition current is I. If the arc current is greater than I, the arc discharge process is described by the Cassie model; otherwise, it is described by the modified Mayr model. 3. This paper supposes that the transition current is continuous. The transition is smooth. It is possible to define a transition factor f, which is an exponential function of the arc current. The transition factor f can be taken as: f = exp( −

i2 ) I2

(4)

A new mathematical arc model, equation (5), was got using equations (1), (3) and (4):

θ

1 dG v2 vi = ( 2 − 1)(1 − f ) + ( − 1) f G dt EO P ( PO + C i i )

(5)

In addition, the arc inherent conductance Gm between the electrodes should be considered in equation (5). Considered equations (4) and (5), the complete model is thus given by

θ

dG i2 i2 i2 i2 i2 G= G exp( − 2 ) − G 2 + Gm − exp( − 2 ) + dt EO EO I P ( PO + Ci i ) I

(6)

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The equation (6) is the new dynamic conductance mathematics model of HID lamp. A 250W of HID lamp was tested at 50Hz in order to obtain the parameters. The parameters were taken as: θ =2*10-4S, EO=120V, I=0.45A, Gm =1.5*10-8S, PO = 250W. P and Ci were obtained by genetic algorithms and other mathematical calculations. P=4.99bar, Ci =499.8V/bar. According to equation (6), the dynamic conductance model of HID lamp in Simulink was established as Figure 1.

Fig. 1. Dynamic Conductance model of HID lamp in Simulink

3 Simulation of the New Dynamic Conductance Model The simulation circuit was shown as Figure. 2. According to the HID lamp current in practical work, the input current of simulation model is set to a sinusoidal current that the peak is 1.5A. In order to observe the voltage and current waveforms better, the input of simout1 in Figure. 2 was amplified to certain multiple.

Fig. 2. Simulation Test Circuit of model

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3.1 Simulation of the Model at Low Frequency

First, the conductance of the model at low frequency is observed, which was shown as Figure.3. It shows that the conductance changes periodically with the sinusoidal current. The peak is not fixed, which changes with the peak of the current. Figure 4 shows the voltage and current waveforms of the model at low frequency. It can be seen that the output voltage of the model is similar to a square wave. Current and voltage are in the same phase, but the voltage has a peak at each zero-crossing point. That is because the phenomenon of re-ignition of HID lamp appears at low frequency. However, as the frequency increases, the peaks gradually become much smaller and then disappear. At this time, the performance of voltage waveform will be improved.

Fig. 3. Conductance at 50Hz

Fig. 4. Voltage and Current at 50Hz

Figure 5 is the V-I characteristics of model at low frequency. It shows that the V-I characteristic performed as the classic hysteresis phenomenon, that is, the current reach the maximum, and then voltage reach the maximum as well. After that, both current and voltage are close to zero. Basically, the parameters of HID lamp are changing with this principle regularly at low frequency. All mentioned above are due to the gas thermal inertia in HID lamp. HID lamp at low frequency performed the nonlinear V-I characteristics. In order to analyze the frequency characteristics of the model deeply, the model in this paper was simulated at 5Hz. Simulation results of voltage and current were shown in Figure 6. It can be seen that the dynamic conductance model at 5Hz can reflect the negative incremental impedance characteristics of HID lamp more clearly. In other words, the lamp voltage decreases as the current increases.

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Fig. 5. V-I characteristics at 50Hz

Fig. 6. Voltage and Current at 5Hz

3.2 Simulation of the Model at High Frequency (50 kHz)

The conductance of the model at high frequency was shown in Figure.7 and Figure.8. It can be seen in Figure 7 that conductance of the model gradually increased from zero to a constant value. When the model reached at high frequency in stable state, the equivalent conductance was shown in Figure.8. The conductance fluctuated in small range, and small-scale periodic fluctuations could be ignored, that is, the conductance was regarded as a constant. This fact is coincided with the actual situation of HID lamp. When HID lamp start at high frequency, the resistance is infinite. Otherwise, when it achieves stable state, the equivalent resistance is a constant value.

Fig. 7. Conductance at 50 kHz

Fig. 8. Conductance at the stable state

Figure 9 shows voltage and current of the model. It shows that the voltage becomes a sine wave and the peak disappears. The voltage and current are in same phase. This indicates that the model appears a pure resistance at high frequency. Figure 10 shows the V-I characteristics which was a linear relationship, while the slope was not fixed, but changed in certain range. It was verified that resistance with HID lamp could be considered as a variable resistor at high frequency.

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Fig. 9. Voltage and Current at 50 kHz

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Fig. 10. V-I characteristics at 50 kHz

4 Conclusion The arc discharge process of HID lamp was described by the classical Cassie model and the modified Mayr model. The new mathematical model was established in this paper followed by the new dynamic conductance model based on the principle of an electric arc. Simulation results showed that the new dynamic conductance model could simulate the main characteristics of HID lamp. The model’s characteristics at low and high frequency are in great agreement with the external characteristics presented by HID lamp. Moreover, the model at a more low-frequency (5Hz) better reflects the negative incremental impedance of HID lamp. Some specific guidance and reference value in future will be provided in the process of electronic ballasts designed. The Cassie model and modified Mayr model describe the arc characteristics only from the physical concept. Therefore, a description of some complex features about HID lamp, such as the phenomenon of acoustic resonance and stroboscopic, has not yet contained in the new model. Such model establishment will become the following direction of HID lamp model.

References 1. 2.

3.

4.

Wang, Y., Wu, W., Wang, J., Yang, L.: Pspice Models of High-Intensity Discharge Lamps. Journal of Shanghai University (Natural Science), 11(6) (2005) Parizad, A., Baghaee, H.R., Tavakoli, A., Jamali, S.: Optimization off Arc Models Parameters Using Genetic Algorithm. In: International Conference on Electric Power and Energy Conversion Systems EPECS 2009, pp. 1–7 (2009) Shvartsas, M., Sam, B.-Y.: A SPICE compatible model of high intensity discharge lamps. In: 30th Annual IEEE Power Electronics Specialists Conference (PESC 1999), pp. 1037–1042 (1999) Wei, Y., Hui, S.Y.R.: A Universal Pspice Model for HID Lamps. In: 37th IAS Annual Meeting,Conference Record of the Industry Applications Conference 2002, pp. 1475–1482 (2002)

242 5. 6. 7.

8. 9.

X. Guan and Z. Li Herrick, P.R.: Mathematical models for high-intensity discharge lamps. IEEE Transactions on Industry Applications, 1A 16(5), 648–654 (1980) Wei, Y., Hui, S.Y.R., Chung, H., Cao, X.H.: Genetic algorithm optimized high-intensitydischarge lamp model. Electron. Lett. 38(3), 110–112 (2002) Zissis, G., Damelincourt, J.J., Bezanahary, T.: Modelling discharge lamps for electronic circuit designers: A review of the existing methods. In: Proceedings of the 2001 Industrial Application Society (2001) Anton, J.C.: An equivalent conductance model for high-intensity discharge lamps. In: IEEE Industry Applications Society-Annual Meeting, vol. 2, pp. 1494–1498 (2002) Tseng, K.J.: Dynamic Model of Fluorescent Lamp Implemented in PSpice. In: Proceedings of Power Conversion Conference PCC 1997, vol. 2, pp. 859–864 (1997)

Coordinated Control for Complex Dynamic Interconnected Systems Xin-yu Ouyang1,2 and Xue-bo Chen2 1

School of Control Science and Engineering, Dalian University of Technology, Dalian Liaoning 116024, China [email protected] 2 School of Electronics and Information Engineering, Liaoning University of Science and Technology, Anshan Liaoning 114051, China [email protected]

Abstract. It provides a coordinated control method for complex systems with dynamic interconnections, that is, by using the dynamic inclusion principle and a desired reverse order permutation transformation, complex system with information constrains of dynamic topology structure is decomposed as a group of pair-wise decoupled subsystems in the expanded space. Then decentralized controllers and coordinators are designed, according to the conditions of dynamic inclusion principle, the overall coordinated compensator also are given. The obtain coordinated controller of the overall system can be contracted and implemented in the original space. Keywords: Complex Systems; Coordinated Control; System Decomposition.

1 Introduction The research on complex systems has been a focus in modern science currently[1-12], there are many literatures discussed this issue. However, there hasn’t uniform definition for complex systems. According to the motive of this paper, we call the system as complex system, which consists of multiple homogeneous subsystems and has many dynamic interconnections between subsystems, such as multi-agent system, electric power systems, multi-vehicle system, etc.. Since this kind of complex systems have the characteristics of high dimensions and variable topology structure constraints, coordinated control for them is a difficult problem. According to these facts, we will introduce dynamic inclusion principle and permuted transformations, and provide a coordinated control method for dynamic interconnected complex systems.

2 Descriptions of Complex Dynamic Interconnected Systems Consider an n -order complex dynamic interconnected system S = {Si } with N subsystems described by M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 243–249. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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N

Si : xi = Aii xi + ∑ eij (t , x) Aij x j + Bii ui ; yi = Cii xi , i = 1, 2," , N ( N ≥ 3 )

(1)

j =1

where xi ∈ R ni , ui ∈ R mi and yi ∈ R li are the state, input and output vectors of the i th subsystem respectively, Aii ∈ R ni ×ni , Aij ∈ R ni ×ni , Bii ∈ R ni ×mi and Cii ∈ R li ×ni are constant matrices; eij (t , x) is dynamic interconnected coefficient between subsystem i and j , which is a function with respect to time t and/or state x ; eij = 0 denotes it hasn’t self-connection in the i th subsystem at i = j . Assume all subsystems of the system are homogeneous, then the matrix form of the system (1) can be described by S : x = Ax + Ea x + Bu ; y = Cx

(2)

The variables satisfying N

N

N

i =1

i =1

i =1

n = ∑ ni , m = ∑ mi , l = ∑ li , x = [ x1T ," , x NT ]T , u = [u1T ," , u NT ]T , y = [ y1T ," , y NT ]T

(3)

Here x ∈ R n , u ∈ R m and y ∈ R l are the state, input and output vectors of the system respectively. Coefficient matrices are

A = blockdiag ( A11 , A22 ," , ANN ), B = blockdiag ( B11 , B22 ," , BNN ), C = blockdiag (C11 , C22 ," , CNN ), Ea = (eij Aij ),

(i, j = 1, 2," , N )

(4)

Let AE = A + Ea , the system (2) also can be rewritten as S : x = AE x + Bu , y = Cx

(5)

In fact, Ea represents dynamic topology structure of the system (2), i.e. dynamic interconnections between subsystems. In order to understand the characteristics of the complex system, let’s give the following notion of multi-overlapping[3,4,13]. Definition 1: The system S is said to possess N ( N − 1) / 2 multi-overlapping pair-wise dynamic interconnected subsystems

Sij : xi = Aii xi + eij Aij x j + Bii ui ;

yi = Cii xi ⎫ ⎪ x j = e ji A ji xi + A jj x j + B jj u j ; yi = C jj x j ⎬ ⎪ i = 1, 2,..., N − 1 j = i + 1,..., N ⎭

(6)

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if eij ≠ 0 and/or e ji ≠ 0 , where the subsystem Si in (1) is a multi-overlapping part of the pair-wise subsystem Sij in (6). If the subscripts of these pair-wise subsystems are arranged as Sij : S12 , S 23 , S13 , S34 , S 24 , S14 ," , Sij , " , S 2 N , S1N i = j − k , j = 2,3," , N , k = 1, 2," , j − 1.

. ()

(7)

we call the special sequence as recurrent reverse order subscripts. According to the definition 1, we know the dynamic interconnected system is a system with structure information overlapping, thus the system can be decomposed.

3 Decomposition of Systems With the system S in (5), we associate a corresponding expanded system described by

, y = Cx S = {Si } : x = A E x + Bu

(8)

where x ∈ R n , u ∈ R m and y ∈ R l are the state, input and output vectors of the expanded system S ; A E , B and C are matrices with appropriate dimensions. It is crucial to assume that n < n , m < m , l < l . In order to analyze the complex system, we give the following definition [4,7]. Definition 2: The system S dynamic includes the system S , or S ⊃ S , if there exists a group of full rank matrices {Vn × n ,U n× n , Rm × m , Qm× m , Tl×l , Sl ×l } satisfying UV = I n ,

QR = I m , ST = I l , such that for any x0 ∈ R n and any fixed u (t ) , the conditions x0 = Vx0 and u = Ru imply x(t ; t0 , x0 , u ) = Ux (t ; t0 , x0 , u ) and y[ x(t )] = Sy[ x (t )] for all t ≥ t0 . Here x(t ; t0 , x0 , u ) , x (t ; t0 , x0 , u ) are the unique solution of the first equation

in (5) and (8) for the initial time t0 respectively. The coefficient matrices of the system S can be obtained by A E = VAEU + M 1 , B = VBQ + M 2 , C = TCU + M 3 .

(9)

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where T

N -1 N -1 ⎛

⎞ V = blockdiag ⎜ I n1 I n1 " I n1 , " , I nN I nN " I nN ⎟ ,

⎟ ⎜ N ⎝ ⎠ N N 1 1 ⎛

⎞ 1 U= blockdiag ⎜ I n1 I n1 " I n1 , " , I nN I nN " I nN ⎟ ,

⎟ ⎜ N −1 N ⎝ ⎠ T

N -1 N -1 ⎛

⎞ R = blockdiag ⎜ I m1 I m1 " I m1 , " , I mN I mN " I mN ⎟ , ⎜ ⎟ N ⎝ ⎠ 1 1 N N ⎛

⎞ 1 Q= blockdiag ⎜ I m1 I m1 " I m1 , " , I mN I mN " I mN ⎟ , ⎜ ⎟ N −1 N ⎝ ⎠

(10)

T

N -1 N -1 ⎛

⎞ T = blockdiag ⎜ I l1 I l1 " I l1 , " , I lN I lN " I lN ⎟ , ⎜ ⎟ N ⎝ ⎠ N -1 N -1 ⎛

⎞ 1 S= blockdiag ⎜ I l1 I l1 " I l1 , " , I lN I lN " I lN ⎟ ⎜ ⎟ N −1 N ⎝ ⎠

and M 1 , M 2 and M 3 are complementary matrices with appropriate dimensions, satisfying M 1V = 0 , M 2 R = 0 and M 3V = 0 . Let’s introduce permuted inclusion and permutation matrices[3] in the following. Definition 3: Consider

SP : x p = A Ep x p + B P u p , y p = C p x p

(11)

Assume there exists a group of full rank matrices {Vn × n , U n× n , Rm × m , Qm× m , Tl×l , Sl×l } such that S ⊃ S , then there exists a group of full rank matrices {U p , V p , R p , Tp } such that the system S p permuted includes S , or S p ⊃ S . Where x p ∈ R n , u p ∈ R m ,

y p ∈ R l ; x p = PA−1 x , u p = PB−1u , y p = PC−1 y ; U p = UPA , V p = PA−1V , R p = PB−1 R and Tp = PC−1T ; PA , PB and PC are non-singular permutation transformation matrices with appropriate dimensions. Definition 4: By partitioning an identity matrix I n× n into M sub-identity matrices,

I1 ," , I k ," , I M , with proper dimensions, we call

Coordinated Control for Complex Dynamic Interconnected Systems

⎡ 0 pk ( k +1) = blockdiag ( I1 ," , I k −1 , ⎢ ⎣ I k +1 ⎡0 p −1k ( k +1) = blockdiag ( I1 ," , I k −1 , ⎢ ⎣Ik

Ik ⎤ , I ," , I M ), 0 ⎥⎦ k + 2 I k +1 ⎤ , I ," , I M ) 0 ⎥⎦ k + 2

247

(12)

as basic column exchange matrix and basic row exchange matrix respectively, and H P = pi ( i +1) p(i +1)( i + 2) " p( j −1) j = Π kj −=1i pk ( k +1) , G (13) P −1 = p −1( j −1) j " p −1(i +1)( i + 2) p −1i (i +1) = Π kj −=1i p −1k ( k +1) , (i ≥ 1, j ≤ M )

are column group permutation matrix and row group permutation matrix respectively. In order to obtain the special sequence of Sij in Definition 1, the following transforms can be used: H H H P = Π iN=1− 2 Π Nj =−1i −1Π kN=(1N+−i ( ji −) −1)i ( j +1) pk ( k +1) G G G (14) P −1 = Π iN=1− 2 Π Nj =−1i −1Π kN=(1N+−i ( ji −) −1)i ( j +1) pkT( k +1) Here permutation matrices P and P −1 represent nonsigular column transformation and nonsigular row transformation respecitively. After the decomposition and permutation above, we can obtain pair-wise subsystems with recurrent reverse order subscripts as in (6) and (7).

4 Coordinated Control of Systems Consider the pair-wise subsystems in (6), we can design decentralized controllers and coordinators for them. The decentralized controllers to control each subsystem can be described by ⎡ kii K ij : uij = ⎢ ⎣0

0 ⎤ ⎡ xi ⎤ k jj ⎥⎦ ⎢⎣ x j ⎥⎦

(15)

and the coordinators can be described by ⎡0 Lij : uij = ⎢ ⎣l ji

lij ⎤ ⎡ xi ⎤ 0 ⎥⎦ ⎢⎣ x j ⎥⎦

(16)

which are used to coordinate the interconnected relations between subsystems i and j . Then the pair-wise closed loop subsystems can be described by ⎡ xi ⎤ ⎡ Aii + Bii kii Scij : ⎢ ⎥ = ⎢ ⎣ x j ⎦ ⎣e ji Aji + l ji B ji

eij Aij + lij Bij ⎤ ⎡ xi ⎤ Ajj + B jj k jj ⎥⎦ ⎢⎣ x j ⎥⎦

(17)

However, these pair-wise coordinators are not contractible to and implemented in the original space S with respect to dynamic inclusion principle. So we need to design a coordinated compensator ΔL for contraction in the expanded space.

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In the expanded space, the controller is K = blockdiag ( K ii ) , i = j − k , j = 2,3," , N , k = 1, 2," , j − 1.

(18)

and the coordinator is L = blockdiag ( Lij ) , i = j − k , j = 2,3," , N , k = 1, 2," , j − 1.

(19)

where ⎡ kii K ij = ⎢ ⎣0

0⎤ ⎡0 , Lij = ⎢ ⎥ k jj ⎦ ⎣l ji

lij ⎤ 0 ⎥⎦

(20)

According to the dynamic inclusion principle and decomposition of the complex system stated above, it is obvious that the structure of ( K + L ) should be the same as the one of A Ep . Therefore, once the position of Aij is fixed in the expanded space, locations of lij will be determined. In this way, the coordinated compensator

ΔL can be established. Let LΔ = L + ΔL , it have ⎡ LΔ11 ⎢L LΔ = ( LΔ ij ) = ⎢ Δ 21 ⎢ # ⎢ ⎣ LΔ N 1

LΔ12 LΔ 22 # LΔ N 2

" LΔ1N ⎤ " LΔ 2 N ⎥⎥ , i, j=1,2,…,N. % # ⎥ ⎥ " LΔ NN ⎦

(21)

here LΔ ij are block matrices with a dimension of (N–1) I ni × (N–1) I n j and have

LΔij

⎧0, ⎪ ⎪ ⎪⎪ ⎡ 0 = ⎨⎢ ⎪⎢ 0 ⎪⎢ # ⎪⎢ ⎩⎪ ⎣⎢ 0

i = j, " lij " 0 ⎤ " lij " 0 ⎥⎥ , # #⎥ ⎥ " lij " 0 ⎦⎥

i < j , non-zero element is in column j − i,

(22)

i > j, non-zero element is in column N − ( j − i ).

Thus we can obtain the coordinator and coordinated compensator by L = P −1 LΔ P

(23)

Here P is recurrent reverse order permuted transformation matrix. At last, we obtain the coordinated controller ( K + L of overall complex system,

）

it can be contractible to and implemented in the original space S by Lk = Qp ( K + L )V p

(24)

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5 Conclusions The paper discussed coordinated control method of dynamic interconnected complex systems by using dynamic inclusion principle and permutation transform. The obtain coordinated controller of the overall system can be contracted and implemented in the original space.

Acknowledgment This research reported herein was supported by the NSF of China under grant No. 60874017.

References [1] Zhang, Z.D., Jia, L.M., Chai, Y.Y.: On General Control Methodology for Complex Systems. In: Proceedings of the 27th Chinese Control Conference, Kunming,Yunnan, China, pp. 504–508 (2008) [2] Ouyang, X.Y., Chen, X.B., Wang, W.: Modeling and decomposition of complex dynamic interconnected systems. The 13th IFAC Symposium on Information Control Problems in Manufacturing, Moscow, Russia, p.1006–1011 (2009) [3] Chen, X.B., Stankovic, S.S.: Decomposition and decentralized control of systems with multi-overlapping structure. Automatica 41, 1765–1772 (2005) [4] Chen, X.B., Stankovic, S.S.: Dual inclusion principle for overlapping interconnected systems. Int. J. Control 77(13), 1212–1222 (2004) [5] Ikeda, M., Šiljak, D.D., White, D.E.: Decentralized control with overlapping information sets. Journal of Optimization Theory and Applications 34(2), 279–310 (1981) [6] Chen, X.-B., Stankovic, S.S.: Overlapping decentralized approach to automation generation control of multi-area power systems. International Journal of Control 80(3), 386–402 (2007) [7] Chen, X., Stankovic, S.S.: Inclusion principle of stochastic discrete-time systems. Acta Automatica Sinica 23(1), 94–98 (1997) [8] Šiljak, D.D.: Large scale dynamic systems: stability and structure. North Holland, New York (1978) [9] Ikeda, M., Šiljak, D.D.: Lotka-Volterra Equations: Decomposition, Stability, and Structrue. Journal of Mathematical Biology 9(1), 65–83 (1980) [10] Tan, X.L., Ikeda, M.: Decentralized stabilization for expanding construction of large-scale systems. IEEE Transactions on Automatic Control 35(6), 644–650 (1990) [11] Šiljak, D.D.: Dynamic graphs. Nonlinear Analysis: Hybrid Systems 2, 544–567 (2008) [12] Wang, Q., Chen, X.B.: Connective Stability Analysis for a Class of Pseudo-Linear Interconnected Swarm Systems. In: Proceedings of the 8th World Congress on Intelligent Control and Automation, Jinan, China, pp. 1195–1199 (2010) [13] Chen, X.-B., Xu, W.-B., Huang, T.-Y., Ouyang, X.-Y., Stankovic, S.S.: Pair-wise decomposition for coordinated control of complex systems. Submitted to Information Sciences (2010)

Study on Calibration of Transfer Character of Ultrasonic Transducer Qiufeng Li1, Quanhong Zhang1, Min Zhao1, and Lihua Shi2 1

Key Laboratory of NDT of Ministry of Education, Nanchang Hangkong University Nanchang, China 2 Engineering Institute of Corps of Engineers, PLA Univ. of Science and Technology Nanjing, China [email protected]

Abstract. Transfer character of ultrasonic transducer often influences on the test signal partly, and then test errors arise. To the problem, a compact method is proposed to calibrate the transfer character in this paper. The experiment data was obtained in water- immerging test of the transducers, and a discrete transfer function is established based on system identification algorithms and then used for transducer calibration. The method is validated effective by experiment. Not only can the characteristic of transducers be indicated, but also a referenced method is presented for calibrating the transfer character of LTI system. Keywords: Ultrasonic transducer, transfer character, LTI system, system identification.

1 Introduction During the course of ultrasonic test, the transfer character of ultrasonic transducer is that test signals could be filtered by a band-pass filter, which would pass the resonant frequency and restrain the deviation frequency. And thus excitation signal selected is always close to the resonant frequency at which greater response signal and SNR would be obtained [1]. Then the transfer character of transducer can be reflected by frequency response of transducer concretely. Nevertheless, the higher resolution needs to be obtained with wideband excitation signal. So the frequency band of excitation signal from transducer is requested more wide, which can obtain more frequency signals [2,3]. It is important to measure the frequency response of transducer. But the measuring device is so expensive and complicated that domestic producers cannot afford to it and provide the graph. For this reason, a compact method of calibrating frequency response of transducer, which could be achieved with common instruments and material, is presented in the contribution. The theory of the method is introduced in next section. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 251–258. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Theory of Calibrating Transducer To continuous linear time-invariant (LTI) system, the relationship between input x(t) and output y(t) can be described by a linear differential equation with constant coefficients as below: N

∑ a (i ) ⋅ i =0

d i y (t ) M d j x (t ) = b ( j ) ⋅ ∑ dt i dt j j =0

(1)

It can express dynamic characteristics of the system. Any linear time-invariant system can be described by a difference equation similarly [4,5]: N

y (n) + ∑ a (i ) ⋅ y (n − i ) i =1

(2)

M

= ∑ b( j ) ⋅ x ( n − j ) j =0

Here a(i) (i=1,2,3…N) and b(j) (j= 1,2,…M) are constant coefficients of the equation. Taking (2) with Z-transform can transform as below: N

M

i =1

j =1

Y ( z )[1 + ∑ a (i) z −i ] = X ( z )[b(0) + ∑ b( j ) z − j ]

(3)

，

From the trait of Z-transform, Y(z)=X(z)H(z) and the discrete transfer function (DTF) of system H(z) can be given by comparing with (3). M

H ( z) =

Y ( z) = X ( z)

∑ b( j ) z

−j

j =0

N

1 + ∑ a (i ) z

(4) −i

i =1

Actually, the calibration of transducer system is the process of calculating H(z), which can be given after evaluating constant coefficients a(i) and b(j) through the system identification algorithms [6-8]. For major actual system, the order is unknown, so important problem is to ensure the order. In theory, higher order can describe the system more accurate. But the oversize order would bring more complicated model, calculation and error in practice. Thus higher order is not advantaged at all time [9]. For lower frequency transducer system, lesser order of the DTF model is required, and the feasible value is M N 5 which has been verified through experiments.

＝＝

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3 Verification of LTI System 3.1 LTI System Character Before calibration, a precondition, the detecting system is LTI system, must be achieved. The results are effective and significant in the condition of assuring the precondition. Therefore, the detecting system established must be assured as LTI system. The detecting devices usually have working range. And these devices are working as LTI system when its work under the range. That is the reason why the model is established with LTI system in this paper. To assuring the precondition, the experiment has been achieved to verifying the LTI system. And the theory of verification can be described as follow. On the assumption that the unit impulse response of detecting system is h(n) the response to input signal x1(n) is y1(n) and the response to x2(n) is y2(n).

，

，

y1 (n) = x1 (n) ∗ h(n)

(5)

y2 ( n) = x2 (n) ∗ h(n)

(6)

If the response to ax1(n)+bx2(n) is ay1(n)+by2(n), as follow equation:

y (n) = [ax1 ( n) + bx 2 ( n)] ∗ h(n) = ax1 (n) ∗ h(n) + bx2 (n) ∗ h( n)

(6)

= ay1 ( n) + by 2 (n) Then the system is linear system. If the input signal is delayed k sampling cycles, the output signal is delayed k sampling cycles correspondingly. And then the system is also a time-invariant system.

y1 ( n − k ) = x1 (n − k ) ∗ h(n)

(7)

The upper content could be described directly as follow. For the given input, the system output has nothing to do with moment of input signal. The system is called LTI system if it is linear and time-invariant [5]. 3.2 Verification Experiment A test system shown in Fig. 1 includes an arbitrary function generator, a pair of 50kHz ultrasonic transducers produced by Kcrt company and signal collection and display unit. signal collection and display unit collects signals by 9812 card and inputs the signals into computer, and then displays the image after the signals are processed with imaging algorithm.

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ֵSignalো

Signal ֵো䞛䲚

Excitation

collection and

থ⫳఼

Ϣᰒ⼎ display

water ∈

Fig. 1. Sketch of test system.

The output signal from transducer need be collected in the experiment. However, in the course of propagation from receiver and collector, the signal would be affected not only by transducer system and but also by the media, and then the established model could not reflect the frequency response of transducer system because it involves the disturbance of the media. Therefore, water-immersion method is introduced for water is a homogeneous and isotropic media. When ultrasonic wave propagates in water, it can be thought that the wave has just attenuation in amplitude, and the effect of attenuation could be removed through adjustment of signal amplitude [10-12]. To bring out the reflected wavepackets facilely, a water tank with 300mm depth is selected which avoids the overlap of reflected waves.

1 0.8

Scaledx(n) amplitude

0.6 0.4 0.2 0

-0.2 -0.4 -0.6 -0.8 -1 -1.5

-1

-0.5

0

0.5

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2.5 -4

t t/ s

x 10

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|X(jamplitude )| Scaled

16

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0

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4

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8

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12 4

f / Hz

x 10

Fig. 2. The waveform and spectrum of excitation signal.

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Two excitation signals, impulse signalδ(n) and 50kHz modulated signal x(n), are selected as input signal respectively. δ(n) is given by difference operation with step signal in fact. And x(n) is a cosine function modulated by Gaussian pulse, which equation is shown as below:

x(n) = cos(2πft ) ⋅ e ( − ((t −ti )⋅ω )

2

)

(8)

Here f is the main frequency, w is a pulse-width coefficient of Gaussian pulse and ti is positional parameter of wavepacket in the transmitting cycle. Actual waveform and its spectrum are shown in Fig. 2. According to the input signals, system responses should be as below respectively.

y1 (n) = δ (n) ∗ h(n)

(9)

y 2 ( n ) = x ( n) ∗ h( n)

(20)

In the light of LTI system trait, it should be y2(n)=x(n)y1(n). And then the system is verified as LTI system if measured output of x(n) is y3(n)=y2(n)=x(n)h(n). Comparison of measured data is shown in Fig. 3.

0.8

Scaled amplitude

0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 1000

2000

3000 4000

5000

Sampling point

Fig. 3. Comparison of measured output data.

In the figure, solidline represents the collected waveform y2(n) after generated by x(n), and dashline describes the convolution result with x(n) and the collected signal after generated byδ(n). In view of existing some errors in the experiment, wavepacket and amplitude of two waveforms could be thought as uniform and anastomotic approximately, which could be verified that the test system is a LTI system.

4 Experiment of Calibrating Transducer 4.1 Signal Collection In the experiment, x(n) is selected as excitation signal. A pair of input and output signals is collected as the data for system identification. From the collected output

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signal, the first wavepacket is the reflected wavepacket from bottom. Contrasting with input signal as Fig. 4, the reflected wavepacket has been changed obviously, and ringing has comparative length.

3

2

1.5

Output signal y(n)

1

H(j2πf) |H(j )|

Scaled amplitude

2.5 2.5

2 1.5 1

0.5

0.5

0

Input signal x(n)

-0.5

0 4

-1

150

200

250

300

Sampling point Fig. 4. Comparison of input and output signals.

4.5

5

f /Hz

350

5.5 4

x 10

Fig. 5. The frequency response of system.

4.2 Calibrating Rresult After amplitude of the output signal is adjusted according to the input signal, the model of the system is established in the light of system identification, and then DTF H(z)=B(z)/A(z) could be obtained. The graph of H(jω) is shown in Fig. 5. The model parameters of measured transducer system are listed in Table I. And then the calculated DTF is calibration of the system. Table 1. Parameter of DTF of 50kHz transducer sytem. a(1) a(2) a(3) a(4) a(5)

0.5739 -2.129 3.064 -2.023 0.5186

b(1) b(2) b(3) b(4) b(5)

-3.338 3.952 -1.520 -0.418 0.3364

4.3 Verification for Calibrating Result To verifying the upper calibrating results, another modulated signal, which waveform is shown in Fig. 6, is selected as input signal to validate the DTF. After the input signal passes through the test system, the output signal compares with the output waveform which is obtained after the input signal passes through the calibrated DTF. The contrasting graph is shown in Fig. 7. In the Fig. 7, solidline represents the output waveform after input signal passes through the test system, and dashline describes the output signal after input signal passes through the calibrated DTF. After comparison with two output signals, it could be discerned that those signals are anastomotic approximately, which shows that the calibrating result is effective.

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1

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0.6

0.8

1 -3

x 10

t /s

-1 0

1000

2000

3000

Sampling point

Fig. 6. Waveform of verified excitation signal. Fig. 7. Comparison of output signal and filtered signal.

5 Conclusion A compact method to calibrate transfer character of transducer has been presented in this paper. At first, the system for detection is verified as a LTI system. And then DTF of the transducer system is established based on system identification algorithms. At last, the transfer function is verified with other test signal, and the result shows that the calibration effect is valid. This kind of method can be used for calibrating others LTI systems. Acknowledgment. This work is supported by Natural Science Foundation of China (10872217), by Open Foundation of Key Laboratory of NDT of Ministry of Education (ZD200929003), and by the Graduate Innovation Base of Jiangxi Province.

References 1. Yuan, Y.Q.: Ultrasonic transducer. Nanjing University Press, Nanjing (1992) 2. Chandrana, C., Kharin, N.A., Nair, A.: High resolution fundamental and harmonic imaging using a MEMS fabricated ultrasonic transducer. In: 2007 IEEE Ultrasonics Symposium, New York, October 28-31, pp. 1183–1187 (2007) 3. Benenson, Z.M., Elizaroy, A.B., Yakovleva, T.V., et al.: Approach to 3-D ultrasound high resolution imaging for mechanically moving large-aperture transducer based upon Fourier transform. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 49(12), 1665–1685 (2002) 4. Parks, T., Burrus, C.S.: Digital filter design. John wiley and sons, Chichester (1987) 5. Hu, G.S.: Digital Signal Processing Theory, Algorithm and implementation. Tsinghua university press, Beijing (2003) 6. Ljung, L.: System Identification: Theory for the User, 2nd edn. Prentice-Hall, NJ (1999) 7. Woo, S.-H., Doo, H.-L.: ‘System identification of structural acoustic system using the scale correction. Mechanical Systems and Signal Processing 20(1), 389–402 (2006)

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8. Dong, X.-J., Meng, G., Peng, J.-C.: Vibration control of piezoelectric smart structures based on system identification technique: Numerical simulation and experimental study. Journal of Sound and Vibration 297(6), 680–693 (2006) 9. Zhang, C.X., Ren, J.S.: The Order Discernment of Transfer Function for Linear System. Journal of Nanjing University of Science and Technology 21(2), 106–109 (1997) 10. Li, Q.F., Shi, L.H., Liang, D.K.: Method of Compensating Transducers Based on Digital Filtering in Concrete Test. Journal of Nanjing University of Aeronautics and Astronautics 40(1), 55–59 (2008) 11. Ohara, Y., Kawashima, K.: Detection of Internal Micro Defects by Nonlinear Resonant Ultrasonic Method Using Water Immersion. Japanese Journal of Applied Physics 43(5), 3119–3120 (2004) 12. Hak-joon, K.I.M., Sung-Jin, S.O.N.G., Lester, W.S.: Modeling Ultrasonic Pulse-Echo Signals from a Flat-Bottom Hole in Immersion Testing Using a Multi-Gaussian Beam. Journal of Nondestructive Evaluation 23(1), 11–19 (2004)

A Novel Non-contact Pulse Information Detection Method Based on the Infrared Sequence Images Weibin Zhou1,2, Bin Jing1,*, Dian Qu1, Guihong Yuan1,3 Chunyan Wang1, and Haiyun Li1 1

School of Biomedical Engineering, Capital Medical University, No.10, Xitoutiao,Youanmen, Fengtai District, Beijing, China 2 School of Pharmacy & Bioengineering, Chong Qing University of Technology, No.69, Hongguang Rd, Banan District, Chongqing, China 3 School of Foundational Education, Peking University Health Science Center, Xueyuan Rd.Haidian District, Beijing, China [email protected], [email protected], [email protected], [email protected], [email protected], [email protected]

Abstract. In this paper, we propose a novel non-contact method to detect the pulse information from the radial artery using the infrared sequence images. First, a ROI (region of interest) can be located on the radial artery by the infrared imaging system because of its higher temperature than surrounding tissue. Then, a short time of AVI video of the radial artery is recorded and the area of the ROI is calculated in every frame, and a time-lapse signal is constructed using the calculated results, which can reflect the pulse information. Compared to the pulse wave from the pressure sensor, our result is acceptable. Our method reveals a novel non-contact way to obtain the pulse information and shows a significant value in the Chinese medicine. Keywords: radial artery, pulse information, infrared sequence images.

1 Introduction Pulse condition is very important in Chinese medicine, and it could give a lot of diagnostic messages. Traditionally, it depends on doctors’ experiences to feel the pulse exactly, but the experiences are relative and not objective. Many kinds of contact sensors have also been used to measure the pulse, but they may bring inconvenience to the subjects. Recently, some new pulse wave measurement methods have been proposed in [1][2][3].Infrared imaging system already has many applications in biomedical measurement. We have used it to detect physiological signals on Lumbar Vertebra and temporal artery in [4][5]. In this paper, we propose a novel objective pulse information detection method from the infrared sequence images. The paper is organized as follows: Section2 introduces the experiment setup. Section 3 introduces the processing method. Section 4 shows the discussion and conclusion. *

Co-first author.

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2 Experiment Setup First, an infrared dynamic image acquisition system is constructed. It has an infrared sensor (Fluke, NETD<90mk) and a video capture card (10moons, DV610) and a workstation (Dell T7500). Fig 1 shows the infrared dynamic image acquisition system.

Subject radiation

Infrared imaging system

Video capture card

Computer workstation

Fig. 1. The infrared dynamic image acquisition system

The experiment was conducted in a thermostatic room, and there were no heat sources such as air-conditioner, computers, and there were no persons who were not associated with the experiment in the room. The subject needed to stay at the room for a short time to get thermal equilibrium with the ambient. Then the subject was asked to sit on a chair near a desk, and lay his hand on the desk in his relaxed manner. After that, the infrared dynamic image acquisition system began to record the video of ROI. The frame rate is 7.5 frame/s, and the record lasted for 5s. In order to verify the detected pulse information, another pulse signal detection system based on pressure sensor is also constructed. It contains pressure sensors, voltage lift circuit, amplifier circuit, filter circuit, A/D (analog/digital) conversion circuit and MCU (microprocessor control unit). Fig 2 illustrates the components of the system.

Subject

Pressure sensor

Voltage lift circuit

Amplifier circuit

PC

MCU

A/D conversion

Filter circuit

Fig. 2. The pulse signal detection system

3 Method Before capturing the video, we first need to locate the ROI on the radial artery. As we know, it is easy to feel the pulse on the radial artery. When the blood passes the radial

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artery, the skin covering the radial artery will have a higher temperature than the surrounding skin and the area of the white ROI will be the largest. When the blood leaves the radial artery, the surface skin temperature will return to normal temperature the same with surrounding skin and the area of the white ROI will be the smallest. So we just need to let the infrared sensor aim to the subject wrist, and change the displayed highest temperature of the infrared sensor properly to display the position of radial artery. Fig 3 shows the anatomy of the radial artery from [6], and Fig 4 shows a captured image by our infrared dynamic image acquisition system.

Fig. 3. The anatomy of the radial artery

Then we calculate the number change of the pixels in the ROI in every frame, and the change can constitute a time-lapse signal. After that, the signal is interpolated to 25Hz using the ‘linear’ method, and the result is illustrated in Fig 5. In order to verify the time-lapse signal, we also design a pulse signal detection system to obtain the pulse wave. Using this system, we could detect the pulse signal of the same subject, the pulse signal is sampled in 200Hz and the result is showed in Fig 6.

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Fig. 4. The thermogram of subject’s left wrist, the white area is the ROI.

Fig. 5. The time-lapse signal obtained from the infrared sequence images

Fig. 6. The pulse wave from the pressure sensor

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Another experiment has also been tested that two wrists of the subjects are captured in the same image at the same time. A captured image is showed in Fig 7. Though two wrists have different temperature, two pulse waves can be obtained separately from two ROI which are illustrated in Fig 8, Fig 9. By comparison, we find that the pulse wave from the left wrist synchronizes with the other.

Fig. 7. The thermogram of two wrists captured together

Fig. 8. The pulse information from the left wrist

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Fig. 9. The pulse information from the right wrist

4 Discussion and Conclusion Compared to the pulse wave from the pressure sensor, the pulse wave from the infrared sequence images loses some detail information mainly due to the low sample frequency. There are many other factors that could affect the wave result, such as the surrounding heat sources like the experiment computer and the subject’s irregular motion in the experiment. Further research is needed to get a more precise pulse wave. In this paper, we firstly propose a novel non-contact pulse information detection method using the thermal sequence images, and demonstrate the synchronization of the pulse wave in left and right wrist radial artery. This method shows large potential in application in Chinese medicine.

Acknowledgements The work is supported by: National Natural Science Foundation of China, Grant No.30670576; Scientific Research Key Program of Beijing Municipal Commission of Education, Grant No. kz200810025011; Medicine and Clinical Cooperative Research Program of Capital Medical University. Grant No. 10JL24.

References 1. Zhang, A., Wang, L.: Information detection of pulse wave base on time-varying images. Chinese Journal of Scientific Instrument 28(5), 820–825 (2007) 2. Yoon, Y.-z., Myeong-Hwalee, Soh, H.-s.: Pulse type classification by varying contact pressure. IEEE Engineering in Medicine and Biology 11, 20–21 (2000) 3. Sorvoja, H., Kokko, V.-M., Myllyla, R., Miettnen, J.: Use of EMFi as a blood pressure pulse transducer. IEEE transactions on instrumentation and measurement 54(6), 2505–2512 (2005)

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4. Jing, B., Li, H.: An Approach to Detection of Physiological Signals on Lumbar Vertebra Based on Infrared Sequence Images. In: The 4th International Conference on Bioinformatics and Biomedical Engineering, Cheng du, China (2010) 5. Jing, B., Li, H.: A non-contact Approach to the Detection of Heart Rate Based on Infrared Sequence Images. Chinese Journal of Biomedical Engineering 29(6), 943–946 (2010) 6. http://web.sc.itc.keio.ac.jp/anatomy/Rauber-Kopsch/ web/1-50.html

A Method of Neurons Classification and Identification Xingfu Li1 and Donghuan Lv2 1

Guilin College of Aerospace Technology, Guilin Guangxi 541004, China [email protected] 2 College of Information and Communication, Guilin University of Electronic Technology, Guilin Guangxi 541004, China [email protected]

Abstract. This paper presents a new method to solve the problem of identification of neurons. Firstly, the neurons are divided into 7 categories based on their functions and geometrical characteristics. In order to get the key feature of neurons, the principal component analysis (PCA) technique was used to do the decorrelation processing with the geometrical characteristic data extracted from space geometric data of neurons. Then the BP neural network algorithm was used to classify and identify the neurons and to establish the mapping relationship between the key characteristics of neuronal geometry and the categories, thus the classification and identification of the neurons was completed. The experimental results shown that this method on neurons identification accuracy is high by 91.4%. Keywords: Neurons, BP neural network, Principal component analysis (PCA), Key geometrical feature.

1 Introduction How to distinguish different types of neurons is still no mature methods [1]. This paper ,considering the geometry of neurons only, using geometric features of neurons, combining with BP neural network and principal component analysis, presents a method of classification and identification of neurons based on their spatial patterns. Using this method neurons can be classified and identified more accurately.

2 The Morphological Classification and the Recognition Algorithm of Neurons Firstly, according to the function of neurons and their geometry characteristics divide them into 7 categories. Then according to the geometrical data of various types of neurons, using principal component analysis, extracte the key geometry characteristics of the neurons. Finally, according to the key geometry characteristics of the neurons , using BP neural network classify them. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 267–273. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2.1 Classification of Neurons It is difficult to achieve the purpose of morphological classification of neurons when name it just from one point of characteristic for the neurons, as neurons in complex and diverse forms. A good naming of neurons is that the corresponding features and structural characteristics should be knew when see the name of neurons. Namely: "See name to know the Meaning". In this paper, the neurons were divided into 7 categories by " See name to know the Meaning ", and the classification results are shown in Table 1. Table 1. Table of neurons’ categories Class NO. C1 C2 C3 C4 C5 C6 C7

Name Motor neuron Purkinje neuron Pyramidal neuron Bipolar interneuron Tripolar interneuron Multipolar interneuron Sensory neuron

2.2 Extraction of Key Morphological Characteristics of Neurons Extraction of key morphological characteristics of various types of neurons is critical for classification and identification of the neurons[2]. As more morphological characteristics are extracted, and they have a certain correlation between, some reflected information overlap. In order to ensure that the extracted geometrical characteristic to be typical , the principal component analysis (PCA) was used to do the data processing, to get the key feature of neurons. 2.2.1 Extraction of Geometric Features of Neurons Based on NeuroMorpho.Org database, extracted the geometry characteristics reflected various neurons from different perspectives. The samples for the spatial structure of 7 categories of neurons (see Table 1)shown in Figure 1. Through observation and analysis of the Figure 1 and its various corresponding spaces geometry data , chose 22 parameters such as Cell body surface area, Trunk number, Number of cell bodies to describe the geometry characteristics of the neurons. Their symbols and descriptions are shown in Table 2. In Figure 1, C1 is similar to C2 in space size, but C1's bifurcation number is less than C2’s obviously, thus the parameter Num_of_Bif has strong distinction between them; C5, C6 and C7 are alike in space size and relatively simple, but they are obviously different from the number of branches, thus the parameter Num_of_Bra can highly differentiate them; the Parameters R_gen_V70, R_gen_V80 and R_gen_V90 reflect the aggregation level of the overall structure of neurons and their root layer by layer; the Parameters R_baoti_V70, R_baoti_V80 and R_baoti_V90 reflect the aggregation level of the overall structure of neurons and cell bodies. These parameters are based on the spatial structure information of C1, C2, C3 and C4 shown in Figure 1. They can Highly differentiate the various morphological features of neurons.

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Table 2. The symbols and descriptions for neuronal morphology parameters Symbols

Descriptions

Soma_Surface

Cell body surface area

Num_of_Stems

Trunk number (AV No. 1)

num_bao_ti

Number of cell bodies

num_zhou_tu

Number of axons

num_shu_tu

Number of dendrites

num_jian_duan_shu_tu

Number of dendritic tip

Num_of_Bif

Number of bifurcation

Num_of_Bra

Number of branch

Mean_Diameter

Average radius of cell bodies, axons, dendrites and dendritic tip

Length

Total length of each branch

Surface

Total surface area of cell bodies, axons, dendrites and dendritic tip

Volume

Mean_L_baoti

Total volume of cell bodies, axons, dendrites and dendritic tip Euclidean distance (The total distance from each leaf node to the root node) Average distance from each leaf node to the cell body

Mean_L_gen

Average distance from each leaf node to the root node

Branch_Order

Bifurcation series Sphere radius that takes root (AV No. 1) as the center and contains 70% of the total volume Sphere radius that takes root as the center and contains 80% of the total volume Sphere radius that takes root as the center and contains 90% of the total volume Sphere radius that takes cell body(AV No. 1) as the center and contains 70% of the total volume Sphere radius that takes cell body as the center and contains 80% of the total volume Sphere radius that takes cell body as the center and contains 90% of the total volume

Eu_Distance

R_gen_V70 R_gen_V80 R_gen_V90 R_baoti_V70 R_baoti_V80 R_baoti_V90

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Fig. 1. The geometric structure of neurons in the sample Table 3. Principal Component Cumulative probability table Cumulative number

Cumulative probability

Cumulative number

Cumulative probability

1 2 3 4 5 6 7 8 9 10

0.408 0.638 0.777 0.844 0.891 0.935 0.957 0.970 0.981 0.989

11 12 13 14 15 16 17 18 19 20

0.993 0.995 0.996 0.998 0.999 0.999 0.999 1 1 1

2.2.2 Extracting the Key Geometrical Characteristic of Neurons with Principal Component Analysis Many geometric morphological characteristics are extracted from 2.2.1.And they usually have a certain correlation. It means that the information of some characteristics reflects a certain degree of overlap. Then the data reduction and the elimination of the correlation between the data is necessary. Principal component analysis is an effective method[3-6]. The concrete achieving Steps are stated as follows:

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STEP1: In NeuroMorpho.Org database, select a certain number of samples from various types of neurons act as the experimental research data.Then,extract geometry characteristic data of neurons in the all experimental data; STEP2: Normalizing the morphological characteristic data extracted from the STEP1, in order to eliminate the dimension; STEP3: Using principal component analysis to extract the key geometrical characteristic of neurons with the normalized data from STEP2. Table 3 shows the principal component cumulative probability of the morphological characteristic data of neurons .In this paper , the former 14 principal components are selected as the key geometrical characteristic of neurons, in order to retain neuronal geometry information as much as possible. 2.3 Neurons Classification and Recognition System Based on BP Neural Network In theory, a three-layer BP network can approximate any nonlinear mapping [7-12].Therefore ,this paper uses BP network to build the classification and recognition system. Select the part of the samples from various neuronal experimental data as a standard training set, and the remaining samples as test sample set. Calculating the key geometric morphological characteristics in all samples and establishing the mapping relationship between the key geometric morphological characteristics and the neurons categories. Then complete to determine the category of the test samples. The concrete achieving Steps are stated as follows: STEP1: Select the part of the samples from various neuronal experimental data as a standard training set. Every sample consists of input information and the output results expect to get. Among them, input information of each neuron samples is the key geometric morphological characteristics, Output is the coding of the neurons’ categories. The specific coding is shown in table 4. Table 4. The coding of neurons’ categories

Category C1 C2 C3 C4 C5 C6 C7

1 0 0 0 0 0 0

0 1 0 0 0 0 0

0 0 1 0 0 0 0

Coding 0 0 0 1 0 0 0

0 0 0 0 1 0 0

0 0 0 0 0 1 0

0 0 0 0 0 0 1

STEP2: Select a sample from the training sample ,and input the information into the network STEP3: Calculate the output of each layer node treated by neurons. STEP4: Calculate the error between actual output and expected output , If the error is acceptable ,then quit; if not, Continue STEP5.

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STEP5: Do the calculating work from the output layer back to the first hidden layer, and adjust the neural network connection weights and thresholds, in accordance with the principles of making progress towards to reduce error. STEP6: Repeat from STEP3 to STEP5 for each sample of the training sample, until the error of entire training sample to be accepted. STEP7: Input the samples of neurons to be tested to the trained system, predict the type of the sample by the output of the system.

3 The Analysis of Experiment and Results In the all kinds of neurons, selected 120 samples as the experimental study data, and choose 100 samples as standard training samples, the rest of the sample data as testing samples. Selected the key geometrical feature in all samples, and built classification and recognition system based on the BP neural network, then completed the identification of categories of the samples belong to. According to the following two principles to create the BP network model[13]: 1: Three layer BP networks can effectively solved the common problem of pattern recognition; 2: In the three layer BP networks, the numbers of hidden nodes L and input nodes m will satisfy approximately the next relationship: L = 2m + 1 .The network's input vector range is [0,1] , the transfer function of hidden layer node using s-type tangent function , the output layer node transfer function using s-type logarithmic function . Measure the performance of the system by the average of recognition rate. The recognition rate of Sample i is:

p(i ) =

p(i, C ) , N = 1, 2,..., 7 N ∑ p(i, Ck ) k =1

(1)

Among them, p (i, C ) represents the output probability of test samples’ category belongs to known types; p(i, Ck ) represents the output probability of test samples’ type belongs to the category Ck . In the experiments, training times is 1000, training target is 0.001, learning rate is 0.1, when the recognition rate of test sample exceed 0.9, we would deem that was an accurate identification. The average of Recognition rate reached 91.4%.

4 Conclusion The method presented in the paper combined the geometrical characteristic of neurons and their functions , could be used to do the naming of neurons by " See name to know the Meaning ", achieved the good effect on the classification and identification of neurons .that would have some reference value to accelerate the understanding of the human brain . Nevertheless the extracting of the geometrical feature of neurons

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based on the morphological characteristic samples of various types of neurons in the NeuroMorpho.Org database primarily ,observed the sample pictures and analyzed the corresponding geometric data. At the same time, the actual morphological characteristics of neurons was much more than that, the sample data selected in the experiment was not comprehensive enough. But we estimated that the effectiveness of the method would not be affect.

References 1. Bedenbaugh, P., Sarko, D.K., et al.: Prosody-Preserving Voice Transformation to Evaluate Brain Representations of Speech Sounds. IEEE Transactions Audio, Speech, and Language Processing (2010) 2. Ascoli, G., Goldin, R.F.: Coordinate systems for dendritic spines: a somatocentric approach. J. Complexity 2(4), 40–48 (1997) 3. lei, Y., shu-qin, L.: Principal component analysis in the application of wheat stripe rust prediction. J. Computer engineering and design 31(2), 459–461 (2010) (in Chinese) 4. fa-jun, Y., yuan-li, Z.: Principal component analysis combined with perceptron application in medical spectral classification. J. Spectroscopy and spectral analysis 10, 2396–2400 (2008) (in Chinese) 5. Panagakis, Y., Kotropoulos, C., Arce, G.R.: Non-Negative Multilinear Principal Component Analysis of Auditory Temporal Modulations for Music Genre Classification. IEEE Transactions Audio, Speech, and Language Processing, 576–588 (2010) 6. Han, X.: Nonnegative Principal Component Analysis for Cancer Molecular Pattern Discovery. IEEE/ACM Transactions Computational Biology and Bioinformatics, 537–549 (2010) 7. Zou, w.-f., Zhu, z.-d.: The color images target recognition method of space and distance conversion. Journal of Nanjing University of Aeronautics 39(5), 601–606 (2007) (in Chinese) 8. Berger, T.W., Dong, S., et al.: The Neurobiological Basis of Cognition: Identification by Multi-Input, Multioutput Nonlinear Dynamic Modeling. Proceedings of the IEEE (2010) 9. Tomas, P., Sousa, L.A.: A Feature Selection Algorithm for the Regularization of Neuron Models. IEEE Transactions Instrumentation and Measurement (2009) 10. Liu, y.-j., Zhu, j.-y., Zeng, j.: The improvment of the BP neural network in engine performance trend analysis and the application of fault diagnosis. Journal of the institute of technology of nanjing university (natural science edition) 34(1), 24–29 (2010) (in Chinese) 11. Zong-Ben, X., Rui, Z., Wen-Feng, J.: When Does Online BP Training Converge? IEEE Transactions Neural Networks (2009) 12. Muzhou, H., Xuli, H.: Constructive Approximation to Multivariate Function by Decay RBF Neural Network. IEEE Transactions Neural Networks (2010) 13. Wang, j.-g., Ding, y.: Long distance pipeline across structure damage test simulation and recognition. Journal of tianjin university 43(3), 229–233 (2010)

A Broadband Image-Rejection Sub-harmonically Pumped Mixer MMIC for Ka-band Applications Fang-Yue Ma, Yang-Yang Peng, Xiao-Ying Wang, and Wen-Quan Sui* Department of Nano Electronics, Zhejiang-California International Nanosystem Institute, Zhejiang University, Hangzhou, People’s Republic of China [email protected]

Abstract. A broadband image-rejection SHP (sub-harmonically pumped) mixer in Ka-band is proposed in this paper. It is composed of two SHP mixers, an LO in-phase power divider, and an RF quadrature-phase divider/combiner. To get smaller chip size, quasi-lumped components are utilized in the design. The simulated conversion loss of the chip is 11-12.5dB, image rejection is better than 20dB in most of the Ka-band, and second harmonic component of LO (2LO) is well suppressed at RF/IF port. The compact chip size, 1.4x1.5mm2 using a 0.15μm GaAs pHEMT technology, makes it useful both as a single device or be an integrated part of a transceiver chip.

Keywords: Sub-harmonically pumped mixer, wide-band mixer, monolithic microwave integrated circuit (MMIC), image-reject, converter.

1 Introduction In recent years, the realization of low cost and high data transmission rates in consumer and professional interactive media system becomes increasingly important. The development of systems and components operating in Ka-band gives millimeterwave communication great significance. One important component of these systems is the mixer, since it is used in both the receiver and transmitter channels. With a number of advantages, such as no DC power supply requirement, superior suppression to all even harmonics when the diode pair are well-matched, and lower requirement for local oscillator, SHP mixer using an ADPA (anti-parallel pair of diodes) become the ultimate solution at high frequency [1]-[4]. To date, few papers are reported on image-rejection SHP mixers working in the whole Ka-band [5], [6], even though expanding bandwidth has been always of interest to both MMIC research and practical applications. In this paper, the broadband image-rejection SHP mixer is implemented using WIN semiconductor’s 0.15μm GaAs pHEMT (pseudomorphic high electron mobility transistor) MMIC technology on a 100μm substrate thickness. An image-rejection SHP mixer design that employs Wilkinson divider as an LO divider and Lange coupler as an RF divider/combiner is described in details. The designed MMIC mixer chip size is as compact as 1.4 x 1.5 mm2. *

Corresponding author.

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2 Configuration of the Broadband Image-Rejection SHP Mixer Fig. 1 shows the structure and topology of the broadband image-rejection SHP mixer. It consists of two SHP mixers, an LO in-phase power divider, and an RF quadraturephase divider/combiner. In practical use, single-sideband and image-rejection are desired in mixer design. Conventional mixers generally occupy large areas and their performances are affected by extra image-rejection filters. On the other hand, for the proposed image-rejection SHP mixer, the image-rejection filter is not needed, therefore, its good performance could be maintained for a wide bandwidth. For down-conversion, the in-phase LO signals and quadrature phase RF signals are fed in separately to two SHP mixers, namely SHP MIX1 and SHP MIX2 in Fig. 1. The frequencies of the main generating components are described as (2fLO±fRF). As shown in Fig. 1, down-conversion USB signals (2fLO+fRF) from SHP MIX1 delay π/2 radians relative to SHP MIX2 while LSB signals (2fLO-fRF) from SHP MIX1 advance π/2 radians relative to SHP MIX2. Signals are combined using off-chip IF quadrature combiner. Therefore, the desired down-converted in-phase LSB signal appears at the output while the out-of-phase USB signal is canceled. The above described circuit also works as an image-rejection mixer. The difference between desired and image frequencies is twice the IF. Therefore, phase after conversion at one of two signals is different from that at the other, so image-rejection signals are filtered while the desired signals are being kept. It can also potentially have similar performance in up-conversion.

π /2

S /2

S /2

S / 2

S /2

S

S /2

S /2

S / 2

S /2

S

Fig. 1. Structure of the broadband image-rejection SHP mixer

3 Design of the MMIC Circuit 3.1 Design of the SHP Mixer The schematic of the SHP mixer is shown in Fig. 2. The ADPA is utilized for signal mixing. The low-pass filter feeding the IF is used to isolate RF and IF ports, as RF high-pass filter does. Similarly, to isolate the RF/IF ports from the LO port, λLO/4

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open stub and λLO/4 short stub are needed. The λLO/4 open stub is simultaneously electrically “short” at fLO, and electrically “open” at fRF and fIF, while the λLO/4 short stub is simultaneously “open” at fLO, and grounded at RF and IF frequency. However, the λLO/4 transmission line occupy a large area since it is a quarter of the wavelength of LO frequency. To make the stubs compact, quasi-lumped stub is used to set its center frequency at LO signal [7]. The layout of the SHP mixer is shown in Fig. 3. 3.2 Design of the Broadband Image-Rejection SHP Mixer The broadband image-rejection SHP mixer consists of two SHP mixers, an LO inphase power divider and an RF quadrature-phase divider/combiner. Wilkinson divider is a most likely candidate for in-phase divider because it divides signals accurately in amplitude and phase, has lower insertion-loss, and can be applied in wide bandwidth. Furthermore, it can achieve multi-way distribution. Because the differences between unit mixers are negligible and Wilkinson divider outputs well-balanced signals for the wide band, the major imbalances in the proposed image-rejection SHP mixer come from the RF divider/combiner. Conventional lumped-element quadrature hybrid is often used as an RF divider/combiner since it occupies smaller area [8]-[10]. Therefore, some approaches have been proposed to minimize the hybrid’s size, but, when size is reduced, bandwidth is decreased at the same time. By contrast, Lange coupler outputs complete quadrature signals with well-balanced amplitude in wide frequency band. Therefore, Lange coupler has been chosen as RF divider/combiner for our MMIC design. The performance of the proposed MMIC mixer design has been simulated using ADS (Advanced Design System) and SONNET software. The EM (electromagnetic) simulations of the circuit layouts are done with ADS, as the layout of the chip is shown in Fig. 4.

Fig. 2. Schematic of the SHP mixer

Fig. 3. Layout of the SHP mixer

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Fig. 4. Layout of the chip

4 Result of the Broadband Image-Rejection SHP Mixer The conversion gain versus RF frequency of the unit SHP mixer for down-conversion is shown in Fig. 5. IF is fixed at 1GHz and the input LO power level is 9dBm. For LSB, conversion gain of -12±1dB is obtained for RF frequency from 26 to 40GHz. Fig. 6 shows the dependence of the conversion gain characteristic on LO input power of the image-rejection mixer. The frequency is fixed at 1GHz for IF and 33GHz for RF. The maximum conversion gain of -8.6dB occurs at the LO power of 6dBm. The best LO power varies with changing RF frequency from 6dBm to 9dBm.

Fig. 5. Conversion gain vs. RF frequency of the unit SHP mixer

Fig. 6. Conversion gain vs. LO power of the unit SHP mixer

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Fig. 7 shows the frequency response of the proposed image-rejection SHP mixer for down-conversion. IF is fixed at 1GHz and the input LO power level is 12dBm. The maximum conversion loss for the RF frequency of 27-39GHz is between 1112.5dB due to port mismatch losses. The isolations of LO and RF ports to IF port is above 21dB as shown in Fig. 8 and is better than 30dB in most of the Ka-band. Fig. 9 presents the isolation of LO port to RF port and the second harmonic component of the LO detected at RF port. The image rejection is better than 18dBc in the whole Kaband as given in Fig. 10. Input/output characteristics is shown Fig. 11 with fixed RF at 33GHz and fixed IF at 1GHz. The 1dB compression occurs for the RF input power of 6dBm. The P1dB RF input power for the whole Ka-band varies from 5 to 7dBm as shown in the inset of Fig.11.

Fig. 7. Conversion gain vs. RF frequency of the image-rejection SHP mixer

Fig. 8. Port isolations vs. RF frequency of the image-rejection SHP mixer

Fig. 9. Port isolations vs. RF frequency of the image-rejection SHP mixer

Fig. 10. Image-rejection vs. RF frequency of the image-rejection SHP mixer

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Fig. 11. IF output power vs. RF input power of the image-rejection mixer (Inset is P1dB RF input power vs. RF frequency)

For up-conversion, frequency response of the proposed image-rejection SHP mixer is shown in Fig. 12. RF is fixed at 28GHz and input LO power level is 12dBm. Conversion gain of -12±1dB is obtained from DC to 10GHz.

Fig. 12. Conversion gain vs. IF frequency of the image-rejection SHP mixer

5 Conclusion A compact size and wide band image-rejection SHP MMIC mixer at Ka-band has been presented. The die size of the chip is only 1.4x1.5mm2. The mixer exhibits an 11-12.5dB conversion loss, high isolation over the whole Ka-band band, image rejection above 18 dBc and P1dB RF input power from 5 to 7dBm for the whole Kaband. This image-rejection SHP mixer is suitable for the prevalent Ka-band receiver and transmitter module applications.

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Acknowledgement This work is partially supported by the Natural Science Foundation under grant number 60971058 and Natural Science Foundation of ZheJiang Province under grant number R107481.

References 1. Cohn, M., Degenford, J.E., Newman, B.A.: Harmonic Mixing with an Antiparallel Diode Pair. IEEE Trans. Microwave Theory Tech. MTT-23, 667–673 (1975) 2. Roberts, M.J., Iezekiel, S., Snowden, C.M.: A W-Band Self-Oscillating Subharmonic MMIC Mixer. IEEE Transactions on Micraowave Theory and Techniques 46(12) (December 1998) 3. Lin, C.-H., Lai, Y.-A., Chiu, J.-C., Wang, Y.-H.: A 23–37 GHz Miniature MMIC Subharmonic Mixer. IEEE Microwave and Wireless Components Letters 17(9) (September 2007) 4. Chen, W.-C., Chen, S.-Y., Tsai, J.-H., Huang, T.-W., Wang, H.: A 38-48-GHz Miniature MMIC Subharmonic Mixer. In: Gallium Arsenide and Other Semiconductor Application Symposium, pp. 3–4 (2005) 5. Singh, P.K., Basu, S., Liao, K.-H., Wang, Y.-H.: Highly Integrated Ka-Band SubHarmonic Image-Reject Down-Converter MMIC. IEEE Microwave and Wireless Components Letters 19(5) (2009) 6. Mahon, S.J., Convert, E., Beasly, P.T., Bessemoulin, A., Dadello, A., Costantini, A., Fattorini, A., McCulloch, M.G., Lawrence, B.G., Harvey, J.T.: Broadband Integrated Millimeter-Wave Up- and Down-Converter GaAs MMICs. IEEE Transactions on Microwave Theory and Techniques 54(5) (2006) 7. Okazaki, H., Yamaguchi, Y.: Wide-Band SSB Subharmonically Pumped Mixer MMIC. IEEE Transactions on Microwave Theory and Techniques 45(12) (1997) 8. Ozis, D., Paramesh, J., Allstot, D.J.: Analysis and design of lumped-element quadrature couplers with lossy passive elements. In: 2006 IEEE Int. Conf. Circuits and Systems in Conf. Rec. pp. 2317–2320 (2006) 9. Chiang, Y.C., Chen, C.Y.: Design of a wide-band lumped-element 3-dB quadrature coupler. IEEE Trans. Microwave Theory and Techniques 49, 179–476 (2001) 10. Hou, J.-A., Wang, Y.-H.: A Ka Band Balanced Third LO-Harmonic Mixer Using a Lumped-Elements Quadrature Hybrid. IEEE Microwave and Wireless Components Letters 18(6) (June 2008)

Study of Phase-Shift Laser Ranging on Travelling Crane Anti-collision System Bisheng Cao, Zhou Wan, Zhongguo Jing, and Xin Xiong Bisheng Cao, Kunming University of Science and Technology, 650093 Yunnan, China [email protected]

Abstract. Combining the research situation of travelling crane anti-collision system, this paper presents a way of phase-shift laser ranging using sine amplitude modulation to solve the measurement precision deficiency of travelling crane anti-collision system. In order to enhance the precision of measuring result, system uses independent inner and external optical receiving circuit, eliminating the additional phase shift caused by receiving circuit time delay. Keywords: laser ranging; phase-shift; travelling crane anti-collision.

1 Introduction To avoid travelling cranes traffic collision with each other working simultaneously on the same track, some research should be done for travelling crane anti-collision system, especially the high-precision measurement of the distance between travelling cranes. At present the tools used to measure the distance between travelling cranes mostly are infrared, ultrasonic, etc. Since travelling crane is running in an execrable and interferefering environment, the measurement reliability of space between cranes is poor. Fortunately, laser which has been widely used owns the advantages of good direction, high energy, anti-interference abilities, so a method of laser ranging is proposed. There are three main methods of laser ranging: pulsed laser ranging: a direct measurement of the time interval between transmitting beam and receiving beam; phase-shift laser ranging: a measurement of the time interval by testing a continuous amplitude modulated wave’s phase difference between emission beam and reflected beam; frequency modulated continuous wave laser ranging: a measurement of the time interval by testing a frequency modulated wave’s frequency difference between transmitter beam and receiver beam[1]. It has been proved that phase method has advantages of higher precision (up to millimeter degree), simpler in frame, and high practical value. Besides, if an inner optical receiver circuit which is the same as external optical receiver circuit is added, the additional phase shift will be eliminated which can enhance the precision of measuring result[2]. Therefore, a method of phase-shift laser ranging with an inner optical receiver circuit is adopted.

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2 Analysis of Phase-Shift Laser Ranging Principle Phase-shift laser ranging is a way of which can obtain the distance (short for S) indirectly by testing a continuous amplitude modulated wave’s phase difference between emission light beam and reflected light beam. When S< λ /2( λ is the wavelength of laser), the general formula is as (1), and the phase difference can be seen in Fig. 1 [3].

S=

c Δϕ ( ). 2 2πf

(1)

where c is the velocity of light, Δϕ is the phase difference, and f is the frequency of sine light wave.

Fig. 1. Phase difference during light emission and return

3 System Design The hardware of phase-shift laser ranging is composed of frequency generator, laser emission, laser receivers of inner and external optical route, signal filter and amplifier, digital phase difference examination and the data processing. System structure is shown in Fig. 2.

Frequency generator

Laser emission

Filter and amplifier

Inner optical laser receiver Lens

Filter and amplifier

Inner optical laser receiver

Digital inspecting phase difference and data processing

Fig. 2. Structure chart of phase-shift laser ranging system

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3.1 Frequency Generator Since the measurement range is 0~ 35000mm, and accuracy is 20mm, 5MHz sinusoidal modulated signal is needed to be generated. Therefore, MAX038 (a highfrequency waveform generator) is selected, and its output frequency and duty cycle can be adjusted to current, voltage or resistance [4].

(a)

(b)

Fig. 3. (a) Frequency Generator chart and (b) laser emission chart

Fig. 3(a) shows the MAX038 chip connection circuit which can generate 5MHz sine wave. Port A1 is needed to be set to high-level. The generated frequency is determined by (2), and the frequency value of sine wave can be different if R2 and C2 are regulated [4]. Sine wave generated by MAX038 outputs by port 19 whose load capacity is very strong. The value of C2 should be 50pF, and the value of R2 should be 20K. fo=5/ (R2C2) .

(2)

3.2 Laser Emission LASER adopted is SLD1133VL which is small, light weight, low power consumption. To achieve phase-shift laser ranging method, we should add a modulated signal to the light, so the intensity of light varies with the modulated changing signal. For a semiconductor laser, when threshold current is less than the injection current, the laser is in stimulated emission state, eradiating laser. Therefore, when modulating the SLD1133VL, an AC signal must be added to the 5 MHZ DC signal to make sure the threshold current lower than the injection current. Laser modulation transmitter circuit is shown as Fig. 3(b).In Fig. 3(b), the input signal by port C provides drive for transistor Q1, and transistor Q2 provides the static bias current which is important for SLD1133VL’s stimulated emission. 3.3 Laser Receiver of Inner and External Optical Route Since laser receivers of inner and external optical route are the same, only the external optical receiver circuit is given.

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3.3.1 Photoelectric Conversion Circuit The photosensitive receiving device adopted is avalanche photodiode (APD) which has Converters with internal photoelectric gain. Before current signal enters amplifier, APD will amplifies that current signal and gains a larger photocurrent signal.therefore that means APD will not cause extra noise. When APD is in a high bias voltage condition, a small temperature change could cause large variation. Therefore, in order to keep the gain constant temperature invariant, a temperature compensation circuit must be added, making the APD bias voltage also change with temperature change. The formula of APD’s best gain avalanche gain constant (Mopt) can be expressed by the following formula[4]. 1

M opt = (

4 KT ) 2+ x . q( I P + I db ) xRL

(3)

where K, q, RL are constants, x is surplus noise coefficient, Ip is Not multiplied the primary current, Idb is Surface leakage current, and T is short for temperature. In order to maximize the receiving system’s Signal Noise Ratio (SNR), APD bias voltage must be designed in the temperature compensation circuit. Therefore, even at various temperatures, APD can work in best gain avalanche gain constant. To this end, selection of a temperature sensor chip MAX6613 and an operational amplifier LM6152 are selected in the temperature compensation circuit design. MAX6613 which is a low power, providing precise analog output temperature sensor provides an analog voltage output proportional to the temperature. The relationship betweenMAX6613's output voltage and temperature is shown as formula (4) [4]. Vout=0.40+0.019T=B+AT .

(4)

where T is short for temperature, and B is a constant. MAX6613’s external circuit is shown in Fig. 4(a).

(a)

(b)

Fig. 4. (a) MAX6613’s external circuit and (b) filter amplifier Circuit

3.3.2 High-Frequency Filter Amplifier Circuit After the photoelectric detection circuit receives the modulated Laser, there is a lot of noise in receiving signal, and the signal is weak. Therefore, to insure the following

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circuit working well and truly, amplifying and filtering is needed. High-frequency filter amplifier circuit is shown as Fig. 4(b). 3.3.3 Digital Inspecting Phase Processing Circuit Design In the digital Inspecting phase circuit, laser receivers of inner and external optical route are separately input to a FPGA chip, and the Schematic diagram is shown as Fig. 5[2].

Fig. 5. Digital inspecting phase processing circuit

Because the structure of inner and external optical receivers circuit are basically the same, so the additional phase shift is eliminated in the inspecting phase pulse. Inspecting phase method used here is doing an integral to integer cycle phase pulse, and then getting the phase information of inspecting phase pulse according to the value of integral voltage along with pulse width of information. Author in [4] shows the value of phase difference ( Δϕ ) as (5).

Δϕ =

NVo 2πNT1 = = K 2T1 . NT /( RC • 2π ) NT

(5)

，

In (5), K2 is a constant T1 is the pulse width of inspecting phase signal. This method can relate phase information and voltage value together, and that has high stability. Therefore the distance value of travelling cranes can be easily calculated by (1) and (5).

4 The Test Results and Analysis To test whether this new phase-shift laser ranging has superiority on accuracy, it is compared with traditional phase-shift laser ranging which has no inner optical receiving circuit. After tests in the real environment, partial data received is shown separately in Table 1 and Table 2 (S is short for distance, and A is short for accuracy).

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Data1/mm 10018.93 17017.61 25012.25 30009.11 35000.71

Data2/mm 10018.09 17017.77 25012.08 30009.23 35000.87

Data3/mm 10018.13 17017.61 25012.33 30009.98 35000.99

Data4/mm 10018.22 17017.61 25012.26 30009.83 35000.39

Data5/mm 10018.91 17017.66 25012.25 30009.11 35000.66

A/mm 18.456 17.652 11.234 9.452 0.798

As can be seen from Table 1, the new phase laser ranging’s measurement accuracy is <18.456mm, more than the project’s required accuracy (20mm).Besides, as the distance becomes large, the accuracy becomes higher. Table 2. Partial data received by traditional phase-shift laser ranging. S/mm 10000 17000 25000 30000 35000

Data1/mm 10058.33 17047.64 25042.26 30041.12 35038.70

Data2/mm 10058.59 17047.47 25042.07 30041.22 35038.77

Data3/mm 10058.53 17047.63 25042.34 30041.99 35038.90

Data4/mm 10058.25 17047.51 25042.25 30041.82 35038.89

Data5/mm 10058.92 17047.66 25042.26 30041.12 35038.68

A/mm 58.524 47.582 42.236 41.454 38.788

In table 2, it is easy to seen that the traditional phase laser ranging’s measurement accuracy is >38.788mm which does not meet the system requirements (20mm). The accuracy of measuring result must be enhanced. That’s why an inner optical receiving circuit is added to eliminating the additional phase shift caused by receiving circuit time delay. Similar to the result of Table 1, as the distance becomes large, the accuracy becomes higher. Through the data and analysis above, we can easily conclude that the system achieved system requirements (meeting the working range of 0 ~ 35000mm and measurement accuracy of less than 20mm).

References 1. Zhang, T., Zhang, K.-s.: Study of phase-shift laser measuring based on Matlab. J. Laser & Infrared 40(1), 22–27 (2010) 2. Zhang, Z., Zhang, J., Zhu, D., Shao, Z.: Research on a Fast and Accurate Phase Shift Laser Range Finder. J. Chinese Journal of Science Instrument (supplement) 26(8), 111–112 (2010) 3. Jin, N., Wang, W., Weng, J.-f., Zhang, Z.-y.: The Design of the Circuit System Used for Phase Laser Range Finder. J. Journal of Optoelectronics·Laser 12(8), 864–867 (2001) 4. Cheng, Y., Yang, J.: Study on emission system of phase-laser range finder. Journal of Changchun University of Science and Technology (Natural Science Edition) 33(1), 29–31 (2010)

Bearing Condition Monitoring and Fault Diagnosis of a Wind Turbine Using Parameter Free Detection Shenggang Yang1, Xiaoli Li1, and Ming Liang2 1 Key Lab of Industrial Computer Control Engineering of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China [email protected] 2 Department of Mechanical Engineering University of Ottawa, 770 King Edward Avenue, Ottawa, Ontario, Canada [email protected]

Abstract. The most frequently used component of a wind turbine is bearing which failure could lead to malfunction and ultimately complete stall in a mission-critical equipment. Hence, bearing fault detection is an imperative part of preventive maintenance procedures in a wind turbine. This paper presents a free parameter approach to implement bearing condition monitoring and fault diagnosis. This approach extracts the amplitude and frequency modulations of faulty vibration signals that measured from a wind turbine system. The amplitude demodulation is inherent, and the fault frequency will be detected from the spectrums of the signal. The effectiveness of this approach has been validated by using simulated signal and experimental evaluation. Keywords: Bearing; Condition Monitoring; Fault Detection; Wind turbine.

1 Introduction The benefits of monitor condition based maintenance such as the reduced down time, repair costs and avoid catastrophic equipments failures which have fueled extensive research activities in wind turbine condition monitoring and fault detection [1]. Most failure wind turbine components due to the heavy load or their harsh operating conditions [2]. In most wind turbine fault detection studies, such fault characteristic frequencies [3] and the associate harmonics are sought in the spectrum of the original or enhanced vibration signal. High Frequency Resonance (HFR) technique and its variants are often used by industry to detect bearing faults [5]. However, the design of a suitable filter requires proper parameters, namely center frequency and bandwidth. Most studies have focused on the development of efficient and robust approaches to estimate proper center frequency and optimum bandwidth of the filter. Qiu et al [6] used minimal Shannon entropy to choose bandwidth and applied a periodicity detection method for center frequency selection. Lin et al [7] selected these parameters based on a kurtosis maximization criterion. Bozchalooi and Liang [8] on the other hand applied a smoothness index to guide the selection of such parameters. Therefore, a parameter free approach will help to mitigate this problem. As such, this M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 289–294. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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study is directed towards the development of wind turbine bearing fault detection with parameter free to implement steps. Consequently, filter parameter selection becomes unnecessary and all the related trouble will be avoided. Furthermore, computational simplicity of the proposed approach makes it suited for industrial applications, particularly realtime processing wind turbine bearing fault diagnosis.

2 Bearing Fault Detection In this section, we intend to detect bearing faults by examining the amplitude demodulated version of bearing vibration with white noise mixture. To illustrate the possibility of this approach, we first examine the spectral density of the amplitude demodulated version of simulated noisy signal, then propose a approach to increase the Signal to Interference Ratio (SIR) of the measured vibration signal. 2.1 Faulty Wind Turbine Bearing Vibrations with White Noise The signal of Figure 1(a) illustrates a series of fault generated impulses simulated at 40 KHz sampling rate with the resonance frequency of 10 KHz and fault characteristic frequency of 77Hz. The spectrum of the signal is shown in figure 1(b). As can be seen from the spectrum, the energy of the signal widely spreads almost over the entire frequency range.

Fig. 1. (a) Faulty bearing vibrations with a fault characteristic frequency of 77 Hz and a resonance frequency of 10 KHz, and (b) the corresponding frequency domain representation

2.2 Maximum Likelihood Dection The Maximum likelihood (ML) function for the signal-noise mixture can be written as follows: N

ln L( z ) = −( N + 1) ln σ 2 + ∑ ln z (n) n=0

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M 1 ⎛ 2 ⎞ ⎜ z ( n) + ∑ A2 (exp( −2 β (nTs − mTp + ϕ ) )u (nTs − mTp + ϕ ) ⎟ 2 n = 0 2σ ⎝ m=0 ⎠ N

−∑

⎛ ⎛ ⎜ ⎜ z ( n) ⎜ ⎜ + ∑ ln ⎜ I 0 ⎜ n= 0 ⎜ ⎜ ⎜ ⎜ ⎝ ⎝ N

M

∑A

m=0

2

⎞⎞ exp (− 2 β (nTs − mTp + ϕ ) )u ( −2 β (nTs − mTp + ϕ ) ⎟ ⎟ ⎟⎟ ⎟⎟ σ2 ⎟⎟ ⎟⎟ ⎠⎠

(1)

Where L(z) is the maximum likelihood function for the signal-noise mixture. The amplitude of fault generated impulses can be obtained by maximizing the above log likelihood function. Then, the existence of fault can be concluded when the ML estimate of the amplitude A of significant value is observed. For cases when prior knowledge of the background noise standard deviation σ is available, the maximization of the log likelihood function should be performed with respect to A, β and φ, otherwise with respect to A, β, φ and σ. Since there are no analytical solutions available for the above maximization problem, optimization methods have to be adopted. 2.3 Increase the Signal to Interference Ratio of the Vibration Signal Which the fault bearing generated impulses excite a resonance in the system, then a very important feature of this phenomenon is that the frequency of resonance is usually much higher than the frequencies of vibration. In the following, we utilize the high frequency characteristic of the excited resonance and propose a approach to increase the signal to interference ratio of the measured vibration signal. Consider the signal model which mixed with a single harmonic as interference has shown below: M

r (t ) = v (t ) + x (t ) = a × cos ω v t + ∑ A × exp( − β (t − mTP + ϕ )) cos ω r (t − mTP )u (t − mTP + ϕ ) m =0

(2)

Where ωv is the frequency of the interfering component and ωr is the excited resonance frequency. We compare the SIR of the signal model of (1) with the SIR of a differentiated version of the same signal. We first derive the SIR of the original signal. For interference power we can write: Pv =

1 T (a cos ωvt ) 2 dt T ∫0

(3)

By differentiating (1) with respect to time we have: dr (t ) dv(t ) dx(t ) = + dt dt dt

(4)

Similarly, we can derive the SIR of the differentiated signal given in (5) by calculating the power of differentiated interference and the power of differentiated fault signal: Pdv = dt

1 T (− ωv a sin ωv )2 dt T ∫0

(5)

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As before, for a single impulse we can write: Pdx = dt

1 TP

TP

∫

0

(− βA exp(− βt ) cos ω r t − ω r A exp(− β t ) sin ω r t ) 2 dt

(6)

3 Simulation Result In this section, we evaluate the proposed method on simulated signal mixture. The signal mixture is obtained by adding white Gaussian noise to the simulated faulty bearing signal of figure 1(a). The resulting signal-noise mixture has a SNR of -6 db and is shown in figure 2(a).

Fig. 2. (a) Simulated signal of figure 1(a) mixed with white Gaussian noise, and(b) signal of part (a) mixed with vibration interferences

Then we further add to the signal-noise mixture vibration interferences including 10 harmonics of gear meshing frequency of 220 Hz and 10 harmonics of shaft rotational frequency of 22 Hz. The resulting signal-noise-vibration interference mixture is illustrated in figure 24(b). The spectrum and the envelope spectrum of this mixture are shown in figures 3(a) and 3(b) respectively. As we can see from the close

Fig. 3. (a) Spectrum, (b) envelope spectrum of the simulated signal of figure 2(b).

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up views, neither the fault characteristic frequency nor any of the associated harmonics can be detected from such spectral information.

4 Experimental Evaluation The raw data and its frequency domain representation are plotted in figures 4(a) and (b), respectively. Figure 4(b) shows that the spectrum of the measured signal is dominated by the shaft rotational frequency (22.9 Hz) and the harmonics as well as gearbox meshing frequency (165.7 Hz) and its harmonics.

Fig. 4. (a) A portion of the measured signal, and(b) spectrum of the signal measured from experiment.

To illustrate the efficiency of the proposed method, the envelope spectrum of the twice differenced signal is compared with the envelope spectrum of the bandpass filtered result. A bandpass filter is again designed using the Parks-McClellan method and is carefully adjusted so that the filter completely covers the high SNR frequency band of the measured signal. Figure 5(a) illustrates the bandpass filtered signal. The fault generated impulses can be easily observed in this figure. Figure 5(b) displays the envelope spectrum of the bandpass filtered signal. The effectiveness of the proposed fault detection approach can be evidenced by comparing figures 4 and 5(b).

Fig. 5. (a) Bandpass filtered signal, and(b) envelope spectrum of the bandpass filtered signal.

5 Conclusion In this paper, a parameter free approach has been developed for wind turbine bearing fault detection and condition monitoring. This study focuses on the mixtures of the faulty wind

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turbine bearing vibration and intrinsic background noise, and the fault signal, background noise or vibration interferences generated by other machine elements. As the signal mixture, we propose a maximum likelihood based fault detection method. It is shown that such signal mixture follows distribution. According to this result, a maximum likelihood function is formulated and maximized to estimate the amplitude of fault generated impulses. It is shown that the vibration interference has diminished the amplitude demodulation effect. Therefore, a differencing technique is proposed to increase the signal to interference ratio of the measured signal in order to effectively apply amplitude demodulation. The results have shown that the proposed method can effectively reveal the wind turbine bearing fault characteristic frequency and the associated harmonics in the presence of intense vibration interferences.

Acknowledgements The authors would like to thank the Editor and reviewers for constructive comments that led to an improved manuscript. This research was partly supported by National Natural Science Foundation of China (61025019, 90820016), Program for New Century Excellent Talents in University (NECT-07-0735), and Natural Science Foundation of Hebei China (F2009001638).

References 1. Hameed, Z., Hong, Y.S., Cho, Y.M., Ahn, S.H., Song, C.K.: Condition monitoring and fault detection of wind turbines and related algorithms: A review. Renewable and Sustainable Energy Reviews 13, 1–39 (2009) 2. Caselitz, P., Giebhardt, J.: Rotor condition monitoring for improved operational safety of offshore wind energy converters, ASME Transactions. Journal of Solar Energy Engineering 127, 253–261 (2005) 3. Tian, Z., Jin, T., Wu, B., Ding, F.: Condition based maintenance optimization for wind power generation systems under continuous monitoring. Renewable Energy 36, 1502–1509 (2011) 4. McFadden, P.D., Smith, J.D.: Vibration monitoring of rolling element bearing by the highfrequency resonance technique- a review. Tribology International 17, 3–10 (1984) 5. Sabini, E.P., Lorenc, J.A., Henyan, O., Hauenstein, K.L.: Bearing defect detection using time synchronous averaging (TSA) of an enveloped accelerometer signal. United States Patent (6), 634–681 (2004) 6. Qiu, H., Lee, J., Lin, J., Yu, G.: Wavelet filter-based weak signature detection method and its application on rolling element bearing prognosis. Journal of Sound and Vibration 289, 1066–1090 (2006) 7. Lin, J., Zuo, M.J.: Gearbox fault diagnosis using adaptive wavelet filter. Mechanical Systems and Signal Processing 17, 1259–1269 (2003) 8. Liang, M., Bozchalooi, S.: An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection. Mechanical Systems and Signal Processing 24, 1473–1494 (2010)

An Approach of K-Barrier Coverage of WSN for Mine Qianping Wang, Liangying Wang, Rui Zhou, Dong Jiang School of Computer, China University of Mining and Technology, Xuzhou, P. R. China

Abstract. In this paper, background the environment of coal mine, we present K-barrier coverage wireless sensor network (WSN) based on redundancy routing protocol, node deployment strategy and optimized physical topology. The proposed node deployment strategy achieves high-accuracy localization and high-reliable communication. And it is proved to supply k-barrier coverage by the formula. Hierarchical topology control is used to constructing virtual backbone of k-barrier coverage WSN, generating an efficient logical network topology for data forwarding. Simulation of k-barrier WSN localization system of coal mine is implemented. The results show that the system is k-barrier and the virtual backbone greatly improves the efficiency of network data forwarding and improves the network survival. Keywords: k-barrier coverage, Node deployment, Topology control.

1 Introduction The first thing to do in constructing WSN of both the ordinary and special environment is to determine the network coverage according to the function. For the ordinary WSN, there are many coverage control, node deployment and topology control algorithms. But most of the algorithms can not be directly applied to the underground applications because of the special environment of coal mine according to [1]. So we analyze and design coverage control and topology control suitable for coal mine WSN in this paper. We built up k-barrier coverage WSN in coal mine for subsequent work as routing protocols and localization algorithms design. Coverage control of WSN defined in [2] optimizes allocation of limited resources through node deployment strategy and routing algorithms, improving the performance of the localization, monitoring, communication and other services. Different coverage schemes have effects on the lifetime and the performance of the network. Through analyzing the challenges posed by WSN in coal mine, coverage control of underground WSN should take into account the following factors: Localization accuracy, Network connectivity, Monitoring capacity. In WSN, network connection at the largest communication range is called "physical topology". After the sensor nodes are deployed, the physical topology of the network is fixed. "Logic topology" is the network connection formed by logical links set up by means of removing unnecessary physical communication links in the physical topology through power control and backbone construction at the premise of M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 295–302. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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network coverage and connectivity. The process of generating logical topology from physical topology is known as "topology control" [3]. Research on topology control for WSN presently focus on generating an efficient data forwarding network topology at the premise of network connectivity and coverage through removing unnecessary wireless communication links by power control and backbone nodes selection [4].

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Coverage Control in Coal Mine

The WSN coverage is divided into point coverage, barrier coverage, area coverage and k-coverage according to the differences of the monitoring region in [5]. Point coverage covers some important targets whose location is known for data collection and monitoring; barrier coverage aims at detecting intruders that attempt to cross the network, reflecting the sensing, localization and monitoring capacity of a given WSN; area coverage covers the whole target area, that is, any point in the target area can be covered by at least one working node; while in k-coverage any point in the target area is covered by k-working nodes as defined in [6]. K-coverage can reach the coverage and localization purpose, but coal mine is a linear topology, and the underground localization system can not only think about coverage, cost control and unknown nodes' movement should be considered.

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K-Barrier Coverage in Coal Mine

It can be concluded that no single coverage scheme can meet the requirements of localization systems of WSN in coal mine, so we introduce k-barrier coverage combining the characteristic of barrier coverage and k-coverage. From [7], we know that a belt region is k-barrier covered if all crossing paths in the region are k-covered. K-coverage guarantees any point throughout the coal mine covered by k sensor nodes, achieving the goal of network coverage even if there is serious signal interference or severe equipment damage. And barrier coverage ensures accuracy coverage of all crossing path of the target nodes to satisfy the underground high-precision localization in the special environment of coal mine. Before building up an underground WSN with all the area covered by k-barrier coverage, we introduce a few definitions and theorems related to k-barrier coverage: The definitions of k-barrier coverage:  The Given Sensed Area (GSA): the target area where sensor networks complete the task.  Sensing Radii: sensing range of sensors, expressed by r.  Disk-based Sensing (DS): the sensing area formed by the sensor node as center and the sensing radius as radius.  The probability of the node activity (Pnode_active): in order to extend the lifetime of WSN, certain scheduling mechanism is used to make the necessary node active, while the other nodes sleep. Wake up the sleeping nodes when they are in need. The network probability of node activity Pnode_active is defined as:

An Approach of K-Barrier Coverage of WSN for Mine Pnode _ active 

lifetime of the network with all nodes active lifetime of the network after employing the scheduling mechanism

297

(1)

 Exposure: the integral of sensing from a given start to a given end, reflecting the coverage performance in a crossing path. Let Sa be the set of sensors involved in sensing q. In time [t1, t2], the target q moves L along the trajectory p (t) = (x (t), y (t)), so in the trajectory, the aggregated sensing of the sensor set Sa, i.e., the exposure of target q in the trajectory p (t) is: 2 (2) d p (t ) E ( S a , q )  E ( S a , p ( t ) , t1 , t 2 ) 

  1

c ( si , q (t ))

i

dt

dt

  (np node _ active ) :a monotonically increasing function of npnode_active, where n is the number of the nodes and npnode_active is the number of the active nodes，there exists： (3)  (np node _ active )=o(loglog(np node _ active ))  C(npnode_active):an assistant function defined for examining the coverage, there exists： 2 n p n o d e _ a c tiv e r (4) C (s) 

s l o g ( n p n o d e _ a c tiv e )

k-barrier coverage theorem：Assuming that topology of a sensor network is the belt region B with length s and the network coverage degree is K, for sufficiently large s, if  ( n p n o d e _ a c t i v e )  ( K  1) lo g lo g ( n p n o d e _ a c t i v e ) (5) C (s)  1  lo g ( n p n o d e _ a c t i v e )

when s   , all crossing paths in region B will reach k-coverage at a probability close to 1, that is say area B is k-barrier coverage, concluded from [8]. Node deployment is the first thing to consider in achieving underground kbarrier coverage. So we first deploy sensor nodes of the localization system of WSN in coal mine.

2.2

Nodes Deployment in Coal Mine

There are many ways to deploy nodes, but most of the researches on node deployment of WSN is based on the random node deployment by aircraft. In practice, random node deployment is rarely used. We employ deterministic deployment in underground WSN localization system as large-scale deployment requires high construction cost and communication break may happen in the random deployment. In order to save cost, we first put one node in the effective communication range every fixed distance. In this situation a dead node will lead to disrupted communication in the whole coal mine. This can not satisfy network connectivity. Moreover there are at most two anchor nodes to do the localization. Thus no matter what method to use for the localization, it is unable to meet the accuracy. Therefore, two more sensor nodes are put between the previous two to improve localization accuracy and network connectivity. We can know from experiments that in an enclosed linear space, two communication nodes placed on

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different walls has lower packet loss rate than that of the nodes placed on the same wall. So the node deployment scenario is designed: one node is placed in the effective communication range every fixed distance, and then two redundant nodes are placed alternately between two nodes of the previous nodes in the equidistant distance. Hence any point shown in Figure 1 has at least six anchor nodes to do the localization, meeting the localization accuracy. And communication links can be re-established by redundant nodes once there are node damages, ensuring the entire network connectivity.

2.3 Validation of K-Barrier Coverage in Coal Mine It is analyzed that the node deployment strategy meets the underground localization accuracy and network connectivity requirements. The following work is done to validate whether the deployment strategy supplies K-barrier coverage over the entire coal mine. We have known from the previous section that an area is K-barrier coverage when all the crossing paths in the area are k-covered by the WSN. We can see any crossing path in the belt region of Figure 1 is covered by at least 6 sensor nodes. That is to say the region is K-barrier covered.

Fig. 1. K-barrier coverage of WSN localization system in coal mine

We use k-barrier coverage theorem to do the verification as follows. From Figure 2 we can know that the network's coverage degree is 6. In addition, the anchor nodes in the coal mine can be powered wired and the unknown nodes are battery-powered by miners' lamps, meaning the network lifetime is infinite whether all the nodes are always active or the nodes employ active scheduling mechanism. So lifetime of the network with all nodes active =1 (6) P  node _ active

lifetime of the network after employing the scheduling mechanism

When s   , path length tends to infinity, and the number of network nodes n is also tends to infinity. Now the equation is: lim C ( s)  1 

 (npnode _ active )  ( K  1) log log(npnode _ active )

s , n 

=

lim

s , n 

2npnode _ active r s log(npnode _ active )

log(npnode _ active )

1

 (npnode _ active )  ( K  1) log log(npnode _ active ) log(npnode _ active )

0

(7)

We can see the entire coal mine is K-barrier coverage with the proposed node

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deployment strategy, ensuring coverage and continuous monitoring of any unknown moving nodes.

3 Topology Control of WSN of Coal Mine Underground WSN constructed according to the above coverage scheme and node deployment strategy only has the k-barrier coverage physical topology. In order to efficiently operating the network, we should optimize the network through topology control. In this way, a real k-barrier coverage underground WSN is established.

3.1 Power Control and Hierarchy Topology Control The topology control of sensor networks can be divided into two categories in accordance with the research: power control and hierarchical topology control. Reference [9] implies that purpose of power control in WSN is to reduce the transmitting power as much as possible without sacrificing system performance. Although power control can effectively reduce energy consumption and improve network connectivity. Most power control technologies stay in the realm of theory; very few in [10] which do not fit special topology of coal mine can be adopted in practical applications. And because of underground localization accuracy, we select RSSI-based localization. If we use power control to eliminate redundant paths for network optimization, different power all over the network makes the signal strength taken by RSSI can’t correctly reflect the distance between two nodes. The whole network must use a unified power to locate the unknown nodes. So power control can not be used in this system. Different from power control, hierarchy topology control uses the uniform transmission power in the entire network. Because wireless communication module consumes much the same energy in idle state with that in sending and receiving states, the hierarchical topology control, for the purpose of energy conservation, selects some nodes as backbone nodes to open their communication module according to certain mechanism and close non-backbone nodes’ communication module. Hierarchical topology control can effectively eliminate redundant communication links and save a lot of energy through sleep scheduling mechanism. So k-barrier coverage WSN in coal mine proposed in this paper using hierarchical topology control to optimize its topology.

3.2

Establishment of the Virtual Backbone Network

Hierarchical topology control elects the cluster heads (virtual backbone nodes) through clustering mechanism in the premise of network topology connectivity. The backbone formed by the cluster heads forwards the data [11]. So in constructing virtual backbone using hierarchical topology control, the first step is to elect appropriate nodes as the virtual backbone nodes by certain algorithm. The election

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algorithms are generally based on residual energy or geographical location. But mine is a linear topology, it is difficult to cluster in the normal way based on energy or geographic location. As a solution, we specify the initial nodes as the virtual backbone nodes (Backbone-nodes) and the nodes joining later as redundant nodes. The virtual backbone nodes and communication links among compose the virtual backbone of WSN of coal mine. Virtual backbone nodes are always in work condition, while the redundant nodes sleep at most time. When needed, the redundant nodes are waken up according to the localization algorithm and routing mechanisms to participate in localization and routing recovery implementation. Optimized logical topology is shown in Figure 2 (b):

（a）physical topology

（b）logical topology

Fig. 2. Topology of WSN localization system of coal mine

From the physical topology and the logical topology in Figure 2, we can see most of the unnecessary communication links are eliminated after establishing virtual backbone of underground WSN. This method effectively reduces energy consumption avoiding the broadcast storm. The whole network using the unified power enables the accuracy of the unknown nodes’ RSSI-based localization. If some of the virtual backbone nodes have been damaged, due to the presence of redundant nodes, route recovery strategy of all cases can be designed, laying a solid foundation for routing protocol design. Though communication through the same link will consume much energy of virtual backbone nodes, it doesn’t do any matter to the practicability of the network because we can easily maintain and update the battery for the nodes are powered wired or by miner's lamp. Besides, the wireless network routing design makes immediate localization of dead nodes. Finally, the K-barrier coverage WSN of coal mine is established.

4 Simulation First, establish the scene and define antenna type, signal transmission mode, routing protocol and the way mobile nodes move. We deploy anchor nodes adopting the node deployment strategy described above. The model has 200 backbone nodes and other 200 redundant nodes. For a clear scene, only a part of the nodes are shown in the figure. To simulate the underground tunnel, the scene is set as a narrow range of 10000m * 10m.

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After establishing the scene, simulations are done to verify coverage control, topology control, routing protocol and localization algorithm. Since we have verified that the system is k-barrier coverage, the simulation of the coverage is aimed at verify node deployment strategy. The number of nodes in the coverage range of two backbone nodes and two redundant nodes as well as the relationship between coverage range and coverage degree are concerned. The simulation result is shown in Figure 4 of [12].

Fig. 4. Simulation of coverage control

Fig. 5. Time delay effect of topology control

From the figure we can know that when the number of the nodes is too small, the coverage is difficult to reach 100% even if the maximum effective communication radius is 30. But 100% coverage can be achieved with the effective communication radius of 10 when there are more than four nodes. That is to say too many nodes are superfluous and a waste of resources in the purpose of achieving 100% coverage. And too many nodes also prone to broadcast storms, increasing the difficulty of routing design. The node deployment strategy of four nodes, implemented with a minimum of nodes, achieves complete coverage requirements within a radius of effective communication. Figure 5 shows the time delay of data packets transmission through the physical topology before topology optimization and logical topology after optimization. The simulation results show that topology optimization make data packet transmission time delay decreased significantly. And the optimized network topology nodes have much higher lifetime. It is obvious that constructing virtual backbone network to optimize network topology by selecting backbone nodes can greatly improves the efficiency of network data forwarding and improves the network survival.

5 Conclusion In this paper, we analyze a variety of typical control schemes and propose a kbarrier coverage strategy for underground WSN localization system. To realize the solution, backbone nodes are alternately placed in the tunnel and other two redundant nodes are placed alternately between each pair of the nodes. The node deployment strategy is verified to be K-barrier coverage. And then an efficient logical topology for data transmission is generated from the physical topology formed after node

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deploying through the virtual backbone structure. We have initially established kbarrier coverage underground WSN through node deployment strategy and coverage control as well as topology control. The ns2 simulation shows that the system has an impressive performance in selecting number of nodes, node deployment strategy, network coverage, topology control.

References 1. Jiang, D., Wang, Q., Wang, K.: The Research and Design of High Reliability Routing Protocol of Wireless Sensor Network in Coal Mine. In: NSWCTC 2009, vol. 5, pp. 568–571 (2009) 2. Ye, F., Zhong, G., Lu, S., Zhang, L.: PEAS: A robust energy conserving protocol for longlivedsensor networks. In: Stankovic, J., Zhao, W. (eds.) Proc. of the Int.Conf. on DistributedComputing Systems (ICDCS), pp. 28–37. IEEE Press, New York (2003) 3. Xu, Y., Heidemann, J., Estrin, D.: Geography-Informed energy conservation for ad hocrouting. In: Rose, C. (ed.) Proc. of the ACM Int. Conf. on Mobile Computing and Networking (MobiCom), pp. 70–84. ACM Press, New York (2001) 4. Li, S., Zhang, K.: Theroy and Application of Wireless Sensor Networks. Machine Press, Beijing (2008) 5. Gui, C., Mohapatra, P.: Power conservation and quality of surveillance in target trackingsensor networks. In: Haas, Z.J. (ed.) Proc. of the ACM Int. Conf. on Mobile Computingand Networking (MobiCom), pp. 129–143. ACM Press, New York (2004) 6. Xing, G.L., Wang, X.R., Zhang, Y.F., Lu, C.Y., Pless, R., Gill, C.: Integrated coverage andconnectivity configuration for energy conservation in sensor networks. ACM Trans. on Sensor Networks 1(1), 36–72 (2005) 7. Chen, B., Jamieson, K., Balakrishnan, H., Morris, R.: SPAN: An energy efficient coordinationalgorithm for topology maintenance in ad hoc wireless networks. ACM Wireless Networks 8(5), 481–494 (2002) 8. Heinzelman, W.R., Chandrakasan, A.P., Balakrishnan, H.: Energy-Efficient communicationprotocol for wireless microsensor networks. In: Nunamaker, J., Sprague, R. (eds.) Proc. of the Hawaaian Int.Conf. on System Science (HICSS), pp. 3005–3014. IEEE Press, Washington (2000) 9. Monks, J., Bharghavan, V., Hwu, W.: A power controlled multiple access protocol for wirelesspacket networks. In: Sengupta, B. (ed.) Proc. of the INFOCOM 2001, pp. 219–228. IEEEPress, Anchorage (2001) 10. Yu, H., li, O.: Theroy, Technology and Implement of Wireless Sensor Networks. National Defence Industrial Press, Beijing (2008) 11. Ding, P., Holliday, J., Celik, A.: DEMAC: An adaptive power control MAC protocol for adhoc networks. In: Walke, B. (ed.) Proc. of the PIMRC 2005, pp. 1389–1395. IEEE Communications Society, Berlin (2005) 12. Jiang, D.: Research and Design of Wireless Sensor Network Localization System in CoalMine Based on Redundancy Routing Protocol and K-Barrier Coverage. China University of Mining and Technology (2010)

Design and Implementation of Intrusion Detection System Tai-ping Mo and Jian-hua Wang School of Electronic Engineering & Automation, Guilin Univ. of Electronic Tech., Guilin 541004, China {mtp,wjh}@guet.edu.com

Abstract. This paper have a research on intrusion detection technology , by analyzing the composition and implementation of intrusion, we designed a network intrusion detection system model: network packet capture - analysis of data package - key elements of the data packet matches with the rule base causing alarm. under VC++6.0 and Access 2000 environment, we designed and implemented a network intrusion detection system. The experiment result show the system can detect both known intrusions and the new intrusion. What's more it can notify users to deal with problems immediately. Keywords: intrusion detection, rules base, invasion.

1 Introduction Since James P. Anderson detail the concept of intrusion detection for the first time in his book “Computer security threat monitoring and surveillance”[4] in 1980. The intrusion detection system has made a significant progress. As more and more companies began to turn their business to network, network security has become an unavoidable problem. With the maturity of the attacker skills, the complication and diversity of attack tools and techniques, the simple firewall technology has been unable to meet our needs. At the same time, network environment has become more complex. Hardware equipment, software systems need to constantly upgrade and worker’s carelessness could cause a major security risk. Intrusion Detection System is in accordance with a certain degree of security policy, through software, hardware, network, the status of the system to found all kinds of attack attempts, aggressive behaviors or attack results, so as to ensure the network resources confidentiality, integrity and availability. Network Intrusion Detection System (NIDS) and Host Intrusion Detection System (HIDS) are the most popular IDS, and the appliance of them are most widely. Network intrusion detection system is more common so this paper has design and implement it. Network intrusion detection technology is a kind of dynamic secure technology, and it is an important part in the security protection system. Firewall is a static security and defense technology well as network intrusion detection system is dynamic. It can detect network intrusion events and processes and make real-time response. Working with network firewall, NIDS become the core of network security equipment. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 303–308. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 IDS and NIDS IDS is a combination of software and hardware and have function of intrusion detection. Through Collecting and analyzing some key points of computer network or computer system, IDS can discover whether the network or system have violation or attack. The data will be analyzed, then useful results can be obtained. The main function of IDS include: Monitor and analyze users’ and systems’ activities, Verificate system’s configuration and vulnerability, Assess critical resources of system and data integrity of the files, Identify of the known attacks, Statistic and analysis of abnormal behavior and Manage the log of the operating system. IDS always contains information collection and data analysis. IDS Can be divided into host-based and network-based. Among them, host-based intrusion detecting systems (HIDS) are often through selecting system log, application log as data source, or through other means (such as calls of the monitoring system) to gather information from the host; Data source of network intrusion detecting system (NIDS) is packets on the network, NIDS often set a NIC of one machine into promiscuous mode, monitor data packets and make judgments. In the shared network, NIDS monitor communications and do data collection, analysis of suspicious phenomenon. Fig. 1 illustrates the principle of it's implementation. Compared with HIDS, NIDS is transparent to the invaders. There is two kinds of NIDS's operation, one is running on the target host to monitor the communications of its own information, the other is runing on a separate machine to monitor all network communication and information devices, such as the Hub, router. NIDS including packet capture, protocol decoding and matching modules. It is always run at real-time status to detect possible intrusions any time. NIDS has high detection speed, better for hiding, wide field of vision, less use of resource of monitor and many other advantages.

Fig. 1. Implementation principle of network intrusion detection system(NIDS).

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3 Design and Implementation of NIDS Based on the design principles of IDS, this paper adopt misuse detection technology, design and implement a network intrusion detection system. Considering machine learning way requires a longer time and more frequency to find new attacks of old loophole and attack methods of new loophole, we adopt artificial way. There is a complex database in the system and new rules also can be set by the user who use it. 3.1 Design Theory Intrusion detection technology contents misuse detection and anomaly detection. Misuse detection, which used to detect those types of the already known attack, needs to judge weather the user’s behavioral characteristics matches the characteristics of the attack feature library or not[3]. Besides, it contents the methods of network intrusion and the knowledge of system defect. The technology has the advantages of high detection accuracy. But it can only testing the known attack mode. When there are some attack means aiming at the new vulnerabilities or some new style attacks for old loopholes, we should add the new attack model towards the characteristics artificially or from some other machine learning system to the attack feature library. Only in this way can the system get the ability of testing a new attack. The usual methods in misuse detection technology are expert system, the state transition and model reasoning. With comprehensive factor considered, we use the technology of misuse detection to design and complete the intrusion detection system. Compared with the intrusion detection systems that using common techniques of misuse detection, our system have a complex rules database and any intrusion rules can be set by user himself. According to these rules, the system can deal with invasions and send alarm to the users. NIDS of this paper have some advantages and disadvantages. Advantages: compared with IDS that using common technology of misuse detection, this system can let user set up their own rules of the invasion. When invasion of a new mode appear, user can add new rules at any time and update the original rule, the discovery of new invasion model which makes the system more flexible. Disadvantages: Due to the technology of after detection, the user must understand the characteristics of invasion so as to set their own rule, Therefore it increase the complexity of user operations, because all rules must be set by the user. 3.2 Framework of NIDS This system contain several functions such as network data collection and testing engine, rule detection, alarm and response and information released, Fig. 2 depicts the functional blocks of the system. Firstly, user create a rule base, after that system will start packet capture program first, then analysis of the data packet, sent the results to the database, extracted data in the database and compare it with rules from database, finally the user can view information.

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Fig. 2. Function module of network intrusion detection system(NIDS).

3.3 Design and Implementation of Functional Modules In the overall framework, network data collection modules , test engine modules, test the rule base modules ,alarm and response information modules will be included. Data Collection and Testing Engine. With the comprehensive consideration, this system is developed by winpcap network package. Independent of the host protocol the main function of winpcap is to send and receive datagram, which offers the following functions: Capture the original datagram, including the sharing of the hosts on the network to send / receive and exchange of data;Before datagram sent to the application, in accordance with the custom of certain special rules to filter out the data reported and Send the original datagram in the network ;Collecting statistics of network traffic. The winpcap packet provide a process of capture data for application, including two different levels of API: packet.dll, wpcap.dll. besides a virtual device driver NPF.vxd will be included. packet.dll calls the kernel directly, and wpcap.dll provide a more friendly, more powerful function. Steps of using winpcap can be seen in Table 1. Database of Detect Rules. The Database which offer detective rules is the most important part of system, it’s the basis of NIDS run effectively. First of all, for some of known specific attacks,we can set these source IP, port, MAC address, protocol type and packet length. Each set is equivalent to a record, you can set multiple records, base of detection rules is constituted by these records. After setting detection rules, you can click Add Record button to add rules. While the system running, we can add the rules according to actual situation manually. Alarm and Response. After the system is running, the system starts to capture network packets and displays them in the list. The length, type, source IP address, source MAC address, source port, destination IP address, destination MAC address, destination port of data packets can be seen in the list. users can click on any one record, then detail information of this paket will be displayed in the left side. At the

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same time system capturing data packets and compare the contents of the packet with the rules of database. when the system detects attacks, it will alarm through the following statements: qqmsg *pMsgWindow=new qqmsg; qqmsg& msgWindow=*pMsgWindow; msgWindow.CreateMsgWindow(); msgWindow.ShowWindow(SW_SHOW); msgWindow.UpdateWindow(); msgWindow.hid(); PlaySound(".\\msg.wav",NULL,SND_ALIAS|SND_ASYNC); m_pRecordset2.CreateInstance("ADODB.Recordset"); View Information. Intrusion information View is the last part of the system. We set up a blocking protocol called "TCP", The destination port is "192.168.2.11", the event code is "100001", the event name is the invasion of "aim at TCP "protocol , the response way is "sound", the invasion level is "high" . Click add record then we can see the rules we just set.After adding the test rules and clicking the "start" button to begin testing, once test the attack then click it and generate alarm, record the attack event, and we can check attack event information at this time . Table 1. Steps of using winpcap in NIDS. Basic Steps Enumeration of NIC information

Open NIC and set it to promiscuous mode

Intercept data packets and save files

Used Functions int pcap_findalldevs (pcap_if_t** alldevsp, char *errbuf) pcap_t* pcap_open_live (char* deivce, int snaplen, int promisc, int to_ms, char*ebuf) pcap_dumper_t* pcap_dump_open (pcap_t* p, const char* fname) int pcap_next_ex (pcap_t* p, struct pcap_pkthdr** pkt_header, u_char** pkt_data) void pcap_dump (u_char* user, const struct pcap_pktdr* h, const u_char* sp)

Parameter Description Alldevsp: pointer of a structure, if sucess we will get a NIC list which store the first poinster of the list errbuf: strings that store the error message device: Get the name of NIC. snaplen: The length of the data package that obtained. promisc: Whether set to "promiscuous mode". to_ms:Timeout, milliseconds pcap_t: Return Value,NIC handle pcap_t* p: NIC handle. const char* fname: the name of files that stored. pcap_t * p NIC handle. pcap_pktdr** pkt_header:header that relevant with paket capture driver. u_char** pkt_data: Pointer that point to the contents of data packets. u_char* user: NIC handle. pcap_pktdr *h: header that relevant with paket capture driver. const u_char* sp:packet pointer of the contents.

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4 Conclusion Based on the study and analysis of the basic theory of IDS, this paper final design and implement the network-based intrusion detection system. By intercepting network packets, analyzing the data packets, matching the key contents of the packet with the rule base, creating an alarm and have achieved the basic functions of network intrusion detection system. Finally, we take a simple test which shows that the system through the rule database which is designed by the user can detect known intrusion activities, along with new detection capabilities of the intrusions. Although the function of the system is quite simple, it is practical.

References 1. Hou, F.-m., Li, D.-x.: Design of a New Intrusion Detection SystemBased on Protocol Tree. J. Application Research of Computer, 150–152 (2005) 2. Verwoerd, T.: Intrusion Detection Techniques and Approaches. J. Computer Communications 25, 1356–1365 (2002) 3. Cheng, Y.-q., Mei, D.-h., Chen, L.-f.: A Model of Intrusion Detection System Based on Data Mining. J. Computer Technology and Development 19(12) (2009) 4. Jiang, J.-c., Ma, H.-t., Ren, D.-e., Qing, S.-h.: A Survey of Intrusion Detection Research on Network Security. Journal of Software, 1460–1466 (2000) 5. Tang, Z-j., Li, J-h.: Intrusion Detection. Tsinghua University Press, Beijing (2004) 6. Ye, N., Emran, S.M., Li, X., et al.: Statistical Process Control for Computer Intrusion Detection. In: DARPA Information Survivability Conference & Exposition Anaheim, California, June 12-14, vol. 1(1), pp. 3–14 (2001) 7. Schulter, A., Reis, J.A.: A Grid-based Intrusion Detection System. Networks and Management Laboratory Federal University of Santa Catarina. In: Proceedings of the International Conference on Networking, International Conference on Systems and International Conference on Mobile Communications and Learning Technologies, IEEE, Los Alamitos (2006) 8. IDS Special Topic, http://www.chinaitlab.com/www/special/ciwids.asp

Self Tuning PID Controller for Main Steam Temperature in the Power Plant Amin Aeenmehr and Alireza Sina Production Technology Research Institute, branch of ACECR, Ahwaz, Iran [email protected], [email protected]

Abstract. In this paper, a PID self-tuning controller is used for the control of main steam temperature in the power plant. A common weakness of these selftuning PID controllers is their inability to cope with dead time processes. In this paper, self-tuning controller based on pole assignment approach, which can overcome constant and known dead time is used and the results show that the controller works well in handling dead-time process. Keywords: Self-tuning PID controller, Smith predictor, Dead-time, Main steam temperature, Power plant.

1 Introduction Many industrial processes inevitably change over time for a variety of reasons that include: equipment changes, different operating conditions, or changing economic conditions. Consequently, a fundamental control problem is how to provide effective control of complex processes where significant process changes can occur, but cannot be measured or anticipated. The main steam temperature controlled process in the power plant is a typical process with nonlinear, dead time, time-varying parameters and it is very difficult to control[1]. Traditionally, PID or PI controllers connected to the plant are tuned with a reduction of gain so that overall stability can be obtained. This results in poor performance of control. One of the most important events in the development of digital control systems is the introduction of the self-tuning controller for single variable systems [3]. The extension of the theory to multivariable systems has since been investigated extensively [4] , [5]. However, in the industrial sector, the process controllers in operation are predominantly PID type due to their simple and robust nature. Various types of self-tuning PID controllers have been proposed in the past. In this paper, a self-tuning pole assignment PID controller which can overcome known and constant dead, is used.

2 Pole Assignment Self-tuning PID The original pole assignment self-tuning PID controller proposed by Wittenmark and Astrom is shown in Fig. 1. Assuming that the process can be described by the difference equation [2]. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 309–315. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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(1) With (2)

A=1+

(3) Given A, B, and 1

(4) (5)

1

(6)

The closed-loop transfer function is given by 1

(7)

1

In order to obtain a closed-loop polynomial P, polynomials Q and R must satisfy the Diophantine equation 1

(8)

The coefficients Q and R can be obtained by solving the simultaneous equations that follow 1

(9) (10)

1

(11) 0

(12)

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Fig. 1. Block Diagram of Pole Assignment Self-Tuning PID.

If 1 is the desired characteristic equation, then controller parameters R and Q can be determined uniquely, provided A and B are coprime. The controlled variable can be determined by the relationship 1

1

(13)

3 A Pole Assignment Self-tuning PID Controller with Smith Predictor Which Overcomes Constant Dead Time Smith has proposed the idea of a predictor to overcome dead time by including a minor feedback loop at the output of the controller. The key idea is that the controller design can be assigned PID controller which incorporates the smith predictor is shown in Fig. 2. Form Fig. 2, the controlled variable can be determined as follows [2]: 1

1

(14)

In this configuration, closed loop stability is not affected by dead time as it can be shown that the controlled variable is independent of the delay. Since (15) (16) (17) Then controlled variable 1 (18)

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1 1 If

then 1 1

(19)

Since equation (19) does not include the dead time, then u is independent of dead time.

Fig. 2. Block Diagram of Pole Assignment Self-Tuning PID Controller with Smith Predictor.

4 Simulation The process considered is the pulverized coal-firing 200MW steam-boiler unit used for electric power generation. It produces 670 tons of steam per hour at maximum continuous rating [1]. Two-stage sprayers were applied for the superheated temperature system in a multi-stage control manner to reduce the time constant of the plant and improve the control performance. The rated superheated steam temperature is 540°C. The task of the super heater in boiler-turbine system is to heat the steam by combustion gas and then send it to turbine[1]. As shown in Fig.4, the steam coming from the boiler drum passes through the lowtemperature super heater and receives a spray water injection for protection before passing through the radiant-type platen super heater. The steam coming out from the radiant-type platen super heater receives a second spray water injection before passing through the high-temperature super heater. The super heater steam temperature reaches the highest point within the whole steam-water process and thus presents great influence on the safety and running efficiency of the whole power plant [1]. A properly control system by super heater water spray should maintain strictly the steam temperature within the permitted range (10°C in transient process and 5°C in steady state). Higher temperature could damage the super heater and the high-pressureturbine by high heat strength. Lower temperature could decrease the running efficiency of the whole steam-boiler. By reducing temperature fluctuations, mechanical stress causing micro cracks is diminished, therefore the useful life of the

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plant is increased and maintenance cost is decreased [1]. The main steam temperature is an important parameter, which is related to the efficiency and safety of the power plant. However, it is affected by many disturbances such as load changing or combustion situation changing or flow of the spray water changing etc. The main steam temperature controlled process is a typical process with significant delay and time-varying parameters. According to many experiments and, the main steam temperature controlled process can be described approximately as[1] 0.4 30 1

(20)

to turbine

1. 2. 3. 4. 5. 6.

low-temperature platen high temperature Spray valve feed water valve spray

Fig. 3. The superheated steam system.

The presented controller is applied to the superheated steam and simulated by MATLAB/SIMULINK. The results are shown in figures (4-6).

Fig. 4. Reference input.

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Fig. 5. Output of controlled superheated steam system..

Fig. 6. The reference input and output of controlled superheated steam system.

Fig. 4 shows the reference input. Fig. 5 shows the response of the controlled system and Fig. 6 shows the reference input and output in a plot. The reference input increases to 4 at t=520 s and consequently the output increases to 4 with sharp undershoot and negligible settling time and overshoot.

5 Conclusion A self-tuning PID controller based on the pole-assignment is designed to control the main steam temperature in the power pant in this paper. The constant and known dead time process can be controlled by the controller with a Smith predictor. The presented controller is applied to the superheated steam and simulated. the simulation results show that the proposed controller copes well with the dead time processes.

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References 1. Xiu-Zhang, J., Gen-Yuan, W., Yao-Quan, Y.: Multi-IMC Adaptive Control System for MainSteam Temperature in the Power Plant. In: Proceedings of the Fourth International Conference onMachine Learning and Cybernetics, pp. 676–681. IEEE, Los Alamitos (2005) 2. Fong-Chwee, T., Sirisena, H.R.: Self-Tuning PID Controllers for Dead Time Processes. IEEETransaction on Industrial Electronics 35(1), 19–12 3. Astrom, J.K., Wittenmark, B.: On Self-Tuning Regulator. Automatica 9(3) (1973) 4. Borrison, U.: Self Tuning Regulator for a Class of Multivariable Systems. Automatica 15(2) (1979) 5. Sirisena, H.R., Teng, F.C.: Multivariable pole-zero placement self tuning controller. Int. J. Syst. Sience 17(2) (1986)

A Novel Transformerless Single-Phase Three-Level Photovoltaic Inverter Xiaoguang Yang and Youhua Wang Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin 300130, China [email protected]

Abstract. Single-phase transformerless inverter is widely used in low-power photovoltaic (PV) grid-connected systems due to its small size, high efficiency and low cost. This paper proposes a novel high efficiency transformerless single-phase three-level-based photovoltaic inverter. The topology is simulated and experimentally validated in a 1.2-kW prototype and a comparison with three-level diode clamped inverter is performed. The results show that the proposed topology retains the positive characteristics of the three-level diode clamped inverter; namely, the nongeneration of variable total common-mode voltage, the reduced current ripple, and the high efficiency. The advantage of the proposed topology is that it reduces the input voltage by half compared with the three-level diode clamped inverter. Keywords: photovoltaic (PV) system, transformerless inverter, three-level diode clamped inverter, common-mode current

1 Introduction Today, Photovoltaic(PV) inverters become more and more widespread. These grid connected inverters convert the available direct current supplied by the PV panels and feed it into the utility grid. According to the latest report on installed PV power in total, about 6,2 GW of PV capacity were installed in the IEA PVPS countries during 2009 (2008:5,5 GW); raising the total installed capacity to 20,4 GW in those countries. By far the greatest proportion (74 %) was installed in Germany and Italy alone. If the US, Japan and France are also included, then over 93% of PV installations in 2009 occurred in five countries [1]. Usually, grid connected PV systems include a line transformer between the converter and the grid. The use of the transformer is to guarantee galvanic isolation between the grid and the PV system. Moreover, the transformer significantly reduces leakage currents between the PV system and the ground, and ensures that no direct current (DC) is injected into the grid. However, the line transformer is big, heavy, and expensive, and it reduces the overall efficiency of the converter. Transformerless PV systems have higher efficiency, smaller size and weight compared with the PV systems with transformers. But transformerless inverters must M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 317–324. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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cover the purposes of the transformer, which are to fulfill safety requirements, to avoid DC current into the grid, and to reduce common-mode currents between PV system and ground [2]. Safety requirements can be fulfilled by including a ground fault detector in the inverter. The common-mode currents might cause severe electromagnetic interferences, grid current distortion, and additional losses in the system. Different inverter topologies have been proposed for transformerless PV systems to minimize the common-mode current and to improve the efficiency of the whole PV system. One of these topologies is the full bridge with bipolar pulse width modulation. However, bipolar modulation causes large current ripple and high switching losses, reducing the overall efficiency of the inverter [3,4,5]. Another topology proposed for transformerless PV systems is the half-bridge inverter [6,7,8]. This topology, besides not generating variable common-mode voltage, guarantees the noninjection of DC current. However, the half-bridge inverter causes a large current ripple and high switching losses, thus reducing the efficiency of the inverter. In order to improve the efficiency and reduce the current ripple, the use of a single-phase three-level diode clamped inverter has been proposed [6,9]. A disadvantage of the three-level diode clamped inverter is that for single phase grid connection they need a 700V DC-link voltage. This paper presents a novel three-level inverter topology for transformerless gridconnected PV system. This topology generates no varying common-mode voltage, has low current ripple, and has high efficiency. The input voltage of the proposed inverter is only half of the traditional three-level diode clamped inverter.

2 Common-Mode System Description Fig. 1 shows a detailed scheme of a transformerless PV system, including the PV array, the DC-side filter, the inverter, the AC-side filter (split in two parts L1 and L2, which are between the line and the neutral) and the grid. L1

Photovoltaic array

P Filter DC

Power Converter Inverter

1 Vdm

L2

Cdm

Vg

Lcm

2 N Ccm

CPVg

Ccm

Ground

icm

Fig. 1. Common-mode currents in a transformerless PV inverter.

~

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There are the stray capacitances CPVg between the PV array and the ground. Due to the large surface of the PV generator, its stray capacity with respect to the ground reaches values that can be even higher than 200 nF/kWp in damp environments or on rainy days [4]. These high values can generate ground currents with amplitudes well above the permissible levels, such as those concerning the standards. The currents can cause severe (conducted and radiated) electromagnetic interferences, distortion in the grid current and additional losses in the system. Considering the requirement of EMC, the differential-mode filter capacitor Cdm, and the common-mode filter elements (Lcm and Ccm) are also included in this system. To study the leakage current into the ground, it is useful to describe the system behavior with the help of the common- and differential-mode concepts. The commonmode output voltage of any circuit is the average value of the voltages between the outputs and a common reference. In our case, it is convenient to use the negative terminal of the dc input, which is point N, as the common reference. Therefore, the common-mode voltage of the converter νcm is

vcm =

v1N + v2 N 2

(1)

The differential-mode output voltage νdm is defined as the voltage between both converter outputs vdm = v1N − v2 N = v12

(2)

The effects of the stray capacitances between the inverter outputs and the ground have been disregarded. The common-mode currents can exist if there are asymmetries in the value of the differential-mode impedances, resulting in an equivalent commonmode voltage [2] vecm = vdm

L2 − L1 2( L1 + L2 )

(3)

The total common-mode voltage νtcm of the system is vtcm = vcm + vecm

(4)

3 Three-Level Diode-Clamped Inverter The use of a single-phase three-level diode clamped inverter has been proposed in PV system, as shown in Fig.2. During the grid voltage positive half cycle, S2 remains on, whereas S1 and S3 commutate at the switching frequency in order to modulate the input voltage. During the grid voltage negative half cycle, S3 remains on, whereas S2 and S4 commutate at the switching frequency. Fig. 3 shows the simulation results of the main electrical variables during a grid period under the condition of vin = 700 V, fsw = 5 kHz, and L1 = 2.5 mH.

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Fig. 2. Three-level diode-clamped inverter

(a) v12

(b) ig

(c) vtcm Fig. 3. Voltage across 1 and 2, grid current and common-mode voltage in a three-level diode clamped inverter

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4 A New Three-Level Inverter The proposed topology is shown in Fig.4, which consists of six switches (S1-S6 ) and two diodes (D7-D8), two capacitors C1-C2 and two inductors L1-L2 . In the positive half cycle, S2 and S6 are on. In order to modulate the input voltage, S1 and S4 commutate at the switching frequency with the same commutation orders. S3 and S5 commute at the switching frequency together and complementarily to S1 and S4. In the positive half cycle, S3 and S5 are on. In order to modulate the input voltage, S1 and S4 commutate at the switching frequency with the same commutation orders. S2 and S6 commute at the switching frequency together and complementarily to S1 and S4.

（

（

）

Fig. 4. Topology of the new three-level inverter

In the positive half cycle, when S1 and S4 are on, and the inductor current, which flows through S1, S2, S6, and S4, increases. In this topology, the clamping diodes (D7D8) and the capacitive divisor limit the blocking voltage of S1 and S4 to half of the input voltage. It can be obtained that: v1N =vin, v2N =0, vdm=v12=vin, vtcm=vcm= vin/2. When S1 and S4 turn off, S3 and S5 are on, the inductor current splits into two paths, one path is through S6 and the freewheeling diode of S3, and the other is through S2 and the freewheeling diode of S5. In this case, the inductor current decreases. Because the diodes clamping effects, v1N=v2N=vin/2, vdm=v12=0, vtcm=vcm=vin/2, in this case, S3 and S5 are turned on with no current. In the negative half cycle, when S1and S4 are on, and the inductor current, which flows through S1, S5, S3, and S4, increases. In this topology, the clamping diodes (D7D8) and the capacitive divisor limit the blocking voltage of S1 and S4 to half of the input voltage. It can be obtained that: v1N =0, v2N =vin, vdm=-v12=-vin, vtcm=vcm= vin/2. When S1and S4 turn off, S2 and S6 are on, the inductor current splits into two paths, one path is through S5 and the freewheeling diode of S2, and the other is through S3 and the freewheeling diode of S6. In this case, the inductor current decreases. Because the diodes clamping effects, v1N=v2N=vin/2, vdm=v12=0, vtcm=vcm=vin/2, in this case, S2 and S6 are turned on with no current. Fig. 5 shows the simulation results of the main electrical variables of the proposed inverter during a grid period. The switching frequency and the inductance are the same as those used for the three-level diode clamped inverter simulation, but the input

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(a) v12

(b) ig

(c) vcm Fig. 5. Voltage across 1 and 2, grid current and common-mode voltage in the new inverter

voltage vin = 350 V. Comparing the results shown in Fig. 3 with that in Fig. 5, it can be observed that the proposed topology remains advantage of the three-level diode clamped inverter, but reduces the input voltage by half.

5 Experimental Results To validate the operation of the proposed topology, a prototype was built. The prototype specifications are an input voltage vin between 350 and 700V, a grid voltage of 220V, a line frequency of 50Hz, a rated power of 1.2kW, a switching frequency of 5kHz, and an inductance of 2.5mH. International Rectifier’s IRG4PC4OUD 600-V fast insulated-gate bipolar transistors were chosen for the switches. Fig. 6 shows the experimental results for the proposed topology. Fig. 6(a) presents the voltage v12, and Fig. 6(b) shows the common-mode voltage. The waveforms confirm and validate the theoretical analysis of the topology operation. As can be

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(a) v12

(b) vcm

Fig. 6. Voltage across 1 and 2, grid current and common-mode voltage in the new inverter

Fig. 7. Waveform of output current and grid voltage

seen, the common-mode voltage does not vary. Fig. 7 shows the grid voltage and current.

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6 Conclusion Transformerless inverters offer a better efficiency, compared to those inverters that have a galvanic isolation. On the other hand, in case the transformer is omitted, the generated common-mode behavior of the inverter topology greatly influences the ground leakage current through the parasitic capacitance of the PV. This paper presents a novel three-level inverter topology for transformerless grid-connected PV system. The topology proposed generates no varying common-mode voltage, has low current ripple, and has high efficiency. The input voltage of the proposed inverter is only half of the traditional the three-level diode clamped inverter. As a conclusion, the proposed topology represents a valuable power-conversion stage for transformerless grid-connected PV systems.

References 1. 2.

3. 4.

5. 6.

7.

8.

9.

Trends in photovoltaic applications: survey report of selected IEA countries between 1992 and 2009. In: IEAPVPS (2010) Gonzalez, R., Gubia, E., Lopez, J., Marroyo, L.: Transformerless single-phase multilevelbased photovoltaic inverter. IEEE Transactions on Industrial Electronics 55(7), 2694–2702 (2008) Mohan, N., Undeland, T.M., Robbins, W.P.: Power electronics: converters, applications, and design, pp. 212–215. John Wiley & Sons Inc, New York (1995) Myrzik, J.M.A., Calai, M.: String and module integrated inverters for single-phase grid connected photovoltaic systems — a review. In: Power Tech Conference Proceedings, Bologna, Italy, June 23-26, vol. 2, pp. 1–8 (2003) González, R., López, J., Sanchis, P., et al.: Transformerless inverter for single-phase photovoltaic systems. IEEE Transactions on Power Electronics 22(2), 693–697 (2007) Kjaer, S.B., Pedersen, J.K., Blaabjerg, F.: A review of single-phase grid-connected inverters for photovoltaic modules. IEEE Transactions on Industry Applications 41(5), 1292–1306 (2005) Shimizu, T., Hashimoto, O., Kimura, G.: A novel high-performance utility-interactive photovoltaic inverter system. IEEE Transactions on Power Electronics 18(2), 704–711 (2003) Tsai-Fu, W., Hung-Shou, N., Hui-Ming, H., et al.: PV power injection and active power filtering with amplitude-clamping and amplitude-scaling algorithms. IEEE Transactions on Industry Applications 43(3), 731–741 (2007) Calais, M., Agelidis, V.G.: Multilevel converters for single-phase grid connected photovoltaic systems—an overview. In: IEEE International Symposium on Industrial Electronics, Pertoria, South Africa (1998)

Image Reconstruction Algorithm Based on Fixed-Point Iteration for Electrical Capacitance Tomography Cuihuan Li1,2, Xiaoguang Yang1, and Youhua Wang1 1

Province-Ministry Joint Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability, Hebei University of Technology, Tianjin, China 2 School of Sciences, Hebei University of Technology, Tianjin,China [email protected]

Abstract. To improve the quality of the reconstruction image for discontinuous media distribution areas, a total variation (TV) regularization based on bounded variation function was employed to solve the ill-posed problem of electrical capacitance tomography (ECT). By using TV regularization, an Euler equation, a non-linear partial differential equation, is derived to describe the ECT problem. However, it is very time consuming, if possible, for traditional methods to solve this Euler equation. This paper presents a new fixed-point iterative algorithm which can get the solution stably and rapidly. Numerical results show that the presented method improves the quality of the reconstruction image for discontinuous media distribution areas in ECT image reconstruction, and leads to the common boundary between different media clearer. Keywords: electrical capacitance tomography (ECT); image reconstruction; illposed; total variation regularization; fixed-point iterative algorithm.

1 Introduction Electrical Capacitance Tomography (ECT) is a process tomography technology based on capacitive sensing mechanism. It is a measurement technique for obtaining information about the contents of process vessels and pipelines. For the advantages of simple structure, low in cost, fast in respond, non-invading, good security, without radiation and wide application, it has been used to the void fraction measurement of many kinds of two-phase flow, such as the oil/gas, gas/liquid in petroleum industry, the gas-solid pneumatic conveying system and fluidized bed [1-5]. The 12-electrode ECT measurement system is shown in Fig.1. It consists of array electrode, data acquisition and signal processing, image reconstruction and display. Capacitances between various different combination electrode pairs are measured, and the data obtained are used to reconstruct cross sectional distribution. The quality and speed of image reconstruction is the key of the practical application for ECT. In fact, there are many aspects affect image quality which include the number and structure of the sensor, C/V conversion circuit, image reconstruction algorithms. When the conversion circuit is confirmed, it’s hardly to M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 325–332. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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increase the number plate, so by increasing the number plate to improve the image quality is extremely limited. Thus, the image reconstruction algorithm is the primary problem to improve quality of image reconstruction. At present, common methods used in ECT image reconstruction include: linear back projection algorithm (LBP), Landweber iteration method [6], conjugate gradient method [7], the novel Gauss-Newton method [8], algorithm accelerated by polynomial [9], neural network algorithm [10], improved trust region based[11], regularization method [12-15] and so on. LBP method algorithm is simple, fast reconstruction, but the image quality is relatively poor, so it is only a qualitative algorithm. Landweber iteration is a steepest descent method essentially, but it is semiconvergence. Conjugate gradient method suit for the coefficient matrix is symmetric positive situation. Neural network algorithm is essentially a pattern recognition method, and there are some difficult to determine the structure of the network for the practical application. Parameters of acceleration algorithm based on polynomial and improved trust region algorithm is often selected by experience which leads to a certain randomness. Image reconstruction is an ill-posed problem, and regularization is often employed to get the stable solution. Tikhonov penalty function is of 2-norm and requires smooth solution which lead to blur boundary and additional artifacts in the reconstruction image. In this paper, total variation regularization based on bounded variation function is presented and a fixed-point iterative algorithm is proposed to get TV regularization solution. Numerical results show that the proposed method can overcome the problem of ill-posedness effectively, and provide high quality images.

Fig. 1. Principle diagram of ECT system

2 Principle of ECT ECT technology includes forward and inverse problem. The forward problem is to calculate the potential distribution for a known distribution of dielectric constant, then to determine the capacitance measurements.

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The capacitance between electrode pair i-j is expressed

Cij = ∫∫ ε (x, y )Sij (x, y, ε (x, y ))dxdy

(1)

D

Where Ci,j is the capacitance between electrode pair, D is the cross section of pipeline, ε(x, y) is the distribution function of dielectric media on the cross section of the pipeline, Sij(x, y,ε(x, y)) is the distribution function of sensitivity for Ci,j, which means the sensitive extent forming from Ci,j to the medium on spot (x, y). In fact, it is often assumed that the dielectric distribution ε(x, y) has no effect on the sensitivity function Sij. Thus, (1) can be simplified to

Cij = ∫∫ ε (x, y )Sij ( x, y )dxdy

(2)

D

After discrete and normalized, the mathematical model of image reconstruction can be written in matrix form

λ = Sg Where λ ∈ R

m

(3)

is the normalization capacitance vector,

g ∈ R n is the normalization

m× n

distribution image vector, S ∈ R is the coefficient matrix (sensitivity matrix).The inverse problem of ECT, also called image reconstruction, is a typical ill-posed problem, and regularization often employ to get stable numerical solution.

3 Total Variation Regularization (TV Regularization) The Tikhonov regularization is given by [16]

M (α , g) = Where

1 2 Sg − λ + αΩ( g ) 2

α is regularization parameter. Ω( g ) = g

2

(4)

, and g must be continuous even

continuous and differentiable. That is to say, most of the regularization methods assume the data sets to be smooth and continuous, which leads to lost boundary information and reduce precision. In fact, distribution of dielectric constant is often blocky, so in order to restore its edge information, the TV method was introduced.

M (α , g ) =

1 2 Sg − λ + αTV ( g ) 2

TV ( g ) = ∫

D

∇g dxdy 2

(5)

(6)

328

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∇g is gradient of g . However, TV ( g ) is not differentiable at zero. In order

β

is added. So TV

1 2 2 Sg − λ + α ∫ ∇g + β 2 dxdy D 2

(7)

to overcome this difficulty a small positive constant value functional can be expressed as

M (α , g ) =

∇g + β 2 is known as the gradient magnitude. Where g is 2

The quantity

bounded variation function. The unique minimum

gδ (α ) of (7) is also the solution

of following Euler equation

( S T S + αL ( g )) g = S T λ

(8)

Where L( u ) is the diffusion operator whose action on a function u is given by

L(g)u = -∇ ⋅ (

1 ∇g + β 2 2

∇u)

(9)

This provides us with the information about the discontinuities in the image. For any α > 0 , β > 0 (9) is well- posed. So the problem turn to select α and β which makes the solution of (8) is also the approximate solution of (3).

4 Fixed-Point Iteration The nonlinearity of (8) poses number of computation challenge, so the fixed-point iterative algorithm is introduced to overcome the difficult. Its basic idea is to successive linearization of nonlinear equation. Equation (8) can be rewritten into the following first-order nonlinear system

G

Where v =

G S T Sg − α∇ ⋅ v = S T λ

(10)

G G 2 − ∇g + ∇ g + β 2 v = 0

(11)

∇g ∇g + β 2

2

.When g of square root is fixed, i.e. set g=g(m) (m is the

G

number of iterations), (8) is linearized. When eliminate v in (10), the format of the fixed point iteration expressed as

( S T S + αL( g ( m ) )) g (m +1) = S T λ

(12)

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Note that at each iteration, one must solve a linear diffusion equation, whose diffusivity depends on the previous iterate g(m). Thus

g (m +1) = ( S T S + αL( g ( m ) )) −1 S T λ

(13)

Equation (13) can be rewritten as

~ g ( m +1) = g ( m ) − ( H ( g ( m ) )) −1 u ( g ( m ) )

(14)

u ( g ) = ( S T S + αL( g )) g − S T λ

(15)

Where

~ H ( g ) is the approximation of Hessian matrix. ~ H ( g ) = S T S + αL ( g )

(16)

Fixed-point iterative algorithm is convergent and with increase in iteration the method converges linearly. But the numerical results show that the convergence is fast, only need one or two steps to get a good approximation of exact solutions. And fixed-point iteration is globally convergent, so it has nothing to do with the choice of initial value.

5 Simulation and Analysis of Results To verify the efficiency of the new algorithm, numerical experiments have been carried out by simulating the typical gas/oil two-phase patterns. In the patterns, dielectric constant of oil is ε oil = 3 , dielectric constant of gas is ε gas = 1 .The number of measurement electrodes is 12, the inner and outer radius of the pipe is 62mm and 75mm respectively, the radius of the grounded shielding is 80mm, and the measurement angle is 26°. The image area (the cross-section area of the pipeline of two-phase flow) is divided into 804 elements. While in experiment iteration error usually satisfied

Sg − λ ≤ δ

(17)

Where δ is observation error of original data and δ = 0.001 , β = 0.1 in this paper. While analysis reconstructing image quality, selecting spatial image error as evaluation index of image quality, its definition as follows:

e= Where

gδ − g g

(18)

g is the grey value of the simulation model, and gδ is the grey value of the

reconstruction image. The experiment results are shown as Table.1.

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Table 1. Comparison of reconstructed images.

Phantom(a)

Phantom (b)

Phantom(c)

LBP Algorithm

Landweber Iteration Algorithm

Tikhonov Regularization Algorithm

TV Regularization Algorithm

Phantom(d)

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Table 1 shows the comparison of the reconstructed image. From Table 1, it can be seen that LBP has poor precision, and can not clearly distinguish the target area for the complex flow pattern; its position has obvious deviation. It can only be used for qualitative analysis; Landweber iteration method generally closes to the original flow for simple flow pattern, but can not distinguish multiple target areas clearly. Tikhonov regularization can distinguish multiple targets basically, but artifacts are more and precision is low. TV regularization can clearly distinguish between single or multiple target area, and the contrast and sharpness of reconstructed images increased significantly. Table 2 is the comparison of the reconstructed image of the error. It can be seen from the table, LBP least accurate, TV regularization algorithm has the highest accuracy. Table 2. Image error (%). Phantom

(a)

LBP

87.63 46.37

33.72

61.46

50.70

Landweber

31.49

56.38

13.77

Tikhonov

44.16

23.54

26.64

13.11

TV

41.05

17.10

22.49

12.19

(b)

(c)

(d)

6 Conclusion In this paper TV regularization based on bounded variation function is introduced to overcome the ill-posed of ECT, and the range of approximate solution can be extended to bounded variation function space. In order to overcome the difficult of solving the nonlinear Euler equations, a fixed-point iteration algorithm is proposed to get TV regularization solution. Numerical experiments show that: the contrast and sharpness of reconstructed images increased significantly, and the proposed algorithm has the advantages both in imaging speed and quality. Selection of regular parameters of regularization method directly affect the accuracy and speed of the reconstructed image, at present parameters selected mainly from experience, which has a certain randomness and uncertainty, so the selection of regular parameter, in particular the more reasonable selection principle and more efficient numerical implementation method is still to be further important issue to be addressed.

Acknowledgment This work was supported by the Hebei Province Natural Science Foundation of China under Grant No. E2007000048.

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References 1. Loser, T., Wajman, R., Mewes, D.: Electrical capacitance tomography: image reconstruction along electrical field lines. J. Measurement Science and Technology 12(8), 1083–1091 (2001) 2. Jaworskia, J., Dyakow Ski, T.: Application of electrical capacitance tomography for measurement of gas-solid flow characteristics in a pneumatic conveying system. J. Measurement Science and Technology 12(8), 89–98 (2001) 3. Zhu, K., Rao, M., Wang, C. H.: Electrical capacitance tomography measurements on vertical and inclined pneumatic conveying of granular solids. J. Chemical Engineering Science 58(18), 4225–4245 (2003) 4. Liu, S., Li, T.J., Chen, Q.: Visualization of flow pattern in the rmosyphon by ECT. J. Flow Measurement and Instrumentation 18, 216–222 (2007) 5. Wang, Z.Y., Jin, N.D., Wang, C., et al.: Temporal and spatial evolution characteristics of two-phase flow pattern based on image texture analysis. J. Journal of Chemical Industry and Engineering (China) 59(5), 1122–1130 (2008) 6. Yang, W.Q., Peng, L.H.: Image reconstruction algorithms for electrical capacitance tomography. J. Meas. Sci. Technol. 14, R1–R13 (2003) 7. Wang, H., Zhu, X., Zhang, L.: Conjugate Gradient Algorithm for Electrical Capacitance Tomography. Journal of Tianjin University 38(1), 1–4 (2005) 8. Chen, D.Y., Chen, Y., Wang, L.: A Novel Gauss-Newton Image Reconstruction Algorithm for Electrical Capacitance Tomography System. J. Acta Electronica Sinica. 37(4), 739–743 (2009) 9. Chen, Y., Chen, D.Y., Wang, L., et al.: Image reconstruction algorithm accelerated by polynomial for electrical capacitance tomography system. J. Chinese Journal of Scientific Instrument. 29(12), 2538–2542 (2008) 10. Zhao, J.C., Fu, W.L., Li, S.T., et al.: Image reconstruction new algorithm for electrical capacitance tomography. J. Computer Engineering 30(8), 54–82 (2004) 11. Wang, L., Chen, Y., Chen, D.Y., et al.: Improved trust region based image reconstruction algorithm for electrical capacitance tomography system. J. Chinese Journal of Scientific Instrument 31(5), 1077–1081 (2010) 12. Sun, N., Peng, L.H., Zhang, B.F.: Tikhonov Regularization Based on Near-Optimal Regularization Parameter with Application to Capacitance Tomography Image Reconstruction. J. Journal of Data Acquisition & Processing 19(4), 429–432 (2004) 13. Wang, H.X., He, Y.B., Zhu, X.M.: Regularization Parameter Optimum of electrical capacitance tomography Based on L-curve Method. Journal of Tianjin University 39(3), 306–309 (2006) 14. Jing, L., Shi, L., Zhihong, L.: mage reconstruction iteration algorithm based on 1-norm for electrical capacitance tomography. Chinese Journal of Scientific Instrument 29(7), 1355–1358 (2008) 15. Wang, H., Tang, L., Yan, Y.: Total variation regularization for electrical capacitance tomography. Chinese Journal of Scientific Instrument 28(11), 2015–2018 (2007) 16. Xiao, T.Y., Yu Sh, G., Wang, Y.: Numerical solution of inverse problems. Science Press, Beijing (2003)

Research on Data Preprocessing in Exam Analysis System Ming-hua Zhu College of Computer and Information Engineering, Jiangxi Normal University, Nanchang330022, china [email protected]

Abstract. Data preprocessing is the key to provide high quality data for Exam Analysis System. In order to get the better useful information from the complex and uncertain exam data, it’s necessary to preprocess the source data. In this paper, the source data of Exam Analysis System was analyzed in detail, and found that the source data is inconsistent, redundancy and so on. Therefore, a general method of data preprocessing is introduced in this paper. Keywords: data preprocessing, data mining, exam analysis system.

1 Introduction Data Mining is a kind of database technology developing with database and artificial intelligence. Currently researches on data mining are mainly concentrated on the discussion of algorithms, as a result that data preprocessing has been neglected. However, data of an actual system can seldom meet the requirement of data mining, which has seriously affected the efficiency of data mining algorithms, even lead to a results deviation. According to statistics, the time and cost on data preprocessing has accounted for 60% to 80% of a whole data mining process. As a result, an effective way of analysis and preprocessing on data source has become a key issue to the achievement of a data mining system. Exam Analysis System has accumulated large amounts of data during years of examinations. Exam data can provide a lot of important information after being processed, which makes contribution to guiding teaching, accurate assessment and making the education standardized, modernized and specific. Decisions of high quality must depend on data of that, so it is necessary to preprocess these data before sent into database. The structure of the Exam Analysis System which depends on data mining is shown as Fig. 1.

2 Analysis of Data Source in Exam Analysis System Data preprocessing is an important section in data mining, which provides clean, accurate and concise data for data mining. But actually the original accumulated data are so-called ‘dirty’ with characters of messy, repetitive and incomplete. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 333–338. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Top: front-end tool

DSO decision support object

OLAP service

Middle layer: Analysis Services

data warehouse Botton layer: SQL-SERVER Servers Data preprocessing

Old warehouse

Registration information

Return data Data Source

Fig. 1. Architecture of the Exam Analysis System

Data of the Exam Analysis System are widely accumulated, mainly for three sections: the first are data form paper system, including information of questions and exams, which are saved in SQL-Servers; the second is information gathered from each exam center, which are saved as Excel files or SQL files; the third are data of exams gathered from each exam center, which are saved as text files. After analyzing these original data, those characters can be concluded below: (1) The original database is designed depending on rules of relational database, so that those data are entire and consistent with small redundancy except for null numeric in some of fields. As a result, some transform and integrated work should be done in order to fulfill those blank fields. (2) Those data of students’ information from each exam center are basically the same in structure, but totally different in the entity and consistency, for example some major codes are presented in registration form while not in majors’ information form. (3) The same kind of data from different exam centers is different in date representation. For example, some use ‘M’ or ‘F’ in the sex field in information table, while some use ‘Male’ or ‘Female’. (4) The same kind of data from different exam centers is different in date type. For example, some use type of date while some use type of integer to represent date field. (5) The same kind of data from different exam centers is presented in some of exam centers, while others don’t have or uncompleted, such as student’s ID. (6) Examinee registration data from each exam center may more or less have noise data so they should be cleaned before sent into database. (7) Some new information will be added in data delivered from each exam center after examination so these kinds of information should be refreshed automatically.

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The following form 1 and form 2 are listed the data uploaded from two different exam centers. Differences in data, blank numeric fields and data redundancy have appeared. Table 1. The examinee registration data of examination site A Registeration number 260605100325

Name

Identity card

Sex

Exam course

Major

Li mei

3601011980

F

260605100029

Fu Min

3601031984

M

260610100287

3601021978

F

professional diploma of CS Undergraduates of CS Network

260605100212

Zhang ping Xu Li

260610100116

Hu Wen

3601011979

MC applications comprehensive subject one comprehensive subject one comprehensive subject two comprehensive subject two …

…

…

F

…

M …

Network Undergraduates of CS …

Table 2. The examinee registration data of examination site B Registeration number 260610100102 260605100208 260610100478 260610100038 260605100291 260610100074 …

Name

Identity card

Sex

Age

Huang an Chen feng Cui En-zan Liu Kai Wan Yan Yang Tan …

3601011983 3601021976 3601011980 3601031985 3601011982 3601021979 …

Male Male Male Male Female Male …

27 34 25 28 31 …

Exam course MC applications subject one subject one subject two subject one subject two …

Major U of CS PD of CS Network U of CS Network Network …

3 Data Preprocessing in Exam Analysis System It is convinced after analyzing the data source from exams that it is necessary to do data preprocessing. Data preprocessing in the Exam Analysis System is implemented by the following four ways that is data extraction, data cleaning, data transformation and data integration: (1)Data extraction is the entrance of database for all data. Because a database is an individual data environment, it extracts data from inner database and external database by the way of extracting procedure. Data extraction is technically involved with interconnection, copying and increment. It is unnecessary for data in database to keep the same with the former database real-timely, so data can be extracted regularly. However, when data extractions are executed at the same time, the intervals, sequences and success or not play an important role to the effectiveness of data in the database. (2)Data cleaning is the standardization before data come into data warehouse which is the identification of data’s integrity and consistency. Data cleaning is technically involved with filling in missing values, smoothing noisy data and resolving inconsistencies. There are many way on filling in missing values, such as

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ignoring the tuple, filling in missing values manually, using a global constant or attribute mean to fill in the missing value and so on. All these methods above should be selected depending on the detail analysis on the source data. The mainly way to deal with noisy data is by smoothing processing. While dealing with data differences, there are data processing in the check on the effectiveness and uniform representation of data. The check on the data effectiveness depends on checking the length of numeric values, while the check on the data’s uniform is usually in the use of value replacing. (3)Data transformation is a way including the transformation of data type, data aggregation and the generalization and normalization of data in order to make the data more appropriate for data mining. The transformation of data type to numeric values is an easy way, for example to transform type of character to type of numeric for the speed of searching numeric is one order of magnitude faster than searching characters. Data aggregation is to summarize and aggregate data in order to reduce the quantity of issues and accelerate the speed of query and analysis in the future. At the same time, aggregating data out of data in the database will help reduce the size of historical data without loss. The conceptualization of data is to stratify data by concepts that the original in the concept of low-level will be replaced with that in the concept of high-level such as replacing the title session property with chapter property. The normalization is to scaling its values so that they fall within a small specified range, such as 0.0 to 1.0. The commonly used methods for normalization are min-max normalization, Z-Score normalization (or zero-average normalization), normalization by decimal scaling, attribute construction and so on. (4)Data integration is a way to combine data from multiple sources into a coherent data store, as in data warehousing. These sources may include multiple databases, data cubes, or flat files. There are a number of issues to consider during data integration:First is data selection problem. How can the data analyst or the computer be sure that studentid in one database and stuid in another refer to the same attribute? Commonly, it can be solved by convince the source data from database or data warehouse or communicating with those business people directly.Second is the detection and resolution of data value conflicts. When there is attribute values from different sources may differ, which one should be chosen as a standard? For example, in data sources of this system, data gathered from different exam centers, it may happen that some student information is added in one exam center while the student id is the same as another student in another exam center. This can result in a data confliction.Third is data loss. In the data source, there are some missing values which make no effect on the original system. However, in the data warehouse, it is much better that it is filled with values than be blank for misconception may come into the result.Fourth is derived data definition. It is involved with functions for calculating sum and average values and analysis of complex business. The derived data are usually redundant for data using for calculating are stored in data warehouse. However, derived data can much simplify the query, so when data coming into data warehouse, those derived data should be checked for consistency and correctness. It can be known from table 1 and table 2 that in the fields of sex, major and exam course, there is a same content presenting inconsistent data and non-standard. Such as ‘M’and ‘Male’, ‘U of CS’ and ‘Undergraduates of CS’ etc. The field of sex should be represented uniformly by using ‘M’ or ‘F’ and coded referring to major and

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examination subjects in order to normalize the data in fields of major and exam course. The contents of ID code uploaded from different exam centers are different so that it can be transformed into numeric to accelerate the speed of query. When the field of age values is missing, it can be filled with the values dropped from the ID code. The field of age is regarded redundantly, but it can simplified query and statistics. According to the data preprocessing by data extraction, data cleaning, data transformation and data integration on examinee registration data, the information is shown as Table 3 that achieving the purpose of the same data format and data types, clear data, in order to content to the requirement for data mining and provide supportive to efficient data mining. Table 3. The examinee registration data after data preprocessing Registeration number 260605100029 260605100208 260605100212 260605100291 260605100325 260610100038 260610100074 260610100102 260610100116 260610100287 260610100478 …

Name Fu Min Chen feng Xu Li Wan Yan Li mei Liu Kai Yang Tan Huang an Hu Wen Zhang ping Cui En-zan …

Identity card

Sex

Age

3601031984 3601021976 3601041981 3601011982 3601011980 3601031985 3601021979 3601011983 3601011979 3601021978 3601011980 …

M M F F F M M M M F M …

26 34 29 28 30 26 31 27 31 32 30 …

Exam course 80702 80701 80709 80709 80701 80902 80709 80702 80702 80709 80709

Major

…

…

2331 2210 2332 2331 2210 2332 2332 2331 2332 2331 2331

Microsoft SQL Server 2005 Data Transformation Services (DTS) is a set of graphical tools and powerful programming objects, that can be taken from completely different sources data extraction, transformation, merging into a single or multiple purposes. Data preprocessing tools of this system is programming with DTS package, in the process, we can design a number of steps to complete the data extraction and conversion, which can be a parallel between steps can also be a serial, you can also According to the results of the previous step to determine the next step of the process. To ensure the data extraction and conversion of integrity and consistency, the design of the system will be a few steps in a transaction through the various steps of the returned results to determine whether to roll back the transaction. In the data preprocessing tools use DTS powerful data extraction and transformation capabilities of the system three different data sources to extract data. DTS data collected will be stored in a temporary table first, then through data cleaning and data conversion functions to the data in the temporary table to clean up and converted into the data warehouse, data preprocessing time work to complete.

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4 Conclusion During the construction of the Exam Analysis System, data preprocessing is an important and necessary work after design the data warehouse, which is the effective guarantee for data mining. This paper has made a detail analysis on the data source and concluded a detail report on the ways of data preprocessing according to the complexity and inconsistency of data. Among these methods, there are still manual parts. During the further work, it can be explored effective detection and automatic resolving methods for data preprocessing to deal with dirty data in order to provide complete entire solutions.

References 1. 2. 3. 4. 5. 6. 7. 8.

Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Higher China Machine Press, Beijing (2007) Liu, M.-j., Wang, X.-f., Huang, Y.-l.: Data Preprocessing in Data Mining. Computer Science 4, 54–57 (2000) Inmon, W.H., Rudin, K.: Building the Data Warehouse. In: Wang, Z.-h. (ed.), 4th edn., Higher China Machine Press, Beijing (2006) Jian, Z.-g., Jin, X.: Research on Data Preprocess in Data Mining and Its Application. Application Research of Computers (7), 117–118 (2004) Yang, F.x., Liu, Y.c., Duan, Z.h.: An Overview of Data Cleaning. Application Research of Computers 3, 3–5 (2002) You, X., Lou, N.-l., Wang, Y.-x.: Research of data preprocessing in education decision support system. Computer Engineering and Design 28(16), 3985–3988 (2007) Yin, J., Chen, Y., Zhang, G.: An OLAM System Based on Data Warehouse. Computer Engineering (19), 49–51 (2004) SQL Server 2005 Online Help

The Development and Application of Environmental Art Project Which Based on Semiotics in Information Age Ke Zuo School of software, Nanchang University, China [email protected]

Abstract. Environment art engineering development faster and faster in the information age, the paper analyzes the semiotic characteristics of the information age, to promoting the rational use of environment art design engineering,. and to promote the cause of China's environment art engineering for further development. Keywords. Information age; environmental art engineering; software; semiotics.

1 Introduction This thesis mainly from the thinking of the digital age way, From semiotics to study the environmental art design. Due to the environmental functions to modeling forms of transformation is used symbols process, in this process modeling form to become transfer information carrier, i.e., the form of the design symbol, it conveys architectural environment to people with various meanings. While in symbols in the process of innovation and composition, because its diversity, repeatability and the reconstruction in digital era under the influence of the produced a new way of thinking. Thus, based on the analysis of semiotics, analyzes the digital age of design thinking mode, the further deepening digital era under the environmental art design method.

2 Concept and Characteristics of EAE Thinking Digital art design and the traditional close relationship between art and design. Creativity is considered in the design of scientific work of art after the session, is to make the visual design goals can be achieved visualization of ideas can be heard. 2.1 The General Concept of Design Engineering The human brain is thinking, the nature of objective things, and regular reflection of the neurons in the physical, chemical, physical exercises in the form of synthesis, and design thinking is the practice of each designers to achieve design goals Provide important subjective design performance conditions. In the design process to achieve the desired results and the effectiveness of play is directly related to thinking, therefore, demands and designers from the creative aspects of practice, designers have to learn to M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 339–344. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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create a psychology based on the results, summarized some with Regular phenomenon, subject to grasp the essence of good design, and better develop their own creative thinking, the ability to improve the innovative design. 2.2 The Features of Design Project 1) Abnormalities: The abnormal form of thinking and often reflect the nature of the mutation for the development of thinking, across or interrupt logic, this is because creative thinking is not primarily on existing concepts, knowledge of the result of a gradual and logical reasoning process, but on inspiration, Such as intuition or insight implemented as non-logical thinking. 2) Differentiation: Composition between the two are opposites, not only different from each other, negative, antagonistic, and complementary, interdependent, united, the resulting contradictions of creative thinking, and promote the development of creative thinking. This dialectic of thought process and often reflected in the form of integrated creative thinking, creative thinking that is actually a synthesis of various forms of thinking. 3) Open: Mainly refers to the need for creative thinking from many angles and sides, in all directions to examine the issue, but no longer confined to logic, single, linear thinking, thus forming a divergent thinking, reverse thinking, lateral thinking, seeking Different thinking, non-linear thinking and open-minded and other forms of creative thinking. 4) Originality: Is a direct manifestation of creative thinking or signs, and often the outcome of specific performance for the creation of novelty and uniqueness. 5) Initiative: Shows that the subject of creative thinking is to create a purposeful activity, rather than the objective world in the human brain is simple, passive direct reflection, it shows the dynamic nature of human activity and initiative. 6) Comprehensive: Knowledge is the foundation of creative thinking, thinking of the main wealth of knowledge to stand above, easy to create new associations and insights. Often create their own "intellectual cross "results, it is both kinds of knowledge of the mutual penetration, combined with, but also a variety of forms and methods of synthesis of thinking. Art and Design in the digital environment, the richer and more intuitive performance fast. As the information age may be, we can reach more of the design works of art, have a more wonderful fantasies and desire for the creation of a stronger, more creative glow of inspiration and insight, and thus a better image of the rich and quick thinking of our intuition , artistic creation in the environment more like a duck.

3 Information Age Environment Art Engineering Characteristics New social form, the design content of the art form has undergone great changes. Design from the static, rational, single, material to create the dynamic, emotional, complex, non-material to create change. Embodies the essence of art created for the free, non-material design features of the past makes the development of a strong artistic nature of the design elements in art, more and more content becoming more and more artistic designs. The development of non-material design, is both an expression of the

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art digital technology on the traditional way of impact, is the perfect combination of technology and art of expression. Material from the traditional design of the transition to non-material design, not only reflects the technology development, but also reflect and meet the people desire for the diversification of lifestyles.Design-led the design direction, different times have different design. When humans gradually from the post-industrial era into the digital age, the interior design concept has changed naturally, mainly in the following two aspects: First, the contradiction from the environment aptitude coordination environment changes,As earth non-renewable resources, environment of increasingly poor escalation, the environmental protection consciousness of people got greatly enhances, hence worldwide open a wave green consumption tide, more and more people in adornment environment, will look to whether it used materials for the whole human environment harmful, and as far as possible need not nonrenewable resources of the earth. To sum up, in the digital environment, environmental art design concept to harmony with environment direction (that is, ecological design concept) change will become a necessity. Second, from the "form follows function" to "form follows emotion " change, environmental design is to meet human "functional requirements" as the core of the movement, and "Spirituality" is only interior design accessories. Therefore, in the design of modernism that "form follows function" design philosophy, and popular. But in the digital environment, network and virtual community does not make the relationship between the people become more closely, but strengthened the solitary and personal survival way, so indoor design also carry the consolation of human spirit and heart of responsibility! Therefore, the famous frog design is put forward "form following emotion" design idea!This equipment will not only be able to design real-time drawing sketches presented in the computer, it can be very realistic to simulate the traditional brush strokes, strength and color. Interior design sketches into a computer brings many benefits, mainly in the following points: (1) computer sketch easily modified so that the designer of the program easier deformation and expansion, thus indirectly inspired the designer's creative inspiration; (2) digital sketch, sketch on the network to get timely and efficient delivery, so that the remote interior design becomes more perfect; (3) sketches the image can be inserted into AutoCAD, as a base map with a fuzzy reference to engineering drawings for the AutoCAD drawing convenience. Design performance is to express the final design, design thinking for designers to visualize the process and specific, and through color renderings, graphic layout, flat layout, flat pattern in elevation, section , The node graph and other means to reflect, its purpose is to allow owners to further understand the designer's design intent, as well as construction workers, construction basis. In the digital environment, environmental art design produced a huge change in performance: 1) The development of computer technology in the past painted flat layout, flat layout, flat pattern in elevation, section, node graph, completely used AutoCAD to draw.Therefore, the design of environmental art show presents a simple, fast, easy to modify, intuitive, and complete benefits. 2) With the three-dimensional network of technology (Network Virtual Reality) of the mature, designers can use Cult3D, Pulse3D, Ser, 3DML other network three-dimensional software, environmental art and design three-dimensional modeling,

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so that the owners and stakeholders through the network Environmental art program designed multi-angle, full of observation and evaluation. In this way, to the environmental art design provides remote technical protection. 3)The target of four-dimensional design,As the digital era, computer art design on the environment has shifted from the secondary to, semiotics and other disciplines to support art and design environment to gradually move toward the front, and to awareness of environmental art and design ways of thinking and theory led to innovative Wave.

4 The Using of Environment Art Engineering Building Semiotics In the semiotic sense, the appearance of buildings, materials, uses, etc., are the use of functions from their abstracts, access to the cultural significance of non-architecture, creating a similar system means the system of linguistic signs. 4.1 Architectural Symbols and Cultural Meaning Determinants of the art of architecture culture.Different cultures have a different architecture, thus forming a different architectural symbol of that culture is a symbol of the determinants that affect the building. Such as traditional Chinese architecture in general are neighbor to create the image of the scale, space and environment, rather than the Gothic architecture as the West to exaggerate the extraordinary scale to symbolize God's space and atmosphere, giving the impression that their own Small and bowed to the feet of God, reflecting on the minds of religious shock. Building a symbol of the human spirit is not only the culture, it is also representative of human emotions. Alone building, it is the provision of human and social activities in the residential function of the carrier, all cultural phenomena have occurred in them. Meanwhile, in order to adapt to a wide range of social needs, building also must reflect the times, geographical, national, public and social life of cultural identity and social order to keep pace.Building a more entrusted with people's thoughts and feelings. 4.2 Environmental Art Project under the Information Age in the Use of Architectural Semiotics The digital age, the form and content of environmental art design, great changes have occurred. Design is no longer the focus of the art of some kind of tangible material products, but from the material level gradually move closer to the spiritual level.Embodies the essence of art created for the free, non-material design features of the past makes the development of a strong artistic nature of the design elements in art, more and more content becomes increasingly designed around art. New technology means not only to bring a new way of thinking space and visual space, it also brings a new sensory needs and psychological needs.People-oriented, demand for services will undoubtedly keep the Art of Design and create one personalized to meet the diversified needs, which will lead to the design would be to diversify the face of art, personal. Gehry design works in small doping social and ideological things. He usually polygonal surface, tilt the structure, form and inverted form of a variety of substances

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applied to the design and visual effects to go. Gehry uses fault geometry to break tradition, to him, breaking a means to explore the social order is not clear. Gehry out in the form of features, created not a whole structure, but a successful abstract ideas and the city agencies. In many ways, he treated the same as building work as sculpture, this structure through three focus on a variety of forms. Art is often inspired by Gehry's birthplace, his interest in the arts can be learned of his architectural works. At the same time, art made him the first time the use of open architecture structure, and people think it is an invisible change, rather than deliberately. Gehry's building is often surreal, abstract and occasionally people are deeply confused by the message so it is often misleading. Even so, Gehry's building is showing its unique, elegant and mysterious atmosphere. Gehry materials using a variety of substances, using a variety of architectural forms, and humor, mystery, and his dreams of building systems integration. He said: "I like to see this in the beauty of the building process, which the U.S. has often lost in the manufacturing process technology. " Gehry in the early work on the bold use of open space, a variety of raw materials And not to carry out the construction of formality. Gehry's architecture also includes a common process, a continuation of life, the evolution of life and the lives of such growth. Presentation of information in this chapter by examples of semiotics in the construction era of environmental art project in the new development. As Roland Barthes in the "semantic object" wrote: "The meaning has always been a cultural phenomenon, is the product of culture; However, in our society, this cultural phenomenon are constantly being Naturalization. words that we believe in a pure object in the transitive situation, and again into the natural meaning of the phenomenon. We believe we find ourselves at a purpose, function of objects, the complete control of the formation of the practice of the world, in fact, through the objects, we find ourselves in a sense, reasons, excuses posed by the world: features derived from Symbols, and this symbol has been transformed into functional re-display. I believe it is this will be a natural process of cultural transformation was established ideology of our society.” Therefore, we conduct multi-disciplinary environment, art and design process, the need to support multi-discipline, but also the so-called Open design, and semiotics, as construction on the environment, the use of art and design, art semiotics also played Environmental Art Design A great role, and has unlimited potential.

5 Conclusion and Outlook As awareness of human expression, the means and methods of conveying information is one of the environmental design is similarly dependent on the support of various disciplines. As designers we have to learn to use other disciplines to design more effective function. Environmental Art and Design, for its part, to include architecture, art and other symbols, by symbols of these elements at different levels, the success of the designer selection, combination, conversion, regeneration of these elements, together become the referent of his thoughts Symbols, as their common recognition of the symbols and the audience, this is the real form of communication, information accurate and complete communication, design of the thinking process is perfect. The purpose of environmental art design is the exchange of people, including the

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application of semiotics in various subject areas, including the principles and methodology in environmental art and design in research and practice is quite common and in-depth, but also undoubtedly become inevitable in our design Tools, and environmental art design to truly become a no limit design.

References 1. 2.

3.

Xige : Art multimedia technology applications in the environment (January 2004) Feng, Y.: Architectural design concept of the software supporting the three key technologies, based on Creator & Vega, 3ds max and AotoCAD, Sichuan Construction (October 2006) Feng, G., Zhang, J.: Architectural decoration based on virtual reality system design and managent. Hebei Architectural Science and technology (June 2006)

Rotor Time Constant Estimation for the Vector Controlled Induction Motor Drive Based on MARS Scheme Hua Li and Shunyuan Zhou Institute of Electric Power System and Motor Drives, College of Information Science and Engineering Northeastern University Shenyang, Liaoning Province, China [email protected], [email protected]

Abstract. In this paper, Model Reference Adaptive System (MRAS) is presented for the rotor time constant estimation for induction motor based on the regulation of instantaneous reactive power. The estimated rotor time constant is used as feedback for calculating the slip speed in an indirect vector control system. This method avoids using pure integration and stator resistance in the reference model. Moreover, the steady state of the reactive power eliminates the derivative terms in the adjustable model. The structure of estimator is very simple and the technique is robust to variations of motor parameters. Simulation results are presented the robustness and accuracy of the proposed schemes and show that good tracking capability and fast responses have been achieved. Keywords: Rotor Time Constant, MRAS, Induction Motor, Vector Control, Reactive Power.

1 Introduction In recent years, the induction motor (IM) has been widely used in industrial application due to its simple structure, great reliability and low costs. Control techniques of these drives are well treated in the literatures. The vector control is a sophisticated control method. It is based on rotor field oriented control according to rotating frame transformation, and has a decoupling control between torque and flux of the IM drive and consequently dynamic performances similar to those of a DC machine. However, rotor time constant (τr) is a very important parameter, which is required in indirect field-oriented control (IFOC) system of IM drive. The rotor time constant may have variation with working condition change, especially temperature change, which may lead to improper flux orientation, improper stator current decoupling, and hence deterioration of dynamic performance of the IM drive. In order to achieve good performance of vector controlled IM drive, many different rotor time constant estimation schemes have been proposed, for example, signal injection-based method [1], Model Reference Adaptive System (MRAS), Extended M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 345–352. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Kalman filter [2]. In these methods, MRAS is popular due to its simplicity, less computation time and good stability [3-7]. In this paper, the instantaneous reactive power has been used in the proposed technique for estimating the rotor time constant of IM drive. Most of the MRAS schemes for estimation of rotor speed or rotor time constant require flux estimation. However, this method is not like that. So, it overcomes the reflections from pure integration and stator resistance [5]. The reference model has no influence from the parameter variations. The steady state of the reactive power eliminates the derivative terms in the adjustable model. The structure of the model is simplified. Therefore, the accuracy of parameter estimation is verified and is not suffered from integratorrelated problems at low speed. The validity and robustness are proved by simulation.

2 Inverse Rotor Time Constant Estimation 2.1 Mathematical Model of Induction Motor The electromagnetic behavior of IM in the d-q synchronously rotating reference frame can be expressed by equation (1). ⎛ ⎜ Rr + ( p + jω ) σ Ls ⎛ vs ⎞ ⎜ ⎜ ⎟=⎜ RL ⎝0⎠ ⎜ − r m ⎜ Lm ⎝

( p + jω ) Lm

⎞ ⎟ Lr ⎟ ⎛ is ⎞ ⎟ ⎜ψ r ⎟ Rr + ⎡⎣ p + j ( ω − ωr ) ⎤⎦ ⎟⎟ ⎝ ⎠ ⎠

(1)

where ω = ωr + ωsl , σ = 1 − L2m / ( Ls Lr ) and p is the differential operator. The voltage, current and flux space vectors are given as x = xd + jxq . The instantaneous reactive power can be expressed as Q1 = vqs ids − vds iqs

(2)

According to the equation (1), the (2) can be rewrote, a new expression of Q1 is

(

)

(

)

Q2 = σ Ls piqs ids − pids iqs + σ Lsω ids2 + iqs2 −

(

)

(

Lm L pψ dr iqs − pψ qr ids + ω m ψ qr iqs + ψ dr ids Lr Lr

)

(3)

In the IFOC system, ψqr=0 and ψdr=ψr. In the running process of motor, ids=ψr/Lm. When the IM drive is working in the steady state, the derivative terms are zero. The equation (3) can be simplified, the expression of reactive power resolves to

(

)

Q3 = σ Lsω ids2 + iqs2 + ω

L2m 2 ids Lr

(4)

In the equation (1) and (4), it is found that the stator resistance does not affect the reactive power. Also, pure integration terms are eliminated, and the question of integral drift can be resolved.

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2.2 Model Reference Estimation Structure The structure of MRAS consists of a reference model, an adjustable model and an adaptation mechanism which is found out to minimize the error between two models, as illustrated in Fig 1. In this paper, Q1 is considered as the reference model, which represents the components of the reactive power in terms of accessible stator variables, that is, stator currents and voltages. Q3 is chosen as the adjustable model which is free from rotor flux and derivative terms, and it is dependent on slip speed (ωsl). The error signal is fed to the adaptation mechanism. According to the conditions of rotor fieldorientation, the following expression can be gained.

ωsl = Lm iqs / (τ rψ r )

(5)

According to equation (5), the rotor time constant can be calculated. The structure of MRAS using the reactive power is illustrated in Fig 1.

Fig. 1. Structure of the new MRAS using reactive power

2.3 Model Reference Estimation Structure

The proposed MRAS based on rotor time constant have been shown in the above part. The reactive power variation is defined by

ε = Q1 − Q3

(6)

Substituting Equation (2) and (4),

ε = vqs ids − vds iqs − σ Lsω ( ids2 + iqs2 ) − ω

L2m 2 ids Lr

(7)

This variation is used by the adaptation mechanism to generate the estimated slip speed and make it converge towards its actual value. The adaptation mechanism must be designed in order to obtain a fast and stable time response. The adaptation mechanism is based on the Popve’s hyperstability concept, which mainly concerns the stability properties of a class of feedback systems as illustrated in

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Fig.2. Usually, r=0 , gain u=-w. Two conditions are required when the system is said asymptotic hyperstable. (a) The transfer function of the feed forward linear time invariant block must be strictly positive real. (b) The nonlinear time varying block must satisfy the Popov’s integral inequality η ( t0 , t1 ) = ∫ yT ( t ) w ( t )dt ≥ −γ 02 t1

t0

∀t1 ≥ t0 , γ 02 > 0

(8)

where w is the feedback block. The term of w is input and ε is the output of the linear forward block.

Fig. 2. Standard block diagram of nonlinear feedback system

According to the hyperstability concept, a state model is represented as pε = Aε − w

(9)

Assume y=Cε, where C=1/[σLs(idr2+iqr2)+(Lm2/Lr)idr2], and constitute the linear part of the standard nonlinear feedback systems, whereas rotor time constant estimation constitutes nonlinear part. The equivalent MRAS is shown in the Fig. 3.

Fig. 3. Equivalent MRAS for the proposed scheme

Substituting y and w into equation (8), the following expression can be gained ⎡

η ( 0, t1 ) = ∫ yT (ωsl − ωsl ) ⎢( ids2 + iqs2 ) + ω t1

0

∧

⎣

L2m 2 ⎤ ids ⎥dτ ≥ −γ 02 Lr ⎦

(10)

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The adaptation mechanism can be chosen by the expression ∧

∧

ωsl = ∫ F1 ( y ,τ , t )dt + F∧2 ( y, t ) − ωsl (0) 0 ωsl (0) t1

According to the actual situation, choose the

, and substitute (11) into (10), then

∧

η ( 0, t1 ) = ∫ ε T ( ∫ F1 ( y ,τ , t )dt − ωsl (0) − ωsl )dτ + ∫ ε T F2 ( y , t )dτ ≥ −γ 02 t1

t1

t0

0

(11)

t1

(12)

0

To make Equation (12) be satisfied, the following inequality can be used

∫

t1

0

pf ( t )kf ( t ) dt =

k 2 k ⎡ f ( t1 ) − f 2 ( 0 ) ⎤⎦ ≥ − f 2 ( 0 ) 2⎣ 2

(13)

So, have pf ( t ) = ε T ∧

kf ( t ) = ∫ F1 ( y ,τ , t )dt − ωsl (0) − ωsl t1

(14)

0

According to the equation (14), the following result can be obtained F1 ( y,τ , t ) = kτ I ε T

kτ I > 0

(15)

F2 ( y, t ) = kτ P ε T

kτ P > 0

(16)

and

The adaptation mechanism can be gained ∧

t1

∧

ω sl = ∫ kτ I ε T dt + kτ P ε T + ω sl (0)

(17)

0

where kτ I , kτ P are the PI controller gains of the adaptation mechanism. Therefore, a PI controller is sufficient to satisfy Popov’ s integral inequality. These satisfy both the Popov’s criterion and confirm the stability of the system.

3 Simulation Results The above presented procedure has been simulated using MATLAB/Simulink to verify the effectiveness of MRAS for estimating the rotator time constant. Fig.4 shows the block diagram of IFOC based on MRAS using the reactive power. The proposed estimation scheme has been simulated on a 1.1-kW four-pole squirrel cage induction motor, and the parameters are summarized in the Table 1. The motor model is represented by equation (1) as well as the following mechanical equation pωr = −

np B ωr + (Te − Tl ) J J

(18)

The performance of the estimator has been studied in terms of its ability to converge to the actual rotor time constant and the response of the estimator towards parameters change. Fig.5 shows the simulation result of rotor time constant for the vector controller IM. The rotor time constant estimator was simulated for a load of 2 Nm and a

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Table 1. Induction motor parameters Parameters Nominal power (kW) Stator line voltage (V) Frequency (Hz) Stator resistance (Ω) Rotor resistance (Ω) Mutual inductance (H) Stator leakage inductance(H) Rotor leakage inductance(H) Pole pairs

Value 1.1 380 50 6.6 5.5 0.454 0.021 0.021 2

Fig. 4. Block diagram of IFOC based on MRAS

reference speed of 800 rpm. At time t=2s, the rotor resistance (Rr) of the induction motor has a step response which is increased by 50% of Rr , and at time t=4s, the rotor time constant is reduced to the normal value, Rr However, the rotor time constant change slowly with time change in the real drive. In this simulation, the step variation is to verify the robustness of the proposed estimator. From the Fig.5., it is found that the value estimated of reactive power ( Qest) can track the reference reactive power (Qref) better compared by the method using flux calculator in [10]. Therefore, the method has good tracking capability and fast response. Fig.6 shows the rotor time constant estimation at low speed. At time t=2s, the rotor resistance is increased by 50%. Compared the method using the rotor flux information, the proposed technique can estimate the rotor resistance at low speed accurately.

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0.4 Qref 0.3

0.2

0.1

0 Qest -0.1 0

1

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6

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(a) 1.2 1 0.8

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(b) 20

15

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0 1

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(c) Fig. 5. Simulation results of the proposed system: (a) the reactive powers, (b) d and q-axis stator current, (c) inverse rotor time constant Tr-1

50 40 30 20 10 0 -10 0

0.5

1

1.5

2

2.5

3

3.5

time (sec)

Fig. 6. Inverse rotor time constant estimation at a low speed

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4 Conclusion In this paper, the rotor time constant estimator based on MRAS is proposed using the function obtained from instantaneous reactive power. The technique has no integrators in the implementation and it is robust to the variation of stator resistance. The rotor time constant can be estimated correctly at steady state of the reactive power. From the simulation results, the rotor resistance estimation has proved the effectiveness of MRAS. The adaptation mechanism has good tracking capability to parameter variations.

References 1. Wade, S., Dunnigan, M.W., Willianms, B.W.: A new method of rotor resistance estimation for vector controlled induction machines. IEEE Trans. Ind. Electron. 44(2), 247–257 (1997) 2. Garcia Soto, G., Mendes, E., Razek, A.: Reduced-order observers for rotor flux, rotor resistance and speed estimation for vector controlled induction motor drives using the extended kalman filter technique. IEE Proc. Electr. Power Appl. 146(3), 282–288 (1999) 3. Peng, F.Z., Fukao, T.: Robust speed identification for speed-sensorless vector control of induction motor. IEEE Trans. Ind. Applicat. 30(5), 1234–1240 (1994) 4. Zorgain, Y.A., Koubaa, Y., Boussak, M.: Rotor resistance estimation for indirect stator flux oriented induction motor drive based on MRAS Scheme.In: STA 2009, REM-627, pp. 1347–1362 (2009) 5. Bin, H., Wenlong, Q., Haifeng, L.: A novel on-line rotor resistance estimation method for vector controlled induction motor drive. In: Proc. Conf. Rec. IEEE IPEMC Conf., vol. 2, pp. 655–660 (2004) 6. Marcetic, D.P., Vukosavic, S.N.: Speed-sensorless AC drive with the rotor time constant parameter update. IEEE Trans. Ind. Applicat. 54(5) (2007) 7. Haron, A.R., Idris, N.: Simulation of MRAS-based speed sensorless estimation of induction motor drive using MATLAB/SIMULINK. In: PECON 2006, pp. 411–415 (2006) 8. Maiti, S., Chakraborty, C., Hori, Y.: Model reference adaptive controller-based rotor resistance and speed estimation techniques for vector controlled induction motor drive utilizing reactive power. IEEE Trans. Ind. Electronics 55(2), 594–601 (2008) 9. Peng, F.Z., Fukao, T., Lai, J.S.: Low-speed performance of robust speed identification using instantaneous reactive power for tacholess vector control of induction motors. In: Conference Record of the 1994 IEEE-IAS Annal Meeting, vol. 1, pp. 509–514 (1994) 10. Ta, C.-M., Uchida, T., Hori, Y.: MRAS-based speed sensorless control for induction motor drive using instantaneous reactive power. In: Conf. Rec. of the IEEE-IECON, pp. 1417– 1422 (2001)

Small-World Request Routing System in CDNs Lan Li* 235 Nanjing Road East, Qingshanhu Distric School of software,Nanchang University 330029 Nanchang, PR. China [email protected]

Abstract. In this paper, we present a novel small-world method for request routing in CDNs. CDNs have multiple servers carry the same content. Request routing system is used to choose a proper replica server that has the requested content and route the incoming request to that sever. To present the proper strategies on choosing the replica server, we use small-world distributed hash tables to create the new replica server and relax the hot spots in replica servers. According to the experiment results, the trade-off between the scalability and robustness gained from small-world distributed hash tables reduce the average query delay. Keywords: We would like to encourage you to list your keywords in this section.

1 Introduction Content delivery(or distribution)networks(CDNs)[1, 2] is an effective approach to improve Internet service quality. It has recently been proposed to improve the performance of the response time, bandwith and the accessibility by using index, cache, stream splitting, multicast and other technologies[2-4]. Request routing system is used to choose a proper replica server that have the requested content and route the incoming request to that sever. There are many techniques have been proposed to guide users to use a suitable server among a set of replica servers, such as client multiplexing, HTTP redirection, DNS indirection, anycasting and peer-to-peer routing, in which peer-to-peer systems build the information retrieval network one the members of replica servers themselves instead of relying on a dedicated infrastructure like the traditional CDNs do. Recent work also has shown that the request interests is a kind of social network and exhibits small-world behavior. Characterizing such behavior is important for the proper evaluation of large-scale content delivery techniques. Inspired by this, this paper present a novel request routing scheme based on small-world theory.

2 Related Work If one object is popular, then the probability of finding more than one replica server to store it becomes higher. Content Distribution Networks (CDN) have multiple servers *

This work is supported by Youth Science Foundation(GJJ11038), Department of Education, Jiangxi Province.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 353–359. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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carry the same content whether it is web content or any other application. Such a problem of multiple serving nodes is similar to peer-to-peer network, however, in contrast to peer-to-peer network, CDNs servers are well maintained by some professional personnel and lost self-organizing features and dynamics. In order to present a suitable content distribution mechanism for the individuals content producers, peer-to-peer network can be introduced into CDNs. When providing servers from individuals, CDNs’ servers are often very dynamic, and might leave a network even if they were in the middle of serving an object to a client node. Thus, it is necessary to provide certain number of replica servers for a client node. In the event that one or more of the serving nodes disappear, a client does not have to restart the download of the entire object from another serving node. According to this, Distributed hash tables(DHT) can be introduced to store index information in CDNs[5]. Distributed hash tables(DHT) associate with an address based on the numerical distance between their hash keys. DHT is widely used to either store index information about the location of data or to store the data itself. However, because a message traverses a typical DHT on links that are completely location unaware, a message originating in two very near nodes may take lots of hops. This problem can be solved by integrating the location information into the DHT. However, the integration of location information often brings the problem of trade-off between the cost of robustness and scalability by reducing the randomness that was intentionally integrated into the design of DHT. In order to achieve the balance of structure and randomness, small-world graphs may give out the solution. Following Watts and Strogatz’ findings[6], the structural properties of small-world graphs typically exhibit a short path length between any two vertices and strong clustering behavior. Small-world graphs exhibit connectivity properties that are between random and regular graphs. Like regular graphs, they are highly clustered; yet like random graphs, they have typically short distances between arbitrary pairs of vertices. It has been shown that many networks have similar small-world property. After Watts and Strogatz’ finding, Kleinberg[7] proposed two engaging questions: “why should there exist short chains of acquaintances linking together arbitrary pairs of strangers?” and “why should arbitrary pairs of strangers be able to find short chains of acquaintances that link them together?” Kleinberg’s studies on the networks with small world characteristics show that searches can be efficiently conducted when the network exhibits the following properties: 1) each node in the network knows its local neighbors; 2) each node knows a small number of randomly chosen distant nodes, with probability proportional to 1/d where d is the distance. A search can be performed in O(log2N) steps on such networks, where N is the number of nodes in a network. Inspired by Kleinberg's work, DHT Symphony is designed by Manku et al.'s. In a Symphony network with n nodes, each node randomly chooses a node identifier from the unit interval [0,1) and positions itself on a ring. The key idea of symphony[8] is to arrange all participants along a ring and equip them with long distance contacts drawn from a family of harmonic distributions. In symphony, each node establishes links with its two immediate neighbors on the ring as well as q long distance links chosen with the help of a probability density function pn(x)=1/xln(x), in which x [1/n,1]. In order to establish a long link, a node draws a distance d from pn(x) and then searches for the manager of the point at distance d away from itself on the ring. Symphony's pn(x)

∈

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implies that the probability density of a long distance link being established at a distance d is inversely proportional to d. According to the recent work, the proposed content distribution mechanism contains two parts: firstly, construct a content mechanism network based on the collections of popular routes queries. Secondly, give the solutions of request routing system in CDNs.

3 Small-World Content Distribution Mechanism 3.1 Construction and Initialization According to the discussion above, obviously, it is important to make data stored close to the requestors in content distribution networks. The proposed small-world DHT determines the distance between nodes by underlining DHT proximity routing, which is different from the measurement of distance between two ip addresses. In order to record the incoming queries, each node maintains a routing table to record the next-hop nodes these queries traveling through before they arrive in the key node. This routing table is a Hash table, in which stores the key and the location of the data. In order to generate a content delivery network based on DHT, each node is given a node identifier based on the content weights and nodes in CDNs is linked to the direct nearest neighbors nodes they know. The identifier of a node is generated by a hash function based on the contents it provides. If there are other links to that node in CDNs, these links connected to the other neighbors. The content weight is decided by the number of queries in CDNs. Algorithm1 describe the initial process of constructing a content distribution network. Algorithm 1: construction of content distribution network

program Construction( int max_query) begin If a key’s query count equals to max_query Choose a new node act as server; New server send joining message; give the node a identifier Ni according to its content weights; end if search the identifier Ni in the routing table with maximum query rate; If no such identifier Ni Add Ni to the routing table with maximum query rate; Inform all nodes in system by sending messages to them; End if end. 3.2 Reinforce the Structure with Small-World Feature According to Algorithm 1 described above, a initial content delivery structure is built. In order to utilize the small-world phenomenon of the interests from users, some additional links are added based on a probability density function over the weights of contents between nodes in the content distribution network. Inspired by symphony, the

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proposed method makes use of the weights of queries’ interests and contents. The smaller the differences between two nodes’ content weights, the higher probability that they are joined by a link. According to this, a node add a new link by computing the difference from the probability density function pn(x)=1/xln(x) and search a node with the difference in the whole network. Then the node sends messages to a number of selected neighbors with its coordinate and the content weight difference. These selected neighbors check their routing tables for any content nodes with the computed content weight difference by probability density function. If find nothing, then a new value is drawn by probability density function and begin the process again. If there are some hits return, then an addition link between the node and the found node is established.

4 Query and Routing Mechanism This section details the query and routing operation in CDNs. Because of the proposed content distribution network construction mechanism adds the content weight according to the query interests, queries can search the replicas in the relevant interests servers. Therefore, when routing a query, a client node looks for the key-k forwards the lookup to its neighbor nodes with identifier-n and these neighbor nodes look up the minimized difference between k and n in the identifier space. If a matched node is found, it returns the query results to the handler, otherwise forwards the query to its next neighbor. Because of using the small-world feature to make the CDN structure revised, the content node make the decision based on the normal links and the added links when selecting the next hop. Algorithm2 present the details of query and routing mechanism. Algorithm 2: query and routing in content distribution network

program Nexthop (key k) begin if Islowest(k-n)//look for the neighbor node n with the lowest difference return nexthop as n ; increase the query weights of node n; end if end. Theorem 1. Given a CDN of N nodes, with the number of maximum query size M, the average search path length for search across both normal links and added lins is O(log(2N/M)). Proof: Note that the basic structure is formed by the normal links and the added links, query is built on all links in the network. During the search process, the algorithm maintains a search-hit list in each server. For every queries, a server node replaces the lowest hit rate in the search-hit list with the highest hit rate with probability of N/(N + M).

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5 Experiments Results and Analysis The proposed strategy inspired by the small-world DHT protocol in peer-to-peer network present solutions for routing a query to the nearest server node in CDNs. In this section, the experiments will show that the proposed algorithm is very efficient at minimizing the query delay. We measure the content distribution system by using a simulator built on the top of Chord. The nodes are generated by the function based on uniform distribution. The parameters of the experiment are given in Table 1. Table 1. The parameters used in the experiment. Descriptions The rate of key production The rate of query arrival The latency of next hop The average throughput of replica data The content weight per node

Value

α=4 qa 5ms 100/s 90% of each capacity

In figure1, we compare the average query delay among normal network without CDN, basic CDN and CDN with small-world links. It shows that the average query delay in CDN with small-world feature has the advantage in he average query delay.

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Fig. 1. Average query delay (qa = 100/s)

Figure2 shows the average query hops comparison among normal network without CDN, basic CDN and CDN with small-world links.

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4 3.5 3

s p o h 2.5 y r e u 2 q e g a 1.5 r e v A

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Fig. 2. Average query hops (qa = 100/s)

It shows that the CDN with small-world links performs better than the normal network without using content distribution method.

6 Conclusions In this paper, we present a content distribution mechanism by using the overlay DHT with small-world features. The initial construction of content distribution network is based on a structured overlay model, afterwards, reinforce the structure by adding additional links according to the query interests. The mechanism present in the paper shows an efficient way of request routing. The experiments show that the delay and hops in CDN with small-world features have advantages compared to the normal network without using CDN and normal CDN without small-world features.

References 1. 2. 3. 4. 5.

Peng, G.: CDN: Content distribution network. Arxiv preprint cs/0411069 (2004) Pathan, M., Buyya, R., Vakali, A.: Content Delivery Networks: State of the Art, Insights, and Imperatives. Content Delivery Networks, 3–32 (2008) Pathan, M., Buyya, R.: A Taxonomy of CDNs. Content Delivery Networks, 33–77 (2008) Chen, Y.: Dynamic, Scalable, and Efficient Content Replication Techniques. Content Delivery Networks, 79–104 (2008) El Dick, M., Pacitti, E., Kemme, B.: Flower-CDN: a hybrid P2P overlay for efficient query processing in CDN, pp. 427–438. ACM, New York (2009)

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Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393, 440–442 (1998) Kleinberg, J.: Small-world phenomena and the dynamics of information, p. 431. MIT Press, Cambridge (2002) Manku, G.S., Bawa, M., Raghavan, P.: Symphony: Distributed hashing in a small world, pp. 10–10. USENIX Association (2003)

Experimental Study on Simulated Cerebral Edema Detection with PSSMI Gui Jin1, Mingxin Qin1,*, Chao Wang2, Wanyou Guo2, Lin Xu1, Xu Ning1, Jia Xu1, and Dandan Gao1 1 College of Biomedical Engineering and Medical Imaging, Third Military Medical University, Gaotanyanzheng street 30, Shapingba district, 400030 ChongQing, China 2 College of Electronic Engineering, Xidian University, Taibai South Road 2, 710126 Xi’an, China [email protected], [email protected], [email protected]

Abstract. Based on PSSMI method, one new detection system and the physical model of cerebral edema, the experimental study of simulated detection of cerebral edema was carried out. We applied three kinds of excitation signals with different frequencies. Three kinds of NaCl solutions were used to simulate the brain tissue, cerebral edema and calibration solution, whose conductivities were 0.133s/m, 0.194s/m and 3.6s/m respectively. To simulate the volume change of cerebral edema, the solutions were increased from 5 to 100 ml with an infusion pump. The phase resolution was up to 0.005° and the range of the gain was -10 35dB in the detection system. The experimental results show that the phase shift is directly proportional to volume, conductivity and frequency. The experimental study suggests that the PSSMI method has the potential of being a simple method for cerebral edema detection.

～

Keywords: magnetic induction; phase shift; cerebral edema.

1 Introduction Phase shift spectroscopy of magnetic induction (PSSMI) method applies a certain frequency range of magnetic fields to induce eddy current in biological tissues and the phase shift spectroscopy between the excitation magnetic field and the inductive magnetic field is then detected. The phase shift spectroscopy will be used to measure the occurrence and progress of cerebral edema and have an important significance for the detection of cerebral edema [1-3]. PSSMI needs the high precision of phase shift. In the previous work, we have established a detection system based on phase locking amplifier(SR844), whose precision was only 0.02° and structure was complicated. So we redesigned a new detection system of cerebral edema. In order to test the new system performance a physical model was used to simulate cerebral edema and detection experiments were made. *

Corresponding author.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 361–367. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Configuration of Our Detection System The detection system includes an excitation source, excitation and detection coils, a physical model of cerebral edema and a phase detector. The excitation current from the excitation source flows into the excitation coils to generate the magnetic field, the magnetic field generates the inductive magnetic field in the physical model. The excitation field and the inductive field are measured by the detection coil. The phase shift between the signal from the detection coil and the reference signal from the excitation source is measured by the phase detector and displayed on a screen.

Fig. 1. Detection System includes (1) phase detector, (2) excitation source, (3) brain edema physical model, (4) excitation coil, (5) detection coil, (6) skin dilator, (7) infusion pump and (8) beaker.

Fig. 2. Schematic Diagram of Detection System.

2.1 Excitation Source The excitation source can output two sinusoidal signals. One signal is used as the excitation signal and the other is the reference signal. The excitation source provides three frequencies and the range of output power from the excitation source is

Experimental Study on Simulated Cerebral Edema Detection with PSSMI

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1.5dB 33db [2-3]. The frequency stability is the order of magnitude of 10-4, the distortion reaches 10-2 10-4 and the SNR is 30 60dB in the excitation source.

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2.2 Physical Model The physical model includes an organic glass ball and a skin dilator[6], which can simulate the irregular geometry, different volumes and different conductivities of cerebral edema. The excitation coil and detection coil are fixed to organic glass ball’s neck and middle separately. The diameter of excitation coils and detection coils are 68mm and 220mm, the number of turns of both coils are 10 and the distance of two coils is 100mm [4-5]. The solution in the beaker is evenly input into the skin dilator by the infusion pump (ZNB-XY1). 2.3 The Phase Detector The phase detector for measuring the phase shift includes the band-pass filter, amplifier, AD, FDGA, DSP, flash memory and LCD. The phase detector can be calibrated and the temperature drift and the noise can be eliminated by a calibration software. You can adjust frequency, set gain, view waveform, measure phase shift and transmite data to computer on the phase detector. The parameters of our phase detector are as follows: the range of phase measurement: 0 180°, the phase precision: 0.005°, the range of gain: -10 35dB, once measurement time: 3 7s. The 12h data measured can be saved in the phase detector.

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3 Experiments on Simulated Cerebral Edema Detection 3.1 Design of Experiments The excitation source was connected to phase detector as Fig 2. The solution of 2800ml NaCl was injected into the physical model to simulate the brain tissue and the conductivity of the solution was 0.133s/m. Two beakers of NaCl solution was prepared to simulate the cerebral edema and physiological saline and the volume of each beaker was 100ml. The conductivities of two solutions were 0.194 s/m, 3.6 s/m respectively. The skin dilator made of plastic film was connected to the infusion pump. The infusion pump evenly transported the solution from the beaker to the skin dilator, the speed of pump was set to 2000ml/h and each time volume was set to 5ml. The phase shifts were measured by the phase detector under the conditions of two different solution volumes and three different frequencies. Process: First, one simulated solution and operating frequency were selected. Whenever each 5ml solution was injected, the phase detector measured the phase shift in time. The solution in the skin dilator was increased from 0 to 100ml, the phase detector got 21 datas in each measurement. After this measurement was over, the infusion pump drew out 100ml. Again, 20 measurements were repeated in the above method. Second, The operating frequency was changed and the above step was repeated. Third, The solution was changed. Step one and two were repeated.

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The data was processed as follows: 20

∑ ( θ ji − θ j 0 ) X obi =

j =1

(1)

.

20

Here, j is the measurment number from 1 to 20, i is the data number in one measurement. θji stands for the phase shift and θj0 represents the phase shift when the skin dilator is empty. Xobi represents the average phase shift over 20 measurments[3]. 3.2 Experimental Results Fig 3 shows the phase shift curves generated by the simulated cerebral edema solution in three kinds of frequencies. From the curves, the phase shift increases as solution volume increases and the change ratios are different. The change of phase shifts are 0.007°, 0.479° and 0.533° using three operating frequencies respectively when the volume increases from 0 to 100ml. 0.6 f1

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phase shift ( °)

Fig. 3. The phase shift of cerebral edema.

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

f1

0

f2

20

f3

（）

40 60 volume of the solution mL

Fig. 4. The phase shift of physiological saline.

80

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Fig 4 shows the curves generated by the physiological saline. From the curves the phase shift increases also as the volume increases, and the change ratios are also different. The change of phase shifts are 0.011°, 0.584° and 0.853° using three operating frequencies respectively when the volume increases from 0ml to 100ml. Fig 5, 6 ,7 are the curves of two solutions using three operating frequencies, in which the experiment datas are the same as Fig 3, 4. The changes of phase shift generated by two solutions are very little, the maximum is only 0.011° when the frequency is f1. The change is larger and the maximum is 0.584° as to frequency f2. The change is the largest and the maximum is up to 0.853° as to frequency f3. From the curves, we know the phase shift of the physiological saline is larger than the cerebral edema’s in the same volume and frequency. 0.012

phase shift(°)

0.01 0.008 0.006 0.004 0.002 0 0

10

20

30

40 50 60 the volume of the solution(ml)

cerebral edema

70

80

90

100

80

90

100

physiological saline

Fig. 5. The phase shift caused by frequency f1.

0.7 0.6

phase shift(°)

0.5 0.4 0.3 0.2 0.1 0 0

10

20

30

40

50

60

70

the volume of the solution(ml) cerebral edema physiological saline

Fig. 6. The phase shift caused by frequency f2.

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G. Jin et al. 0.9 0.8

phase shift ( °)

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

10

20

30

40

50

the volume of the solution cerebral edema

60

（mL）

70

80

90

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physiological saline

Fig. 7. The phase shift caused by frequency f3.

4 Discussion According to our experimental results, it can be seen: (1) From the curves in Fig3, 4 our new detection system can detect the phase shift less than 0.01° for frequency f1. The phase precision of our system is higher than the phase locking amplifier(SR844). (2) From Fig 3, 4 the greater the volume, the greater the phase shift generated is, under the condition of the same conductivity solution and the same frequency. The higher the signal frequency, the greater the phase shift generated is, under the condition of the same conductivity solution and the same volume. For frequency f1 the phase shift generated is less than 0.1° and for frequency f3 the phase shift generated is more than 0.5°. (3) From Fig 5, 6, 7 the phase shift generated by physiological saline is larger than cerebral edema with regard to the each frequecncy. (4) According to the formula (2) the phase shift is directly proportional to the conductivity, the volume and the frequency [6] and our experimental results are consistent with the formula (2).

θ ∝ kωσ

.

(2)

θ is the phase shift, k is the geometry coefficient, σ is the conductivity, ω is the angular frequency. From above discussions our new experimental results are consistent with the results of our previous tests, so the new system is proved to be feasible. There are two problems in our experimental study. One is that the excitation and detection coils are exposed to the surrounding environment and suffered from various EMI, such as human body influence and power interference and so on. The other is that the self-calibration needs too long time in our phase detector. In order to solve the above problems the physical model should be shield to eliminate EMI and the selfcalibration time of the phase detector should be reduced by optimizing our selfcalibration software.

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Acknowledgments. This work was supported by National Natural Science Foundation of China (61072254).

References 1. Hu, X., Qin, M., Liang, W., et al.: Study on techniques of phase detection in brain magnetic induction tomography. In: Proceedings of ICBBE 2007, pp. 782–785 (2007) 2. Zhou, W., Qin, M., Li, K., et al.: Study on detection phase deviation in brain MIT system. In: IFMBE Proceedings, vol. 25, pp. 328–330 (2009) 3. Liang, W., Qin, M., Jiao, M., et al.: Phase Detection Based on the Lock-in Amplifier SR844 and Experiments of Brain Neuron Cells in MIT System. In: Proceedings of ICBMEI 2008, pp. 638–642 (2008) 4. Li, K., Qin, M., et al.: The calculation and Measurement on phase shift in Single-channel BMIT system. Chinese Journal of Medical Physics 26(3), 1097–1101 (2009) 5. Jiao, M., Qin, M., Liang, W., et al.: Design and implementation of a new type excitation source and the optimal excitation coil for MIT. In: Proceedings of ICBBE, pp. 538–541 (2008) 6. Gfiffiths, H., Stewart, W.R., Cough, W.: Magnetic induction tomography: A measuring system for biological tissues. Ann. N Y Acad. Sci. 873, 335–345 (1999)

Medium Choice of Chinese Consumers in Obtaining Advertising Information about Minitype Automobile* Dao-ping Chen** School of Economics and Management, Chongqing Normal University, Chongqing 400047, P.R. China [email protected]

Abstract. This paper explores the media choice of Chinese consumers in obtaining advertising information about minitype automobile. A worldwide survey which involves a majority of Chinese areas is conducted. The survey focuses on consumers’ demographic characteristics, media choice and consumption things (first or repeated consumption). The result of the survey shows that consumers for obtaining information about minitype automobile chiefly choose friend or relative, newspaper, TV or automobile dealer as the media. The study finds that there isn’t a significant difference between first and repeated consumers in the media choice and that the demographic characteristics significantly affect the media choice of consumers. Consumers’ living city, occupation and education are the most important characteristics which affect the media choice of consumers. Keywords: medium choice, advertising information, minitype automobile, consumer.

1 Introduction A steady and rapid growth of China economy in longer time provides basic condition for development of Chinese automobile industry. China takes near 40 years, from 1953 to 1992, to make its automobile production volume reach 1 million units; However, China’s automobile production volume reaching 2 million units only spent 8 years, from 1992 to 2000, and then it only takes 2 years that China's automobile production volume reaches 3 million units in the end of 2002 (Zhang and Sun 2004). Weng (2004), vice-minister of Chinese Ministry of Communications, predicts that the total amount of civil automobile will reach 140 million or so in 2020. Facing such huge chance, multinational automotive companies, such as GM, Ford and Kreisler, invest one after another in China. In order to lure consumers to their brands, automakers place lots of advertisements in media. Statistics shows that advertising expenditure by automotive firms reach RMB 4.6 billion in 2003, doubling in size since 2002, with RMB 2 billion on TV and RMB 2.6 billion spent on print (Savage 2004). In this situation, automakers are confronted with an important *

Funded by Doctor Foundation of Chongqing Normal University (No. 11XWB004). ** Dao-ping Chen: PhD; Asso. Prof.; Research interests applied statistics. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 369–377. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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decision: media decisions, namely, what medium should be chosen to advertise about automobile? If a manufacturer advertises in a certain medium, but consumers don’t like to choose the medium to obtain the advertising information. The advertising information in the media can’t reach or can’t effectively reach these target consumers even though the manufacturer has taken a lot of expenditure for it. Accordingly, it is important to study the medium choice of consumers’ obtaining advertising information. This paper studies medium choice of Chinese minitype automobile consumers. In the remainder, the previous relevant literatures are briefly reviewed. Next, a questionnaire is designed and accounted for responses. Then, the results are present and interpreted. Finally, conclusion and implication are given.

2 Literature Review Advertisers and media planners generally think that media differentially impact the effectiveness of advertising and media context has an important influence on the value of advertising (Ducoffe 1995). Among conventional media, Magazine advertising is especially efficient because it targets consumers by demographics and lifestyle (LaReau 2005). Therefore, automobile companies are increasingly working with food and travel magazines to sponsor consumer promotions and special events (Bernstein 2004). A research (Marketing (UK) 1999) indicates that potential automobile buyers find television and newspaper the most valuable media when it comes to making a new purchase, while radio and posters are least helpful. Since Internet appeared, there have been a lot of researches about the web medium. It is reported that more automobile buyers turn to Web for information, especially in the initial stage of purchasing automobile (Milsom 2003). Yoon and Kim (2001) think that one of the most significant differences may be the interactivity of Internet advertisements. In fact, Internet, as one of all kinds of media, is only a tool that closes relationships with automotive customers even if it possesses powerful function (Washington1998). Thus, mass media advertising is still very important in strengthening the bond between automakers and consumers (Serafin 1994). Therefore, the consumers’ needs and preferences on the advertising medium are vital for automakers to be faced with medium decisions. A study (Pollay, Tse, and Wang 1990) suggests that Chinese consumers are more positive about advertising than consumers in the West. Another study (Zhou, Zhang, and Vertinsky 2002) has also gotten similar results that urban Chinese has similar or more positive attitudes toward advertising than their America counterparts. Chinese households now possess more TV, newspapers and computers than before, which offers a condition for advertisement reaching consumers. Moreover, television viewing is considered the most popular leisure time activity in China (Wei and Pan 1999). Chinese local firms, therefore, tend to place a special emphasis on television advertising (Li 2004). Meanwhile, newspapers in China have also grown in ways similar to television stations, although they tend to take on a stronger regional than national feature (Yao 2004). Like television and newspapers, magazines are changing rapidly in China (Zenith Media 2000/2001). After compared with television, newspapers, and magazines’ effectiveness to reach particular consumer segments in

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China, Hung, Gu and Tse (2005) find that magazines have the highest targetability among the three most popular mass media. Another survey (Zenith Media 2000/2001) on the proportion of reaching target audience reported that there are the different reaching rates for different media in China: television reaches 89%, newspapers reaches 72%, and magazines reaches 13% of the population. It is not doubt that these research results are very helpful for automakers to make media decision on advertisements. But authors don’t more consider consumers’ preference to advertising media.

3 Questionnaire and Response Nine media are selected in this questionnaire. They are newspaper, magazine, TV, street poster or billboard, Internet, automobile dealer, automobile manufacturer, automobile exhibition and friend or relative. That friend or relative is included in the questionnaire is because compared with the West, China is considered a high context culture (Hall Hall 1990). A research (Prendergast et al. 2001) shows that personal relationships with friends and family are more important, and these personal relationships can be extended to affect business relationships in a high context culture. In questionnaire, gender, living city, occupation, education, age and monthly household income are used to describe consumer characteristics. Gender has two levels (1=male, 2=female), living city four levels (1=big city, 2=city of middling size, 3=county seat, 4=villages and towns), occupation seven levels (1=government servant, 2=employee of national enterprise, 3=employee of private enterprise, 4=employer of individual enterprise or partnership enterprise, 5=farmer, 6=professional (lawyer, accountant, teacher, doctor, athlete, reporter etc.), 7=other), education six levels (1=junior high school or below, 2=senior high school, 3=technical secondary school, 4=junior college, 5=college or university, 6=graduate student), age seven levels (1=18-21 years, 2=22-25, 3=26-29, 4=30-34, 5=35-39, 6=40-59, 7=60 or above 60 years), monthly household income four levels (1=less than RMB 2000, 2=RMB 2000 to less than RMB 5000, 3=RMB 5000 to less than RMB 8000, 4=more than RMB 8000). This survey is a large what is supported by an automotive group company. The area involved in the survey amounts to 29 provinces, municipalities directly under the central government or autonomous regions. The survey is conducted between December 2003 and March 2004 by means of 263 automobile dealers that sell minitype automobile. Each dealer is with responsibility for providing and regaining 10 questionnaires, thus the questionnaires add up to 2630. The object of the survey is the consumer selected randomly from consumers entering the shop of dealer and having intention to purchase minitype automobile. A total of 2623 questionnaires are returned. Out of 2623, 280 are discarded due to their incompleteness and the remaining 2343 questionnaires are used for the final analysis. The data are analyzed using SPSS 15.0.

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4 Results 4.1 Media Choice of Chinese Minitype Automobile Consumers The result of the media choice of consumers is shown in able 1. Of total 2343 cases, 1305 are from consumers of first purchasing automobile and 1038 are from consumers of repeatedly purchasing automobile. According to Table 1, the percentage of the media choice to consumers of first purchasing automobile is as follows: friend or relative (64.7%), newspaper (45.1%), automobile dealer (43.2%), TV (39.8%), automobile manufacturer (27.0%), automobile exhibition (26.6%), Internet (23.1%), magazine (13.6%), and street poster or billboard (5.2%). For the repeated consumers, the percentage of media choice breakdown is as follows: friend or relative (69.2%), automobile dealer (47.2%), newspaper (43.4%), TV (37.4%), automobile manufacturer (26.3%), automobile exhibition (23.2%), Internet (18.5%), magazine (11.3%), and street poster or billboard (6.6%). The percentage of media choice between first consumers and repeated consumers only has a small difference. Is the difference significant A MANOVA is conducted with two levels (1=repeatedly and 2=first) as the factor on the full set of 9 dependent variables, friend or relative, newspaper, magazine, TV, street poster or billboard, Internet, automobile dealer, automobile manufacturer and automobile exhibition (Wilks' Lambda=0.988 (Exact F=2.950, Sig.=.002<.01)). The result indicates that there isn’t a significant difference between them. Therefore, consumers can be considered as a whole in analyzing media choice. Friend or relative variable has the highest percentage, which reveals that there is the most possibility that friend or relative is selected as an information channel of Chinese consumers. The percentage of newspaper is higher than that of TV, which shows that it is more possible for consumers to obtain advertising information from newspaper than from TV though Chinese families almost have TV. Why? A likely cause is that the advertising in newspaper provides more detail for consumers to intend to purchase automobile while the advertising in TV does not. Though Internet has had a rapid development in China in recent years, it is early for Internet to be regard as a main medium of automobile information. It is surprised that the percentage of magazine, as an important traditional medium, is only 13.6% (first) or 11.3% (repeated), which may be because minitype automobile consumers aren’t known as chief target of magazine. According to the percentage of media choice, the 9 media can be classified into 4 groups as follows: friend or relative is first group, newspaper, TV and automobile dealer are second group, automobile manufacturer, automobile exhibition and Internet are third group, and magazine and street poster or billboard are fourth group. When Chinese consumers intend to purchase minitype automobile, it is most likely for them to choose friend or relative to learn information, and it is more likely for them to choose newspaper, TV and dealer to obtain information. Evidently, it isn’t very high that Chinese consumers select manufacturer, automobile exhibition and Internet to obtain automobile information. In addition, consumers seem not to like to choose magazine and street poster or billboard to obtain the information about automobile.

？

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Table 1. Proportion of Media Choice of Consumers

Media Friend or Relative Newspaper Magazine TV Street poster or Billboard Internet Automobile Dealer Automobile Manufacturer Automobile Exhibition Other

First Consumption Count Pct of Cases 844 64.7 589 45.1 177 13.6 519 39.8 68 5.2 302 23.1 564 43.2 352 27.0 347 26.6 107 8.2

Repeatedly Consumption Count Pct of Cases 718 69.2 450 43.4 117 11.3 388 37.4 69 6.6 192 18.5 490 47.2 273 26.3 241 23.2 87 8.4

4.2 Influence of the Demographics on the Media Choice In order to study the affection of demographics on media choice, Optimal Scaling Regression with gender, living city, occupation, education, age and monthly household income as independent while one of 9 variables, friend or relative, automobile dealer, newspaper, TV, automobile manufacturer, Internet, automobile exhibition, magazine and street poster or billboard as dependent is conducted using statistic software SPSS. The result is shown in table 2. Friend or Relative. The result of ANOVA shows that the regression model with friend or relative as dependent variable is statistically significant at the 0.01 level (F=5.678, Sig.=.000<0.01). From table 2, it can be known that living city (F=5.078, Sig.=.006), occupation (F=17.156, Sig.=.000), education (F=14.848, Sig.=.000) and MHI (F=4.066, Sig.=.017) produce significant influence. The four demographics are put in descending order of importance as follows: occupation (43.8%), education (39.5%), living city (13.9%), MHI (0.2%). Occupation, education and living city are three main characteristics. Gender and age have not significant affection on friend or relative. Table 2. Proportion of Media Choice of Consumers Media Friend or Relative

Newspaper

Characteristics Gender Living City Occupation Education Age MHI Gender Living City Occupation Education Age MHI

ANOVA

F= 5.678 Sig.=.000

F=3.796 Sig.=.000

Beta -.029 -.051 -.105 .100 -.028 -.044 .030 .112 -.056 -.036 .037 -.060

F 1.78 5.08 17.16 14.85 1.77 4.07 1.89 26.59 5.15 2.06 3.00 7.49

Sig. .182 .006** .000** .000** .171 .017* .169 .000** .000** .128 .050 .006**

Importance -.004 .139 .438 .395 .031 .002 .030 .596 .041 .073 .055 .206

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Magazine

Gender .029 1.82 .178 Living City -.024 1.08 .340 F=2.324 Occupation .068 8.86 .000** Sig.=.002 Education -.090 14.49 .000** Age .044 4.06 .007** MHI -.008 .12 .725 TV Gender -.019 .76 .384 Living City -.086 15.56 .000** Occupation .048 4.81 .000** F=1.943 Sig.=.016 Education -.059 6.86 .000** Age .025 1.36 .254 MHI .027 1.58 .209 Street Poster Gender .029 1.78 .183 or Billboard Living City -.035 2.39 .122 F=1.901 Occupation -.022 1.05 .389 Education Sig.=.019 .072 9.68 .000** Age .043 3.95 .047* MHI -.056 6.73 .000** Internet Gender -.007 .11 .742 Living City .043 4.22 .015* F=16.852 Occupation .173 54.09 .000** Sig.=.000 Education -.215 79.35 .000** Age .080 15.57 .000** MHI -.055 7.34 .000** Automobile Gender .033 2.38 .123 Dealer Living City -.087 16.16 .000** F=2.886 Occupation -.077 12.53 .000** Sig.=.001 Education -.046 4.17 .041* Age .017 .63 .535 MHI -.002 .01 .921 Automobile Gender .009 .16 .692 Manufacturer Living City .037 2.72 .100 Occupation -.074 10.87 .000 F=1.470 Education .038 2.81 .060 Sig.=.108 Age .013 .34 .794 MHI .061 7.27 .000 Automobile Gender -.049 5.13 .024* Exhibition Living City .042 3.44 .032* F=2.463 Occupation -.062 8.20 .000** Sig.=.001 Education -.064 7.80 .000** Age .011 .24 .627 MHI .048 4.78 .009** Note: MHI = Monthly Household Income. * Sig.<0.05. ** Sig.<0.01.

.008 -.026 .351 .534 .121 .013 .040 .522 .162 .152 .059 .063 .076 .154 .068 .424 .105 .172 -.003 .045 .365 .502 .057 .034 .050 .413 .382 .131 .023 .001 .011 .126 .457 .125 .014 .267 .192 .163 .268 .292 .021 .064

Newspaper. As shown in table 2, when newspaper is used as a dependent variable while six demographic characteristics as independent variable, the regression model is statistically significant at the 0.01 level (F=3.796,Sig.= .000<0.01). Three characteristics, living city (F=26.59, Sig.=.000), occupation (F=5.15, Sig.=.000) and MHI (F=7.49, Sig.=.006), have the significant influence on newspaper. Living city

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(59.6%) is the most important characteristics, followed by MHI (20.6%) and occupation (4.1%). The other characteristics, gender, age and education, have not significant affection on newspaper. Magazine. The result of ANOVA shows that the regression model with magazine as dependent variable has statistical significance at the 0.01 level (F= 2.324, Sig.=.002<0.01). Occupation (F=8.86, Sig.=.000), education (F=14.48, Sig.=.000) and age (F=4.06, Sig.=.007) produce significant influence on magazine. The three characteristics breakdown in descending order of importance are as follows: education (53.4%), occupation (35.1%), age (12.1%). From table 2, it can be known that gender, living city and MHI have not significant affection on choice to magazine. TV. The result of ANOVA on TV shows that the model is statistically significant at the 0.05 level (F= 1.943, Sig.=.016<0.05). Three of six characteristics, living city (F=15.57, Sig.=.000), occupation (F=4.81, Sig.=.000) and education (F=6.86, Sig.=.000), produce significant influence on TV. The three characteristics are put in descending order of importance as follows: living city (52.2%), occupation (16.2%), and education (15.2%). Moreover, gender, age and MHI have not significant affection on dependent variable TV. Street Poster or Billboard. The result of ANOVA shows that the model on street poster or billboard is statistically significant at the 0.05 level (F=1.901, Sig.=.019<0.05). And, education (F=9.67, Sig.=.000), age(F=3.95,S ig.=.047) and MHI (F=6.72,Sig.=.000) produce significant influence on street poster or billboard. The most important characteristic is education (42.4%), followed by MHI (17.2%) and age(10.5%). Other three characteristics of consumers, city, gender and employment, have not significant affection on street poster or billboard. Internet. The result of ANOVA on Internet shows that the regression model is statistically significant at the 0.01 level (F=16.852, Sig.=.000<0.01). Living city (F=4.22, Sig.=.015), occupation (F=54.09, Sig.=.000), education (F=79.35, Sig.=.000), age (F=15.57, Sig.=.000) and MHI (F=7.34, Sig.=.000) produce significant influence on Internet. Owing to table 2, education (50.2%) is the most important, followed by occupation (36.5%), age (5.7%), living city (4.5%) and MHI (3.4%). Gender is only characteristic which is not statistically significant in this case of Internet. Automobile Dealer. The result of ANOVA shows that the model on dealer is statistically significant at the 0.01 level (F= 16.852, Sig.=.000<0.01). And, living city (F=16.16, Sig.=.000), occupation (F=12.53, Sig.=.000) and education (F=4.17, Sig.=.041) produce significant influence on dealer. The three characteristics are ranked in descending order of importance as follows: living city (41.3%), occupation (38.2%), and education (13.1%). Gender, age and MHI have not significant affection on dealer. Automobile Manufacturer. The result of ANOVA on manufacturer shows that the regression model isn’t statistically significant at the 0.05 level (F= 1.47, Sig.=.108>0.05). Automobile Exhibition. The result of ANOVA shows that the regression model on automobile exhibition is statistically significant at the 0.01 level (F= 2.463,

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Sig.=.001<0.01). Five characteristics, gender (F=5.13, Sig.=.024), living city (F=3.44, Sig.=.032), occupation (F=8.20, Sig.=.000), education (F=7.80, Sig.=.000) and MHI (F=4.78, Sig.=.009), produce significant influence on automobile exhibition. The characteristics are ranked in importance as follows: education (29.2%), occupation (26.8%), gender (19.2%), living city (16.3%), and MHI (6.4%). Age has not significant impact on automobile exhibition.

5 Conclusion and Implication In this paper, the study which focuses on the media choice of Chinese minitype automobile consumers in obtaining advertising information about automobile and the influence of demographic characteristics on the media choice is conducted. Friend or relative among nine media is the most-used channel by which consumers obtain information about automobile. Moreover, newspaper, TV and automobile dealer are also often selected as the media by which Chinese consumers obtain advertising information about automobile. The investigation shows that automobile manufacturer, automobile exhibition and Internet are not the main media of minitype consumers which obtain advertising information about automobile. Again, Chinese consumers appear not to like to select magazine and street poster or billboard in obtaining information about automobile. The study finds that the demographics of Chinese consumers have a significant impact on their selection to media in obtaining automobile information. Living city, occupation and education are the most important in six characteristics variables which affect the choice of consumers on media. Gender of consumers has not significant influence on almost all choice of consumers toward media but automobile exhibition. Considering the importance of friend or relative in Chinese consumers’ obtaining automobile information, manufacturers should hold a persistent praise toward brand from consumers which have purchased minitype automobile because these consumers may become a friend or relative of potential consumers which intend to purchase minitype automobile. The results also provide substantial implications for minitype automobile manufacturers which face media decision on advertisement. If an automaker intends to select a medium to advertise from some media, newspaper or TV should first be considered, and the next should be Internet or automobile exhibition. Specially, automobile dealer or automaker self may also be regarded as an important medium which can provide automobile information. Another implication derivable from this study is that manufacturers should do a classification on consumers according to their living city, occupation or education characteristics, and then integrate several media to publish advertising information suitable for consumer categories, which may help manufacturers well to consider the different media choice of different consumer categories. This study is somewhat limited in the sense that the sample is confined to consumers entering the shop of dealer and having intention to purchase minitype automobile, therefore, our results are only a first step toward understanding Chinese minitype automobile consumers. A more comprehensive survey that focuses on all consumers of automobile in China should be conducted in the future. If so, a more representative sample can be obtained. By means of the new sample, a contrast on the media choice between consumers of minitype automobile and that of other automobile categories can be done.

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References 1. Zhang, H.S., Sun, J.: Zuiju touzi qianli de qiche shichang–Zhongguo qiaoran cong qiche daguo xiang chanye daguo maijin(The most potential automobile market–China strides forward industry country from automobile country). International Financing 1, 17–24 (2004) 2. Weng, M.Y.: nian quanguo mingyong qiche baoyouliang jiang biaosheng zhi 1.4 yi liang (The possessing amount of Chinese civil automobile will reach 140 million in 2020 (2020) http://www.pcauto.com.cn/news/yjpl/medium/0409/130866.html (accessed August 8, 2005) 3. Savage, M.: Nielsen Study Casts Doubts on Impact of China Car Ads. Media Asia 8, 1/8, 1c (September 2004) 4. Ducoffe, R.H.: How Consumers Assess the Value of Advertising? J. of Current Issues and Research in Advertising 17, 1–18 (1995) 5. LaReau, J.: Magazines Are Pricey–But a Bargain. Automotive News 79(6139), 46, 1/4, 1c (2005) 6. Bernstein, M.: SI Stays No. 1 in Magazine Auto Ads. Automotive News 79(6116), 26D, 1/3 (2004) 7. Marketing (UK): Radio and Poster Ads Fare Badly in Mirror’s Research. 07/22/99, 4, 1/9 (1999) 8. Milsom, P.: More Car Buyers Turn to Web for Research. New Media Age,13, 1/2, 1c (August 21, 2003) 9. Yoon, S.J., Kim, J.H.: Is the Internet More Effective Than Traditional Media? Factors Affecting the Choice of Media. J. of Advertising Research 41, 53–60 (2001) 10. Washington, F.S.: Consumers in Command with Internet. Automotive News 71(5749), 28, 1/2 (1998) 11. Serafin, R.H.: Cleveland Still no Substitute for Traditional Ads. Automotive News, Cars & Customers 68(5544), S-24, 2/3, 6c (1994) 12. Pollay, R.W., Tse, D.K., Wang, Z.: Advertising, Propaganda, and Value Change in Economic Development: The New Culture Revolution in China and Attitudes toward Advertising. J. of Business Research 20, 83–95 (1990) 13. Zhou, D.S., Zhang, W.J., Vertinsky, I.: Advertising Trends in Urban China. J. of Advertising Research 42, 73–81 (2002) 14. Wei, R., Pan, Z.D.: Mass Media and Consumerist Values in the People’s Republic of China. International J. of Public Opinion Research 11, 75–97 (1999) 15. Li, Y.: Huwai guanggao weixie dianshi guanggao de bazhu diwei (Outdoor ads threaten TV as the top advertising media). Zhongguo jingying bao, 23 (2004) 16. Yao, L.: nian baokan guanggao shichang xinshi fenxi (Analysis of, print ad market) (2003) http://www.mediasinobnet.com/wzjx/0402121.htm (accessed June 11, 2004) 17. Zenith Media: China: Market and Media Fact. London: Zenith Media (2000/2001) 18. Hung, K., Gu, F.F., Tse, D.K.: Improving Media Decisions in China. J. of Advertising 34, 49–63 (2005) 19. Hall, E.T., Hall, M.R.: Understanding Cultural Differences. Intercultural Press Inc., Yarmouth (1990) 20. Prendergast, G., Shi, Y.Z., West, D.: Organizational Buying and Advertising Agency– Client Relationships in China. J. of Advertising 30, 61–71 (2001)

The Finite Element Modeling for Mechanical Feature Analysis of Human Lumbar L4-L5 Segment Juying Huang1, Haiyun Li1,∗, and Hao Wu2 1

School of biomedical engineering, Capital Medical University, Beijing, China 2 Xuan Wu Hospital affiliated to Capital Medical University, Beijing, China [email protected], [email protected], [email protected]

Abstract. In this paper, we propose an approach to calculation and visualization of mechanical features for human lumbar L4-L5 segment. A finite-element analysis model of an intact lumbar L4-L5 segment is formed from a series of CT-scanning images. Axial compressive loads, which simulate the pressure from above, are applied to the FEA, and 10Nm moment are applied in flexion, extension, lateral bending and axial rotation to simulate bending. The simulation calculation shows the stress-strain distribution and deformation of the spine. The finite element method model can help surgeons analyze biomechanical characteristics of lumbar effectively and optimize individual therapy for orthopedic clinical. Keywords: motion segment; intervertebral disc herniation; FEM; stress-strain.

1 Introduction The lumbar intervertebral disc is located between vertebrae, which are very flexible and can transfer loads, balance the body, stabilize the spine and absorb vibration[1]. The lumbar intervertebral disc consists of endplate, nucleus pulposus, and annulus fiber. Annulus fiber surrounds nucleus and prevents nucleus extruding out. Nucleus pulposus will produce strong reverse force and absorb the vibration. If the reverse force is too strong, nucleus will make annulus fiber broken and extrude out. Lumber disc herniation is an important cause of low back pain. The number of patients with interbertebral disc herniation is increasing. Changes from normal mechanical behaviour in the spinal motion segment may cause disc herniation. Thus, it is quite important to understand of mechanical behaviour in spinal motion segments in clinical diagnose and therapy for the lumbar disc herniation. The mechanical relationships of the lumbar have been examined using finite-element analysis(FEA). In this paper, we propose an approach to calculation and visualization of mechanical features for human lumbar L4-L5 segment, thus obtain the stress-strain distribution and deformation of the spine. This paper is organized as follows. We first illustrate the significance of this study in Section1. The methodology ∗

Corresponding author.

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is introduced in Section2. The results of calculation are given in Section 3. This paper is summarized in Section 4.

2 Methodology 2.1 Geometric Model A healthy young man with no disc disease was selected as normal subject. L4-L5 motion segment data were taken in axial direction. The CT slice images has a slice thinness of 0.8 and 512*512 resolution. In order to reconstruct the geometry model of the lumbar L4-L5 model segment, DICOM data were read in the Mimics10.01. Then after modifying the model and smoothing the surface, and removing some of the meaningless miscellaneous point, the whole process of reconstruction of the spine model could minimize the artificial interference, perfectly reflect the geometric patterns and local morphological details. Then we transmit the geometrical models into the SOLIDWORKS10.0 and get 3D geometry solid model which the ANASYS can read. 2.2 The Finite Element Model The 3D geometry model is imported into ANSYS12.0, after setting up real constant, defining material properties, setting boundary conditions, applying loads, we can obtain the different biomechanics behaviors of three-dimensional finite element models under different loading conditions. Details of the finite element model developed have been given and is briefly summarized here: the shape of the lumbar segment is reconstructed from data obtained from CT scans of an intact human L4-L5 motion segment, as shown in Fig.1.

Fig. 1. The finite model of L4-L5 segment

Each vertebral bone, consisting of cancellous bone and cortical bone, is modeled as a 8-node isoparametric material element using homogeneous and isotropic material properties [2]. L4-L5 vertebral consist of 64211 elements and 75025 nodes. Nucleus pulposus is modeled using solid elements to simulate an incompressible behavior with a low Young modulus [2]. Annulus fiber is modeled a flexible material element also using homogeneous and isotropic material properties. In order to appropriately model

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the changes of contacting areas of facet articulating surface, facet articulations are modeled using contact elements. Here, we hypothesize that the strain of spine is a small strain. The material properties used in the study are listed in Table 1. The complete model consists of 94794 elements and 134518 nodes. Table 1. Material properties of vertebrae, nucleus pulposus, annulus substance and ligaments material Cortical bone Cancellous bone Nucleus puposus Annulus fiber

Young’s modulus/MPa 12000 100 1 4.2

Poisson ratio 0.3 0.2 0.4999 0.45

2.2 Boundary Conditions With regard to the validation and accurateness of model analysis, we applie the boundary conditions on the finite element model. The boundary conditions on the model use pressure and restrains assigned to surface areas of the model. The inferior surface of L5 vertebral body and its spinous process are fixed in all directions. The restraints are used to limit the model movement with six possible values at the node on the surface, three translations and three rotations. The value of freedom is zero. Then couple the inferior surface of vertebral L4 body and the upper surface of intervertebral disc body, as well as the bottom intervertebral disc body and upper surface of L5 vertebral body in all directions of translation. The facet articulation is modeled as a 3D contact unit using interface elements [3]. 2.3 Load Cases Different axial compressive force distribute across superior surface of L4. L4 superior surface can bear 500N loads when a healthy person weighted 66 kg stands straight in a relaxation state. But the data goes up to 2000N when he lifted 100N with two arms stretch straight. The heavier he lifts, the more L4 superior surface bears. In order to simulating bending, we apply 10 Nm moment to beams attached to the upper surface of L4. These moments are applied in flexion, extension, lateral bending, and axial rotation. All moments are applied independently and are not combined. At the caudal end, L5 is constrained in all 3 translations and in all 3 rotations.

3 Results 3.1 Stress Distribution of Model Fig.2 shows the stress distribution of spine when applied 1000N axial compression load. It shows that the high stress concentrations are around spinal canal, pedicle region, and the upper body of vertebral.

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Fig. 2. Vos Mises distribution of L4–L5 segment at 1000N axial compressed load

3.2 Disc Stress Distribution Fig.3 shows the stress distribution of disc when applied 500N axial compression load. The disc consists of annulus fiber and nucleus pulposus. In the normal healthy disc, the hydrated nucleus pulposus exerts a hydrostatic pressure (intradiscal pressure (IDP)) on the annulus fibrosus (annulus) fibers [4]. The fiber bears a higher IDP [5],[6]. From the figure, we can see that from the annulus fiber to the nucleus pulposus, intensity of stress grows weaker and weaker (Fig. 3). It indicates that nucleus pulposus can absorb some compressive loads. That’s why the fiber shows higher stress than nucleus pulposus. And we can also see that the stress and strain distribution of lumbar is mainly concentrated on the posterior and bilateral posterior of annulus fibers in the normal disc. That is why the herniation often occurs in the posterior and bilateral posterior of annulus fibers. We can also observe that the stress becomes higher correspondingly as the loads applied to the disc increases. The relationship between IDP and external load is approximately linear as is shown in Table 2. It also illustrates that intervertebral disc has flexible properties in a certain extent.

Fig. 3. Vos Mises distribution of disc at 500N axial compressed loading

Fig.4 shows Vos Mises distribution of disc under different moment conditions. In flexion, the maximum stress is found in the posterior part. For lateral bending, the maximum stress is found in the posterolateral part of disc. These results are supported by clinical studies[7] that the greatest proportion of herniation (about 90%) are located

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in the posterior and posterolateral part of the disc. In rotation (to the left), the stress concentrates within the left and postero-left annulus because the torque is resisted primarily by the annulus of the disc[8]. When lumbar L4-L5 motion segment is loaded to simulate an extension movement of the lumbar spine, then approximately two third of bending is resisted by the neural arch, and one third by the disc[9]. The stress mainly concentrates in the posterior-lateral annulus. Table 2. The maximum stress of disc in different axial loads stress of disc

Vos Mises

200 150 100 50 0 500N 1000N 1500N 2000N 2500N

As shown in table 3, the stress level in the nucleus is quite low and is higher in the annulus than in the nucleus for all cases. Stress levels generated for the rotation case are the lowest and for flexion and extension are the highest in all loading cases.

(A)

(C)

(B)

(D)

Fig. 4. Vos Mises distribution of disc under different moment conditions:(A) flexion; (B) lateral bending; (C) rotation; (D) extension.

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flexion

lateral bending

rotation extension

the stress of disc

4 Discussion and Conclusion In the above, we have shown that mechanical behavior of the lumbar L4-L5 motion segment for various loading conditions can be clarified through current study by a 3D FEM. The study indicates the biomechanical characteristic as follows: (1) The strain of L4-L5 motion segment under axial compressive load increased with the performed load. (2) The stress level in the intervertebral disc is quite low but higher in the annulus than in the nucleus in different loading conditions. (3) Stress levels generated for rotation case are the lowest and for flexion and extension are the highest in all loading cases. The model is just one segment of the whole spine. The results may have differences if done with the whole spine. However, it still simulates the biomechanical characteristics of the lumbar preferably. Since the model set up here can give correct information of mechanical behavior in L4-L5 motion segment and provide medical areas valuable guidance in solving herniation. The finite element model of L4–L5 segments based on medical images can be considered as a useful tool to predict the stresses undergone by the discs under different loading conditions, and may be considered as a first step for patient-specific models to predict the appearance and evolution of different pathologies. This study can help surgeons analyze mechanical characteristics of lumbar effectively and help optimize individual therapy for orthopedic clinical. Our next step is to extend current work to simulate the relationship between disc bulge and overall disc mechanics.

Acknowledgement The authors would like to acknowledge funding from the National Natural Science Foundation of China (Grant No. 30670576) , Scientific Research Key Program of Beijing Municipal Commission of Education and Medicine ( Grant No.kz200810025011) and Clinical Cooperative Research Program of Capital Medical University (Grant No.10JL24).

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Lee, K.K., Teo, E.C.: Effects of laminectomy and facetectomy on the stability of the lumbar motion. Med. Eng. Phys 26, 183–192 (2004) Qin, Y.-X.: In: Proceeding of the First Joint BMES/EMBS Conference Serving Humanity, pp. 13–6 (1999) Tanaka, E., del Pozo, R., Tanaka, M., Asai, D., Hirose, M., Iwabe, T., et al.: Three-dimensional finite element analysis of human temporomandibular joint with and without disc displacement during jaw opening. Med. Eng. Phys 26(6), 503–511 (2004) Wagner, D.R., Lotz, J.C.: Theoretical model and experimental results for the nonlinear elastic behavior of human annulus fibrosis. J. Orthop. Res. 22(4), 901–909 (2004) Dolan, P., Adams Michael, A.: Recent advances in lumbar spinal mechanics and their significance for modeling. Clin. Biomech. 16, 8–16 (2001) Joshi, A., Fussell, G., Thomas, J., et al.: Functional compressive mechanics of a PVA/PVP nucleus pulposus replacement. Biomaterial 27(2), 176–184 (2006) Latorraca, A., Forni Niccolai Gamba, C.: Analysis on 149 consecutive cases of intervertebral disc prolapse operated with microendoscopic (MetrX) technique. Reumatismo. 56, 31–35 (2004) Krismer, M., Haid, C., Rabl, W.: The contribution of annulus fibers to torque resistance. Spine 21, 2551–2557 (1996) Adams, M.A., Dolan, P., Hutton, W.C.: The lumbar spine in backward bending. Spine 13(9), 1019–1026 (1988)

Hybrid Control Using Sampling PI and Fuzzy Control Methods for Large Inertia and Time Delay System Jia Xie1, Shengdun Zhao1, Zhenghui Sha2, and Jintao Liang1 1

Department of Mechatronics Engineering, Xi’an Jiaotong University, Shaanxi, Xi’an 710049, China 2 Department of Materials and Mechanical Engineering, Washington State University, Pullman, WA, 99163, USA [email protected], [email protected], [email protected], [email protected]

Abstract. In this paper, a hybrid control strategy for large inertia and time delay system was proposed. The proposed hybrid control strategy combines the sampling PI control and the linear control methods together. Respectively, the sampling PI control method is used to overcome the disadvantages of the control problems which are caused by the large inertia and time delay; while the linear control method is applied to achieve stable control of the steady-state control process and improve the system robustness. Since the parameters of the linear control are varied in the control process, the fuzzy control method is utilized to get the optimal parameters so that can realize the effective control. The hybrid control strategy is implemented and practically applied to a large inertia and time delay system ─ cement mill in a cement plant. The practical control results illustrate that feasibility the proposed strategy both effective and implemental. Keywords: Hybrid control strategy, Sampling PI control, Linear control, Fuzzy control, Large inertia and time delay system.

1 Introduction In the industrial process, large inertia and time delay systems are widely utilized. In practice, the system also has other features such as nonlinear, time-varying and uncertainties generally. The features of the system complicate the control problems and increase the control difficulties. In order to obtain a good control performance of the system, lots of researches have been done. At present, the main methods for the control of large inertia and time delay system are Smith Predictor [1], Forecast PID [2], Delay Compensator [3], Neural Network PID [4], Grey Forecast [5], and Hybrid Control [6], etc. Although each control method has its own advantages, they all have some shortcomings to embarrass the applications of the control methods. Specifically, Smith Predictor depends on accurate mathematical model and it is very sensitive to the external disturbances. Forecast PID can suppress noise, but the output response is slow. Delay Compensator has poor robustness. Neural Network PID relies on the training samples. So the control performance deteriorates when the control process is M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 387–393. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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changed. Grey Forecast causes forecast error when the system stays in the status of long-time delay, and will become worse in the multi-step prediction. Hybrid control seeks to achieve multiple performance objectives in a locally adaptive sense by switching between numbers of control methods. It is apparently different from the classical control that seeks to achieve balance trade off between multiple performance objectives using a single control method. Fluctuation, overshoot, and furthermore oscillation, which are difficult to avoid using classical control for the large inertia and time delay system, can be effective eliminated or reduced by hybrid control. Furthermore, compared with classical control, hybrid control has more advantages such as high speed of response, steady-state accuracy, disturbance attenuation, robustness, adaptation, and satisfaction of constraints. Therefore, hybrid control is an ideal alternative to the large inertia and time delay system. Sampling PI control offers enhanced performance if one considers the slow sampling rate system. Compared to the input, the response is time lag in the large inertia and time delay system. The sampling PI control can ignore the wrong response to the input received at this time and can effectively avoid the overshoot of the control process. In order to obtain stable control of the steady-state process and improve the robustness of the control process, a linear function is applied to the system when the control process enters into a range of a small error. But it is difficult to determine the parameters of the linear function because the large inertia and time delay system often features time varying. Fuzzy control is one of the most successful intelligent technologies for adaptive control which is especially useful for determining the values of parameters of the linear function in the control process of the large inertia and time delay system. This paper is organized as follows. Section 2 presents the sampling PI control method and describes the control algorithm in detail. In section 3, the linear control algorithm in the range of the small error is presented and the fuzzy control method for parameters modification is discussed. Section 4 proposes the hybrid control strategy and applies it to a large inertia and time delay system ─ cement mill in a cement plant. The control results are then shown to validate the control method. Section 5 concludes to sum up the work.

2 Sampling PI Control PID control is widely applied to the industrial process control. However, differential control is not required in the large inertia and time delay system. On the other hand, compared with the positional PI control, the incremental PI control has some advantages for using in this paper. Firstly, at each sampling time, the incremental value which is output by the incremental PI is filtered by a logic system to avoid the malfunction of the control system. The input value of the controlled object of the last sampling time can be kept when the improperly incremental value at this sampling time is ignored, which is able to maintain the stability of the system control process. The above control process is difficult to performance by positional PI. Then, in the control process of the hybrid control system, the control method applied to the controlled object is switched between several control methods according to the status

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of the system operation. In such case, the smooth switching is easy to realize by the incremental PI control because the incremental value is only concerned with this and last errors of the control system. Therefore, the incremental PI is selected to control the large inertia and time delay system in this paper. The incremental sampling PI control algorithm is expressed as

k = 0,1,..., n ⎧r (i ) − c (i ) kT ≤ t ≤ kT + Tc , e(i ) = ⎨ kT + Tc < t < (k + 1)T , k = 0,1,..., n ⎩0

(1)

Δm(i ) = K p [(1 + T / Ti )e(i ) − e(i − 1)]

(2)

where e(i) and e(i-1) are the i-times and (i-1)-times samples of the error of the control system respectively, and r(i) and c(i) are the i-times samples of the given and the output respectively, and T and Tc are the sampling period and control time respectively, and Δm(i) is i-times sample of the incremental value of the incremental sampling PI control, and Kp and Ti are the proportional gain and integral time constant of the incremental sampling PI control respectively. Then the i-times sample of the input of the controlled object m(i) is obtained as

m(i ) = m(i − 1) + Δm(i) .

(3)

Assuming that the incremental value is linear increase, the schematic diagram of the input of the controlled object generated by the incremental sampling PI control is obtained, and is shown in Fig. 1. In the period of control, the system is controlled by the incremental PI. Then waiting a period of time (T-Tc), the incremental PI control is performed again.

Fig. 1. Schematic diagram of the input of the controlled object generated by the incremental sampling PI control

3 Fuzzy Control for Parameters Modification PID control is particularly useful when the process dynamics are benign and the performance requirements are modest [7]. The incremental sampling PI control described in section 2 can effectively control the large inertia and time delay system

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when the error of the system is larger. However, when the error is smaller, the oscillation is difficult to avoid if the incremental sampling PI is still involved in the control system. At this time, the incremental sampling PI control should be replaced by other control method. The local linearization is an ideal alternative in the range of the small error. The control algorithm is a linear function which is expressed as

m(i ) = a (i )r (i ) + b(i )

(4)

where a(i) and b(i) are the slope and the initial value for linearization in the range of the small error at the time of i-times sampling respectively. The parameters a(i) and b(i) are varied in practice because the steady-state point is impossible to remain unchanged during the control process. Then fuzzy control is used to modify the parameters in the control process. The parameters a(i) and b(i) are varied according to the history of the system operation. The average value of the error of the system and the time spent during the output of the system beyond the range of the small error properly reflect the historical situation of the system running. Then the average error and the spent time at a cycle of the parameters adjustment are the inputs of the fuzzy control. The outputs of the fuzzy control are the adjustment steps of the parameters a(i) and b(i). Fig. 2 shows the block diagram of the fuzzy control for parameters modification.

Error

Average calculation

Input 1 Input 2 Input 1

Time record

Input 2

Adjustment step of a(i) Fuzzy control I Output

Fuzzy control II Output

Adjustment step of b(i)

Fig. 2. Block diagram of the fuzzy control for parameters modification

4 Hybrid Control System Realization and Application 4.1 Hybrid Control System According to the analysis in previous sections, the input of the controlled object is expressed as

⎪⎧m(i − 1) + Δm(i ) m(i ) = ⎨ ⎪⎩a(i )r (i ) + b(i )

e(i ) ≥ B e(i ) < B

(5)

where B is the range of the small error as mentioned above. Then the block diagram of the hybrid control system realization is established and shown in Fig. 3.

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Fig. 3. Block diagram of the hybrid control system realization

4.2 Application to a Large Inertia and Time Delay System In this paper, we apply the hybrid control system to a large inertia and time delay system ─ cement mill in a cement plant. The cement clinker and some supplementary materials are fed in the cement mill to produce the cement. When the mill is running, the steel balls, the steel segments and the materials, whose total weight is near 100t, are loaded in the mill. That means the mill is a large inertia system. In addition, it needs about 3 - 5 minutes to convey the materials to the mill by conveyor belt. The system has a considerable time delay. The system parameters are often changed because of the variation of the cement clinker and the surroundings disturbance. As shown in Fig. 3, control selector is used to select the input of the controlled object the cement mill between the sampling PI control and the linear control in small range. The parameter of the control selector is the range of the small error B. The parameters setting of the sampling PI control complies with the purpose of making the transition process fast and smooth. The linear control in small range is used to ensure that the control process is stable in the small range and that the control process enters into steady state quickly. The parameters modification of the linear control is performed by the fuzzy control method. Here, we only introduce the design of fuzzy control I. The design of fuzzy control II is similar to that of fuzzy control I. In this paper, the linguistic qualifications of the fuzzy control are positive large, positive medium, positive small, zero, negative small, negative medium, and negative large. Both input variables and one output variable of the fuzzy control have seven membership functions. Triangular membership functions are used for the middle membership functions, while trapezoidal ones are used at the two ends [8]. The range of the input variables the average error and the spent time are 2 % - 8 % and 2 min - 6 min respectively. The range of the output variable the adjustment step of a(i) is 1.3 % - 4.7 %. Each parameter in the system is normalized to the range of 0 to 100 for comparison and calculation. After the fuzzification, the min-max operator is used for the fuzzy reasoning and the centre of gravity method is used for defuzzification. The lookup table of the fuzzy control I output (adjustment step of a(i)) for control is shown in Table 1. The unit of the adjustment steps is also %. The other control data, which are not included in Table 1, can be calculated by interpolation.

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2 3 4 5 6 7 8

2 1.3336 1.4026 1.5557 1.7500 2.4438 2.8695 3.0000

2.5 1.3751 2.2663 2.3150 2.5537 2.6844 2.9470 3.0739

3 1.4722 2.4438 2.7024 2.7534 2.8525 3.1475 3.2976

Spent time [min] 3.5 4 4.5 1.6012 1.7500 2.2663 2.5428 2.5826 2.8525 2.8695 2.9221 2.9470 2.9261 3.0000 3.0739 3.0528 3.1305 3.2976 3.4171 3.4460 3.5562 3.7337 4.2500 4.3986

5 2.7024 2.8779 3.1475 3.2976 3.6849 3.7337 4.5278

5.5 2.9261 3.0528 3.4660 3.7337 3.8453 3.9849 4.6249

6 3.0000 3.1305 3.5562 4.2500 4.4443 4.5973 4.6666

Given/Output [%]

Average error [%]

Table 1. Lookup table of the fuzzy control I output (adjustment step of a(i)) for control

Fig. 4. Actual control results of the hybrid control system in a fifteen-minute period

The actual control results of the hybrid control system in a fifteen-minute period for the cement mill are shown in Fig. 4. The control process is stable and smooth. After the hybrid control system is applied to the cement mill in a cement plant, the yield of the mill has an increase of 9.15%, and the eligible ratio of the pulverous grade of the cement is improved from 84.21% to 95.15%. The data indicate that the hybrid control system is a valid strategy to control the large inertia and time delay system. The hybrid control system is effective in practical applications.

5 Conclusion To sum up, a hybrid control strategy using sampling PI and fuzzy control methods is proposed in this paper. The control strategy aims to achieve effective response, high robustness, and stable control of the steady-state process in the large inertia and time

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delay system. The sampling PI control method is applied when the error of the system is larger, and the linear control method is applied when the error is smaller. In order to track the system parameters variation in the control process, the fuzzy control method is used to adjust the parameters of the linear control instantaneously. Whereafter, the hybrid control strategy is successfully applied to a large inertia and time delay system ─ cement mill in a cement plant. The application results indicate that the proposed hybrid control system is effective and feasible to realize the control of the large inertia and time delay system.

Acknowledgments The authors gratefully acknowledge the contribution of National Natural Science Foundation of China under Grant 50575175, and the Significant Scientific Innovation Foundation of Shaanxi Province, China, under Grant 2007ZKC(2)03-04.

References 1. 2.

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Vrečko, D., Vrančić, D., Juričić, D.H., Strmčnik, S.: A New Modified Smith Predictor: the Concept, Design and Tuning. ISA Transactions 40, 111–121 (2001) Han, Z.X., Li, D., Pan, G., Huang, H.P.: Applying GIC to Improve Response of System with Time-varying Parameters and Big-inertia. In: 2nd International Symposium on Systems and Control in Aerospace and Astronautics, pp. 1–5. IEEE Press, New York (2008) Dikyar, S., Leblebici, T., Ozcelik, D., Unel, M., Sabanovic, A., Bogosyan, S.: Delay Compensation for Nonlinear Teleoperators Using Predictor Observers. In: 36th Annual Conference on IEEE Industrial Electronics Society, pp. 1483–1488. IEEE Press, New York (2010) Fang, M.C., Zhuo, Y.Z., Lee, Z.Y.: The Application of the Self-tuning Neural Network PID Controller on the Ship Roll Reduction in Random Waves. Ocean Engineering 37, 529–538 (2010) Huang, S.J., Huang, C.L.: Control of an Inverted Pendulum Using Grey Prediction Model. IEEE Transactions on Industry Application 36, 452–458 (2000) Li, H.X., Tong, S.C.: A Hybrid Adaptive Fuzzy Control for A Class of Nonlinear MIMO Systems. IEEE Transactions on Fuzzy Systems 11, 24–34 (2003) Åström, K.J., Hägglund, T.: Advanced PID Control. ISA, Research Triangle Park, NC, USA (2006) Neto, P., Mendes, N., Pires, J.N., Moreira, A.P.: CAD-Based Robot Programming: the Role of Fuzzy-PI Force Control in Unstructured Environments. In: 6th Annual IEEE Conference on Automation Science and Engineering, pp. 362–367. IEEE Press, New York (2010)

An Operator Controllable Authentication and Charging Solution for Application Store Jianbing Xing1,2, Zhaoxia Li3, Xiongwei Jia3, and Zizhi Qiao1,2 1

China United Network Communications Group Company Limited, 100033 Beijing, China 2 Beijing University of Post and Telecommunication, 100876 Beijing, China 3 China Unicom Research Institute, 100032 Beijing, China {xingjb3,lizx101,jiaxw9,qiaozz3}@chinaunicom.cn

Abstract. Application store is a very important supply channel for mobile internet applications. A suitable authentication and charging system is necessary for application store’s success service. Current application stores only authenticate and charge in the download process. This paper introduces a new method combining operator’s Business Operation Support System (BOSS) with application store’s authentication and charging process in both download and running stage, which better fits the requirements of the application store. Keywords: Network Application; Application Store; Charging; Authentication.

1 Introduction Huge amount of personalized and user centric mobile internet application is evolving in the mobile internet age. Traditional terminal pre-installation method for application distribution is not catching up with the mobile internet age. Application store as a new distribution and sales channel can provide a better connection between appdevelopers and end customers. This feature is very important to the operators [1]. Telecom operators and service/product providers are aggressively deploying application store service, trying to remain on the central position in the ecosystem. Currently the most successful store is Apple’s App Store which hits 10 billion downloads in 2011.01 [2]. Following Apple’s lead, different application stores are lunched by terminal vendors, operation system vendors and telecom operators. Examples including OVI store, Android Market, Chrome Web Store, App World, App Catalog, Mobile Market and China Unicom’s WO Store. Application store provides an open display and sales platform for developers, which encourages them to invent different applications. On the same time, telecom operators can provide more value-added services through different applications in the store, which can meet different user needs. Authentication and charging (A&C) is a basic function of the application store platform. Current application stores only authenticate and charge user on purchase. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 395–400. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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However, many applications in the store will need to call network or remote resources while running. So it is necessary to consider application running period A&C. This paper introduces application store’s A&C requirements in Chapter 2. Current A&C techniques are then presented in Chapter 3. Finally, a new A&C solution fulfilling the business requirements is presented in Chapter 4.

2 Authentication and Charging Requirements for Stores 2.1 Technical Architecture of Application Stores Currently, architectures of the application stores are similar. They include 3 parts: client, store platform and the development tools including the developer community. Clients are installed in the user terminal/PC or embedded in browsers. End user can use the client to browse, purchase and download applications. Different applications can be displayed in the store platform. User can browse, search and download application in the application stores through the link provided by the client. The store platform also takes care of the listing, delisting, price change and other management features. Development tools can provide support for the developers. App-developers can download latest API from the developer support part and upload the finished application here for approval and publication. 2.2 The Needs for In-Application Authentication and Charging Applications can be cataloged into native application or network application. Native applications utilize only the native resources, while network application can utilize remote network resources when running. In the life circle of an application, there are two stages requiring A&C: the downloading stage and the running stage. Table 1. Different applications needs for authentication and charging in different stages. Application Type Download/install Running stage

Native Application A&C needed No A&C needed

Network Application A&C needed A&C needed

Current application stores have developed A&C process during the downloading process. For those native applications, a one-off fee is enough. On the other hand, network applications will need to call network functions and remote application resources to run. Those network functions e.g. SMS, Internet data usage normally can not be freely provided. Therefore, it is necessary for the store operators to develop a new mechanism or system to authenticate the user and charge for the usage of the network resource when network application utilizes it.

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3 Current Store Authentication and Charging Techniques 3.1 Three Application Store Charging Methods Currently there are three methods for stores to charge users for their purchase. The first method is to charge the user on their bank cards (credit card or debit card). This method needs the store operator to interface with banks and utilize the bank’s charging facility to bill the user. But this method requires the store operator to interface with different banks, which is normally difficult to negotiate and program. Further more, potential risks associated with bank information will discourage users to make the purchase. The second method is the so called “third party payment” method. The application store operator is interfaced with a trusted third party e.g. Paypal or Alipay instead of different banks. By involving such a third party, the store operator can avoid the difficult negotiation and interface with different banks. However, the potential risk of financial information leak is still preventing the user from buying applications. The third method is to charge the user through their account with the telecom operators. When a user downloads an application from the store, he can be identified with an ID linked to their phone number and charged by the operator. 3.2 In-Application Purchase In-Application Purchase is a common charging method used in application stores for download A&C. A good example of the In-Application Purchase is the Apple’s IAP method [3]. IAP allows the developers to build-in store functions in their applications. Apple uses the Store Kit Framework running on an iOS device to communicate with the App Store, represent the applications to process the purchase safely and perform the IAP function. When the user make the purchase through a store embedded application, the application calls the Store Kit to collect purchase related information, and relies on the Store Kit to prompt the user for A&C. After successful purchase, the application can indicate the user about the result of the purchase. IAP function can also help the developers to charge advantage part/functions of the application. Those payable item or product could be electrical contents e.g. e-books, e-magazines, game levels or even advance features of applications. Before the developers list their applications, they need to register in App Store through iTunes Connect and provide name, description, price and other necessary meta data about their application products. A unique product ID is generated to identify the purchase status of the application in Store Kit and App Store. Once a user purchased an application product through an application, the application can identify the product as “purchased”. There are two ways to deliver the item, one is to unlock functions in the application (Built-in Product Model) and the other is to allow an external server to provide the item (Server Product Model).

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3.3 Standardization Efforts in Application Store A&C Techniques Many international standardization organizations e.g. OMA and 3GPP have formalized standards for A&C process in the telecom system. However, the application store has only become a powerful business model in the last few years. The related technical standards are not well developed. China Communications Standards Association (CCSA) is developing a technical report about mobile application store [4]. OMA has established the TAS (Teleco’s App Store) working group to study the application store’s requirements and system architecture standards. A&C as a very important function of an application store is yet to be standardized. 3.4 A&C Connected with Telecom Operator’s BOSS System It is possible for the application store platform to connect to the telecom operator’s BOSS system and fulfill the A&C requirements of both download and running period charging. Following are the advantages of such connection. (1) Different and flexible charging methods in the matured telecom OSS system can be leveraged to provide flexible tariffs in application stores. For example telecom operators billing system can support both pre-paid and post-paid method. (2) Matured Service Provider (SP) revenue sharing scheme can be utilized to support the business connection between the telecom operator and the application store operator. Previous cooperation model already created many successful value-added services including video on demand, mobile music and multimedia ring tones. (3) Users have already established billing and payment relationship with the telecom operators. Therefore with the integration user can continue to use their existing payment method for the consumption of new application store items without worrying about potential bank details leakage through the application purchase process. This will raise user’s experience level and boost the application sales. (4) Running period A&C requirement can be met by this connection. As discussed in previous chapters, network applications may need to call different chargeable network or communication resources while running. Current application store operators, especially those are not in the telecom industry, can not authenticate and charge users for those utilization of telecom resources. This connection can fill the gap and provide a solution to such running period charging requirements.

4 A New Common Charging Solution for Application Stores 4.1 Architecture of the Charging Solution This paper introduces a method to integrate the telecom operator’s A&C system with the app store platform and meets the download and running period charging need. The framework of new system is shown in the following figure (Fig.1). The system has these function blocks: Client, App Store Front, Developer Support, Capability Resources Management, Developer Portal, Application Portal, Operator’s authentication centre and BOSS, Operator and Internet capability resources.

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Fig. 1. The architecture of the new authentication and charging method is shown.

Application Store Front module is in charge of providing applications to the user. Terminal Client or Browser can browse, search and download application from the Application Store Front. They also exchange information about installation or registration status of application items. Developer Support module is in charge of collecting and auditing developer uploaded new applications. After information checked and audited, new applications can be listed in the Application Store Front for user to download. Developer Portal providers the environment for developers to program, test and upload new applications. Capability Resources Management module is in charge of the capability resources information. These capabilities can be called after registration with this module. Operator’s Capability Resources includes SMS, MMS and other telecom features. Internet Capability Resources include different IP services e.g. news, map and so on. They can be provided by standard API e.g. REST (REpresentational State Transfer). Application Portal provides interface and functions to installed applications so they can access different capability resources. It can also trigger A&C process during the running period of the installed application. The A&C message can be send to the operator’s A&C system, e.g. the value-added services management platform. The result can be send back to the Application Portal to control the usage. The A&C is based on telecom operator’s well established BOSS system so the reliability and safety is guaranteed by the operator. This reliable feeling will encourage the user to purchase and use the application items. If there is no A&C requirement for an installed application, there is no need to trigger the A&C process from the Application Portal. 4.2 Flow Chart of the Authentication and Charging Process When a user makes the purchase, a purchase relationship is established between the user and the purchased application item (Fig.2). Then the Application Store Front can send this relationship to the operator’s BOSS system, for the A&C of the purchased item. When user start to install or use the purchased item, the Application Portal will receive authentication request from the Terminal Client or Browser, for checking the user’s status, purchase relationship and the service status. If the check is passed i.e. the user is entitled to use the application item, the Application Portal will request the operator’s charging system e.g. Online Charging System (OCS) to charge for the

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usage. When the charging success message arrives, the Application Portal will send an approval for the user to use the application item. Based on the usage, Application Portal will generate CDR records and inform the charging system to bill the user.

Fig. 2. Flow chart of the new authentication and charging method is shown.

5 Summary The A&C methods, especially the A&C methods during the application running period, are very important for the application store ecosystem. This paper introduces a new application store A&C system integrated with the matured telecom operator’s BOSS system including the A&C function. This makes the A&C process more reliable, trustable and easy to use for the end users. The system architecture of the new method is introduced here and more technical details need to be formalized before such system can get to commercial lunch.

References 1. Min, D., Liu, D.M.: Study on Mobile Application Store. Telecommunications Network Technology 2, 13–18 (2010) 2. CNTV, Apple App Store Hits 10 Billion Downloads (2011), http://english.cntv.cn/20110126/107837.shtml 3. Wooldridge, D., Schneider, M.: The Business of iPhone App Development: Making and Marketing Apps that Succeed. Apress (2010) 4. China Communications Standards Association: Mobile Application Store Research V0.8_0320. Technical Report pre-published by CCSA as TC11-WG2&WG3-2011-003 (in Chinese), http://www.ccsa.org.cn 5. Open Mobile Alliance: Telco’s Application Store. OMA-ER-TAS-V1_0-20110309-D Draft Version 1.0 March 9 (2011), http://www.openmobilealliance.org

A Modified Kalman Filter for Non-gaussian Measurement Noise Muhmmad J. Mirza Faculty of Engineering and Applied Sciences Riphah International University, I-14, Islamabad, Pakistan [email protected]

Abstract. A novel modiﬁcation is proposed to the Kalman ﬁlter for the case of non-Gaussian measurement noise. We model the non-Gaussian data as outliers. Measurement data is robustly discriminated between Gaussian (valid data) and outliers by Robust Sequential Estimator (RSE). The measurement update is carried out for the valid data only. The modiﬁed algorithm proceeds as follows. Initially, the robust parameter and scale estimates of the measurement data are obtained for a sample of data using maximum likelihood estimates for a t-distribution error model through Iteratively Reweighted Least Squares (IRLS). The sample is dynamically updated with each new observation. Sequential classiﬁcation of each new measurement is decided through a weighting scheme determined by RSE. State updates are carried out for the valid data only. Simulations provide satisfactory results and a signiﬁcant improvement in mean square error with the proposed scheme.

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Introduction

In many dynamic modeling and control problems estimates of the parameters in the equations and/or state of the systems at some time are required. The values of these quantities are dependent upon the assumed model structure, measurements and statistics associated with the measurements, the model, and the inputs of the system. Kalman ﬁltering provides an integrated technique to estimate, predict and smooth state variables of a system. Kalman ﬁlter is an optimal linear ﬁlter, but the optimality does not hold in general for non-Gaussian observations and/or process noises. In real world applications the error distributions are more likely to be leptokurtic and/or contaminated by anomalous values, or outliers. By an outlier, we mean an observation that deviates so much from the others as to arouse suspicion that it was generated by an entirely diﬀerent mechanism [1]. This type of noise can be represented by non-Gaussian heavy tailed distributions [2]. There are many real world noise processes that are not Gaussian [3,4], which justiﬁes the need for the work to optimize the Kalman ﬁlter for such processes. In an earlier attempt Sorenson and Stubberud [5] focused on approximating density functions. However, due to ﬁnite number of terms the approximated densities tend to be negative in certain cases and increasing the number of M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 401–409. c Springer-Verlag Berlin Heidelberg 2011 springerlink.com

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terms makes the method computationally unappealing. To alleviate this difﬁculty Sorenson and Alspach [6] developed Gaussian Sums method but this method too becomes computationally intractable as the ﬁlter propagates. A new direction was proposed by Masreliez [7] using score functions. This method involved convolution operators and had implementation diﬃculties. An eﬃcient computational scheme was proposed by Wen and Amlan [8] to alleviate this problem. Maryak et. al. [9] developed probabilistic criterion for the characterization of quality of state estimates when the true distribution is non-Gaussian. In this paper a novel modiﬁcation is proposed to the Kalman ﬁlter that checks the input measurements at each time step before passing it on for the a posteriori state update. The bifurcation step of measurement data into valid data points and outliers is carried out by Robust Sequential Estimator (RSE) [10] through its weighting scheme. Weights are generated by maximum likelihood (ML) estimates for a t distribution error model with a degree of freedom f. The ML estimates based on a t distribution model is a form of M estimation, yielding robust location estimates with a redescending inﬂuence function. The proposed modiﬁcation works in the following ways: at each time step RSE computes robust location and scale estimates for a small sample of the observation data using IRLS. The sample is dynamically changed with each new observation. The new measurements are assigned weights based on the location and scale estimates computed by RSE. The observations whose weights fall below a threshold are sequentially rejected as outliers and only the valid data points are used to update the state estimates. This eﬀectively keeps the elegance and optimality of Kalman Filter by restricting the eﬀects of outliers on state estimates. Simulations provide satisfactory results and a signiﬁcant improvement in mean square error with the proposed scheme.

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Mathematical Preliminaries

The paper fuses the results obtained from RSE with Kalman ﬁlter. It will be instructive to give the key results from both these methods to make this paper easily readable. 2.1

Kalman Filter

Consider a linear discrete-time dynamic system described by linear stochastic diﬀerence equation xk+1 = φk xk + wk (1) where φk is an n × n state transition matrix, wk is n-dimensional process disturbance vector and k is the time index. The problem is to estimate the n × 1 dimensional state vector xk from m × 1 dimensional noisy measurements yk given by the observation equation yk = Hk xk + vk

(2)

Hk is an m × n observation matrix and vk is an m × 1 observation noise vector. Kalman ﬁlter is a recursive unbiased minimum variance state estimator under

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the assumptions that wk and vk are mutually independent, white, Gaussian noises with known covariance matrices Q and R, respectively. The initial state vector x0 is also assumed to be independent of wk and vk . The Kalman ﬁlter estimates the system states with two set of equations: time update equations (a priori estimates) and measurement update equations (a posteriori estimates). In the proposed modiﬁcation the correction step is interrupted by the RSE and only those measurements are allowed to update the a posteriori estimates that are qualiﬁed by RSE as valid data points. The qualiﬁcation procedure is detailed in the derivation of RSE while the key results are repeated to make this document self contained. 2.2

Robust Sequential Estimator

The Robust Sequential Estimator (RSE) is a robust extension of sequential least squares (SLS) estimator which diﬀerentiates between the data and the outliers through a weighting mechanism. Weights are obtained iteratively by maximum likelihood error analysis for a wide class of non-normal distributions. The salient features of this technique are given below. A general linear regression model can be expressed in a matrix form z = Xθ +

(3)

where z is an m-vector of observations on the dependent variable, X is an m × p matrix of observations on the explanatory variables having rank p, θ is a p-vector of regression coeﬃcients to be estimated, and is an m-vector of disturbances with diagonal covariance matrix. Let p(i ) be an error density function which can be written in the form p(i ) ∝ σ −1 g{(

i 2 ) } σ

(4)

where σ is a scale parameter, g{•} denotes a functional form, and i is the actual ith error. Given a sample z of m observations, the log likelihood for θ and σ 2 is given by [10] l (θ, σ2 |z) = K −

1 i n ln σ 2 + ln g{( )2 } 2 σ i

(5)

where K is some constant. Diﬀerentiating the log likelihood function l with respect to θ and equating these to zero,we get the maximum likelihood estimates of θˆ and σ ˆ 2 as the solution of nonlinear equations [10] wi (zi − θˆj xji )xki = 0 k = 1, 2, . . . , p (6) i

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where ∂ ln g{(i /σ)2 } wi = wi (θ, σ ) = −2 ∂(i /σ)2 2

(8)

Heavy tailed error distributions are more realistically represented by a t distribution having degree of freedom f and scaled by a parameter σ, for which i i 2 g{( )2 } = {1 + } σ f σ2

(−1/2)(1+f )

(9)

Substituting this value of g in (8) gives the individual weights wi =

1+f f + (ri /s)2

(10)

where the residual ri = zi − zrobust and zrobust is the location parameter obtained for a sample of data using IRLS. The degree of freedom parameter f acts as a robustness tuning parameter. When f < ∞, the maximum likelihood estimates under the t model are robust in the sense that outlying cases with large Mahalanobis distances (ri /s)2 are downweighted as wi clearly decreases with increasing distance. The scale parameter is sequentially updated by the relationship [10] (k − 1)s2k−1 + wk rk2 s2k = (11) k where k is the time index.

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Proposed Modification

Given the state transition matrix φk , the observation matrix Hk , the process noise covariance matrix Q and the initial state and error covariance estimates, the modiﬁed algorithm proceeds as follows. 3.1

Time Update

The time update projects the previous a posteriori state and error covariance estimates to predict the new a priori state and covariance estimates similar to the standard Kalman Filter recursion. x ˆ− ˆ− k = φk x k−1

(12)

Pk− = φk Pk−1 φTk + Q

(13)

where P is the error covariance matrix E(˜ xx ˜T ) and x ˜=x−x ˆ.

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Measurement Update

The measurement update is comprised of series of steps and our proposed modiﬁcation is incorporated in this stage. – Obtain the robust measurement estimate yrob at time instant k for a sample l of the observations yk , yk−1 , ..., yk−l+1 using IRLS. – Compute the weight wk of the new observation yk using (10) by subsituting ri = rk = yk − yrob . – Update the scale estimate using (11) and assign this value to the measurement covariance matrix R. The measurement covariance matrix is not assumed to be known a priori rather computed dynamically. – Compute the Kalman gain and update the error covariance matrices as below. Kk = Pk− H T (HPk− H T + R)−1

(14)

Pk = (I − Kk H)Pk−

(15)

The last step in the recursive cycle is to update the state vector. This is the second major contribution of the proposed modiﬁcation and a departure from the standard Kalman Filter. The weighted residual is added to the a priori state estimates only if the current observation is not an outlier. In order to declare an observation as an outlier we compare its weight with a threshold. From (10) it is obvious that the weight wi is a random variable that takes on values between 0 and (1 + f )/f . Using a uniform target distribution model with extreme values as its parameters and based on our empirical ﬁndings we have set one third of the mean value as our threshold. – Compute the mean value of the observations’ weights in the sliding window. – Assign the threshold and classify the new observation as valid data or outlier. – Obtain the conditional a posteriori state estimate as below. x ˆk =

4

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if yk an outlier otherwise

Simulations

In order to demonstrate the validity of our novel ﬁndings we apply it to a practical application for maneuvering radar target tracking where the observation glint noise is highly non-Gaussian. The three state dynamic model of the target is given below. The state vector is composed of the target position, velocity and acceleration. The target is moving

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with a constant acceleration and its maneuvers are modeled by zero mean white Gaussian noise. ⎡ ⎤ ⎡ ⎤ ⎡ T3 ⎤ 2 ⎤⎡ dk dk−1 1 T T2 6 ⎣ d˙k ⎦ = ⎣ 0 1 T ⎦ ⎣ d˙k−1 ⎦ + ⎣ T 2 ⎦ wk−1 (16) 2 00 1 d¨k d¨k−1 T wk ∼ (0, σa2 ) where T is the sampling time. The received range data of the target position is yk = dk + vk

(17)

where vk is the observation glint noise. The glint spikes can be modeled as a Gaussian noise with large variance (covariance) [3] or equivalently a Laplacian noise. In the current study the probability density function (pdf) of measurement noise v is given by f (vk ) = mN (0, 2) + (1 − m)L(a, b)

(18)

the mixture variable m (percentage of contamination) is a number between 0 and 1. L(a,b) denotes a Laplacian distribution with mean a and variance 2b2 . In our simulation we have used m=0.5, a = 0 and b=5. The variance of the mixture model is computed and sequentially updated using (11) and that value is used for measurement variance R in the modiﬁed Kalman ﬁlter. The position estimates obtained using the standard Kalman ﬁlter along with the exact position and the noisy measurements are displayed in Figure 1(a). It is obvious from this plot that the Kalman ﬁlter state error increases as the measurement data deviates from the Gaussian assumption: a direct consequence of giving equal weights to each observation. The proposed modiﬁcation requires a robust estimate of measurement data at each time step. We have used a sliding window concept to cater for the time evolution of the state trajectory. At each step a robust measurement value for the data points in the sliding window is computed using IRLS as delineated earlier. The results of this step are shown in Figure 1(b) along with the noisy measurement. The major step in the proposed modiﬁcation is the decision whether the current observation is an outlier or not. This decision is based on the weight of the current measurement against a rejection threshold. Figure 1(c) gives the resulting weights that justiﬁes our selection of a uniform target distribution for outlier rejection. The problem of large state errors for non-Gaussian data points stems from the fact that Kalman ﬁlter assumes an underlying Gaussian measurement noise and assigns equal weights to each measurement. Our novel modiﬁcation takes care of these data points by restricting the inﬂuence of outliers in the measurements update. Figure 1(d) displays the results of the position estimates using the proposed modiﬁcation for the same measurement data and model parameters.

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Figure 1(e) gives the plot of the two estimates for comparison purposes. It can be seen that the state estimates obtained by the modiﬁed Kalman Filter remain very close to the exact state while the standard Kalman ﬁlter wanders with very large peaks. To give a more vivid picture of the results the mean square error for each iteration for the two estimates is plotted in Figure 1(f). There are very high peaks in case of standard Kalman ﬁlter which are pretty damped in the modiﬁed case. The qualitative comparison of the two cases is self explanatory from these ﬁgures. Mean Squared Error (MSE) was used as a quantitative comparison measure for the two estimates. We made forty runs of the simulations and the average MSE for Kalman Filter was 80 while for modiﬁed Kalman Filter it was around 30.

5

Conclusion

In this paper we have developed a novel modiﬁcation to the standard Kalman ﬁlter for symmetric non-Gaussian measurement noise. At the heart of this amendment lies the Robust Sequential Estimator, a statistically robust and computationally eﬃcient algorithm that robustly discriminates between the valid data points and the outliers. We have shown that non-Gaussian measurement values can be eﬀectively modeled as outliers and their eﬀect can be minimized by rejecting them in the measurement update stage of the standard Kalman ﬁlter. The algorithm is applied to a real world problem of target tracking in glint noise and the simulations conﬁrm our ﬁndings which are reﬂected in more than 60 % reduction in the mean squared error in the state estimates.

References 1. Hawkins, D.: Identiﬁcation of outliers. Chapman and Hall, Boca Raton (1980) 2. Lange, K.L., Little, R.J.A., Taylor, J.M.G.: Robust Statistical Modeling Using the t Distribution. Journal of the American Statistical Association 84(408), 881–896 (1989) 3. Hewer, G.A., Martin, R.D., Zeh, J.: Robust Preprocessing of Kalman Filtering of Glint Noise. IEEE Trans. Aerosp. Electron. Syst., AES 23, 120–128 (1987) 4. Bilik, I., Tabrikian, J.: Target Tracking in Glint Noise Environment Using Nonlinear Non-Gaussian Kalman Filter. In: 2006 IEEE National Radar Conference Proceedings, art. no. 1631813, pp. 282–287 (2006) 5. Sorrenson, H.W., Stubberud, A.R.: Nonlinear ﬁltering by approximation of the a posteriori density. Int. J. Control 18, 33–51 (1968) 6. Sorrenson, H.W., Alspach, D.I.: Recursive Bayesian Estimation using Gaussian Sums. Automatica 18, 456–476 (1971) 7. Masreliez, C.J.: Approximate non-Gaussian ﬁltering with linear state and observation relations. IEEE Tran. Automat. Contr. 18 (AC-20), 107–110 (1975) 8. Wen-Rong, W., Amlan, K.: Kalman ﬁltering in non-Gaussian environment using eﬃcient score function approximation. In: Proc. of IEEE International Conference on Circuits and Systems, pp. 403-410 (1989)

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9. Maryak, J.L., Spall, J.C., Heydon, B.D.: Use of the Kalman Filter for Inference in State-Space Models With Unknown Noise Distributions. IEEE Trans. Automat. Contr. 49(1), 87–90 (2004) 10. Boyer, K.L., Mirza, M.J., Ganguly, G.: Robust Sequential Estimator: A General Approach and its Application to Surface Organization in Range Data. IEEE Trans. Pattern Anal. Machine Intel. 16(10), 987–1001 (1994)

A Novel Framework for Active Detection of HTTP Based Attacks Liang Jie, Sun Jianwei, and Hu Changzhen Department of Software Engineering, Beijing Institute of Technology, Beijing, 100083, China {liangj228,ejwsun}@gmail.com

Abstract. Web application vulnerabilities represent a substantial portion of the security exposures of computer networks. Considering HTTP protocol is stateless, we explore the effectiveness of HTTP-session model to effectively describe http behavior. Based on the HTTP-session model and the analysis of http attack behavior, we present a novel framework to actively detect http attacks. Our method takes http requests as input and calculates anomalous probability for each session attribute and for the session as a whole as output. All the probabilities are weighted and summed up to produce final probability, and this probability is used to decide whether http session is attack or not. We demonstrate the effectiveness of the proposed methods via simulation studies using real-world web access logs. Experiments prove that our detection framework achieves high detection rates under very few false positives. Keywords: HTTP-session, anomaly detection, http attacks.

1 Introduction With the rapid expansion of the HTTP-based applications, Web-based attacks and abuse become more serious. For this reason, web applications are subject to greater and greater levels and types of attacks as hackers exploit vulnerabilities within the software like DoS, DDoS, and XSS. In this type of attacks, attackers abuse HTTP request protocol and send malicious HTTP requests to victim web server. To combat these attacks, many studies were done on traffic measure and identification. These studies can be categorized as two kinds: network layer approach and application layer approach. The former methods use network layer protocol features, like TCP headers, and IP headers, to characterize traffic feature and to detect the attacks. The latter methods generally use application layer protocol features to model the traffic, unlike the network layer features, these methods extract high level user access information to semantically model the user access behavior. In this paper, we propose a web-based, active anomaly detection system to protect a target web server. Our approach analyzes webpage traversal history of users using the HTTP-session model to actually describe user’s access behavior. HTTP-session is defined as one user access process on the web , either for seeking information, or buy some things with online business application. Web based attacks, such as DDoS, DoS, may be different from the normal access process. For example, DoS attackers may M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 411–418. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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access the main page for many times in a short period to exhaust the web server resources, which make web server unable to answer no more user request. Based on this assumption, our approach is to detect web based attack based on the analysis of HTTP-session behavior.

2 Related Works HTTP-session is defined as a sequence of HTTP requests sent out by a user in a time period. Session concept is adopted in web usage mining area to analysis user access behavior. Many research in web usage mining uses HTTP session (i.e. web session) to model user access behavior to find frequent path traversal patterns of web users[1], classifying user navigation patterns[2] and optimizing the website organization[3]. Indentifying HTTP-session is important for our model. Traditional research identified HTTP-session by defining some thresholds[4], like visiting time intervals, requests under the time threshold are defined as one session. In our framework, we use time interval between requests to define a session, which is 600s in our framework. As for the detection of web based attacks, Kruegel proposed a number of different anomaly detection techniques [4][5] to detect web application attacks. In these studies, it proposes several models using different features extracted from the HTTP information to identify web application attacks as anomaly detection approach. Also, different types of learning-based anomaly detection techniques have been proposed to analyze different data streams. Valeur et al. proposed an anomaly-based system [6] that learns the profiles of the normal database access performed by webbased applications using a number of different models. Estevez. et al. proposed an approach [7] which used Markov model to detect the web application attacks. Their approach is based on the monitoring of incoming HTTP requests to detect attacks. Naiman uses the statistic methods [8] to analyze the data which are collected from web servers. These approaches detect anomaly behaviors on http requests one by one, and ignore some semantic meanings between different requests. Since http protocol is stateless, http requests alone are unable to obtain the relationships between the http requests and user access behavior. Our proposed method considers the relationship between requests sequence and user access behavior into the model to describe the user access session, and we divide http requests into HTTP-sessions to find interesting patterns between requests. Based on the analysis of web based attack behavior, we explore our model to detect web based attack effectively.

3 Analysis of User Access Behavior and HTTP-Session Model In this section, we present analysis of web user access process, and the web attack process. Based on the analysis of web access behavior, we propose HTTP-session model in details.

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3.1 Analysis of User Accessing Behavior and Web Based Attacks Web accessing is the major behavior of users on the web. Clients connect web server through http protocol. In this process, Clients send out requests to web application, and web server answers requests separately. Generally, user access process contains various requests either through input URLs or to click the links on the web to access web page. User access process is described in Fig. 1.

Fig. 1. Illustration of the user access process

As shown in Fig.1, there are two types of requests: user sends out requests to obtain web pages, and there are some inline requests in the web pages to obtain inline objects, like advertisements and script. There are many requests in http protocol (POST, HEAD), however, we only consider GET requests. The reason is that GET request is the main part of user access process demonstrating the behavior of user. HTTP GET request example is shown in Fig.2. GET /scripts/access.jsp?user=johndoe&cred=admin 200 GET /scripts/access.jsp?user=christie&cred=user 400 Fig. 2. Example of HTTP GET request

Unlike normal access patterns, web based attacks have different access patterns: (1) DoS attack tool may send out many requests to query URL at a short period to exhaust web server. Their target may be the main web page, or to imitate normal users to request a random URL. The key point is in the time of requests and interval between requests. (2) DDoS attacks are usually initiated from many attackers to send out requests to web server at a short period to exhaust web server resource[9]. Although some attacks can imitate normal user behavior to send HTTP requests, they are unable to imitate the session of users. So HTTP-session model can effectively describe user access behavior.

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The fact that normal access requests patterns are different from attacks can be used in detecting attackers. In this paper, we extract some statistical features from HTTPsession to identify web based attacks. The features includes numbers of addressed, numbers of sessions in a given time period, requests length in one session, URL length of the requests and so on. 3.2 Using HTTP-Session Model to Describe Access Behavior Our proposed HTTP-session model for web based attack detection and filtering is based on two results obtained by web mining [8]: (1) about 10% of the web pages of a website may draw 90% of the access; (2) web user browsing behavior can be abstracted and profiled by users’ request sequences. This section illustrates HTTP-session model for web based attack detection. Different from session model in web usage mining area, we define HTTP-session as a requests sequence launched by a user. The input to the HTTP-session mainly consists of an ordered set U = (u1 , u2 ,..., um ) of URLs extracted from GET requests. HTTPsession starts at the time user sends out requests. We adopt the interval threshold to define the HTTP-session. If user’s request is empty for a given time period, HTTPsession is ended at that time.

4 Anomaly Detection Approach Based on HTTP-Session Model The HTTP-session based detection model aiming at web based attacks, as shown in Fig.3, contains four modules which are respectively HTTP requests preprocessing, HTTP-session model building, web based attack detection, and anomaly behavior processing.

Fig. 3. HTTP-session based anomaly detection process

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4.1 HTTP Requests Preprocessing and HTTP-Session Model Building As mentioned above, our HTTP-session mainly consists of an ordered set U = (u1 , u2 ,..., um ) of URLs extracted from HTTP GET requests. Also, other requests, like HEAD and POST, which is also possible to launch web based attacks, will be considered in further studies. First, we extract HTTP information from given resources. In this paper, we extract HTTP information on the web log file to extract HTTP requests. Also, we can extract HTTP requests from packet sniffed packets. Let U = (u1 , u2 ,..., um ) denote the stochastic variable expressing the observed

S = ( s1 , s2 ,..., sl ) denote the sessions in a given time period, where M i be the total number of queries in session si , and L be the number of IP addresses in a

URLs,

given time period, i.e. session number in a given time period. Let si = (u1 , u2 ,..., ul ) denote useri HTTP-session, which is subset of

U = (u1 , u2 ,..., um ) , where ul denote l th URL user queries in the session si ,and Ti be the session time period(in this paper we define Ti < 600 s ). Thus, HTTPsession can be extracted. 4.2 Anomaly Detection Approach Based on the analysis of HTTP-session, web based attacks detection is to distinguish normal HTTP-sessions from dubitable attack HTTP requests by the different time of HTTP-session according to the original information offered by the resources. From the analysis of section III, we concluded that web based attack send out http requests in different ways, and attack and normal behavior differs in URLs, requests order, and the time of requests, which is modeled by our proposed session model. In order to detect various web based attacks, the anomaly detection process uses a number of different statistics to identify web based attacks within a set of sessions associated with user access behavior. These statistical features of session includes the number of requests in one session, the number of sessions in a given time periods, length of queries in sessions, and the duration of a session, etc. Each feature is associated with characteristics of a session by means of learning normal access behavior. Consider, for example, the length of the requested queries statistics for the detection of XSS attack. The task of the detection model is to assign a probability value to either a session or one of the session's features. This probability value reflects the probability of the occurrence of the given feature value with regards to an established normal session. The assumption is that a session with a sufficiently low probability (i.e., abnormal values) indicates a potential attack. Based on the model outputs (i.e., the probability values of the session and its individual attributes), a decision is made (that is, the session is either reported as a potential attack or as normal). This decision is reached by calculating an anomaly score individually for each session attribute and for the session as a whole. When one or more anomaly scores (either for the session or for one of its attributes) exceed the

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detection threshold determined during the training phase, the whole session is marked as anomalous. This is necessary to prevent attackers from hiding a single malicious attribute in a session with many ‘normal’ attributes. The anomaly scores for a session and its attributes are derived from the probability values returned by the corresponding HTTP-session models that are associated with the sessions or one of the attributes. The anomaly score value Pm is calculated using a weighted sum as shown in Equation 1. In this equation, we represent the weight associated with every factors, while pm is its returned probability value. The probability

pm is subtracted from 1 because a value close to zero indicates an anomalous

event that should yield a high anomaly score.

AnomalyScore = ∑ wm *(1 − pm )

(1)

The detection approach includes two phases: training normal HTTP-session and detection of web based attacks. The training phase is required to determine the characteristics of normal events, and to establish anomaly score threshold to distinguish normal sessions and attack sessions.

5 Experiments 5.1 Datasets for Experiment and Summary of System Parameter In this section, we implement our model for HTTP-session processes based on the real traces of web log files. Table 1 shows the compositions of the access log for evaluation. And, the access log of DDoS and DoS attack is collected by launching DDoS and DoS flood attacks to the web server by tools. Table 1. Compositions of access log for evaluation

Source IP address Get requests

Normal clients 100 3291

Attackers 10 43121

Total numbers 110 46412

The data consists of three weeks of training data and two weeks of test data. In the training data there are two weeks of attack-free data and one week of data with labeled attacks. We trained the model on the HTTP dataset of the dataset using week 1 (5 days, attack free) and week 3 (7 days, attack free), then evaluate the detector on weeks 4 and 5. Retaining the ordering in the requests, we separate the GET requests from the web log to different users, and learn the session models as proposed in this paper. After building HTTP-session model, statistics are summarized in Table 2. From these figures, we can find that the weights for different features are 0.421, 0.389, and 0.2352 respectively. Thus, we can show equation(2) as:

A Novel Framework for Active Detection of HTTP Based Attacks

#.of .sessions ) num.of .session #.of .queries. per.session +0.389*(1 − ) num.of .queries. per.session int erval.of .int er.request. per.session +0.2352*(1 − ) duration.of .int er.request. per.session

417

Pm = 0.421*(1 −

(2)

Table 2. Statistics of normal data sets used Name Duration of the traces Total number of sessions Avg. number of sessions Avg. number of queries per session Avg. user Inter-request duration

Features 5 weeks 1343 568 2.6 115 sec

We simulate normal users and attackers to send out access requests to test the performance of our proposed method. And we use the detection ratio (DR) and false positive ratio (FPR) to measure the performance of anomaly detection approach. In our experiment, we implement payload based web attack detection method, and arrival based detection approach to compare the performance of anomaly detection. The results shows our method is better than the remaining for the attack type which our paper is focused.

Fig. 4. ROC curves of various anomaly detection approaches

From the above experiments, we can see that our method has a high detection rate and low false positive rate.

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6 Conclusions In this paper, we present a new method of detecting application layer attacks. We use the HTTP-session model to describe the users’ access behaviors. In the detection phase, we calculate every observation sequence’s average probability against the normal model, and detect the potential attacker based on their average anomaly likelihoods. The experiment results show that our method works well.

References 1. Chen, M., Park, J., Yu, P.: Data mining for path traversal patterns in a web environment. In: ICDCS (1996) 2. Liu, H., Kešelj, V.: Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users’ future requests. Data & Knowledge Engineering (2007) 3. Srikant, R., Yang, Y.: Mining web logs to improve website organization. In: International Conference on World Wide Web (2001) 4. Nuzman, C., Saniee, I., Sweldens, W., Weiss, A.: A compound model for TCP connection arrivals for LAN and WAN applications. Computer Networks 40(3), 319–337 (2002) 5. Kruegel, C., Vigna, G.: Anomaly detection of web based attacks. In: Proceedings of the 10th ACM conference on Computer and communications security, pp. 251–261 (2003) 6. Kruegel, C., Vigna, G., Robertson, W.: A Multi-model Approach to the Detection of Webbased Attacks. Journal of Computer Networks 48(5) (2005) 7. Valeur, F., Mutz, D., Vigna, G.: A learning-based approach to the detection of SQL attacks. In: Julisch, K., Krügel, C. (eds.) DIMVA 2005. LNCS, vol. 3548, pp. 123–140. Springer, Heidelberg (2005) 8. Kantardzic, M.: Data Mining Concepts, Models, Methods and Algorithm. IEEE Press, New York (2002) 9. Yatagai, T., Isohara, T., Sasase, I.: Detection of HTTP-GET flood Attack Based on Analysis of Page Access Behavior. In: Proceedings of the 2007 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, Victoria, Canada, pp. 232–235 (2007)

Model Predictive Control for Single Phase Inverters Yecheng Lv, Ningxiang Xie, and Kai Wang School of Electric Engineering, Wuhan University, Wuhan, China [email protected], [email protected], [email protected]

Abstract. Tracking control for inverters are commonly used in load flow control, adjustable speed motor and smart grid. In this paper the model predict control (MPC) technique is introduced and then used in the current control of single phase full-bridge inverters. To conquer the inherent time delay of MPC, an improvement is made. Finally, with several simulation results, the method proves to enjoy the characteristics of quick response, small overshoot and high accuracy. Keywords: Model Predict Control, current tracking, inverters.

1 Introduction Inverters are playing an increasingly important role in areas such as Direct Current Transmission, Uninterruptible Power Supply and distributed generation [1]. The evolution of switching devices has greatly promoted the research of the inverter control technology. On the other hand, the rapid development of computer technology has enabled the implementation of more complex control strategies. Some of these strategies are Immune Genetic Algorithm [2], BP neural network algorithm and predictive control [3]. The latter is gradually becoming popular for it’s simplicity in implementation. Paper [4] introduced the principles, classification, development and applications of Predictive Control and Paper [5] introduced an application of Predictive Control in power system. A main branch of predictive control is Model Predictive Control (MPC) [6]. In MPC, a model of the system is established. Based on the model, the future value of the variables can be predicted. According to the comparison of the predicted value and the desired reference value, the control action at the present time is determined. MPC can be conveniently applied to the control of single phase inverters for the structure of which is very simple for modeling. The switching patterns of the inverter are finite so the prediction can be made based only on the possible switching patterns [7]. And this makes the calculation of predicting can be completed during a very brief time. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 419–425. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Model Predictive Control The control process is illustrated in Fig. 1, in which the predictive model is constructed according to the physical characteristics and situation of the controlled object. The generic form of the predictive model is x(k+1)=Ax(k)+Bu(k)

(1)

y(k)=Cx(k)

(2)

The optimality criterion and cost function are also decided in terms of the controlled object. UN

FRVW IXQFWLRQ

RSWLPDOLW\ FULWHULRQ

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\N SUHGLFWLYH PRGHO Fig. 1. MPC system chart

3 The Application of MPC in Current Tracking Control Fig. 2 shows the circuit topology of single phase full-bridge inverter, the load is represented as a series connection of a resistance and an inductance. From Fig. 2, the equation of the inverter can be derived as follows:

input1

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Fig. 2. The topology of single phase full-bridge inverter

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di −iR = +U dt L

(3)

or t +Δt

i (t + Δt ) = ∫t

(

−iR + U )dt + i (t ) L

(4)

where iLD is the load current and vout is the out put voltage of the inverter. In this paper, it is assumed that the switching devices are ideal switches so the process of switching-on and switching-off is not taken into consideration. All switches work synchronously with the sampling clock signal and the upper and lower switches on the same leg are always working complementarily, i.e., when an upper switch receives a control signal, the lower one on the same leg receive the reverse signal at the same sampling time. The dc voltage source is assumed as a constant dc voltage source with no ripple so there is no change of the output voltage level within a sampling period. Table 1 shows the possible switching patterns and the corresponding cases of output voltage levels. Table 1. Possible switching patterns and the corresponding cases of output voltage levels

switching patterns S1,S4 ON; S2,S3 OFF S2,S3 ON; S1,S4 OFF

output voltage level E -E

If the sampling period is sufficiently brief, iLD R within one sampling period can be regarded as a constant. It is assumed that the load current at sampling time k is iLD (k ) , so from the discretization of (4), the load current at the next sampling time will be

T ⎛ R ⎞ iLD (k + 1) = iLD (k ) + ΔiLD ( k ) = ⎜ 1 − Ts ⎟ iLD (k ) + s vout (k ) L ⎝ L ⎠

(5)

T ⎛ R ⎞ In reference to (1), we have A= ⎜1 − Ts ⎟ , B= s ,and C=1. vout ( k ) is the inverter L L ⎝ ⎠ output voltage level determined by the switching patterns of the inverter which has been shown in Table 1. (5) is the predictive model of the inverter circuit. From Table 1 we know that iLD (k + 1) varies with different choices of vout ( k ) . The process of MPC is as follows: A. Obtain the load current at sampling time k ( iLD (k ) ) with sampling instrument. B. Predict the load current at the sampling time k+1( iLD (k + 1) ) with (5) in consid-

eration of different choices of vout (k ) . Different cases of prediction are thus obtained. C. In different cases of iLD (k + 1) , calculate respectively the absolute error of the prediction and reference value of load current at sampling time k+1, namely

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| e(k + 1) |= iLD (k )+

vout − iLD (k ) R Ts − iref (k + 1) L

(6)

D. Select the case of vout (k ) which results in the minimum of | e(k + 1) | .

In this way, MPC ensures the minimum of every e ( k + 1) thus make the load current accurately track the reference wave.

4 Improvement of MPC To conquer the delay caused by sampling process. In the discussion above, the process of predicting has been regarded as ideal which would take up no time. However, this process does result in a delay, i.e., the vout (k ) is actually emitted briefly later than the sampling time k which is expected. If the delay is sufficiently long, the MPC will not work properly. In order to conquer the delay which is an inherent drawback of MPC, an improvement is made (in the paper it is expressed as improved MPC). However, improved MPC predicts the inverter output voltage at next sampling time, which is different from MPC. At sampling time k( k ≥ 1 ), from (5), it can be derived as follows: T ⎛ R ⎞ iLD ( k + 1) = ⎜1 − Ts ⎟ iLD ( k ) + s vout ( k ) L ⎝ L ⎠

(7)

T ⎛ R ⎞ iLD (k + 2) = ⎜ 1 − Ts ⎟ iLD (k + 1) + s vout (k + 1) L ⎝ L ⎠

(8)

where iLD (k ) is the actual load current at sampling time k, vout ( k ) is determined at sampling time k-1, iLD (k + 1) and iLD (k + 2) are predicted value at sampling time respectively k+1 and k+2, and vout (k + 1) is the inverter output voltage level at sampling time k+1, while which is to be decided at sampling time k. Special attention should be given to iLD (k + 1) , which has been predicted at previous sampling time k-1 according to the decision of vout (k ) . Although here in improved MPC iLD (k + 1) is a predicted value (in (5) in the discussion of MPC, iLD (k ) is an actual value obtained from sampling process), the error between the predicted iLD (k + 1) and actual iLD (k + 1) to occur at sampling time k+1 will be small enough so to be neglected. So the predicted value iLD (k + 1) can be used to predict iLD ( k + 2) according to different cases of vout (k + 1) . After the prediction of iLD (k + 2) , the absolute error | e(k + 2) | will be calculated as is done in (8) in the discussion of MPC. Then the optimum vout (k + 1) is decided.. Next we will consider the determine of vout (1) . In difference from vout (k ) ( k > 1 ), vout (1) is a constant and vout (1) = E in order to make the actual load current track the reference (which is a sinusoidal wave).

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Because the unnecessity of deciding vout (1) , the delay at sampling time 1 is eliminated. And because every vout (k ) ( k > 1 ) is calculated a sampling period in advance, the delay at every sampling time k( k ≥ 1 ) is eliminated.

5 Simulation Analysis

g

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e Min

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x Model y m

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m

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C

To analyze some aspects of the performance of MPC (together with improved MPC), simulation environment has been constructed with Matlab/Simulink which has been shown in Fig. 3. For the simulation, the initial value of load current is 0A, the sinusoidal reference current wave is expressed as 6.35sin(2πft), the frequency is f=50Hz, the inductance is L=5mH , the resistance is R=0.1Ω and the sampling frequency is 10kHz. Several indicators are used in the simulation: The maximum of the error (between the actual value and reference value) at every sampling time, which is denoted by |emax|, to measure the accuracy of the current tracking; the ratio of the effective value of fundamental component of load current to that of reference current (denoted by I1/Iref) to demonstrate the steady-state behavior, and total harmonic distortion (THD) to evaluate the quality of the load current. Because of the simulation environment, the effect of the delay resulting from MPC is so little that MPC and the improved MPC actually have the same performance (while in actual experiment the performance would be different). So in this part, both methods are considered as one. The simulation has been done in comparison with hysteresis control method, the band of which is 2%.

B1 B2

E

+

i -

i RL

+ v -

E

g m

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VD4 m

VD3

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RL

Fig. 3. Simulation environment in Matlab/Simulink

The values of the indicators are listed as follows: Table 2. Values of the indicators

|emax| I1/Ir THD

MPC 3.28% 100.20% 2.06%

Hysteresis 6.58% 99.76% 3.40%

Scope

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The inverter output voltage and load current controlled by MPC has been plotted in Fig. 4 and Fig. 5.

Fig. 4. Waveform of inverter output voltage

Fig. 5. Waveform of load current

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Simulation results indicate that MPC has very perfect performances. A. MPC reduces |emax| to a very small value (especially when compared with hysteresis control), with which the reference current is better tracked. B. The effective value of fundamental component is only 0.2% larger than that of reference current. This result shows that the steady-state behavior of MPC is nice. C. The THD of load current controlled by MPC is very low (which can bee seen in comparison of hysteresis control). This result indicates the quality of load current is satisfactory.

6 Conclusion In the paper, Model Predictive Control is introduced and then applied to the control of single-phase full-bridge inverters. The method is based on the prediction of the load current at next sampling time according to different cases of inverter output voltage. Then the case which results in the least error is chosen. In this way, reference wave is perfectly tracked. To conquer the delay caused by MPC, an improvement has been made. Simulation results indicate MPC enjoys accurate tracking capability which makes the quality of load current greatly optimized. Thus it is proved that MPC is an ideal control strategy of inverters.

References 1. Blaabjerg, F., Chen, Z.: Power electronics for modern wind turbines (synthesis lectures on power electronics). Morgan Claypool Publishers, San Rafael (2006) 2. Yuan, J.X., Chen, B.C., Tian, C.H., Jia, J.B.: The Research of Inverter’s Control Based on Immune Genetic Algorithm. J. Proceedings of the CSEE 5, 110–118 (2006) 3. Yang, M.X., Dai, Y.X., Wang, W.G.: Application of BP Algorithm in Deadbeat Control of Single Phase Inverter. J. Low Voltage Apparatus 24, 11–13 (2009) 4. Xu, C., Chen, Z.G., Shao, H.H.: A Survey on Predictive Control Technique and Its Application. J. Control and Instruments In Chemical Industry 3, 1–10 (2002) 5. Li, G.Q.: Study on Prediction Control for Transient Stability of Power Systems. J. Automation of Electric Power Systems 3, 25–31 (1994) 6. Linder, A., Kennel, R.: Model Predictive Control for Electrical Drives. In: 36th Power Electronics Specialists Conference, pp. 1793–1799. IEEE Press, New York (2001) 7. Rodriguez, J., Cortes, P., Kennel, R., Kazrnierkowski, M.P.: Model predictive control – a simple and powerful method to control power converters. In: 6th International Power Electronics and Motion Control Conference, pp. 41–49. IEEE Press, New York (2009)

Evaluation Method and Standard on Maintainability of Cockpit Junwei Zhao1,*, Lin Zhou1, Haitao Zhao1, Xuming Mao1, and Youchao Sun2 1 Shanghai Aircraft Design and Research Institute, General Configuration and Aerodynamic Design Division, Caoxi Road 222,200232 Shanghai, China 2 Nanjing University of Aeronautics and Astronautics, Civil Aircraft Institute, Yudao Road 29, 210016 Nanjing, China

Abstract. Maintainability is an important factor to determine the maintenance quality for aircraft. Favorable maintainability design can make the maintenance more simple, speedy and economical. Maintainability design and validation need evaluation method and standard. This article takes control column and side stick with related standards as an example. Then establish evaluation method. At last, contrast and analyze the related cockpit parameters of A320 and B737. Keywords: Maintainability evaluation evaluation method and standard.

， control

column and side stick

，

1 Introduction Maintainability is an important factor to determine the maintenance quality for aircraft, and it reflects the ease of maintenance. At first it is important to bring maintainability to the full life circle of a civil aircraft. Secondly if the maintainability request can be carried out by demonstrate, design, produce and validation, it will reduce the cost for life circle of civil aircraft and improve self attribute. Meanwhile maintainability has the consanguineous relationship with maintenance, and it refers to the production factor such as structure, connection, installation and collocation. It is an important character of making maintenance more simple, speedy and economical. Thirdly maintainability is a design attribute, and its request must be carried out by maintenance design. Favorable maintainability design can make the maintenance more simple, speedy and economical. Maintainability design and validation need evaluation method and standard [1]. At last evaluation method is established as a necessary condition for validating maintainability. Evaluation method and standard is provided as a guide to maintenance design of the cockpit.

*

Junwei Zhao(1981-), engineer, doctor's degree. Now working in General Configuration and Aerodynamic Design Division of Shanghai Aircraft Design and Research Institute.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 427–432. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Now a new generation glass cockpit is widely used on civil aircraft. Its main features includes that as follow: Firstly, the display divice is changed from scattered electromech indicator to integrated electronic indicator, and combined scale and digital, combined raster scan and stroke vector display mode are adopted. Secondly, combined information mode is adopted, and parameters and alert information of aircraft systems is integrated. Thirdly, autocontrol system is upgrated, and fly-bywire, drive control and full authority digital engine control technology are introduced. Fourthly, automatic integrated management systems such as integrated avionics system, multifunction flight management system are adopted. Finally, as information using efficiency , integrated management and automation degree are increased, the service persons on plane such as telegraphist, maintenance person are cancelled. And two attendants system is put into practice. [2] Research for maintainability of a new generation glass cockpit in civil aircraft is rare. Now there is no mature evaluation method and standard on maintainability of cockpit in civil aircraft. This article takes control column and side stick with related standards as an example. Then establish evaluation method. At last, contrast and analyze the related cockpit parameters of A320 and B737.

2 Establishment for Evaluation Indicator of System and Equipment in Cockpit By studying the international and national standards, referring the correlated aircraft data, considering the characteristic of civil aircraft, consulting a variety of maintenance programs and carrying through reliability prediction and allocation, evaluation standard on maintainability of cockpit is established. The use indicator of maintainability is divided into target value and threshold value. And the compact indicator is divided into provide value and minimum acceptable value. Relevant factors and constraints should be defined , while indicator of maintainability is ensured. These factors are indispensable to make a description of indicator including preconcerted maintenance program, the validation method of indicators. The indicator of maintainability is ensured by the following factors: First, ensuring indicator is mainly based on use requirement. Maintainability is not only needed by maintain department, but also by use. Second, ensuring indicator should refer to the maintainability level of similar national and international equipment. It is important to learn more about the actual maintainability level which similar equipment has reached. And it is starting point of ensuring indicator of maintainability for new research equipment. The indicator of maintainability for new research equipment should be better. Third, ensuring indicator is based on the level of maintenance which the anticipate technology could make the products reach. Fourth, the existing security system of maintenance, maintenance responsibilities, time constraints at all levels of maintenance are important factors of ensuring indicator. Fifth, ensuring indicator must be balanced with reliability, life cycle cost, development schedule and other factors.

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3 Illustration This section takes control column and side strick of aircraft for example. evaluation method and standard on maintainability of cockpit is put forward, and it is referred to correlative standards. First, each cockpit control must be located to provide convenient operation and to prevent confusion and inadvertent operation. The controls must be located and arranged, with respect to the pilots’seat, so that there is full and unrestricted movement of each control without interference from the cockpit structure or the clothing of the minimum flight crew when any member of this flight crew, from 5&foot; 2&inch; to 6&foot; 3&inch; in height, is seated with the seat belt and shoulder harness(if provided) fastened. [3,4,5] Second, identical powerplant controls for each engine must be located to prevent confusion as to the engines they control. Third, FBW and AP control are usually located on side stick. AP emergency quick release control must be located the two side sticks, and the location must make pilot use quickly and easily.[6] Fourth, electric connection of side stick must be designed for providing security when the two pilots amend and input override control. The announcement of control state can not cause confusion. Fifth, it must be shown that the use of side stick can not bring inapposite control characteristics of man-machine loop, when do precise pitch control tasks and consider the turbulence through the flight test.

4 Characteristics of the Maintenance of Related Equipment on A320 and B737 The characteristics of the maintenance of related equipment are analyzed through the collection for information. Fly-by-wire system and side stick are first adopted for A320, which can provide the same aircraft maneuverability for different scale and layout aircraft. Fly-by-wire system and big screen display are applied to achieve glass cockpit for new generation B737. And it enhances the maintainability of the cockpit. Column control and side stick are taken for example to analyze maintainability.[7] There are prodigious differences between control column and side stick. Control column is for B737-800, while side stick is for A320. It is shown in Fig. 1. Stabilizer trim switch and autopilot cut-off switch are located at side of control column, while aileron trim indicator is located on the top of control wheel for B737800. Autopilot cut-off switch and PPT switch are located on side stick for A320. Control column is adopted by B737-800. It is located in front of the pilot, so it influences the pilot's field of vision. Otherwise, it also influences the maintainability of the parts in front of itself(such as rudder pedal). And it reduces the visibility and accessibility when rudder pedal is maintained.

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Fig. 1. control column for B737-800 and side stick for A320

Side stick is adopted by A320. Compared with the traditional control column, side stick has the advantage of light and simple structure, but the lack of visual feedback. Side stick is a console which located at side of captain. And another one is a console which located at side of copilot. Its position does not make itself influence the pilot's field of vision. And it do not produce interference to other components when it is maintained. So its maintainability is better. There are PPT interphone systems on control column and side stick for B737-800 and A320. PPT switches are on control column and side stick for B737-800 and A320. There are the same function, easy maintenance,.and the maintainability is relatively well.

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Fig. 2. PPT switches on control column and side stick for B737-800 and A320

References 1. Wang, Y.X.: The method of ergonomic analysis based on emulation of virtual maintenance. Nanjing University of Aeronautics and Astronautics, Nanjing (2007) (in Chinese) 2. Wang, W., Sun, Y.C., Kong, F.F.: Analysis and Study of Ergonomics on Virtual Maintenance of Civil Airplane Based on DELMIA. In: Advanced Materials Research, 2008 International Conference on Advances in Product Development and Reliability, pp. 821–828. Trans Tech Publications Ltd, Laublsrutistr (2008) 3. CCAR25: Airworthiness Standards for Transport Category Airplanes(2001) (in Chinese)

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4. FAR25: Airworthiness Standards: Transport Category Airplanes (1968) 5. CS25: Certification Specifications for Large Aeroplanes (2008) 6. Cheng, B.S.: Aircraft design handbook, vol. 5. Aviation Industry Press, Beijing (2002) (in Chinese) 7. GJB/Z91-97: Maintainability design technique handbook (1997) (in Chinese)

Smart Mobile User Adaptive System with Autonomous Sensor Networks Ondrej Krejcar1,2 and Robert Frischer1 1 VSB Technical University of Ostrava, Center for Applied Cybernetics, Department of measurement and control, 17. Listopadu 15, 70833 Ostrava Poruba, Czech Republic [email protected], [email protected] 2 University of Hradec Kralove, FIM, Department of Information Technologies, Rokitanskeho 62,Hradec Kralove, 500 03, Czech Republic [email protected]

Abstract. In this article we present a smart mobile user adaptive system with autonomous sensor networks as a modern way of use an energy harvesting for powering of intelligent sensors. Gaining the energy to supply sensors is possible even from immediate sensor’s environment. They are supplying the energy from vibration less environment and without need of high intensity incident light. For running they are need only small thermal gradient of order of degrees. Thanks to evolution of new MOS-FET transistors with very low value of RDSON (order of mOhm) is possible to use even very low voltage produced by thermal sources called Thermo Electric Generators. Keywords: User Adaptive System; Mobile Device; Autonomous Sensor Network..

1 Introduction Advances in low power technology are making it easier to create wireless sensor networks in a wide range of applications, from remote sensing to HVAC (Heating, Ventilating, and Air Conditioning) monitoring, asset tracking and industrial automation. The problem is that even wireless sensors require batteries that must be regularly replaced. It is a costly and cumbersome maintenance project. A better wireless power solution would be to harvest ambient mechanical, thermal or electromagnetic energy from the sensor’s local environment. This technique is called energy harvesting. It is process, by which free ambient energy is derived, captured and stored. Typically, harvestable ambient power is on the order of tens of microwatts to tens milliwatts, so energy harvesting requires careful power management and sophisticated circuits with latest technology design in order to successfully capture a few microwatts of ambient power and store it in a useable energy reservoir. As a reservoir usually serve a capacitor with huge capacity (order of farads). One common form of ambient energy is mechanical vibration energy, which can be caused by motors running in a factory, airflow across a fan blade or even by a moving vehicle. A piezoelectric transducer can be used to convert these forms of vibration energy into electrical energy, which in turn can be used to power wireless sensors. To manage the energy M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 433–438. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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harvesting and the energy release to the system, the LTC3588 piezoelectric energy harvesting power supply. It uses an efficient energy harvesting algorithm to collect and store energy from high impedance piezoelectric elements, which can have shortcircuit currents on the order of tens of microamps (1). (1) Advantage of mechanical-vibration energy is relatively high output voltage, which is suitable for another processing. On the other hand, thermal environmental energy has a great disadvantage just in value of output voltage (order of tens millivolts). This very low voltage is very difficult to transform up to the useable level (order of volts). Losses, which originates by conversion are relatively high and deteriorate whole circuit efficiency. Nevertheless, thanks to the new technology new types of MOSFET transistors and control circuits with extremely low quiescent power appear. This MOSFET transistors with very low RDS(on) and control circuits are key blocks in power sources design which are utilizing ambient environmental energy. Further described power source is intended to supply intelligent sensors with using surrounding thermal energy from immediate sensor’s surroundings. Thermal energy is very suitable because there is no need of vibrations or intensive electromagnetic radiation. TEG (thermo electric generator) device acts as source of electricity. Output voltage level is in order of tens millivolts (hundreds millivolts at max). Low voltage disadvantage is compensated by relatively high supply current in order of hundreds of milliamps (1). Using thermal ambient energy is very gainful when supplying sensors which are monitoring oxygen content in the water, because during the day is the air temperature usually higher than the water temperature and at night vice versa. In addition in winter time is thermal gradient between frozen air and liquid water constant and relatively high. Another indisputable advantage against mechanic-vibration energy is lack of any moving parts. Regarding to its character, thermal power sources are stable during a long time period. Basic common feature of the power sources which using free environmental energy is discontinuous operation. Many wireless sensor systems require only low average power, which make from them front candidates for powering by energy harvesting technique. Many sensors in addition are monitoring variables with very slow process system type (for example oxygen content in water, slow temperature process etc.). That’s why the measurement can proceed discontinuously, resulting on a low duty cycle of operation correspondingly low average power requirement. For example, if a sensor system requires 3.3V at 50mA (165mW) while awake, but is only active for 100ms out of every second, then the average power required is only 16.5mW, assuming the sensor system current is reduced to microamps during the inactive time (sleep time) between transmit bursts. If the same wireless sensor only samples and transmits once per a minute instead of once a second, the average power plummets under 30μW. This difference is significant, because most forms of energy harvesting offer very little steady-state power. In case of utilizing TEG, we have got about tens of milliwatts but only in case of thermal gradient in order of few degrees.

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The less average power required by an actual application, the more likely it can be powered by harvested energy. A typical wireless sensor system powered by harvested energy can be broken down into five fundamental blocks. With the exception of the power management block, all of these blocks have been commonly available. For example, microprocessors that run on microwatts of power (MICROCHIP nanoWAT technology), and small RF transmitters and transceivers (MICROCHIP’s MRJ24J40) that also consume very little power are widely available. Low power analog and digital sensors are also ubiquitous. The missing link in completing this energy harvesting system chain has been the power converter/power management block that can operate from one or more of the common sources of free energy. This block wasn’t able to realized until the new special MOSFET transistors with very low RDS(on) appears. This transistors has merit in rocket boost of new technologies, which are engaged in using surroundings residual energy. MOSFET transistors have no undesirable voltage decay called UCE. That’s why is possible to use power sources with voltage level about tens millivolts and accumulate little energy bursts.

Fig. 1. Supplied TEG's power dependence on temperature gradient

2 TEG (Thermo Electric Generator) Thermoelectric generators (TEGs) are simply thermoelectric modules that convert a temperature differential across the device, and resulting heat flow through it, into a

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voltage via the Seebeck efffect. The polarity of the output voltage is dependent on the polarity of the temperature differential across the TEG. Reverse the hot and cold siides of the TEG resulting to chaanges output voltage polarity. TEGs are basically Peltieer’s cells, only utilized in reverssed mode. These Peltier’s cells are made from many sm mall semiconductor blocks connected together with copper tape. We have made several tests t of selected TEGs, which have to tell us relation between the thermal gradient to TEG output power. TEG worked into constant lload (power resistor with value 0R27). 0 Results can be compared on (Fig. 1.). There were compared two t Peltier’s cells. Both of them were tested in reveerse mode. The first one has nom minal supply voltage 8.6V, the second one double the vvoltage, 15.4V. Purpose of thiss test was to tell, how wide difference in output power w will we notice. From (Fig. 1). We W can assume that cell with higher nominal voltage repport better results..

3 HW Part As a power management co omponent were chosen an integrated circuit from LINE EAR TEHCNOLOGY, LTC310 08. The LTC3108s self-resonant topology steps up frrom input voltages as low as 20 0mV. This energy harvester power source is designed for applications which are usin ng very low average power, but requiring periodic pulses of higher load current. This is i typical for intelligent sensors, which are continuouusly measure oxygen content in n water and transmit data into superior system. Oxyygen content in water is very slo ow varying variable. Therefore one measure per minutee or even hour is sufficient. Ox xygen content in water is very important parameter w with regard to life under the icee in winter season. Continuous thick ice cover may reesult into decreasing oxygen disssolved in water and mean risk for life under the water ssurface. Thermal gradient whicch is among the air and water is sufficient to power sim mple sensor and successive comp ponents. Blocks schematic of used power source is shoown on (Fig. 2).

Fig. 2. Circuit diiagram of TEG power source with two level outputs

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The main advantage rest on self-oscillating design. Feedback loop consist of secondary transformer windings, capacitor Cosc and resistor Rosc. Without these the circuit would scarcely do its job. If the TEG generate a proper voltage spike, or the leading edge is sufficiently slope (2), the secondary winding generate much more higher voltage spike (3)(4), which opens the gate of switching MOSFET. So through the primary winding flow maximum obtainable current defined by TEG type and environment conditions. Steady state on primary side lead to no energy is flow to the secondary side, so there is no energy to power up the MOSFET’s gate (the Cosc is acting as differentiator). Transistor will switch off and the residual energy stored in primary winding creates another spike which will make the loop infinite. It is very simple and clever idea.

4 Smart Mobile User Adaptive System A smart mobile user adaptive system can be developed based on previously discussed solution. The usage of such created system can be founded in areas like large ocean remote monitoring of water quality or water currents. For example a use of remote sensor networks for monitoring of expansion of oil after sea damage (in Mexican gulf in 2010). For continental areas a similar usage can be found in monitoring of content of oxygen in fish ponds or lakes to be informed about a need to increase oxygen content in water to fish survive in winter periods. For both mentioned usage areas a 24 hours monitoring cycle is needed. Use of battery for power in such remote sensors HW solution is uneconomical because there is a need of hundreds or thousands of sensors with a final price of several dollars.

5 Conclusion Problems examined above reflect the possibilities which we have, when designing energy harvester. Water environment is suitable for heat based energy harvesters. Relatively constant thermal gradient and high heat capacity of water guarantee good conditions to running sensors array. Described power source can supply one intelligent sensor with measure frequency about one per minute. By using more powerful TEGs or their cascade connection we can achieve much more power. Power management basis will stay the same, with few minimum modifications.

Acknowledgement This work was supported in part by (1) „Centre for Applied Cybernetics“, Ministry of Education of the Czech Republic under project 1M0567, (2) „SMEW – Smart Environments at Workplaces“, Grant Agency of the Czech Republic, GACR P403/10/1310, (3) „SCADA system for control and monitoring of processes in Real Time“, Technology Agency of the Czech Republic, TACR, TA01010632 and (4) "User Adaptive Systems", VSB - Technical University of Ostrava under project SP/2011/22.

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References 1. Technology, Linear. Wireless Remote Sensor Application Powered From A Peltier Cell. LT Journal of Analog Innovation (April 2010) 2. Ultralow Voltage Energy Harvester Uses Thermoelectric Generator. LT Journal of Analog Innovation, 1–11 (October 2010) 3. Krejcar, O., Frischer, R.: Non Destructive Defects Detection by Performance Spectral Density Analysis. Journal Sensors, MDPI Basel 11(3), 2334–2346 (2011) 4. Krejcar, O.: Problem Solving of Low Data Throughput on Mobile Devices by Artefacts Prebuffering. EURASIP Journal on Wireless Communications and Networking, Article ID 802523, 8 pages (2009), doi:DOI 10.1155/2009/802523 5. Brida, P., Machaj, J., Duha, J.: A Novel Optimizing Algorithm for DV based Positioning Methods in ad hoc Networks. Elektronika Ir Elektrotechnika (Electronics and Electrical Engineering) 1(97), 33–38 (2010) ISSN 1392-1215 6. Cerny, M., Penhaker, M.: Biotelemetry. In: IFMBE Proceedings 14th Nordic-Baltic Conference an Biomedical Engineering and Medical Physics,, Riga, Latvia, June 16-20, vol. 20, pp. 405–408 (2008) 7. Tucnik, P.: Optimization of Automated Trading System’s Interaction with Market Environment. In: 9th International Conference on Business Informatics Research, Univ. Rostock, Rostock, Germany. LNBIP, vol. 64, pp. 55–61 (2010) 8. Mikulecky, P.: Remarks on Ubiquitous Intelligent Supportive Spaces. In: 15th American Conference on Applied Mathematics/International Conference on Computational and Information Science, pp. 523–528. Univ. Houston, Houston (2009) 9. Krejcar, O., Janckulik, D., Motalova, L.: Complex Biomedical System with Biotelemetric Monitoring of Life Functions. In: Proceedings of the IEEE Eurocon 2009, St. Petersburg, Russia, May 18-23, pp. 138–141 (2009), doi:10.1109/EURCON.2009.5167618 10. Cerny, M., Penhaker, M.: The Circadian Cycle Monitoring. In: Conference Proceedings Conference Information: 5th International Summer School and Symposium on Medical Devices and Biosensors, Hong Kong, PEOPLES R CHINA, June 01-03, pp. 41–43 (2008) 11. Augustynek, M., Penhaker, M., Korpas, D.: Controlling Peacemakers by Accelerometers. In: 2010 The 2nd International Conference on Telecom Technology and Applications, ICTTA 2010, Bali Island, Indonesia, March 19-21, vol. 2, pp. 161–163. IEEE Conference Publishing Services, NJ (2010) ISBN 978-0-7695-3982-9, DOI: 10.1109/ICCEA.2010.288 12. Pindor, J., Penhaker, M., Augustynek, M., Korpas, D.: Detection of ECG Significant Waves for Biventricular Pacing Treatment. In: 2010 The 2nd International Conference on Telecom Technology and Applications, ICTTA 2010, Bali Island, Indonesia, March 19-21, vol. 2, pp. 164–167. IEEE Conference Publishing Services, NJ (2010) ISBN 978-0-76953982-9, DOI: 10.1109/ICCEA.2010.186 13. Labza, Z., Penhaker, M., Augustynek, M., Korpas, D.: Verification of Set Up DualChamber Pacemaker Electrical Parameters. In: 2010 The 2nd International Conference on Telecom Technology and Applications, ICTTA 2010, Bali Island, Indonesia, March 19-21, vol. 2, pp. 168–172. IEEE Conference Publishing Services, NJ (2010) ISBN 978-0-76953982-9., DOI: 10.1109/ICCEA.2010.187 14. Krawiec, J., Penhaker, M., Krejcar, O., Novak, V., Bridzik, R.: Web System for Electrophysiological Data Management. In: Proceedings of 2010 Second International Conference on Computer Engineering and Application,s ICCEA 2010, Bali Island, Indonesia, March 19 -21, vol. 1, pp. 404–407. IEEE Conference Publishing Services, NJ (2010) ISBN 978-07695-3982-9, DOI: 10.1109/ICCEA.2010.85

Reduction of Player’s Weight by Active Playing Using Motion Sensors Ondrej Krejcar1,2 and Dalibor Janckulik2 1

University of Hradec Kralove, FIM, Department of Information Technologies, Rokitanskeho 62,Hradec Kralove, 500 03, Czech Republic [email protected] 2 VSB Technical University of Ostrava, Center for Applied Cybernetics, Department of Measurement and Control, Faculty of Electrical Engineering and Computer Science, 17. Listopadu 15, 70833 Ostrava Poruba, Czech Republic [email protected]

Abstract. This project was inspired by the idea of the connection of amusement and useful thing respectively the movement by the playing games, because as is known global problem of the Word is increasing obesity caused by the addiction on the PC games at the young people. This project connects pleasurable with useful, respectively playing of the game with physical effort. By the help of the accelerometer is possible to handle by simple games to whose operating is needed physical impulsion of the player. After it would be possible to take up on this project by the beat measurement of the player in order to check on effectivity of the suggested system. Software was programmed in background C#. Keywords: accelerometer; weight reducing; mobile device, C#.

1 Introduction This project is engaged in the interconnection of physical effort and playing of the games of mobile equipments in order to reduce the weight of the player. Is necessary to scan the movement of the whole body or its parts that in this project simulates hardware controlling elements. About that will care electronic component called accelerometer. Accelerometer is an appliance that measures vibrations or acceleration by the movement of the structures (constructions, parts of the machine etc.) The power causing vibrations or the change of the movement (acceleration), effects on the mass of the sensor, that than overpress piezoelectric element generating electric charge comparable to compression. Becouse the electric charge is comparable to energy and the mass of the sensor is constant, so also electric charge is comparable to acceleration [1]. By the accelerometer is possible to measure dynamic acceleration of the gravitation resulting from the movement of the player [2]. We can imagine accelerometer as a box that is in cosmic area – far- far from all of the cosmic orbs, or if is difficult to find such place, so at least as space ships orbiting over the planet, where everything is in weightlessness. Imagine every wall is sensible on the pressure. If we will at once move by the field on the left (we will speed up M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 439–446. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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acceleration 1g = 9,8 m/s 2), the ball will get at on the wall – X. We measure compressive energy and the output value on the axe X, that evolves the ball/ globule touching the wall [3]. For the practical realization was chosen linear 3-axes ( x ,y ,z) accelerometer LIS3LV02DQ with digital exit and communication up to bus I2C/SPI. The information obtained by the accelerometer can be transferred to the PC after the three communication interfaces. The first one parallel port, but that is rather outdated, and newer PCs are free. Another option is the serial port, after which it is not so difficult to communicate as after USB version for the third time. By using of the communication USB interface would be necessary to develop hardware that would be required to obtain information about location of the users hand from accelerometer, convert it to a data packed, and that send over USB. The development of that hardware is quite a complicated and too much time-consuming, when nowadays is ordinary converter between serial port and USB. From that follows that it is preferable to use serial communication interface. Standard RS-232, respectively the latest version of the RS-232C, from 1969, (also the serial port or serial line) is used as the communication interface of personal computers and other electronics. RS-232 allows connection and mutual serial communication between two devices, it means that the individual bits of transmitted data are transmitted in successively on after another (in series) over a single wire, similarly as by the network technology. Ethernet or USB interface [4]. In this paragraph have been described resources that would be used to build electronic equipment in order to reduce the weight of playing games on mobile devices. For laboratory testing will be used mobile transmission equipment HTC Touch HD with just built-in accelerometer. By these devices motion sensor isn´t used just to recording the screen of display, but also to facilitate the control of various applications, without the use of buttons, both hardware and software. Method GetGvector returns the vector that describes the direction of gravitation and acceleration in relation to the device screen. When the unit face up on a flat surface methods are returned to quality 0, 0, -9.8. The value of -9.8 implies that the acceleration is in the opposite direction of the screen orientation. If the device is standing method returns to 0, -9.8, 0. The quality Y -9.8 means that the appliance accelerates in the direction of the bottom of the screen. Conversely, if the device is held upside down method returns 0, 9.8, 0 [5]. The values measured by an accelerometer, are than visualized by the application in this case the game that was written in Visual Studio 2008 C # for Windows Mobile platform. The application simulates acts as a jump, and the frequency of jumps over certain time. This game can play two or more players who compete with each other. The problem with the conversion of measured values from the accelerometer can occur with improper handling with mobile devices.

2 Problem Definition The movement effects on development-development effects on the move, thanks to the modern era full of technological innovations that lead to a reduction of movement and to decrease of physical exertion, is an irreplaceable factor for many companies to develop physical activity in gaming, which is directly linked to that to give physical

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proficiency. This combination of nice with useful leads to targeted result, respectively to weight loss of players during playing the games. There are many games that needs physical exertion such as dancing simulators, virtual broad jump or mobile applications that by the help of accelerometer response on the tilt of mobile device as at the game „Cube Runner“ , a simple game developed for iPhone mobile, iPhone 3G, iPod Touch. Controlled is the ship (arrow), which travels through a maze of cubes. To passing game is needed a ship without hitting the dice to the finish line. In this game control is very sensible, just very little to tilt the ship. Control is provided by a built-in accelerometer. This game does not always bring such a great effect as the movement of player, it is de facto all about tilting of a mobile device [6]. Another way to move by playing games is using detection by help of 3D camera. The company Mgestyk presented the technology that allows you to control your computer just through simple gestures. It recognizes gestures by using 3D camera and can work with the most of programs under the Windows. It is about controlling your computer by using only hand gestures. You don´t need any peripherals like a mouse or keyboard. Just to connect special camera and buy special software by Mgestyk. There is no need to use sweeping gestures. It´s enough with saving moves by just one hand, in some cases you will use both. It is not known if some applications are so complicated that you would use also the legs, but if it happens, it will be especially the excesses. But the technology by itself in any case isn´t the excess. It should manage any application running under the Windows, and so in the full range of its possibilities. If you will learn to gesture and control computer by that, you will have no limits in its control [7]. iPhone 4 offers the games that are controlled by a gyroscope. You might ask why you should be interested in data from the gyroscope, while accelerometer provides directive angles in all of three axes. The answer is simple – we can´t always rely on the accelerometer. The results, even if the accelerometer is in a relatively stable position, are very sensitive to vibrations and to mechanical noise at all. This is the main reason why the most of the system for the measurement of inertial force uses a gyroscope to annihilation of errors of the accelerometer [8]. All of these methods how to achieve the movement of players by playing games have something good in itself and also some imperfections. In this project, when will be developed simple but effective game where the player will develop a physical activity and so that the mobile device THC Touch HD has just built-in accelerometer, which will be mounted on the body will do “sit-lye“. The device will record the change in the tilt angles according to the number of changes within a given range. The result will be displayed on the display of the device. This game will play one or more players.

3 Developed Software Parts of Platform In this chapter we introduce with a specific solution. Developed applications for mobile device HTC Touch HD have the task of using G sensor, which is part of the device, scan the body position when performing the exercise (or movement in the simulation, simply by tilting the mobile device) called sit-lying. There are several

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types of sit-lying. One of them is that at the exercise, we lie on the ground. When sitting-lying, we lie on your back, bend the legs at the knees and feet push against the ground and hands can be in the rear. The movement begins slowly lift the upper torso from the ground through a rounded back. The scapulae should be lifted from the ground, while the lower back remains on the ground. In the second phase, the body return to its original position, but is not running the floor. During the exercise, it is necessary to maintain a neutral position of the cervical spine and regular breathing (stroke-touch return - exhale). Another type is to do sit-supine on an inclined bench. Lie on the bench upside down. The greater the slope, the greater burden. Slowly we lift the hull and hands crossed on his chest. The exercises on a inclined bench with his own body weight (you can add ballast in the form of wheel weights - it keeps on the chest) is graded load - the heaviest at the beginning of the exercise, when we were the lowest - surpass the greatest weight. This can be used during exercises tops - at the end of the exercise will remain in the down position and only a very short and slow movements up and down the muscles maximally heating up - here but watch out for injuries - strained or torn abdominal muscle and make it impossible for a few weeks of any workout! For better practice oblique muscle while moving up the appropriate body turns slightly left and right dial. The structure features is shown in (Fig.1). Body of a program is a form on which the controls are initialized in the method FrmToCondition() and Initialization(). In the form is included TabControl, in which are two TabPages. In one are the elements of control and in the second are instructions. The main form (TabPage_1), are the following controls: The first is the ComboBox, which is used for the selection of activity (not implemented versions of choice for more activities). The second is the NumericUpDown-nudInterval-determined interval measurementactivity. One of the other Labek, one of the major is one that displays the number of repetition scores. Button-btnStart-used to start the exercise. Label-lblTime-The label displays the time practice starts and the time during which the exercise is conducted. PictureBox, PictureBox PictureBox3-in are two pictures that demonstrate the movement. Label - label6 this label displays the angle of rotation. After initialization, the application will serve the main form load event FrmToConditional_Load. Here we set the head of file that is used for storing the measured values from the G sensor. Event btnStart_Click used to initialize the element values before you start exercising. The method included features DataTime system time to recover. Next run timer, which is primetime for the calculation. The event includes timer1_Tick as mentioned above calculation principle. If the time of exercise not equal to the system time + TimeInterval program continues the calculation otherwise, the info about the end of exercise. If the angle is greater than 50 and less than 150 in the BUP is changed from FALSE to TRUE. This applies to the situation when the angle increases. If the angle is greater than150 or less than 50 in bDown changes false to true. If both achieve both truei32Counetrer + +. Then BUP and bDown back to false. If-it will be when the angle of growth of less than50 and will fall again BUP'll always be in the condition is false, because the condition is not complete, which is such that for changing the status true, we need a angle greater than

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50 and less than 150. This problem may occur for example when it is wrong to tutor training. In the eventbtnStart_click is contained in a file and saving. In the latest incident is resolved CloseMenu_Click quit. DialogResult, remembers what it was last pressed, then ask if dr == DialogResult if so, that application shall be terminated if not return.

Fig. 1. Application Structure

The project is ready for further enlargement exercises. You need only get the code for the next exercise, therefore, is to write the formula for calculating algorithm danny issues such as handles and add the application graphically.

4 Implementation This chapter will talk about how to implement new solutions. The application was developed using C # language compatible. NET in Visual Studio2010 from Microsoft. The application was created in the GUI, a graphical user interface library, in this case for mobile devices (mobile service). Were used as traditional components as Button, Label, PictureBox and components that run in the background like Timer and main

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menu. HCT_X7500 Mobile devices connected over USB to the PC and the connection and synchronize via ActiveSync program to play into the mobile device. You can then disconnect the mobile device from your PC and run the actual applicationHTC_X7500. Pressing the ToCondition application starts and loads the graphics window (Fig.2).

Fig. 2. Application GUI

After setting an interval workout that is every one minute, for example as shown in (Fig.2) user need to press Start. When pressed on the upper right shows the time when the end of the exercise.

Fig. 3. Time Machine view (top)

After opening exercises the next time when the exercise will end the current time (Fig.3), a time that flows in real time and stops on a time basis, which is set to start after the Start button. After opening exercises the next time when the exercise will end the current time (Fig.3), a time that flows in real time and stops on a time basis, which is set to start after the Start button.

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5 Testing of Developed Application This chapter deals with the testing and applications created by comparing the results from other authors. Given that a similar application for mobile devices has been found will be tested only in testing applications developed. Task was to create an application for mobile devices, which already includes a built-in accelerometer (G sensor). In this project, namely HTC_X7500. As a first test were tested, which took place after the first measurement interval setting exercise 1 minute and then another, without that application has been turned off. This means that after 1 min when it was done 34 Sit-Lying as the chart shows (Fig,4). Valid Sit-Lie is one that is in the range of 50 – 150. The second measurement was performed five Sit-Lies, as shown in the chart at the end (Fig, 4).

Fig. 4. Repeated exercises

Red dots represent unsuccessful attempts. The conclusion was one counted, because it met the set range.

6 Conclusion On this project we tried to work with the accelerometer control in the mobile deviceHTC_X7500. So everything related to getting from the accelerometer data from the device to communicate. We also tried working with graphics for mobile devices in mobile servicing and discovered some new features and commands. Utilization of this project is such that at practice, we can concentrate fully on the exercises and you do not guard interval exercise or count the number of repetitions. The application was designed more as a game and therefore serves to entertain, such as two or more people who do much for the lying-sit any unit of time. The application is ready, maybe in the future to extend to more activities such as push-ups and the like. It would be enough only to write up the algorithm and compute and modify graphics window.

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Acknowledgment. This work was supported in part by (1) „Centre for Applied Cybernetics“, Ministry of Education of the Czech Republic under project 1M0567, (2) „SMEW – Smart Environments at Workplaces“, Grant Agency of the Czech Republic, GACR P403/10/1310 and (3) "User Adaptive Systems", VSB - Technical University of Ostrava under project SP/2011/22. We also acknowledge support from student Marek Smolon.

References 1. Augustynek, M., Penhaker, M., Korpas, D.: Controlling Peacemakers by Accelerometers. In: 2010 The 2nd International Conference on Telecom Technology and Applications, ICTTA 2010, Bali Island, Indonesia, March 19-21, vol. 2, pp. 161–163. IEEE Conference Publishing Services, NJ (2010) ISBN 978-0-7695-3982-9, DOI: 10.1109/ICCEA.2010.288 2. Krejcar, O., Frischer, R.: Non Destructive Defects Detection by Performance Spectral Density Analysis. Journal Sensors, MDPI Basel 11(3), 2334–2346 (2011) 3. Krejcar, O.: Problem Solving of Low Data Throughput on Mobile Devices by Artefacts Prebuffering. EURASIP Journal on Wireless Communications and Networking, Article ID 802523, 8 pages (2009) DOI 10.1155/2009/802523 4. Brida, P., Machaj, J., Duha, J.: A Novel Optimizing Algorithm for DV based Positioning Methods in ad hoc Networks. Elektronika Ir Elektrotechnika (Electronics and Electrical Engineering) 1(97), 33–38 (2010) ISSN 1392-1215 5. Tucnik, P.: Optimization of Automated Trading System’s Interaction with Market Environment. In: 9th International Conference on Business Informatics Research. LNBIP, vol. 64, pp. 55–61. Univ. Rostock, Rostock (2010) 6. Mikulecky, P.: Remarks on Ubiquitous Intelligent Supportive Spaces. In: 15th American Conference on Applied Mathematics/International Conference on Computational and Information Science, pp. 523–528. Univ. Houston, Houston (2009) 7. Krejcar, O., Janckulik, D., Motalova, L.: Complex Biomedical System with Biotelemetric Monitoring of Life Functions. In: Proceedings of the IEEE Eurocon 2009, St. Petersburg, Russia, May 18-23, pp. 138–141 (2009) DOI 10.1109/EURCON.2009.5167618 8. Pindor, J., Penhaker, M., Augustynek, M., Korpas, D.: Detection of ECG Significant Waves for Biventricular Pacing Treatment. In: 2010 The 2nd International Conference on Telecom Technology and Applications, ICTTA 2010, Bali Island, Indonesia, March 19-21, vol. 2, pp. 164–167. IEEE Conference Publishing Services, NJ (2010) ISBN 978-0-76953982-9, DOI: 10.1109/ICCEA.2010.186 9. Labza, Z., Penhaker, M., Augustynek, M., Korpas, D.: Verification of Set Up DualChamber Pacemaker Electrical Parameters. In: 2010 The 2nd International Conference on Telecom Technology and Applications, ICTTA 2010, Bali Island, Indonesia, March 19-21, vol. 2, pp. 168–172. IEEE Conference Publishing Services, NJ (2010) ISBN 978-0-76953982-9., DOI: 10.1109/ICCEA.2010.187 10. Brad, R.: Satellite Image Enhancement by Controlled Statistical Differentiation. In: Innovations and Advances Techniques in systems, Computing Sciences and Software Engineering, International Conference on Systems, Computing Science and Software Engineering, ELECTR NETWORK, December 03-12, pp. 32–36 (2007)

Use of Neural Networks Library for Material Defect Detection Diagnosis Ondrej Krejcar 1

University of Hradec Kralove, FIM, Department of Information Technologies, Rokitanskeho 62,Hradec Kralove, 500 03, Czech Republic [email protected] 2 VSB Technical University of Ostrava, Department of measurement and control, 17. Listopadu 15, 70833 Ostrava Poruba, Czech Republic [email protected] Abstract. Aim of this project is to prepare support for design and realization of application, which is developed in object orientated programming language C#. This application classifies data that are obtained within industrial processes. Data used in this project was received during measurement of material diagnostics and its structural defects. Core of developed application is based on neural networks design, which is capable to classify whether the material has defect or not. Before classification itself it is necessary to set structural parameters of the neural network to obtain a good quality results. Afterwards application outputs are compared with outputs from Statistica programme, which is also used for classification purposes. Keywords: artificial neural networks, supervised learning, AForge.Neuro, material diagnostics, Statistica programme.

1 Introduction Neural networks as part of artificial intelligence issue, is quickly evolving branch of computer engineering, which allows solving tasks that would have (when used conventional computing algorithms) high performance requirements on computing power because of the complex mathematical description. Generally many real tasks can be solved by linear models described by complicated mathematics, usually systems of inhomogeneous differential equations. Realization of this kind of solutions is often time consuming, sometimes even impossible because of too complex mathematical description [3]. To design solution even for this complicated tasks, neural networks were developed during 20th century. Neural networks are a non-linear models that allows to solve specific tasks from almost all areas of engineering. Many artificial neural networks architectures were developed since the first moments of their introduction. Practical usage of these various architectures is very broad and many tasks may be solved by them. 1.1 Basic Elements of the Neural Networks In principle each of these neural networks architectures has basically one element in common. This element is called neuron. Neuron in computer environment is very M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 447–454. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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similar to its biological pattern. It can be imagined as object with many inputs and only one output. Every input has its own weight value which is important for defining of the priority during neural processing. Another basic element of the neuron is its threshold which determines input values needed to activate neuron processing so that it can work as designated [3]. Last basic element of the neuron is its transfer function which determines the way of neuron processing. Output value of the neuron is computed by this transfer function which transfers input values combined by threshold. There are many kinds of transfer functions used in application based on neural networks. The most common transfer function is threshold function or sigmoid. Details of this transfer function will be provided in other chapters of this project. Architecture of the artificial neural network which uses only one neuron is called perceptron. This architecture can classify only to two classes, that’s why it is used rarely in practical applications [1]. For the real purposes the architecture composed of more neurons is used. These neurons are set in layers. In principle there are three layers: input layer, hidden layer and output layer. Input layer’s task is to adjust raw input data so that it can be computed by the rest of the neural network afterwards. Hidden and output layers are computing power of the application [4]. Details of this kind of neural network architecture will be provided hereinafter. 1.2 C# Neural Networks Library Support for the solution of this project in .NET framework is C# neural network library which was introduced by the British programmer Andrew Kirillov in 2006. Kirillov won the prize of the Codeproject.com server [5]. This library contains a huge amount of classes and objects that can be used for design of the various architectures of the artificial neural networks. Programmer also attached some demonstrative application developed in library which shows the principles of neural networks design.

2 Problem Definition Many methods and algorithms are applied in material diagnostics issue nowadays. Some of them are very precise and are able to diagnose materials correctly, some are less efficient when determining required material properties. An important subset of diagnostics is material defects diagnostics. Researchers from VSB Technical University of Ostrava came up with a defect diagnostics method which uses the emission of pulses which are fetching to the diagnosed body. Accelerometers then measure the body’s response to these pulses. Obtained data is transformed by Fast Fourier Transformation and modified by filters in MATLAB afterwards [2]. Adjusted data is needed to be computed by an instrument, which allow researchers to classify the material as convenient or inconvenient. Statistica programme developed by Statsoft company has been this classification instrument so far. This commercial software contains neural network’s design and can be used for classification purposes. Aim of this project is to substitute this commercial software by application that works in .NET framework as less expensive possibility of classification of the measured, adjusted and modified data.

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Fig. 1. Measurement network scheme [2].

Essence of the whole problem is classification of the material diagnostics. It is required that from the programme outputs is possible to assess whether the material has defects and if so, what kind of defect that would be. When designing the architecture of the neural network it is necessary to take into consideration, that there has to be as many neurons in input layer as the count of the classes of input samples. The neurons count in the hidden layer is needed to find out empirically. The neurons count in the output layer is equal to count of classes in which the input data is classified. Architectures with so called supervised learning algorithms are implemented for the classification purposes. In these architectures it is necessary to learn designed neural network for the first time with some reference data (samples of measurement materials with no defects), so that the neural network can recognize whether the other samples are obtained from materials with defects of without defects.

Fig. 2. Three-layer neural network.

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2.1 Supervised Learning Process In principle the supervised learning process of the multi-layer neural networks runs method of setting the neurons input’s weights to random values, usually in the range from 0.1 to 0.3. As transfer functions the threshold functions are usually applied for the first time learning. Number of iterations is set. This count determines the length learning process. The reference data is then submitted to the neural network’s inputs. In these data the correct output values are known. After that the learning process begins. During this process the input’s weights, thresholds and transfer functions of neurons in all layers are set. The first settings usually take place in input layer. The aim of this part of the learning process is to set the neural network’s parameters so that the errors in comparison to required values in output values are minimalised. A certain count of iterations runs through. After that it is necessary to check whether the error’s values are in range of tolerance which depends on the chosen type of the neural network or on type of the application. If there are still too high errors, the learning process runs through again until the required range of tolerance is achieved. It is possible that after running through the all preset iterations the neural network is not learned in a good quality [4]. In such cases it is necessary to take into consideration of reset the values of weights and transfer functions before the learning process begins. As soon as the neural network is learned in sufficient quality, the process of validation and testing takes place. The learning set of samples serves for searching the neural network model, the validation set for model validating and testing set for testing usability of model [8]. For the purposes of diagnostics the three-layer neural network architecture is implemented in Statistica programme. This architecture is based on perceptron learning process, however there are more neuron in this architecture (in comparison to typical perceptron architecture), neurons are spread into three layers. 2.2 Perceptron Learning Algorithm • • • •

Values of the weights are randomized If the output is correct, the weights don’t change If the output value is supposed to be equal to 1, but it’s 0, weights of the active inputs are incremented. If the output value is supposed to be equal to 0, but it’s 2, weights of the active inputs are decremented.

Inputs are active when its value on inputs above threshold has non-zero value. Quantity of weight’s values change (when incrementing or decrementing) depends on the chosen option: • • •

Solid additions are applied when incrementing or decrementing Addition’s values change depends on error values. It is convenient the increase them when the error is higher and conversely. This speed-up convergence can lead to learning inability. Variables and non-variables are combined depending on error [1].

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Although the network is based on perceptron learning system, it acts like a multi-layer network. 2.3 Elementary Properties of Multilayer Neural Networks Let φ: RM (input) => RN (output) be a continuous mapping, then hidden layers and weights (thresholds) values can be found in such way that φ is realised by this network with arbitrary precision [3]. If some network (its weights) exists to such arbitrary continuous mapping φ, it is possible to find x to approximate value y ≅ φ(x) by this algorithm: •

• • •

to the inputs of neurons in input layer values of all all elements of vector x are submitted. Outputs of neurons in input layer obtain values yi = xi, i = 1,…, m, and are transferred along the lines with weights, which are marked wji to the inputs of all k-neurons of hidden layer (j = 1, …, k) these values are computed by neurons from the hidden layer to its response and proceed them to inputs of the neurons in higher layer this activity is continuously spread until from the output layer neuron the response vector x of the entire network y is created.

3 New Solution This chapter is devoted to detailed description of the C# neural network library, which, as aforesaid, is possible to use for design of application that classifies outputs of spectral analysis of material diagnostics. Library AForge.Neuro.dll is based on Microsoft Visual Studio Solution called AForge, which contains elementary classes, methods, components and other elements that can be used not only for design of neural networks, but even for design genetic algorithms, fuzzy systems, image processing, robotics and many other practical applications. AForge.Neuro.dll namespace contains interface and classes for computing purposes of neural networks. Following description of all classes that are contained in the library helps for better insight of neural network architectures design. 3.1 Classes in AForge.Neuro Library Neuron class is elementary class of the whole library. Method Neuron has only one input parameter in its argument which is count of neuron’s inputs. This class also contains vector of all neuron inputs weights and one output variable. After neuron is created, all its input weight values are set by Randomize method. The range of these randomized values is from 0 to 1. Weight value are during following learning process changed, so that the results and its errors are minimalised. Layer class is basically similar to Neuron Class and summarizes common functions of all neural layers. In this class particular parameters of the whole layer are determined, that means count of neurons in layer, overall count of layer inputs. Resulting vector of layer outputs is generated by using method Compute.

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In standard practice is Network class the most popular class. If some specific kind of neural network architecture ought to be implemented, it is necessary to include this class and extend it with required parameters of given architecture, e.g. Kohonen selforganizing maps that are capable to recognize colors. Network class contains declaration of overall number of inputs to neural network, number of layer in network and number of neurons in each layer. From this generally designed class inherits classes ActivationNetwork and DistanceNetwork, which can be used for particular design of applications.

Fig. 3. Sigmoid progression [7].

3.2 SigmoidFunction Class SigmoidFunction class which implements non-linear sigmoid with 0 to 1 range is much more common. Presicion of neural network learning depends on its steepness that is defined as λ = tg α. In this class default setting of λ value is 2. It is possible to change this value if the neural network is not trained in a good quality.

4 Conclusions In this project we provided support for the design and realisation of application developed in object orientated programming language C#, which will classify data obtained on basis of the material defects diagnostics. The application implements three layer perceptron architecture that was so far used pro classification purposes in commercial programme Statistica. After realization of this application it will be possible to substitute Statistica programme. Support for programming of the application is C# neural network library called AForge.Neuro.dll, which was introduced on codeproject.com server by programmer Andrew Kirillov in 2006. Author of this library reserves rights to its usage and it is forbidden to use it for the commercial purposes without his agreement. When compiling the theoretical part of this work, we used particularly expert materials focused on neural networks and we understood the basic principles of their

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design. As far as my opinion is concerned, we think that the future of neural network is very perspective, because very complicated and complex tasks can be solving by these algorithms. Acknowledgement. This work was supported in part by (1) „Centre for Applied Cybernetics“, Ministry of Education of the Czech Republic under project 1M0567, (2) „SMEW – Smart Environments at Workplaces“, Grant Agency of the Czech Republic, GACR P403/10/1310, (3) „SCADA system for control and monitoring of processes in Real Time“, Technology Agency of the Czech Republic, TACR, TA01010632 and (4) "User Adaptive Systems", VSB - Technical University of Ostrava under project SP/2011/22. We also acknowledge support from student Jakub Hlavica.

References 1. Jirsik, V., Horacek, P.: Neural networks, expert systems and speech recognition. Study support FEKT VUT, 7–46 2. Krejcar, O., Frischer, R.: Detection of the Internal Defects of Material on the Basis of the Performance Spectral Density Analysis. Journal of Vibro Engineering 12(4), 541–551 (2010) 3. Krejcar, O.: Problem Solving of Low Data Throughput on Mobile Devices by Artefacts Prebuffering. EURASIP Journal on Wireless Communications and Networking, Article ID 802523, 8 pages (2009) DOI 10.1155/2009/802523 4. Neural Networks in Statistica program (2010), http://www.statsoft.com/textbook/neural-networks/ (quoted 3/11/2010) 5. Neural Networks on C# - The Code Project 1(November 9,2006), http://www.codeproject.com/KB/recipes/aforge_neuro.aspx (quoted 25/10/2010) 6. Krejcar, O., Janckulik, D., Motalova, L.: Complex Biomedical System with Biotelemetric Monitoring of Life Functions. In: Proceedings of the IEEE Eurocon 2009, St. Petersburg, Russia, May 18-23, pp. 138–141 (2009) DOI 10.1109/EURCON.2009.5167618 7. Mikulecky, P.: Remarks on Ubiquitous Intelligent Supportive Spaces. In: 15th American Conference on Applied Mathematics/International Conference on Computational and Information Science, pp. 523–528. Univ. Houston, Houston (2009) 8. Krejcar, O., Frischer, R.: Non Destructive Defects Detection by Performance Spectral Density Analysis. Journal Sensors, MDPI Basel 11(3), 2334–2346 (2011) 9. Brida, P., Machaj, J., Duha, J.: A Novel Optimizing Algorithm for DV based Positioning Methods in ad hoc Networks. Elektronika Ir Elektrotechnika (Electronics and Electrical Engineering) 1(97), 33–38 (2010) 10. Augustynek, M., Penhaker, M., Korpas, D.: Controlling Peacemakers by Accelerometers. In: 2010 The 2nd International Conference on Telecom Technology and Applications, ICTTA 2010, Bali Island, Indonesia, March 19-21, vol. 2, pp. 161–163. IEEE Conference Publishing Services, NJ (2010) ISBN 978-0-7695-3982-9, DOI: 10.1109/ICCEA.2010.288

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11. Pindor, J., Penhaker, M., Augustynek, M., Korpas, D.: Detection of ECG Significant Waves for Biventricular Pacing Treatment. In: 2010 The 2nd International Conference on Telecom Technology and Applications, ICTTA 2010, Bali Island, Indonesia, March 19-21, vol. 2, pp. 164–167. IEEE Conference Publishing Services, NJ (2010) ISBN 978-0-76953982-9, DOI: 10.1109/ICCEA.2010.186 12. Tucnik, P.: Optimization of Automated Trading System’s Interaction with Market Environment. In: 9th International Conference on Business Informatics Research. LNBIP, vol. 64, pp. 55–61. Univ. Rostock, Rostock (2010) 13. Labza, Z., Penhaker, M., Augustynek, M., Korpas, D.: Verification of Set Up DualChamber Pacemaker Electrical Parameters. In: 2010 The 2nd International Conference on Telecom Technology and Applications, ICTTA 2010, Bali Island, Indonesia, March 19-21, vol. 2, pp. 168–172. IEEE Conference Publishing Services, NJ (2010) ISBN 978-0-76953982-9., DOI: 10.1109/ICCEA.2010.187

Multi-sensor Measurement Fusion via Adaptive State Estimator Li-Wei Fong No. 168, Shiuefu Rd., Tanwen Village, Chaochiao Township,36143 Miaoli, Taiwan [email protected]

Abstract. An adaptive state estimator is developed to fuse the measurements extracted from multiple sensors for tracking the same maneuvering target. The proposed approach consists of Multi-Band Standard Kalman Filter (MBSKF) and a learning processor. Based on Bayesian estimation scheme, the likelihood function of learning processor can be approximated by the Gaussian basis function whose smoothing factor is related to the estimated bandwidth by taking an average of innovation error covariance matrices of MBSKF. Based upon learning processor and MBSKF, adaptive state estimation is extended to handle the switching plant in the multi-sensor environment. The simulation results are presented which demonstrate the effectiveness of the proposed approach. Keywords: Measurement fusion, adaptive state estimator, learning processor.

1 Introduction In the modern surveillance systems, multi-sensor data fusion algorithms have been applied to many fields such as air traffic control, tactical weapon defense, and C3I (Command, Control, Communication and Intelligence) systems where measurements extracted from multiple sensors are used to estimate the states (position, velocity, and acceleration etc.) of the moving objects [1]. Tracking targets by fusing measurements from Standard Kalman Filter (SKF) in a central level fusion process (a global estimation process) is acknowledged to be one of the most powerful tracking techniques [2]. It is known that the target dynamics may vary rapidly. Using only one single-level of process noise, the SKF can be resulted in performance degradation. In order to accommodate the possibly varying accuracy requirement, different levels of system process noise are required for the adaptation of state estimator. In the literature, a few adaptive approaches [3], [4], [5] introduced to overcome the filtering dilemma were to use a group of two parallel filters set up for two different models, namely, the system process noises of narrow bandwidth and wide bandwidth. In this paper, we extend the works of [6] and [4] by using Multi-Band SKF (MBSKF) and the learning processor to develop an Adaptive State Estimator (ASE) for central measurement fusion, as shown in Fig. 1.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 455–462. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Fig. 1. The adaptive state estimator in the measurement fusion center.

In particular, a semi-Markov process is incorporated into the learning processor this ensuing adaptive bandwidth capability, that is, the abilities to switch between low-level-band SKF in the absence of maneuvers and middle- or high-level-band SKF in the presence of maneuvers or very highly skilled maneuvers. Hence, the learning processor becomes an unsupervised Bayesian learning algorithm [7]. The resulting estimator makes natural for target tracking that provides significantly better tracking performance than each individual SKF.

2 Adaptive State Estimation In this section, the target model, measurement model, and the computational structure of the measurement fusion center are described. In the fusion center, the estimation architecture of ASE is divided into two parts, MBSKF and learning processor. The candidate central fused estimates are produced by the MBSKF. The learning processor is used to generate the model probabilities to classify which one of the candidate estimates for output. Meanwhile, Bayesian estimation scheme of ASE is developed and briefly outlined. 2.1 Target and Measurement Models Consider a dynamical target which is tracked by a multi-sensor system. Assume that the target dynamics can be modeled by one of M hypothesis models. The model set is

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denoted as ΨM := {1,2, …, M } . The event that model j is in effect during the sampling

period (k,k+1] is denoted by α kj+1 . For the j-th hypothesized model α kj+1 , the target dynamics and the N sensor measurements are modeled by the following discrete-time state-space model: xk +1 = Φ kj xk + Γkj wkj ,

j = 1,2, … , M

(1)

z ki +1 = H ki +1 xk +1 + vki +1 ,

i = 1,2,… , N

(2)

where xk is nx -dimensional state vector, Φ kj is the state transition matrix, Γkj is input matrix, z ki is the n z -dimensional measurement vector of the i-th sensor, and H ki is the observation mapping matrix. The wkj and vki vectors are assumed to be zeromean, white sequences with known covariance matrices. The covariance matrices for the wkj and vki vectors are given by cov[wkj , wlj ] = Qkjδ kl and cov[vki , vli ] = Rki δ kl

(3)

where the Kronecker delta δ kl = 0 for k ≠ l and δ kl = 1 for k = l . The two noises are also assumed to be uncorrelated as cov[wkj , vli ] = 0,

∀k , l .

(4)

By measurement fusion, the N sensor models can be integrated into following single model: z k +1 = H k +1 xk +1 + v k +1

(5)

where z k +1 = [( z1k +1 ) T , ( z k2+1 ) T , H k +1 = [( H k1+1 ) T , ( H k2+1 )T , v k +1 = [(vk1 +1 ) T , (vk2+1 ) T ,

, ( z kN+1 ) T ]T ,

(6)

, ( H kN+1 ) T ]T , , (vk3+1 ) T ]T ,

R k +1 = cov[ v k +1 , v k +1 ] = diag[ Rk1+1 , Rk2+1 ,

, RkN+1 ].

(7)

(8)

(9)

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2.2 Multi-Band Standard Kalman Filter

Given the system model described by (1) and (5), the MBSKF is based on a bank of M parallel standard Kalman filters set up for M different models. The equations of the j-th standard Kalman filter are given by [8] Pk +j 1|k = Φ kj Pk |jk (Φ kj ) T + Γkj Qkj (Γkj ) T

(10)

S kj+1 = H k +1 Pk +j 1|k (H k +1 ) T + R k +1

(11)

K kj+1 = Pk j+1|k (H k +1 ) T ( S kj+1 ) -1

(12)

Pk j+1|k +1 = Pk j+1|k − K kj+1 S kj+1 ( K kj+1 ) T

(13)

xˆkj+1|k = Φ kj xˆ kj|k

(14)

d kj+1 = z k +1 − H k +1 xˆ kj+1|k

(15)

xˆkj+1|k +1 = xˆ kj+1|k + K kj+1d kj+1

(16)

where xˆ kj+1|k and xˆkj+1|k +1 are predicted and filtered state vectors of the α kj+1 , Pk +j 1|k ( Pk +j 1|k +1 ) is the covariance matrix of the estimation errors before (after) processing the measurement, d kj+1 is the innovation , S kj+1 is the covariance of the innovation, K kj+1 is the filter gain matrix, Rkj+1 is the variance of the measurement noise, and Qkj+1 is the variance of the process noise. 2.3 Learning Processor Assume that the unknown target maneuver model randomly switches at random times between a finite set of M possible models. The rate of switching is assumed to be considerably slower than that of the observation sampling rate, and the random switching process among the models will be determined by a semi-Markov process. The model probabilities are calculated by the learning processor. The likelihood function of learning processor can be approximated by the Gaussian basis function whose smoothing factor is related to the estimated bandwidth by taking an average of innovation error covariance matrices of MBSKF. Based on the Bay's estimation procedure, a weighted state estimate can be written as follows: xˆk +1|k +1 =

M

∑μ j =1

j k +1

xˆ kj+1|k +1 .

(17)

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The probability μ kj+1 of the α kj+1 is given by Bayes’ rule as below M

μ kj+1 =

f (α kj+1 | d kj+1 , S k +1 )∑ θ jm μ km m =1 M

M

∑ f (α

j k +1

j =1

(18)

| d , S k +1 )∑ θ jm μ km j k +1

m =1

where θ jm is the transition probability. The likelihood function of the α kj+1 is given by the innovation d kj+1 and the averaged covariance of the innovation S k +1 as f (α kj+1 | d kj+1 , S k +1 ) =

1 (2π )

nz / 2

S k +1

1/ 2

⎞ ⎛ −1 exp⎜ ( d kj+1 ) T ( S k +1 ) −1 d kj+1 ⎟ 2 ⎠ ⎝

(19)

where S k +1 =

1 M

M

∑S

j k +1

.

(20)

j =1

3 Simulation Results The results of computer simulation are presented for the performance comparison of ASE, low-level-band SKF (denoted as SKF1; maneuver variance given by 133m2/s4), middle-level-band SKF (denoted as SKF2; maneuver variance given by 833m2/s4) and high-level-band SKF (denoted as SKF3; maneuver variance given by 2133m2/s4). The results of 100 runs of Monte Carlo simulation are processed and presented in the Figures of following case study. The descriptions of scenario and system parameters are similar to but a little different in [3], [4], [5]; the interesting readers may consult these papers for realizing the method of simulation processing. The performance is evaluated by using the Root Mean Square Error (RMSE). Averaged Root Mean Square Error (ARMSE) is defined as the performance index. There are two maneuvering turns of the target trajectory called circular-turn and U-turn shown in Fig. 2. The errors of the position estimate are plotted in Fig. 3. Similar RMS-type quantities are plotted in the other figures. Figs. 3-5 show the estimation errors of ASE, SKF1, SKF2 and SKF3, respectively. For a quantitative performance comparison, the timeaverages of estimation errors in Figs. 3-5 are listed in Table 1. As shown, ASE demonstrates adaptive capability in the tracking process. ASE performs better tracking performance than SKF1, SKF2 and SKF3. Table 1. Time-Averages of Estimation Errors. Method SKF1 SKF2 SKF3 ASE

Position (m) 21.4431 19.4546 19.9763 18.5923

Velocity (m/s) 31.0124 29.2598 32.4038 25.5496

Acceleration (m/s2) 22.0567 22.7735 26.8376 19.1526

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12000 Target 10000

z-axis (m )

8000

6000

4000

2000

0

2000

4000

6000

8000 10000 x-axis(m)

12000

14000

16000

Fig. 2. The target trajectory with both circular- and U-maneuvering turns.

POSITION 40 SKF1 SKF2 SKF3 ASE

35

RMS ERROR (m)

460

30

25

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15

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0

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30

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50 60 Time(sec.)

70

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Fig. 3. RMS errors in position estimate.

90

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VELOCITY 90 SKF1 SKF2 SKF3 ASE

80

RMS ERROR (m/s)

70 60 50 40 30 20 10

0

10

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30

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50 60 Time(sec.)

70

80

90

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Fig. 4. RMS errors in velocity estimate.

ACCELERATION 110 SKF1 SKF2 SKF3 ASE

100 90

RMS ERROR (m/s 2)

80 70 60 50 40 30 20 10 0

0

10

20

30

40

50 60 Time(sec.)

70

80

Fig. 5. RMS errors in acceleration estimate.

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4 Conclusion In this paper, an adaptive state estimator for use of the centralized measurement fusion is introduced to improve tracking accuracy of the multi-senor surveillance systems. With a semi-Markov process, learning processor is developed as an on-line learning algorithm to choose an appropriate target model for the standard Kalman filter performing sub-optimal state estimation. Simulation results are provided for comparison of tracking accuracy between proposed estimator and each individual standard Kalman filter. For target trajectory with both circular- and U-maneuvering turns, the proposed estimator demonstrates better tracking accuracy than the averaged estimates of multi-band standard Kalman filter with about 8.37%, 17.29%, and 19.83% improved in position, velocity, and acceleration, respectively. The results indicate that proposed estimator provides significant performance improvements.

References 1. Hall, D.L., Llinas, J.: An Introduction to Multisensor Data Fusion. Proc. of the IEEE 85(1), 6–23 (1997) 2. Gan, Q., Harris, C.J.: Comparison of Two Measurement Fusion Methods for Kalman-filterbased Multisensor Data Fusion. IEEE Trans. Aerosp. Electron. Syst. 37(1), 273–280 (2001) 3. Fong, L.W.: An Adaptive Filter for Multi-sensor Track Fusion. In: Proceedings of International Conference on Signal Processing, pp. 231–235 (2008) 4. Fong, L.W.: Multi-sensor Track Fusion via Multiple-model Adaptive Filter. In: Proceeding of the 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, pp. 2327–2332 (2009) 5. Fong, L.W.: Adaptive Information Matrix Filtering Fusion with Nonlinear Classifier. In: Proceedings of SICE Annual Conference, pp. 2214–2219 (2010) 6. Moose, R.L., Wang, P.P.: An Adaptive Estimator with Learning for a Plant Containing Semi-Markov Switching Parameters. IEEE Trans. Syst. Man Cybern. 3(3), 277–281 (1973) 7. Duda, R.O., Hart, P.E., Stork, D.G.: Patter Classification, 2nd edn. Wiley, New York (2001) 8. Bar-Shalom, Y., Li, X.R.: Estimation and Tracking, Principles, Techniques, and Software. Artech House, Norwood (1993)

A Simple Automatic Outlier Regions Detection Kitti Koonsanit Department of Computer Science, Faculty of Science, Kasetsart University, Bangkok, Thailand [email protected]

Abstract. Automatic determination of the outlier regions is often needed to eliminate that outlier region. In this paper, a method has been developed to determine the outlier regions in satellite image using a data mining algorithm based on the co-occurrence matrix technique in order to determinate that outlier. Our method consists of four stages, the first stage estimate a number of region by co-occurrence matrix, the second stage cluster dataset by automatic clustering algorithm, the third stage detect outlier regions by automatic threshold value and the final stage defines outlier regions, which are lower than threshold value, to be outlier regions. The proposed method was tested using data from unknown number of regions with multispectral satellite image in Thailand. The results from the tests confirm the effectiveness of the proposed method in finding the outlier regions. Keywords: Outlier regions, Anomaly Detection, determination outlier regions, co-occurrence statistics, Outlier regions detection.

1 Introduction Clustering is a popular tool for data mining and exploratory data analysis. One of the major problems in cluster analysis is the determination of the outlier clusters for unlabeled data, which should be eliminated. Outlier is the data which has obviously difference with clustering. In 1980, Hawkins made the definition of it: an outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism [1]. Usually, this kind of data has special behavior or model. In effective data set, outlier is a small part and recognized as the byproduct of clustering [2]. So, outlier is always canceled or neglected simply. However, certain outlier probably is the real reflection of normal data. These data are worthy to be study more. In this paper, we propose a new easy method for automatically estimating the outlier regions in unlabeled data set. Pixel clustering technique in a color image is a process of unsupervised classification of hundreds thousands or millions pixels on the basis of their colors. In this paper, a method has been developed to determine the outlier regions in satellite image clustering application using a data mining algorithm based on the co-occurrence matrix technique. Therefore, automatic determination of the outlier regions can greatly help with the unsupervised classification of satellite Image. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 463–470. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Related Work 2.1 Background Mutispectral imaging is characterized by its ability to record detailed information about the spectral distribution of the received light. Mutispectral imaging sensors typically measure the energy of the received light in tens or hundreds of narrow spectral bands in each spatial position in the image, so that each pixel in a mutispectral image can be represented as a high-dimensional vector containing the sampled spectrum. Since different substances exhibit different spectral signatures, mutispectral imaging is a wellsuited technology for numerous remote sensing applications including target detection. When no information about the spectral signature of the desired targets is available, a popular approach for target detection is to look for objects that deviate from the typical spectral characteristics in the image. This approach is commonly referred to as anomaly detection [3], and is related to what is often called outlier detection in statistics. If targets are small compared to the image size, the spectral characteristics in the image are dominated by the background. An important step in outlier detection is often to compute a metric for correspondence with the background, which then can be thresholded to detect objects that are unlikely to be background objects. Two approaches are of particular interest. One was developed by Reed and Yu [4][5][6] and is referred to as the RX detector (RXD), which has shown success in outlier detection for multispectral and hyperspectral images [7][8]. Another was proposed in [9][10] and is referred to as low probability detection (LPD), which was designed to detect targets with low probabilities in an image. The benchmark of anomaly detection is RX algorithm which is derived from the Generalized Likelihood Ratio Test (GLRT) with the assumption of Gaussian background [11]. However, background may be consisted of different ground cover types in real remote sensing images, such as water body, grass land, trees. This will lead to miss detection in complex background. Many researches attempt to use the Gaussian mixture model [12][13]. In reference [12], Ashton employs K-means cluster clustering the image into a number of statistical clusters and models each cluster with the Gaussian distribution. Subsequently, Carlotto proposes a similar approach using vector quantization to reduce the computational time [13]. However, the K-means based algorithm only considers the spectrum information of background. This will lead to miss clustering background pixels during the cluster process. Besides, all the methods, these methods are unsuitable for our application that needs to implement software fast and to ease the difficulty of implement software for beginners. In this paper, propose an outlier region detection algorithm. The new method is compatible with the k-means algorithm and it overcomes the limitation of having to indicate the outlier regions by co-occurrence matrix and automatic threshold define which is a apply technique in this proposed paper.

3 The Proposed Algorithm 3.1 Our Algorithm In this paper, we propose a new method for determination of the outlier regions, which is based on co-occurrence matrix scheme. While a traditional co-occurrence

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matrix specifies only the transition within an image on horizontal and vertical directions. The proposed method can be used to automatically select a k range in multispectral satellite image as shown in Table. 1. Table 1. Algorithm finding outlier region Algorithm Simple Outlier region detection Input: A spectral image Output: Outlier regions Processing 1. Estimation: Estimate a number of region by co-occurrence matrix [14][15][16][17] 2. Clustering: Cluster image by a clustering algorithm and input a number of region from step 1. 3. Threshold: Detect outlier regions by automatic threshold or input from user 4. Detection : Define regions which are lower than thresholding value as outlier regions

The proposed technique consists of four main steps: estimation, clustering, threshold and detection. 3.2 Estimate Number of Region The proposed technique first, the co-occurrence matrix scheme is employed to automatically segment out the object region in an image. Then, the local maximum technique is used to count a number of regions, which a number of region. Our definition of a co-occurrence matrix [15][16][17][14] is based on the idea that the neighboring pixels should affect region of clusters. Hence, we define a definition for a co-occurrence matrix by including the transition of the gray-scale value between the current pixel and adjacent pixel into our co-occurrence matrix illustrated in figure 1 and figure 2.

Fig. 1. right and bottom of pixel in a cooccurrence matrix

Fig. 2. Creating a Co-Occurrence Matrix

Let F be the set of images. Each image has dimension of P×Q. Let tij be an element in a co-occurrence matrix depending upon the ways in which the gray level i follows gray level j

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P

tij = ∑ x =1

Q

∑δ y =1

( Fk ( x, y ) = i ) and ( Fk ( x, y + 1) = j )

or

( Fk ( x, y ) = i ) and ( Fk ( x + 1, y ) = j )

Where δ = 1 ,δ = 0

otherwise .

(1) th

where Fk denotes the k band in the image set, F If s,0 ≤ s ≤ L − 1 is a threshold. Then s can partition the co-occurrence matrix into 4 quadrants, namely A, B, C, and D shown in Figure.3. Clusters

Fig. 3. An example of blocking of co-occurrence matrix

Since two of the quadrants shown in Figure.3, B and D, contain information about edges and noise alone, they are ignored in the calculation. Because the quadrants, which contain the object and the background, A and C, are considered to be independent distributions. The idea of proposed method is to select the results of co-occurrence matrix into a diagonal matrix. After threshold processing, the result of diagonal matrix was shown in figure 5. Diagonal matrix is used to show some clustered pixels. The gray level corresponding to local maximum which give the optimal number for object- classification in image as shown in figure 6. 3.3 Clustering After K estimate processing, we got a number of region, we cluster data by clustering algorithm such as k-means which is the most popular clustering techniques. 3.4 Automatic Thresholding After cluster processing, thresholding techniques was assigned an outlier score to each instance in the test data depending on the degree to which that instance is considered an outlier. In this paper, we apply a method for automatic thresholding, which is based on standard deviation. Standard deviation is a statistical evaluate of spread or variability. First, we have to find the mean for standard deviation. Mean is represented by the division of sum of all values and the total number of values. The standard deviation is the root mean squares deviation of the values from their arithmetic mean. It is calculated by take the square root of the variances and is symbolized by s.d, or s. as shown in (2).

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∑ (x − Mean )

467

2

σ= where

(2)

n −1

Ʃ = sum of Mean = Mean of all point

x = individual point n = sample size (Number of point)

The proposed method can be used to select Mean − σ is an appropriate automatically threshold values in order to estimate a cut-off threshold value to select the outlier region as shown in Figure.6. Thus the output of our techniques use a cut-off threshold to select the outlier regions which less than Mean − σ value. 3.5 Automatic Outlier Region Detection Finally, after threshold processing, we get outlier region which is an outlier region as shown in fig 4- fig 7.

Fig. 4. an example of original image

Fig. 5. an example of co-occurrence matrix of image from Figure 4.

Figure 5. illustrates outlier in a simple 2-dimensional data set. The data has 4 normal regions, A, B, C and D, since most observations lie in this one region. Points that are different from the regions, e.g., “A” points in red region, are outlier region.

Fig. 6. Example histogram from diagonal of cooccurrence matrix (with threshold value = 30)

Fig.7. X Region = a result of outlier detection

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4 Experiment and Results 4.1 Dataset We used the eight sets of raw data from different CCD Multi-spectrum images [18]. The dataset are obtained from small multi mission satellite project (SMMS), a department of Electrical Engineering, Kasetsart University. We would like to analyze data, which was registered in Thailand and thus try to determinate of the outlier region in interesting areas of Thailand. 4.2 Unsupervised Classification Method The experiments performed in this paper use the simple K-mean from the Weka software package. The simple K-Mean is the common unsupervised classification method used with remote sensing data. 4.3 Experimental Result Our experiment was tested with CCD Multi-spectrum images and shown in Table.2. The experiments demonstrate the robustness and effectiveness of the proposed algorithm. Table 2. Algorithm finding outlier region Original image

Outlier Region Detection

Outlier

Outlier

From the experimental result, it was found that outlier region solved by cooccurrence statistics techniques gives the nearest outlier regions with ground truth. It can be noticed that the oulier region in clustering between original images and solving by co-occurrence statistics techniques are very closed.

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5 Conclusions In this paper, a method has been developed to determine the outlier regions in satellite image using a data mining algorithm based on the co-occurrence matrix technique in order to determinate that outlier. Our method consists of four stages, the first stage estimate a number of region by co-occurrence matrix, the second stage cluster dataset by automatic clustering algorithm, the third stage detect outlier regions by automatic thresholding and the final stage defines regions, which are lower than threshold value, to be outlier regions. The proposed method was tested using data from unknown number of regions with multispectral satellite image in Thailand. The results from the tests confirm the effectiveness of the proposed method in finding the outlier regions.

Acknowledgment This work was supported by budget for overseas academic conference from the graduate school, Kasetsart University. The authors would like to thank TGIST.

References 1. Hawkins, D.: Identification of Outliers. Chapman and Hall, London (1980) 2. Liu, J.: Study and implementation of clustering and outlier detection algorithm : (Master thesis).LIAO NING: Shenyang Institute of Computing Technology Chinese Academy of Sciences (2006) 3. Stein, D.W., Beaven, S.G., Hoff, L.E., Winter, E.M., Schaum, A.P., Stocker, A.D.: Anomaly detection from hyperspectral imagery. IEEE Signal Process. Mag. 19, 58–69 (2002) 4. Reed, I.S., Yu, X.: Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Trans. Acoust., Speech, Signal Processing 38, 1760–1770 (1990) 5. Yu, X., Reed, I.S., Stocker, A.D.: Comparative performance analysis of adaptive multispectral detectors. IEEE Trans. Signal Processing 41, 2639–2656 (1993) 6. Yu, X., Hoff, L.E., Reed, I.S., Chen, A.M., Stotts, L.B.: Automatic target detection and recognition in multispectral imagery: A unified ML detection and estimation approach. IEEE Trans. Image Processing 6, 143–156 (1997) 7. Ashton, E.A., Schaum, A.: Algorithms for the detection of sub-pixel targets in multispectral imagery. Photogram. Eng. Remote Sens., 723–731 (July 1998) 8. Stellman, C.M., Hazel, G.G., Bucholtz, F., Michalowicz, J.V., Stocker, A., Scaaf, W.: Real-time hyperspectral detection and cuing. Opt. Eng. 39, 1928–1935 (2000) 9. Harsanyi, J.C.: Detection and classification of subpixel spectral signatures in hyperspectral image sequences. Ph.D. dissertation, Dept. Elect. Eng., Univ. Maryland-Baltimore County, Baltimore, MD (1993) 10. Harsanyi, J.C., Farrand, W., Chang, C.-I.: Detection of subpixel spectral signatures in hyperspectral image sequences. Proc. Amer. Soc. Photogram. Remote Sens., 236–247 (1994) 11. Reed, R., Yu, X.: Adaptive multi-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Trans. Acoust., Speech, Signal Process. 38, 293–305 (1990) 12. Ashton, E.A.: Detection of Subpixel Anomalies in Multispectral Infrared Imagery Using an Adaptive Bayesian Classifier. IEEE Trans. Geosci. Remote Sensing 36, 506–517 (1998)

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13. Carlotto, M.: A cluster-based approach for detecting man-made objects and changes in imagery. IEEE Trans. Geosci. Remote Sensing 43, 374–387 (2005) 14. Koonsanit, K., Jaruskulchai, C.: Automatic Determination of the Initialization Number of Clusters in K-means Clustering Application by Using Co-occurrence Statistics Techniques for Multispectral Satellite Image. In: The 2010 International Conference on Information Security and Artificial Intelligence (ISAI 2010), Chengdu, China, December 17-20 (2010) 15. Pal, N.R., Pal, S.K.: Entropic thresholding. Signal processing 16, 97–108 (1989) 16. Chanwimaluang, T., Fan, G.: An efficient algorithm for extraction of anatomical structures in retinal images. In: ICIP 2003 Proceedings, September 4-17 (2003) 17. Koonsanit, K., et al.: Metal Artifact Removal on dental CT Scanned Image by Using Multi-layer Entropic Thresholding and Label Filtering Technique for 3-D Visualization of CT images. In: Proc. of International Conference on Biomedical Engineering: ICBME 2008.IFMBE Proceedings, 13th International Conference on Biomedical Engineering, Singapore (December 2008) 18. Small Multi-Mission Satellite (SMMS) from the World Wide Web: http://smms.ee.ku.ac.th/index.php (data Retrieved: May 26, 2010)

The Feature Parameters Algorithm of Digital Signal in Circuit Based on Simulation Xiaodong Ma, Guangyan Zhao, and Yufeng Sun School of Reliability and System Engineering, Beihang University, 100191 Beijing, China [email protected], [email protected], [email protected]

Abstract. Aiming at fault judgment issue of digital signal in circuit fault simulation, a fault judgment method of time domain feature parameters based on Cadence simulation was made deep research in this paper. Firstly, the commonly used feature parameters of digital signal were analyzed. Secondly, as per data structure feature of Cadence software, the digital signal was divided into two kinds, one was expressed by analog form, and the other was expressed by numeric form. Then the specific algorithm of relative parameters of these two kinds of digital signal in Cadence was provided respectively. By the end, a representative case was made simulation analysis to validate the correctness of the algorithm. The result also indicates that the method is practical in reliability design of circuit. Keywords: feature parameter, digital signal, algorithm, fault simulation.

1 Introduction Circuit fault simulation technology based on EDA is an effective way to achieve integrated circuit design and analysis. By using of this technology, we can realize batch injection and simulation for component failure, then got large numbers of fault response. On basis of judgment made on such response signal, we can achieve circuit reliability analysis then provide foundation for improving the circuit design. It is very simple to make manual judgment on output signals of fault simulation; however, automatic judgment is necessary facing hundreds of thousand of simulation results. Signal analyzing and processing, as well as fault criterion technology are mainly applied in fault judge of circuit signal. At present, there are 3 main methods for signal analysis and processing, they are times domain method, frequency domain and time-frequency analysis method [1]. Frequency domain analysis takes frequency of input signal for variables, and studies the relationship between system structural parameters and performance in frequency domain. It reveals the inherent frequency features of signal and the relationship between time features of signal and its frequency ones. Frequency domain analysis is widely used in communications, automatic control and other fields [2,3]. However, for the purposes of judging the fault of circuit signal, just knowing its frequency feature is not enough. Thus, frequency domain method is not the focus of this paper. Time-frequency analysis triggerred from 1940’s is a new method which can clearly M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 471–478. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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indicate the evolution relations of spectrum over time, and is mainly used in nonsteady signals or time-varying signal analysis and processing, it is used extensively in speech signal analysis and processing[4,5]. Although it is already applied in system fault judge and diagnosis[6,7], it is not easy to achieve due to complicated principle and large amount of calculation. In contrast, time domain analysis is a direct and more accurate analysis method. Time-domain feature parameters can reveal system’s instantaneous state and steady state performance, this method is widely used in automatic control, system fault diagnosis and other fields [8,9]. In view of the contents of this paper is fault judge of circuit signal in Cadence-based environment, time domain signal feature parameters can effectively reflect variation of amplitude over time. We can extract a series of feature parameters from the given signal and analyze whether these parameters meet requirements then realize signal fault judgment. Thus, this paper mainly studies the algorithm of time-domain feature parameters based on Cadence.

2 Feature Parameters of Circuit Signal Circuit signal typically contains analog signal and digital signal. Different types of signals have different feature parameters. Analog signal’s feature parameters and its algorithms have been given in literature [10] and this paper does not mention it again. Digital signal can be furtherly divided into single-pulse signal and multi-pulse signal, and its commonly used feature parameters are, rise/fall time, slew rate, period, duty cycle, pulse width, delay time, over/under overshoot, time interval of two positive /negative pulses, time interval between two positive/negative jump, and so on. Some feature parameters’ definition and algorithm are given as follows. (1) Delay Time It refers to time deflection of the measured signal against the reference one under the selected high and low level. Here it does not require that the measured signal keep same shape as the reference one, but they must be in the same graph region. Specifically: under selected high and low level, it’s the difference of jump points between measured signal and reference one in the time coordinates. (2) Slew Rate Also known as conversion rate, it refers to variable range of output voltage within unit time, expressed as:

SR=

V(H)-V(L) t (R)

(1)

Where, SR refers to slew rate, V (H) means high level potential, V (L) means low level potential, and t (R) is the rise time of signal. In Fig.1, V (H) = 3.7566v, V (L) =0.011494v, the got t (R) = 540.17us (see Fig.1) as per rise time calculation method, slew rate can be obtained by formula (1), SR = 5571.8 (v / s). (3) Period Period is the time interval between two adjacent rising edge (or falling edge). We take the median point of rising (or falling) edge in calculation. The determination of the rising / falling edge should be noted. As shown in Fig.2. The period is the time interval between the left point and the right one.

The Feature Parameters Algorithm of Digital Signal in Circuit Based on Simulation

Fig. 1. Definition of Slew Rate

473

Fig. 2. Definition of Period

(4) Duty Cycle It is the ratio of duration of high level to total period.

DC=

t (1) T

(2)

Where, DC refers to duty cycle, t (1) is the duration of high level, T is the period of the signal. (5) Over / Under Overshoot Over overshoot refers to the difference between the maximum of a signal and the high level within a signal cycle. Under overshoot refers to the difference between the minimum of a signal and the low level within a signal cycle.

V(Over)=V(max)-V(H)

(3)

Where, V (Over) refers to the over overshoot, V (max) refers to the maximum voltage of a signal, V (H) is the voltage of high level.

V(Under)=V(L)-V(min)

(4)

Where, V (Under) refers to the under overshoot, V (min) refers to the minimum voltage of a signal, V (L) is the voltage of low level.

3 Data Structure of Circuit Signal in Cadence Cadence is a kind of common used EDA simulation software, which can simulate digital/analog mixed circuit. Its output signal types include analog signal and digital signal. All signal information during the whole simulation period is recorded in simulation output file .dat. Data structure of the output file of time domain in Cadence is shown in Table 1. As shown in Table 1, analog signal is described as the amplitude of the signal at different time, more data points are recorded in region with higher variation rate of amplitude, and vice versa, that is, analog signal is recorded in the way of non-equal time spacing in Cadence. Digital signal is described as 0,1, R, F, X and Z, where 0 represents the low level, 1 represents the high level, R on behalf of the rising edge, F on behalf of the falling edge, X on behalf of uncertain state, Z represents highimpedance state, and record signal state after jump when digital signal is jumping.

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X. Ma, G. Zhao, and Y. Sun Table 1. Data structure of circuit signal in Cadence

Signal Type

Analog signal

Digital signal

Data Structure #H <settings*> #N [‘] or #C [‘] or #C … [circle descriptions for analysis once again] #H <settings*> #N ‘ #C ‘ #C … [circle descriptions for analysis once again]

Notice

“**”—more than one, “<>”—Required, “[]”— Options, “[…] or […] or […]”— — at least one.

“**”—— more than one, “<>”—— Required, “[]”—— Options.

4 Cadence-Based Algorithm for Digital Signal Parameters In view of the feature of digital/analog mixed simulation in Cadence, an invisible DtoA interface exists between digital device and analog one where they are connected, it switches original digital signal to analog one then connect it with the analog device. Therefore, when these signals are displayed or judged, user sees the analog signals while they concern about its digital properties. For such signals, we treat it differently from digital signal or analog signal, and call it as digital signal expressed by analog form. For the digital signal expressed by 0, 1 etc discrete variables, we call it as digital signal expressed by digital form. Algorithm for feature parameters of these two types of digital signals is introduced as follows. 4.1 Algorithm of Digital Signal Expressed by Digital Form For convenience, the meanings of symbols are agreed as follows:

—— － —— ＋ —— —— —— － ——

S(i) The i-th state of a specified state sequence. S i (y ) The state before the i-th state “y” in a specified state sequence. S i (y ) The state behind the i-th state “y” in a specified state sequence. n(y) The number of state "y" in a specified state sequence. ti(y) The time of the i-th state “y” in a specified state sequence. ti(y ) The time of the state before the i-th state “y” in a state sequence.

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＋)—— The time of the state behind the i-th state “y” in a state sequence. —— The total number of state in a specified state sequence.

ti(y N

From Part 3 of this paper, we know that state of digital signal after jump is recorded with 0, 1 and other discrete variables, it is lack of description of the features of signal amplitude, and therefore we can only get part of the feature parameters. The specific algorithms are given in Table 2. Table 2. Algorithm of feature parameters of digital signal expressed by digital form Feature parameters

Algorithm

Note

n

Pulse Width

Delay Time

∑ [t (1+) − t (1)] i

PW =

i

1

n

DT = M ax[ t b ( y ) − ti ( y )] n [ S (1)]− 1

Period

∑ {[t

T =

n −1

Duty Cycle

∑

DC =

[ S (1)] − ti [ S (1)]}

1

n[ S (1)] − 1 [ t i (1+ ) − t i (1)] ti + 1 [ S (1)] − t i [ S (1)] n −1

n −1

Time Interval between t= Two Positive Jumps

i +1

1

∑ [t

Pulse numbers after n= one pulse is triggered

i +1

(0 + ) − ti (0 + )]

1

{

tb(y) is the jump time of reference signal at each state; ti(y) is the jump time of measured signal at each state. For periodic signal, it is the average of periods. It has no sense for non-periodic signal. where , n

n(0), the first edge after t is rise edge n(1), the first edge after t is fall edge

= n[ S (1)]

where , n

n −1

{

n (1)−1, S ( N ) =1 others

where , n = n (1),

−1, S ( N ) =0 = { nn(0) (0), others

t is action end time of excitation signal——t2[S(1)]. n(0)/ n(1) is the number of state 0/state 1.

4.2 Algorithm of Digital Signal Expressed by Analog Form Part of feature parameters of digital signal expressed by analog form can be converted to digital signal expressed by digital form via certain algorithm, and then use the algorithm listed in Table 2 to calculate feature parameters. However, some parameters cannot be converted; it should be calculated directly by means of the output data of simulation. Digital signal expressed by digital form is used to record the state of signal after jump, thus we just need consider the two data points near high / low level threshold. Because of data points with amplitude equal to high or low level threshold may not exist, this paper takes time points as state transition ones, time points are got by means of linear interpolation algorithm. Low level threshold is set as UL, high threshold is set as UH. Two adjacent state points of digital signals expressed by analog quantity are S1 (t1, u1) and S2 (t2, u2). Transformation rules are given in Table 3.

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conditions

＜ ≦ u ＜U ≦ ＜U ≦u ＜＜U ≦ u

u 1 UL u 1 UL UL u1

2

H

H

2

H

2

State

Time

conditions

R R, 1 1

t(R) t(R), t(1) t(1)

UL u2 u 2 UL u 2 UL

＜ ≦ U ＜u ≦ ＜U ≦ u ≦ ＜u ＜U H

1

H

1

1

H

State

Time

F F, 0 0

t(F) t(F), t(0) t(0)

Where, t(R) and t (0) can be obtained from:

、 t (0) = ut -−tu 1

t (R )

2

1

× (U L -u1 )+ t1

(5)

× (U H -u1 )+ t1

(6)

2

t (F) and t(1) can be obtained from:

、 t (1) = ut -−tu

t (F)

1

1

2

2

Over / under overshoot of digital signal expressed by analog form can be calculated by formula (7) and formula (8). V (O ver)=M ax ( u i )-V (H )

(7)

V (U nder)= V (L)-M in ( u i )

(8)

Where, UL refers to low level threshold, UH refers to high level threshold, ui is the amplitude of digital signal expressed by analog form.

5 Case Study Figure 3 is a typical digital/analog mixed circuit. We apply the arithmetic of feature parameters described in this paper to determine circuit fault. In which, OUT is digital signal expressed by digital form, and U3B:Y is digital signal expressed by analog form. Normal simulation and fault one is made for circuit above by Cadence respectively, we observe the waveform of OUT and U3B:Y. The results of normal simulation and fault simulation are shown in Fig.4 and Fig.5 respectively.

Fig. 3. Circuit Schematic Diagram

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Fig. 4. Waveform of normal simulation

(a) C1- Parameter drifts

(b) C1-Short-circuited

Fig. 5. Waveform of fault simulation

U3B:Y is a clock signal, and period and duty cycle are two important feature parameters for clock signal. Therefore, we choose period and duty cycle as judgment parameters. OUT is a square signal, we also choose period and duty cycle as judgment parameters. Feature parameters for two observation points in fault simulation output calculated as per algorithm of digital signal time domain feature parameters mentioned in this paper, together with fault judgment result is shown in Table 4. Table 4. Value of feature parameters and the results Signal Name

OUT

U3B˖Y

Feature parameters

period

DC

period

DC

Minimum permitted

2.3us

0.4

1.2 us

0.4

5us

0.6

2.5 us

0.6

Maximum permitted C1- Parameter drifts

C1-Short-circuited

Value Parameter Signal Value Parameter Signal

1.2588us 0.5122 Fault Normal Fault -1 -1 Fault Fault Fault

0.6293 us 0.5232 Fault Normal Fault -1 -1 Fault Fault Fault

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The values in the table are calculated by arithmetic given by this paper. By comparison, the results keep same as simulation graph, which indicates that the algorithm is correct. Since there is no amplitude variation after waveform achieves stable with C1 shortcut, it cannot calculate relevant feature parameter, which is expressed as -1, which also proves circuit fault judgment could be achieved by use of this algorithm.

6 Conclusion Aiming at the data structure of Cadence, this paper divides digital signal into signal expressed by analog quantity and signal expressed by digital quantity, and gives specific algorithms respectively for relevant feature parameters of these two types of digital signals in Cadence-based environment, By using a typical case, it validates that the arithmetic is correct and then provide necessary technical support for the circuit fault simulation based on Cadence. On the base of realization of fault simulation to obtain large numbers of fault simulation judgment, it then can make further reliability analysis, including fault mode effect analysis (FMEA), reliability parameter prediction, test-purpose parameter prediction and etc.

References 1. Zhao, G.: Signal Analysis and Processing. China Machine Press, Beijing (2010) 2. Lu, K., Zhuo, Y.: Extraction of Multipath Timedelay in Frequency Domain to UWB Chirp Signal. J. Communications Technology. 43(12), 15–17 (2010) 3. Li, L.M.: Analysis of Nonlinear Oscillators Using Volterra Series in the Frequency Domain. Journal of Sound and Vibration 330(2), 335–337 (2011) 4. Xu, Y., Wang, G., Guo, Y.: Wavelet Package Based Speech Enhancement Algorithm Using Time-Frequency Threshold. Journal of Electronics & Information Technology 30(6), 1363–1366 (2008) 5. Ayat, S., Manzuri, M.T.: Wavelet based speech enhancement using a new thresholding algorithm. In: International Symposium On intelligent Multimedia, Video and Speech Processing, pp. 238–241. IEEE Press, Hong Kong (2004) 6. Yang, C.: Study on Feature Extraction of Fault Circuits Based on Wavelet. Jilin University, Jilin (2004) 7. Yu, G.: A cluster-based wavelet feature extraction method for machine fault diagnosis. J. Applied Mechanics and Materials 10, 522–548 (2008) 8. Sreejith, B., Verma, A.K., Srividya, A.: Fault diagnosis of rolling element bearing using time-domain features and neural networks. IEEE Press, New York (2008) 9. Lee, H., Nguyen, N., Kwon, J.: Bearing diagnosis using time-domain features and decision tree. In: Region 10 Colloquium and 3rd International Conference on Industrial and Information Systems, pp. 952–960. IEEE Press, New York (2008) 10. Zhang, M., Zhao, G., Sun, Y.: Research on Arithmetic of Fault Judge in typical circuit. Journal of Naval Aeronautical and Astronautical University 24(1), 115–117 (2009)

Research of Stereo Matching Based on Improved Median Filter Huiyan Jiang, Rui Gao, and Xiaojie Liu Software College, Northeastern University, 110819 Shenyang, China [email protected]

Abstract. In the refinement process of traditional median filter for the disparity map, the estimation of disparity for some pixels is not always precise. In order to solve this problem, the thesis presents an improved median filter for disparity refinement method. The proposed method first uses the left/right consistency test and color segmentation to detect the mismatch regions and discontinuities sections, and then filter out these regions in the filter window so as to achieve a better estimate of disparity for current pixel. Experimental results clearly indicate that this method can obtain a more precise disparity map compared with conventional method. Keywords: Stereo matching; Binocular vision; Filter; Median filtering.

1 Introduction Stereo vision is the research focus of computer vision [1]. With the development of computer technology, stereoscopic vision in robot vision, industrial measurement, object recognition and military field have been widely used. Stereo matching plays an important role on the three-dimensional visual field, but as a result of noise and other factors during the imaging process, we have not found a universally applicable method for stereo matching currently [2]. Thus, the research for the stereo matching method has important theoretical significance. At present, the research for the stereo matching algorithm mainly concentrates in two directions. One is a local optimal algorithm based guidelines for local support neighborhood, and the other is an algorithm based global strategy to implement global optimization. Both of algorithms have advantages and disadvantages. For the local optimal algorithm, the main advantage is easy to implement and the algorithm is fast, while the disadvantage is that the accuracy of obtained disparity map is low. The disparity map accuracy of the algorithm based global strategy to implement global optimization is high, but it is difficult to obtain a global optimal solution [3]. Therefore, accurate and fast stereo matching algorithm has been an important research direction. After the analysis of the problem that traditional median filter applied to the disparity map refinement process, this thesis presents an improved median filter for M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 479–486. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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disparity refinement method, and then make a comparison between traditional filter and improved median filter. Moreover, introduce the evaluation standard of the stereo matching. Experimental results show that this method can obtain a more precise disparity map compared to conventional method, and it is more effective than the traditional median filter based on stereo matching.

2 Filters on Image Processing Gauss filter is a linear smoothing filter based on the shape of Gaussian function to select the right value [4]. It’s very effective on wiping out normally distributed noises. Filter is a mathematical model, and it can convert the image data to the energy. When the energy is low, exclude low energy which stands for the noise. Generally, by choosing proper weights and template, the value of a specific pixel is the result of weighted mean on its own and nearby pixels [5]. Mean filter algorithm is one of the classic linear filtering algorithms by replacing the value of the central pixel with the average value of its most closely surrounded eight other neighbor pixels and itself. The normal processing course is choosing a kind of template consisted with a certain amount of neighbor pixels for current pixel ( x, y ) , then calculating the mean value of this template as the final gray scale value g ( x, y ) of ( x, y ) which is

g ( x , y ) = 1 ∑ f ( x, y ) m

(1)

m here is the amount of pixels in the template [6]. Median filter algorithm based on statistical theory of sorting, is one of the nonlinear signals processing technology to inhibit noises effectively. Weak textures in some areas, due to relatively low signal to noise ratio images will cause the failure of matching, so there will be some mismatch point, so the median filter can be filled on the point of these errors The basis course of Median filter algorithm is replacing a specific value of a numeric sequence or digital image with the middle value of its neighborhood, so the pixels values of surrounding area will be close to the actual one so that eliminate isolated noise points [7]. Median filter in image processing commonly is a classic method of smoothing the noise, and it is used to protect the edges. Firstly, sort the value of pixels in a specific shaped 2-D floating template monotonously in ascending order or reverse order, then replace the current pixel value, as in

g ( x, y ) = med { f ( x − k , y − 1), (k , l ∈ W )}

(2)

Where f ( x, y ) is the original image while g ( x, y ) is the processed one. W stands for 2-D floating template which generally is an area with size of 2*2 or 3*3[8], but it also can be shapes like bars, circles, crosses and rings, etc.

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3 Stereo Matching Based on Improved Median Filter By analyzing and comparing different filter algorithms, it is not hard to find Gauss filtering is suitable for the image containing gauss noise. Mean filter may produce some influence for the image edges, and the disparity contains some isolated point, while the median filter is more suitable for the process of disparity map refinement [9]. There may be two problems in the process of disparity map refinement. The one problem is that filter window may contain some errors matching area, and the other second problem is that filter window may contain some disparity discontinuity region. Therefore, estimating the parallax of current pixel, if taking the parallax of the mismatching and discontinuity pixel into account, it may lead to a further error of the current pixel evaluation. For the purpose of solving the above problem, the paper proposes an improved median filter method. Although there are many kinds of stereo matching algorithms, their solving steps are basically the same. Scharstein divides it into four steps: matching cost calculation, cost value accumulation, disparity calculation and disparity refinement [1, 10]. The paper uses two images each time during the experiments. One is the reference image as left view and the other is the target image as right view. Firstly process the two images, and cost the value accumulation. Then achieve the disparity calculation based on Winner-Take-All (WTA). The most important step is to detect the mismatch regions and discontinuities sections through using the left/right consistency test and color segmentation, and then filter out these regions in the filter window so as to achieve a better estimate of disparity for current pixel. The specific steps are described as follows. Matching cost calculation. For each pixel ( x, y ) of the reference image L* ( x, y ) ,

calculating the cost ( x + d , y ) of maxim to the corresponding pixel in the target image R* ( x, y ) , d is (0, d max ) , d max is a different value for different stereo images, we can get the matching cost for each pixel in the reference image, the calculation formula is as follow [11]

C0 (x, y, d) =

∑

c∈{r, g,b}

Lc (x + d, y) − Rc (x, y)

(3)

Cost value accumulation. The disparity of one pixel has certain correlation with the pixels around it. Local algorithm usually sets the matched pixel as the center of the window whose size is (2 L + 1) ∗ (2 L + 1) , then choose the mean value of the pixels in the window as the value of the center pixel.

C ( x, y , d ) =

1 x+ L y+ L ∑ ∑ C0 ( x , y , d ) 9 i= x− L j= y− L

(4)

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Disparity calculation and refinement. From the last step, we can get C ( x, y, d ) . For * each pixel (u , v) in the disparity, choose the minimum value of C ( x, y, d ) among

(0, d max ) , and then d * is the value of DL (u, v) . Before the process of median filter, we can use the left/right consistency test and color segmentation to detect the mismatch regions and discontinuities regions. After eliminating these regions which are detected in the filter window, use the remaining pixels as the elements of the median filter to estimate the parallax of current pixels. There are three steps as follows. The first step is left/right consistency test. Exchange the reference image and target image, making the right image as the reference image and making the left image as the target image, and then get the disparity image DR of the right image R according to step C and step D. For each pixel of disparity image DL of the left image, if

DL (x, y) ≠ DR (x + dL (x, y), y) , then DL ( x, y ) = 0 ,and if DL (x, y) = DR ( x + d L ( x, y ), y ) , else DL (x, y) = DL (x, y) . The second step is detection of discontinuous region based on Mean Shift algorithm. Use Mean Shift to segment the reference image L* ( x, y ) in order to get the tagged image Label . Those regions in Label whose colors are similar will be assigned the same label. The third step is improved median filter. According to the disparity image DL which is obtained under the first and second step and the tagged image Label , use the median filter to process DL . Detailed steps are as follows. For each pixel ( x, y ) in the disparity image DL , choose a window which size is (2 L + 1) ∗ (2 L + 1) and identify ( x, y ) which belong to the U = {( u, v) | u ∈ ( x − L, x + L), v ∈ ( y − L, y + L)} as the

centre. Then select (u , v) within the set of coordinate point and make that DL (u , v) is not zero and Label (u, v) = Label(x, y) .That means the set is U* = {( u, v) | u ∈ (x − L, x + L), v ∈ ( y − L, y + L), DL (u, v) ≠ 0, Label (u, v) = Lable( x, y )}

Finally, choose the middle value of coordinate set DL (U* ) as the final disparity of the DL (x, y) , and DL is the final disparity image.

4 Experiment Results This paper uses the four pairs of stereo image pairs: map, tsukuba, sawtooth, venus, they are chosen from the Middlebury stereo image library. The results shown in Fig. 1, where (a), (e), (j), (o) for the corresponding threedimensional images on the left view , (b), (f), (k), (p) for the corresponding threedimensional view of the right image, (c), (g), (l), (q) for the three-dimensional view of the true image of the left disparity map, (d), (h), (m), (r) for the traditional algorithm the experimental results obtained, (e), (i), (n), (s) for the experimental results obtained by the paper .

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(a)

(b)

(c)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

(l)

(m)

Fig. 1. Compared experiment results.

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(n)

(o)

(p)

(q)

(r)

(s)

Fig. 1. (continued)

Compared with the traditional median filter in Fig.1 and the paper eliminates most of the obvious mistakes region and relatively gets smooth disparity in the absence of texture regions. The effect is quite satisfactory, as it is smooth on the background at the top right corner of the image tsukuba, venus the left of the image, etc. Table1 is the table of false match rate .The data appears from the results that this method resulting in false match rate of non-matching block area, no texture regions and disparity discontinuities is superior to the traditional methods. In particular, the average error matching rate increases 9.36% in non- block area and achieves satisfactory results. But in the right half of the lamp in Figure tsukuba can see that the color segmentation result is not accurate because of the shadow, and then disparity estimation error occurs. On the other hand, the paper uses color segmentation and detection of about consistency during the process of disparity map refinement, so there is an increase in running time of the algorithm. From the running time of view in Table2, despite the increase in the running time, the time complexity is still within the acceptable range. In the last line the Mean Shift color segmentation algorithm is used in each process of image segmentation, and it takes almost half the whole time. Looking for faster and more accurate color segmentation approach is still to this next step to conduct research.

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Table 1. Comparison of algorithm mistakes rate.

Parameter

Tsukuba (%)

Map (%)

Sawtooth (%)

Venus (%)

Traditional Method

Paper Method

BO

8.31

4.37

BT

11.2

4.92

BD

15.7

10.7

BO

3.40

2.21

BT

0.71

0.57

BD

13.9

6.77

BO

3.91

5.96

BT

1.84

8.50

BD

3.91

5.96

BO

1.84

8.50

BT

3.91

5.96

BD

1.84

8.50

Table 2. The runtime(s) comparison of different methods

Parameter

Mean Shift

Traditional Method

Paper Method

Tsukuba

2.44

1.43

5.27

Map

1.56

1.20

3.92

Sawtooth

4.03

2.42

8.31

Venus

3.92

2.40

8.41

In Table1 and Table2, this method is superior to the traditional methods in false match rate of non-matching block area, no texture regions and disparity discontinuities. There is an increase in the running time, but the time complexity is still within the acceptable range.

5 Conclusion Based on the Improved Median Filter this paper is compared with the traditional median filter and implements the Stereo Matching, and introduce evaluation of stereo matching algorithm proposed by Scharstein. The experiment results can eliminate most obvious mistake area and relatively get smooth parallax in the absence of texture areas. Moreover, it can obtain a more accurate matching result. In the process of

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disparity refinement, mean-shift algorithm is time consuming, so researching a faster and more accurate color segmentation method is the future work.

Acknowledgement This research is supported by the National Nature Science Foundation of China (No: 60973071, No: 50834009) and the Liaoning province Natural Science Foundation (No: 20092004).

References 1. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 7–42 (2002) 2. Van Meerbergen, G., Vergauwen, M., Pollefeys, M., Van Gool, L.: A hierarchical stereo algorithm using dynamic programming. In: Proceedings of IEEE Workshop on Stereo and Multi-Baseline Vision, pp. 166–174. IEEE Computer Society Press, Washington, USA (2001) 3. Birchfield, S., Tomasi, C.: A pixel dissimilarity measure that is insensitive to image sampling. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 401–406. Springer, Heidelberg (1998) 4. Hartley, R.I.: Theory and practice of projective rectification. In: International Journal of Computer Vision, pp. 115–127. IEEE Computer Society Press, Los Alamitos (1998) 5. Chan, R.H., Ho, C.W., Nikolova, M.: Salt and pepper noiseremoval by median type noise detectors and detail preserving regularization. IEEE Trans.Image Process, 1479–1485 (2005) 6. Bobick, A.F., Intille, S.S.: Large occlusion stereo. International Journal of Computer Vision, 181–200 (1999) 7. Intille, S.S., Bobick, A.F.: Disparity-space images and large occlusion stereo. In: Eklundh, J.-O. (ed.) ECCV 1994. LNCS, vol. 801, Springer, Heidelberg (1994) 8. Dornaika, F., Chung, R.: Cooperative Stereo Motion: Matching and Reconstruction. In: Computer Vision and Image Understanding, pp. 408–427. Springer, Heidelberg (1998) 9. Fukunaga, K., Hostetler, L.D.: The estimation of the gradient of a density function with application in Pattern recognition. IEEE Trans. Information Theory, 32–40 10. Gong, M.L., Yang, Y.H.: Fast stereo matching using reliability-based dynamic programming and consistency constraints. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 610–617. IEEE Computer Society Press (2003) 11. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient belief propagation for early vision. International Journal of Computer Vision, 261–268 (2006)

Generating Method of Three-Phase Voltage Sag in EV Charging System Performance Testing Xiaoming Yue, Hui Fan, Jin Pan, Xiaoguang Hao, and Zhimeng Zhang Hebei Electric Power Research Institute, Shijiazhuang, Hebei 050021, China [email protected]

Abstract. In order to analyze the impact between Electric Vehicle (EV) charging system and grid influence. In this paper, a method of three-phase voltage sag generating based on DQ transformation is proposed. With the ‘Back to Back’ linked two PWM voltage-source converters, normal and fault state of infinite grid is simulated. Aiming at seven typical voltage sag faults (including all the voltage sag types in symmetric, asymmetric excluding zero-sequence and asymmetric contain zero-sequence circumstances), compound operation is completed in synchronous rotational axes (dq0) under symmetrical component method applied. Especially, abc-dq0 transform is amended for asymmetric contain zero-sequence voltage sag situation. So the space vector pulse width modulation (SVPWM) control signal is generated. Three-phase voltage sag generated with this method could satisfy the phase relationship and amplitude relationship of the inter-phase automatically. As a result, common connection point (PCC) fault voltage could be accurately generated. And the full scope of the depth of voltage sag adjustable and fault duration of any settings are realized. Simulation and experiment results show that this method is the rationality and feasibility. Keywords: Electric Vehicle, Voltage Sag, Voltage-source Converter, DQ Transformation.

1 Introduction Along with the progress of the society and the development of economy, the energy issue gradually becomes the focus. Electric power is essential in modern society. Meanwhile, with the increasing of cars, pollution from cars becomes more and more serious. So Electric Vehicle becomes a good solution for that. But wide-scale EV charging will bring a lot of trouble to grid, and the charging performance of the electric vehicle charger will be influenced by grid, too. Thus, the mutual influence between the wide-scale EV charging and the grid has important significance. In the analysis of the electric car battery and grid interaction, the grid voltage sag is one of the common problems. But the grid voltage sag fault has its randomness and uncertainty, so how to correctly generated grid fault situations becomes a problem must be solved. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 487–496. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Generally, a lot of different grid fault types can occur, such as single-phase-toneutral, phase-to-phase, 2-phase-to-neutral, and 3-phase faults. The form of grid voltage sag can be divided into seven types (A, B, C, D, E, F and G) [1-5]. There are some methods of generating voltage sag, but they all have their own shortcomings and couldn’t accurately reflect the actual situation of power grid faults. This paper proposes a voltage sag generating method based on DQ transformation. The main topology of this method is two three-phase PWM voltage-source converter (VSC) connected by the back-to-back structure, generating a symmetrical, asymmetric excluding zero sequence and asymmetric contain zero sequence three circumstances of all types of voltage sag, realized the full scope of the voltage sag depth and fault duration adjustable of any settings.

2 Main Topology and Principle The principle diagram of the generating method in this paper is shown in Fig. 1. Main circuit adopts two three-phase PWM voltage-source converters (VSC), which is connected in "back-to-back" form. This could simulate the power supply characteristics of infinite power grid in normal and various fault conditions. The energy between grid and converter can flow bilaterally. Thereby fault generating side PWM voltage-source converter can connect passive load, also can connect active load. The grid side PWM voltage-source converter, used SVPWM double closed loop control method [6], produces a constant DC voltage in the DC side, realizes the AC unit power factor controlled, and reduces grid harmonics pollution.

Fig. 1. The principle diagram

The vector control principle diagram of the fault generating side converter is shown in Fig. 2. The grid fault voltage is decomposed into positive sequence voltage, negative sequence voltage and zero sequence voltage by the symmetric component method [7-8]. Seven kinds of typical of voltage sag signals (A, B, C, D, E, F and G) come through the stator side 3-phase stationary reference frame to synchronous revolution frame by abc-dq0 transformation. With the compound operation in dq0 frame and the dq0-abc transformation, space vector pulse width modulation (SVPWM) control signals are generated. Accordingly various-type three-phase voltage sag waveform of arbitrary voltage sag depth and arbitrary duration are gained.

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L

Grid Side Converter

Charger C

abc

SVPWM

Fault Type

dq0

Symmetrical Phasor Synthesis

Eigenvalues Phasor

Fault Time Sag Depth

Phasor Decompositio n

Fig. 2. SVPWM control schematic diagram of fault side converter

3 Generating Method of Three-Phase Voltage Sag Grid short-circuit fault can be divided into two kinds such as symmetrical fault and asymmetric fault. The most common asymmetric short-circuit fault are single-phaseto-neutral, phase-to-phase, 2-phase-to-neutral. And asymmetric short-circuit faults are divided into two kinds. One kind contains zero sequence components; another kind is excluding zero sequence components. All kinds of voltage sag fault has his different features. Voltage sag fault is divided into seven typical types, such as Type A, Type B, Type C, Type D, Type E, Type F and Type G. Type A is the three-phase symmetric voltage sag caused by three-phase short-circuit fault; Type B is the asymmetric voltage sag caused by single-phase-to-neutral fault; Type C, D are the asymmetric voltage sag caused by phase-to-phase fault; Type G, E, F are the asymmetric voltage sag caused by 2-phase-to-neutral fault. Each phase voltage of the entire voltage sag fault at the PCC

．．．

、、

point [9-12] is shown in Table 1. U a U b U c are the phase voltage phases at the point of common connection (PCC). U is the characteristic value of the voltage sags. Based on the symmetrical component method, voltage sag in PCC can be resolved into positive-sequence component, negative-sequence component and zero-sequence component. The transform equation as follows:

⎡ . ⎤ ⎢U1 ⎥ ⎡1 a ⎢ . ⎥ 1⎢ ⎢U 2 ⎥ = ⎢1 a 2 ⎢ . ⎥ 3⎢ 1 1 ⎢U ⎥ ⎣⎢ ⎢⎣ 0 ⎥⎦ D

Where a is defined as a = e j120 .

a

2⎤

⎥ a⎥ ⎥ 1⎥ ⎦

⎡ . ⎤ ⎢U a ⎥ ⎢ . ⎥ ⎢U b ⎥ ⎢ . ⎥ ⎢U ⎥ ⎢⎣ c ⎥⎦

(1)

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U a

U b

U c

A

U

1 3 − U + j U 2 2

1 3 − U + j U 2 2

B

U

1 3 − −j 2 2

C

1

−

D

U

1 3 − U − j 2 2

1 3 − U + j 2 2

E

1

1 3 − U − j U 2 2

1 3 − U + j U 2 2

F

U

G

2 1 + U 3 3

1 3 − j U 2 2

−

−

1 3 + j 2 2

1 3 + j U 2 2

1 3 3 1 3 3 − U − j ( + U ) − U + j ( + U) 2 3 6 2 3 6

1 1 3 − − U − j U 3 6 2

1 1 3 − − U + j U 3 6 2

Vector Chart

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The positive sequence components, negative sequence components and zero sequence components of all the typical voltage sag fault corresponding PCC point can be seen in Table 2. Obviously voltage sag in Type B and E contain zero sequence components, Type A, Voltage sag in Type C, Type D, Type F and Type G exclude zero sequence components. Only voltage sag in Type A is symmetrical, the other types are asymmetric situation. Table 2. Positive, negative and zero sequence voltage for each sag type

Type

.

.

.

Np

Nn

N0

A

U

0

0

B

(2 + U ) / 3

(U − 1) / 3

(U − 1) / 3

C

(1 + U ) / 3

(1 − U ) / 2

0

D

(1 + U ) / 2

(U − 1) / 2

0

E

(1 + 2U ) / 3

(1 − U ) / 3

(1 − U ) / 3

F

(1 + 2U ) / 3

(U − 1) / 3

0

G

(1 + 2U ) / 3

(1 − U ) / 3

0

.

As. shows in Table 2, N P is the characteristic value of positive-sequence voltage, . the N n is characteristic value of negative-sequence voltage, the N 0 is characteristic value of zero- sequence voltage. Therefore, the phase voltage of PCC voltage sag in three-phase static axes (a, b, c) can be expressed as: .

.

.

.

U PCC = N p e jωet + N n e− jωet + N 0 e jωet

(2)

With the abc-dq0 transformation, the phase voltage of PCC voltage sag in the synchronous rotational axes (d, q, 0) shown in (3): .

.

.

.

U PCC = N p + N n e− j 2ωet + N 0

(3)

Thus, phase voltage sag at PCC is composed of the symmetrical components from seven typical voltage sag decomposition. Then d-axis, q-axis, o-axis components can be received by abc-dq0 transformation. After the compound operation, three-phase fault voltage modulation signal is got by the dq0-abc transformation. For excluding the zero sequence components of voltage sag, the traditional dq0-abc transformation matrix [13] as follows:

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⎡ ⎤ ⎢ cos ω t − sin ω t 1⎥ ⎡ ua ⎤ ⎢ ⎥ ⎡ud ⎤ ⎢u ⎥ = ⎢ cos(ω t − 2π ) − sin(ω t − 2π ) 1⎥ ⎢ u ⎥ ⎢ b⎥ ⎢ ⎥⎢ q⎥ 3 3 ⎢⎣uc ⎥⎦ ⎢ ⎥ ⎢⎣ u0 ⎥⎦ ⎢cos(ωt + 2π ) − sin(ωt + 2π ) 1⎥ ⎥⎦ 3 3 ⎣⎢

(4)

And for the voltage sag containing zero sequence components, dq0-abc transformation matrix (4) should be revised to ⎡ ⎤ ⎢ cos ω t − sin ωt cos ωt ⎥ ⎡ ua ⎤ ⎢ ⎥ ⎡ud ⎤ ⎢ u ⎥ = ⎢ cos(ω t − 2π ) − sin(ω t − 2π ) cos ω t ⎥ ⎢ u ⎥ ⎢ b⎥ ⎢ ⎥⎢ q⎥ 3 3 ⎢⎣ uc ⎥⎦ ⎢ ⎥ ⎢⎣ u0 ⎥⎦ ⎢cos(ω t + 2π ) − sin(ω t + 2π ) cos ω t ⎥ ⎢⎣ ⎥⎦ 3 3

(5)

In the generating method of three-phase voltage sag in this paper, the dq0-abc transformation uses transform matrix (5). So any type of voltage sag is generated, including symmetric, asymmetric excluding zero sequence and asymmetric containing zero sequence.

4 Simulation and Experimental Results Grid side converter side: Power frequency f Z is 50Hz; Source voltage Us is 220V; The dc given voltage Vdc is 400V; The carrier frequency f C is 3kHz. Fault generating converter side: Filtering inductances L is 0.4mH; Filter capacitance C is 140μF ; Symmetric three-phase resistor load R is 50Ω . The duration of voltage sag is 100ms. The simulation results is shown in Fig. 3-Fig. 9. And part of experimental results is shown in Fig. 10-Fig. 11.

(a) Voltage sag 70% UN

(b) Voltage sag 30% UN

Fig. 3. Waveforms of three-phase voltage sag in type A

Generating Method of Three-Phase Voltage Sag in EV Charging System

(a) Voltage sag 70% UN

(b) Voltage sag 30% UN

Fig. 4. Waveforms of three-phase voltage sag in type B

(a) Voltage sag 70% UN

(b) Voltage sag 30% UN

Fig. 5. Waveforms of three-phase voltage sag in type C

(a) Voltage sag 70% UN

(b) Voltage sag 30% UN

Fig. 6. Waveforms of three-phase voltage sag in type D

(a) Voltage sag 70% UN

(b) Voltage sag 30% UN

Fig. 7. Waveforms of three-phase voltage sag in type E

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(a)Voltage sag 70% UN

(b)Voltage sag 30% UN

Fig. 8. Waveforms of three-phase voltage sag in type F

(a) Voltage sag 70% UN

(b) Voltage sag 30% UN

Fig. 9. Waveforms of three-phase voltage sag in type G

Part of the experimental results is depicted in Fig. 10 and Fig. 11. Fig. 10 shows the waveforms of three-phase voltage sag in type A with voltage sag 70%UN. Fig. 11 shows the waveforms of three-phase voltage sag in type G with voltage sag 80%UN.

Fig. 10. Waveforms of three-phase voltage sag sag with voltage sag 70% UN in type A

Fig. 11. Waveforms of three-phase voltage with voltage sag 80%UN in type G

The simulation and experimental results show the three-phase voltage sag waveforms of seven typical voltage sag with different voltage sag depth. The simulation and experimental results show that the generating method of the three-phase voltage

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sag in this paper can generate all the kinds of voltage sag fault, with voltage sag depth and fault duration set arbitrary. Meanwhile each phase voltage automatic meet phase relationship and amplitude relationship between any two phases. So the power system fault condition is reflected factually.

5 Conclusion This paper adopts "back-to-back" PWM converter as the main circuit of three-phase voltage sag generator with which energy can flow two-way. The method of threephase voltage sag proposed in this paper is based on DQ transformation. The voltage sag signal is decomposed into the positive and negative, zero sequence voltage using symmetric component method. The compound operation is completed with abc-dq0 transformation. And the traditional dq0-abc transform type is revised, and then the voltage sag of SVPWM modulation signal is generated. The three-phase voltage sag generated by this method automatic meet the phase relationship and the amplitude relationship between any two phases. The PCC voltage is reproduced more accurately, which includes symmetric, asymmetric excluding zero sequence and asymmetric containing zero sequence. And the full scope of the depth of voltage sag adjustable and fault duration of any settings are realized. The simulation and experimental results verify the correctness of the generating method described in this paper.

References 1. Liu Wanshun, C.: Electric Power System Fault Analysis. China Electric Power Press, Beijing (1998) 2. Xiao, X.: C: Power Quality Analysis and Control. China Electric Power Press, Beijing (2004) 3. Bollen, M.H.J.: C.: Understanding Power Quality Problems: Voltage Sags and Interruptions. IEEE Press, Piscataway (2000) 4. Xiao, X., Tao Shun, M.S.: Voltage sags types under different grounding modes of neutral and their propagation Part I. J. Transactions of China Electrotechnical Society 22(9), 143–147 (2007) 5. Xiao, X., Tao, S., M.S.: Voltage sags types under different grounding modes of neutral and their propagation Part I. J. Transactions of China Electrotechnical Society 22(10), 156–159 (2007) 6. Zhang, C., Zhang, X.: C: PWM Rectifier and Control. China Mechanic industry Press, Beiing (2003) 7. Yan, X., Zhang, B., Gu, X., et al.: M.S.: Closed-loop control of three-phase five-level PWM current source inverter. J. Transactions of China Electrotechnical Society 22(sup1), 60–64 (2007) 8. Collins, E.R., Morgan, R.L.: M.S.: A three-phase sag generator for testing industrial equipment. J. IEEE Trans- actions on Power Delivery 11(1), 526–532 (2003) 9. Yan, X., Venkataramanan, G., Yang, W.: M.S.: Fault Tolerance of DFIG Wind Turbine with a Series Grid Side Passive Impedance. Industry Applications Society Annual Meeting, 1–8 (2009)

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10. Boonmee, C., Kumsuwa, Y., Premrudeepchacharm, S.: M.S.: Implementation of real time three phase balanced voltage sag generator 1 kVA: using microcontroller and PC control. In: ICROS- SICE International Joint Conference, pp. 903–907 (2009) 11. Oranpiroj, K., Premrudeepreechacham, S., et al.: M.S.: The 3-phase 4-wire voltage sag generator based on three dimensions space vector modulation in abc coordinates. In: IEEE International Symposium on Industrial Electronics (ISIE 2009), pp. 275–280 (2009) 12. Collins, E.R., Morgan, R.L.: M.S.: A three-phase sag generator fortesting industrial equipment. IEEE Trans. Power Delverry II, 526–532 (1996) 13. Nanjing Institute. C.: Power System. China Electric Power Press, Bei Jing (1982)

Improved Predictive Control of Grid-Connected PV Inverter with LCL Filter Huijie Xue, Wei Feng, Zilong Yang, Chunsheng Wu, and Honghua Xu Department of Renewable Energy Power Generation Institute of Electrical Engineering, CAS No.6 Beiertiao, Zhongguanchun Haidian District, Beijing, China {xuehuijie,fengwei,zlyang,wcsxg hxu}@mail.iee.ac.cn

Abstract. LCL filter has high insertion loss and is expected to replace LC filter in the grid-connected PV inverter. However, the inverter with LCL filter is hard to be control and instability is liable to be incurred. So it is necessary to research reliable control strategy for the inverter with LCL filter. This paper researched the problem in detail. Discrete state space model of the inverter with LCL filter was firstly obtained with Clarke transformation and ZOH method. Then, improved predictive control strategy was proposed and explained in detail. Simulation was carried on and the results verified control effect of the proposed control strategy. Compared with prior control strategies, the proposed control strategy has the merits of simplicity, fast response and robustness. Keywords: PV Inverter LCL Filter Predictive Control.

1 Introduction As a kind of clean renewable energy resource, solar PV is undergoing rapid growth in the past several years [1, 2]. Grid-connected operation is the main utilization method of the PV system. The inverter is an important equipment of the grid-connected PV system. As a switching mode power electronics converter, the inverter will produce plenty of high frequency ripple current during normal operation. If not filtered out adequately, the ripple current will be injected into the grid and decreases the power quality even produces EMI to other electric and electronic equipment. So the filter with high insertion loss is desirable in the PV inverter. At present, most PV inverters adopt the LC inverter. In the view of the filter performance, the LCL filter is prior to the LC filter because of higher insertion loss, lower profile and lower cost. However, the LCL filter is hard to be controlled and liable to oscillate if not controlled appropriately [3]. The scholars have proposed many different control strategies for the inverter with the LCL filter [4, 5]. However, the strategies have respective shortcoming such as extra signal sensing [4] or limitation on the filter parameter [5]. This paper proposed an improved predictive control strategy of the PV inverter with the LCL filter. The proposed control strategy has explicit physical sense and the merit of simplicity, fast response and robustness. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 497–502. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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The rest of the paper is organized as follows: Section 2 models the PV inverter with the LCL filter. Section 3 proposes the improved predictive control of the PV inverter with the LCL filter in detail. Simulation results are given in Section 4.

2 Modeling the Inverter with LCL Filter The 3-phase PV inverter with the LCL filter is shown in Fig. 1. The line frequency transformer may be included into the model if necessary. The leakage inductance of the transformer can be included in L2 in that case.

Fig. 1. Schematic of the Inverter with LCL Filter

Assume that the parameters of the LCL filter of every phase of the inverter are identical. Take the voltage across the filter capacitor C , the current through the filter inductor L1 and the L2 as the state variables, the output voltage of the inverter bridge U inv as the input variable and the grid voltage U grid as the external disturbance, the state equation of Fig.1 can be expressed as: i

X o = Ao X o + BoU o + N oWo , Yo = C o X o where

X o = ⎡⎣uCa

iL1a

iL 2a

uCb

iL1b

iL 2b

uCc

iL 2c ⎤⎦

iL1c

(1) T

U o = ⎡⎣uinva

T

uinvb

uinvc ⎤⎦

T

Ao = diag{ Ao a, Aob, Ao c} Yo = ⎡⎣iL 2a iL 2b iL 2c ⎤⎦ Wo = ⎡⎣u grida ugridb u gridc ⎤⎦ Bo = diag{Bo a, Bo b, Bo c} No = diag{No a, Nob, Noc} Co = diag{Co a, Cob, Co c} T

⎡ ⎢ 0 ⎢ ⎢ 1 Ao a = Ao b = Ao c = ⎢ − ⎢ L1 ⎢ 1 ⎢ ⎣ L2

⎡ Bo a = Bo b = Bo c = ⎢ 0 ⎣ C o a = C o b = C o c = [0

1 ⎤ ⎥ C ⎥ ⎥ 0 ⎥ ⎥ R ⎥ − 2⎥ L2 ⎦

1 C R − 1 L1

−

0

1 L1 0 1] .

⎤ 0⎥ ⎦

T

T

⎡ 1⎤ , N o a = N ob = N o c = ⎢0 0 − ⎥ L2 ⎦ ⎣

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Principally, the current through L2 should be controlled to inject sinuous current into the grid. However, the current through L1 is almost the same with that through the inductor L2 because of the low capacitance of C. Moreover, the current through it is easy to be compensated if necessary. So, satisfactory results can be achieved by controlling the current through L1 in the grid-connected inverter. With the control objective of the current through L1 , the model of (1) can be simplified as (2) by looking the current through L2 as external disturbance and eliminating the redundancy with Clarke transformation. i

X t = At X t + BtU t + N tWt , Yt = Ct X t

(2)

where X t = ⎡uCα ⎣

iL1α

uC β

iL1β ⎤ ⎦

T

U

t

= ⎡ u in v α ⎣

u in v β ⎤ ⎦

T

T

Yt = ⎡iL1α iL1β ⎤ W t = ⎡ iL 2α iL 2 β ⎤ ⎣ ⎦ ⎣ ⎦ At = diag{ Atα , At β } Bt = diag{Btα , Bt β } N t = diag{N tα , N t β } Ct = diag{Ctα , Ct β } T

⎡ ⎢ 0 Atα = At β = ⎢ ⎢− 1 ⎢⎣ L1

1 ⎤ C ⎥ ⎥ R − 1⎥ L1 ⎥⎦

⎡ B tα = B t β = ⎢ 0 ⎣

1 ⎤ ⎥ L1 ⎦

T

⎡ 1 N tα = Nt β = ⎢ − ⎣ C

⎤ 0⎥ ⎦

T

C tα = C t β = [ 0 1] .

In order to design digital controller directly, discrete model should be obtained. Assuming the sampling period is Ts , the discrete model can be obtained by applying zero order holder method [6] and can be expressed as:

X d ( n + 1) = Ad X d ( n) + BdU d ( n) + N dWd ( n) Yd ( n) = Cd X d ( n)

,

(3)

where Ad = e At ⋅Ts , Bd = e AtTs BtTs , N d = e A T N tTs and Cd = Ct . t s

3 Proposed Control Strategy of Inverter with LCL Filter The proposed predictive control of the inverter with the LCL filter is easy to understand according to the physical sense of the controlling behavior and is explained as follows. In order to control the current through L1 at the instant (n + 1)Ts reach I ref ( n ) , the reference at the instant of nTs , the control input, i.e. the output voltage of the inverter U d (n) , can be obtained according to (4).

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U d (k ) = (Cd Bd )−1 ( I ref (k ) − Cd Ad X d (k ) − Cd N dWd (k )) ,

(4)

Generally, the sampling instant of the control system occurs at the middle of the control period to avoid alias effect. However, the updating of the modulation signal is finished at the end of the control period due to the computation delay. Besides, updating at the end of the control period is desirable to avoid asymmetric pulse width modulation of the inverter. So the delay of half switching period is produced by the computation and updating behavior. Moreover, one switching period is necessary for the inverter to finish the current control. So total one and half switching periods delay is unavoidable in the predictive control. In the high power PV inverter, the time delay can’t be omitted because the switching frequency is limited to only several kilohertz to avoid high switching loss. So it is necessary to overcome the delay. The delay can be compensated with the following measure. Denote the components of control input of the α axle and β axle with U dα and U d β respectively, the improved strategy can be expressed with U dαβ (k ) = e jθ (U dα (k ) + jU d β (k )) ,

(5)

where the real part and imaginary part is the control input of the α axle and β axle respectively and θ = 2π f ref t delay .The delay compensation is achieved by introducing a pure imaginary number e jθ , which is a phase shift factor corresponding to the time delay in αβ coordinate. It can be implemented easily with simple complex number operation and has no much operation time consuming.

4 Simulation Results In order to verify the proposed control strategy, simulation was carried on PSIM. The schematic is shown in Fig. 2. The parameters of the inverter used in the simulation are as follows: L1 = 1.5mH R1 = 0.1Ω L2 = 900 μ H R2 = 0.1Ω C = 66 μ F f sw = 5.4kHz .

Fig. 2. Schematic of the Simulation

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Fig. 3. Simulation Result with Nominal Parameters

Simulation result of the nominal parameter and ideal U grid is shown in Fig.3. The upper waveform is U grid and the lower one is the iL1 respectively. It can be seen that the current is in phase with the voltage and has no distortion except for the current ripple. The control effect in the case of distortion and unbalance of the grid voltage are shown in Fig. 4 and Fig. 5 respectively. In Fig. 4, the grid voltage has 5th harmonic distortion. In Fig. 5, it has negative sequence component. The results show that both distortion and unbalance has no influence on iL1 .

Fig. 4. Simulation Result with Grid Voltage Distortion

Fig. 5. Simulation Result with Grid Voltage Unbalance

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To verify the robustness of the proposed control strategy, L1 is changed to 2.0mH from the nominal 1.5mH , which means a variation of over 33%.

Fig. 6. Simulation Result with Grid Voltage

The simulation result with the revised parameter in Fig. 6 manifests the good robustness of the proposed control strategy. Variation in other parameters has similar results.

5 Conclusion The LCL filter has higher insertion loss than the LC filter. However, control of the inverter with the LCL filter is much more difficult than that with the LC filter. An improved predictive control of the grid-connected PV inverter with LCL filter was proposed in this paper. The control strategy has explicit physical sense and is easy to understand and implement. Simulation results show that the proposed strategy has good reduction effect to grid unbalance and distortion. Moreover, the robustness to the parameter variation is satisfactory.

References 1. International Energy Agency, Solar Photovoltaic Energy Technology Roadmaps, pp. 6–10 (2010) 2. European PV Industry Association, Market Outlook for Photovoltaic until 2014 (March 2011), http://www.epia.org/ 3. Wang, T.C., Zhihong, Y., Gautam, S., Xiaoming, Y.: Output Filter Design for a Gridinterconnected Three-Phase Inverter. In: Proceeding of 34th Annual IEEE Power Electronics Specialist Conference, vol. 2, pp. 779–784 (2003) 4. Liu, F., Yan, Z., Duan, S., Ji, Y., Liu, B., Liu, F.: Parameter Design of a Two-Current-Loop Controller Used in a Grid-Connected Inverter System With LCL Filter. IEE Transactions on Industrial Electronics 56(11) (November 2009) 5. Gabe, I.J., Montagner, V.F., Pinheiro, H.: Design and Implementation of a Robusr Current Controller for VSI Connected to the Grid Through an LCL Filter. IEEE Transactions on Power Electronics 24(6) (2009) 6. Dorf, R.C., Bishop, R.H.: Modern control systems, 10th edn. Addison Wesley Longman, Menlo Park (2005)

Study on Application of Demand Prognosticating Model Based on Grey Theory Han Qingtian, Li Lian, and Zhang Yi Naval Aeronautical and Astronautical University, Yantai, China [email protected]

Abstract. Grey theory has been widely used in the predicting, estimation and assessment. In the paper, GM(1,1) model was introduced for demand prognosticating. Firstly grey theory model was studied on, including data transform, data modeling. Secondly, the prognosticating steps were given out. Finally, a spare prognosticating application example was carried out, and the prognosticating result and test are presented. The result shows that prognosticating model is more adaptive and comprehensive than other models. Keywords: grey theory; exponential smoothing; prognosticating model.

1 Introduction The gray theory treats the whole random variables all make gray a number, the transaction of the gray number is not to seek probability distribution or statistics law, it is the way that makes use of data handling to look for law between data. Carry on a transaction through the data in the logarithm row, creation new array, with this to excavation and seek number of law method, be called number of born. Number of the born mode has a variety and mainly introduce here tired apply born and tired reduce born and all be worth born. A novel grey-based modeling strategy for a dynamically turned gyroscope random drift model is studied in [1]. Fu YU-sun et. gives grey system theory, data preparation and their application [2]. And the grey model is also used for prognosticating in some combination methods [3]. In the paper, GM(1,1) model is introduced for demand prognosticating.

2 Grey Theory Model The gray model is to make use of discrete random values. Through transforming the data can follow some low. Then the pattern of the differential equation form of establishment, so it becomes easy for the change process progress search and present. 2.1 Data Transform For assure the mass of the model and the accurate result of the systematic analysis. Data transform and transaction to the raw data should be carried on for accumulating to make its cancellation measure a key link and have comparability. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 503–508. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Suppose there is a sequence and the data transform

x = ( x(1), x(2)," , x(n)) f :x→ y f ( x( k )) = y ( k ), k = 1, 2," , n

(1)

2.2 GM(1,1) Model

Let

array

x (0) = ( x (0) (1), x (0) (2)," , x (0) (n))

,AGO k

x (1) = ( x (1) (1), x (1) (2)," , x (1) ( n)) . Here x (1) (k ) = ∑ x (0) (i ) i =1

array

of

x (0)

is

（ k = 1, 2,", n ）.

Then d (k ) = x (0) (k ) = x (1) (k ) − x (1) (k − 1)

(2)

Let z (1) be the average array of x (1) , z (1) (k ) = 0.5 x (1) (k ) + 0.5 x (1) (k − 1) . So z (1) = ( z (1) (2), z (1) (3)," , z (1) ( n)) . Define the differential equation of GM(1,1) d (k ) + az (1) (k ) = b , that is x (0) ( k ) + az (1) ( k ) = b

(3)

When k = 2,3," , n , it becomes ⎧ x (0) (2) + az (1) (2) = b ⎪ (0) (1) ⎪ x (3) + az (3) = b ⎨ ⎪""" ⎪ x (0) (n) + az (1) (n) = b ⎩ ⎡ − z (1) (2) ⎢ (1) Let Y = ( x (0) (2), x (0) (3)," , x (0) (n))T , u = (a, b)T , B = ⎢ − z (3) ⎢ # ⎢ (1) ⎣⎢ − z ( n) model can be denoted as matrix equation

Y = Bu

(4)

1⎤ ⎥ 1⎥ , then GM(1,1) #⎥ ⎥ 1⎦⎥

(5)

Using LSE it can be calculate

uˆ = (aˆ , bˆ)T = ( BT B) −1 BT Y

(6)

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2.3 Prognosticating Steps 2.3.1 Data Test For assuring the feasibility of model method, it is needed to make a necessary inspection transaction to the known data sequence. Suppose original data x (0) = ( x (0) (1), x (0) (2)," , x (0) (n)) , then

λ (k ) = 2

x (0) (k − 1) , k = 2,3," , n x (0) (k )

(7)

2

If all λ (k ) are in (e− n +1 , e n +1 ) , then x (0) can be used for the prognosticating. Oth-

erwise, x (0) should be transformed. The usual data transform is parallel moving transform. For a constant c , y (0) (k ) = x (0) (k ) + c , k = 1, 2," , n . So for y (0) = ( y (0) (1), y (0) (2)," , y (0) (n)) .

λ y (k ) =

y (0) (k − 1) ∈ X , k = 2,3," , n . y (0) (k )

(8)

2.3.1 Prognosticating Model b⎞ b ⎛ xˆ (1) (k + 1) = ⎜ x (0) (1) − ⎟ e − ak + , k = 1, 2," , n − 1 a a ⎝ ⎠

(9)

And xˆ (0) ( k + 1) = xˆ (1) ( k + 1) − xˆ (1) ( k ) , k = 1, 2," , n − 1

(10)

2.3.2 Prognosticating Value Test

Let ε (k ) be the error, calculate

ε (k ) =

x (0) (k ) − xˆ (0) (k ) , k = 1, 2," , n x (0) (k )

(11)

Then the test is taken. Firstly, using x (0) (k − 1) and x (0) (k ) , λ0 (k ) can be calculated. Then the index a is used for calculate the ρ (k ) . ⎛ 1 − 0.5a ⎞ ⎟ λ0 ( k ) ⎝ 1 + 0.5a ⎠

ρ (k ) = 1 − ⎜

(12)

Lastly the prognosticating precise level can be estimated, and Table 1 can be referenced.

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Precise level

ε (k )

ρ (k )

Bad Ordinary Better

ε (k ) > 0.2 ε (k ) < 0.2 ε (k ) < 0.1

ρ (k ) > 0.2 ρ (k ) < 0.2 ρ (k ) < 0.1

3 Application of Demand Prognosticating Model For an enterprise, the spare consume quantity from 1998 to 2009 is shown in Table 2. The demand prognosticating is needed for the quantity of 2010. Table 2. Spare consume quantity from 1998 to 2009

Year 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1 season 35 34 33 38 36 35 40 37 39 42 43 40

Spare consume quantity 2 season 3 season 30 29 30 30 31 30 32 33 30 31 34 33 32 37 32 33 34 32 39 37 37 39 32 35

4 season 18 20 23 21 24 26 26 30 29 25 27 29

3.1 Ratio Test

Create the sequence of the spare consume data of spring season as follows. x (0) = ( x (0) (1), x (0) (2), x (0) (3)," , x (0) (11), x (0) (12))

= (35, 34, 33, " , 43, 40)

(13)

Firstly, λ (0) (k ) is calculated.

λ (0) = (λ (0) (2), λ (0) (3)," , λ (0) (12)) (14) = (1.029,1.030,0.868,1.056,1.029, 0.875,1.081, 0.948,0.928, 0.976,1.075) (0) Then, the value is estimated. since λ (k ) ∈ [ 0.8574, 1.1663] , k = 2,3," ,12 , so

x (0) is satisfactory model.

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3.2 GM(1,1) Model

Step 1: Accumulate the data x (0) . x (1) = (35, 69,102,140,176, 211, 251, 288,327,369, 412, 452) Step 2: Construct the array B and vector Y . ⎡ 1 (1) (1) ⎢ − 2 ( x (1) + x (2)) ⎢ ⎢ − 1 ( x (1) (2) + x (1) (3)) B=⎢ 2 ⎢ # ⎢ ⎢ 1 (1) (1) ⎢ − ( x (11) + x (12)) ⎣ 2

⎤ 1⎥ ⎡ x (0) (2) ⎤ ⎥ ⎢ (0) ⎥ 1⎥ x (3) ⎥ ⎥ ,Y = ⎢ ⎢ # ⎥ ⎥ #⎥ ⎢ (0) ⎥ ⎣⎢ x (12) ⎦⎥ ⎥ 1⎥ ⎦

(15)

Step 3: Calculate uˆ . ⎛ −0.0215 ⎞ u = (a, b)T = ( BT B) −1 BT Y = ⎜ ⎟ ⎝ 32.8503 ⎠ Then a = −0.0215 , b = 32.8503 . Step 4: Prognosticating modeling

dx (1) − 0.0215 x (1) = 32.8503 dt

(16)

(17)

And

b b x (1) (k + 1) = ( x (0) (1) − )e − ak + = 1562.9e0.0215 k − 1527.9 a a

(18)

3.3 Prognosticate Result and Test

We can get xˆ (1) (k + 1) and xˆ (0) (k + 1) . xˆ (1) (1) = xˆ (0) (1) = x (0) (1) = 35 . Let k = 1, 2,...,14 , then we have xˆ (1) (1) = 35.0000, xˆ (1) (2) = 68.9662, xˆ (1) (3) = 103.6705, xˆ (1) (4) = 139.1291 xˆ (1) (5) = 175.3583, xˆ (1) (6) = 212.3749, xˆ (1) (7) = 250.1959, xˆ (1) (8) = 288.8389 xˆ (1) (9) = 328.3217, xˆ (1) (10) = 368.6626, xˆ (1) (11) = 409.8802,xˆ (1) (12) = 451.9935 xˆ (1) (13) = 495.0221,xˆ (1) (14) = 538.9859 So, the prognosticating model is as follows xˆ (0) (k + 1) = xˆ (1) (k + 1) − xˆ (1) (k )

(19)

The prognosticating results are shown in Table 3. Model test can also be carried out based on tab. 1. as validated, the relabive error is smaller than 10%, so the precise is satisfactory.

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Original value 35 34 33 38 36 35 40 37 39 42 43 40 — —

Model value 35 33.9662 34.7043 35.4586 36.2292 37.0166 37.8210 38.6430 39.4828 40.3409 41.2176 42.1133 43.0286 43.9638

error 0 0.0338 -1.7043 2.5414 -0.2292 -2.0166 2.1790 -1.6430 -0.4828 1.6591 1.7824 -2.1133 — —

Relative error 0 0.09 -5.16 6.69 -0.64 -5.76 5.45 -4.44 -1.24 3.95 4.15 -5.28 — —

Precise 100 99.91 94.84 93.31 99.36 94.24 94.55 95.56 98.76 96.05 95.85 94.72 — —

References 1. Fan, C.-l., Tian, W.-f., Jin, Z.-h.: A novel grey-based modeling strategy for a dynamically turned gyroscope random drift model. Journal of Shanghai Jiaotong University 38(10), 1741–1743 (2004) 2. Fu, Y.-s., Tian, Z.-h., Shi, S.-j., et al.: Grey system theory, data preparation and their application. Journal of Shanghai Jiaotong University 35(2), 267–271 (2001) 3. Coulson, N.E., Robins, R.P.: Forecast Combination in a Dynamic Setting. Journal of Forecasting 12(1), 63–67 (1993)

Research on Bayes Reliability Assessment for Test Data Han Qingtian, Li Lian, and Cui Jia Naval Aeronautical and Astronautical University, Yantai, China [email protected]

Abstract. Inverse Weibull distribution model has been recently proposed as a model in the analysis of life testing data. According to the distribution function of the Inverse Weibull distribution, concerning characters of failure data, the uniform distribution was taken as an prior distribution of the failure probability, in order to estimate the failure probability using the Bayes method. The least squares estimates of the distribution parameters were given, and Bayes estimation of failure probability was presented, so that the reliability assessment was obtained. Keywords: reliability assessment; Bayes inference; inverse Weibull distribution.

1 Introduction In the time-censored life tests, especially in high reliability and small sampling tests. there maybe small number items failure, sometimes, there only one failure occurs. [1,2] It is also very important of the statistic analysis for this condition. In the existing literatures many familiar distributions such as Exponential, Weibull, Normal and Log Normal distributions have been studied as the life distributions of the test items. In this paper, a new life distribution, Inverse Weibull distribution, is introduced into the reliability assessment for data with only one failure.[3,4,5]. Inverse Weibull distribution model has been recently proposed as a model in the analysis of life testing data. Keller et al. derived this model on the basis of physical considerations on some failures of mechanical components subject to degradation phenomena. Erto has provided other physical failure processes leading to such distribution. Furthermore, he has shown that the Inverse Weibull distribution gave a good fit to life testing data reported in Nelson. Estimation procedures, in classical and Bayesian approaches for such distribution, are given in the literature. Calabria and Pulcini have been investigated the statistical properties of the maximum likelihood estimators (MLE’s) of the parameters and reliability for a complete sample. Erto (1989) used the Least-Square (LS) method for obtaining the estimators of the parameters and reliability. Calabria and Pulcini derived the MLE’s and the LS of the parameters. Calabria and Pulcini derived the Bayes estimator of the parameters and reliability. Finally, Calabria and Pulcini derived the prediction for some future variables. The probabilistic design of complex systems requires estimating the reliability function of any part (or component) of the whole system. The system component fails when the stress induced by the operating conditions exceeds the stress resisting capacity (strength) of the M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 509–513. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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component. Both stress and strength are, generally, random variables since they depend on several operating and manufacturing variables such as temperature, size, surface finish, etc. Once distributions of these r.v.s are determined over the complete range of stress r.v. or only in the inference range, then the reliability of each component can be evaluated. In this framework, Keller and Kamath and Calabria and Pulcini proposed a statistical model, Inverse Weibull, to evaluate the reliability function of a component subjected to a given degradation phenomenon[6,7]. Distribution function of Inverse Weibull distribution

F (t ) = e

η

− ( )m t

, m > 0,η > 0

(1)

2 Test Data Analysis Supposed that the life of the product follows Inverse Weibull distribution. n products are chosen and separated into k groups, and the numbers in every group are n1 , n2 ," , nk . The start time is the same and the end time is different. Test time is recorded as t1 .t2 ," , tk , and t1 < t2 < " < tk . For the products in i group, test is ended at the predefined time ti . In the whole test, there is only one failure occurs. It is supposed that the failure occurs during (tm −1 , tm ) , ant the group has and only has one failure. Let k

sl = ∑ ni , so the situation can be denoted as tl , sl , rl , l = 1, 2," , k ,. Here sl , rl denote i =l

the number of test products and the number of failure products. Obviously s1 ≥ s2 ≥ " ≥ sk , when l ≤ m − 1 , we have rl = 0 , and when l ≤ m − 1 , we have rl = 1 . From the information above, we can know that: When t = 0 , the failure probability p0 = 0 (or approximately to 0). Because of 0 < t1 < t2 < " < tk , let pi = P (T < ti ) , then p1 < p2 < " < pk , when sk is large, pi (i = 1, 2," , k ) are all small. Under the suppose of the Inverse Weibull life distribution and the test information, we can estimate the parameters m and η and assess the reliability of the product.

3 Failure Probability Parameters Estimation 3.1 Prior Distribution of Failure Probability

Because there is only one failure occur until time tk , so we can judge that the reliability of the product is very high during time (0, tk ) . That is failure probability pk is small, especially when sk is large pk approximately to 0. So the failure probability pk ≤ 0.5 before time tk . In generally, through collect the design experience from experts, we can give the more precise upper limit λk for pk . So pk is in the area of (0, λk ) , and λk ≤ 0.5 .

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As the life follows Inverse Weibull distribution, then

f (t ) =

mη m − (ηt )m e t m +1

(2)

so ∂ 2 f (t ) mη m (m 2η 2 m − 3mη m (m + 1)t + (m + 1)(m + 2)t m ) − (ηt )m e = t 3( m +1) ∂t 2

(3)

∂ 2 f (t ) > 0 , distribution function F (t ) is concave function of t . ∂t 2 Because pk < λk ≤ 0.5 , so 0 < t1 < t2 < " < tk < μ , based on the quality of concave function, we know that

In some situation,

0<

p1 − p0 p2 − p0 p − p0 < <"< k t1 − t0 t 2 − t0 t k − t0

(4)

Here, t0 is a predefined time, and t0 < t1 , p0 = P(T < t0 ) . When t0 = 0 , p0 is small, and approximately to 0. So

p p1 p2 < <"< k t1 t2 tk

(5)

p λ p1 p2 < <"< k < k t1 t2 tk tk

(6)

0< Since pk < λk ≤ 0.5 , then 0<

When the product life follows Inverse Weibull distribution , the failure probability λt pi follows eqation (0). So the valure range of pi is [0, λi ] , and λi = k i . We can tk estimate the failure probability pi using the prior information. If pi is supposed follow uniformity distribution during [0, λi ] , the prior distribution of pi is ⎧1 ⎪ ,0 < pi < λi π ( pi ) = ⎨ λi ⎪0, other situation ⎩

(7)

3.2 Bayes Estimation of Failure Probability

Because there are si products are tested and ri products failure before time, so the likelihood function is

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⎛r ⎞ L(ri , pi ) = ⎜ i ⎟ pi ri (1 − pi )si − ri ⎝ pi ⎠

(8)

Combined the prior distribution and the likelihood function, and use the Bayes equation ,we can get the posterior distribution of pi is

π ( pi | si , ri ) =

L(ri , pi )

∫

λi

0

, i = 1, 2," , k

L(ri , pi )dpi

(9)

Under the square mass loss, the Bayes estimation of pi is obtain. When i < m , ri = 0 , the posterior distribution of pi is

π ( pi | si , ri ) = pˆ i = 1 −

( si + 1)(1 − pi ) si , 0 < pi < λi 1 − (1 − λi ) si −1

si + 1 1 − (1 − λi ) si + 2 ⋅ , 0 < pi < λi si + 2 1 − (1 − λi ) si +1

(10)

(11)

When i ≥ m , ri = 1 , the posterior distribution of pi

π ( pi | si , ri ) = pˆ i = 1 −

si + 1

⋅

si ( si + 1) pi (1 − pi ) si −1 , 0 < pi < λi 1 − (1 + si λi )(1 − λi ) si

2 − [2 + si λi + si ( si + 1)λi 2 (1 − λi ) si , i = m, m + 1," , k ( si + 2)[1 − ( si + 1)(1 − λi ) si + si (1 − λi ) si +1

(12)

(13)

All the probabilities follow the relationship p1 < p2 < " < pk , at the same time, the estimations of p1 , p2 ," , pk also follow the relationship pˆ1 < pˆ 2 < " < pˆ k .

4 Reliability Assessment Model For the product with life following Inverse Weibull distribution. m time-censored test were taken. The test data are (ti , ni ) , i = 1, 2," , m ni , so the LSE estimation of the parameters m and η are mˆ =

1 mD − AC BC − AD μˆ = , ηˆ = exp( μˆ ) σˆ = 2 , σˆ mB − A mB − A2

m

m

m

m

i =1

i =1

i =1

i =1

(14)

2 Here, A = ∑ xi , B = ∑ xi , C = ∑ yi , D = ∑ yi , xi = ln(1 / ln(1 / pˆ i )) , yi = ln ti .

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And the weighed LSE estimation of m and η are mˆ =

1 D ′ − A′C ′ B ′C ′ − A′D′ , μˆ = , ηˆ = exp( μˆ ) . σˆ = 2 σˆ B ′ − A′ B ′ − A′2 m

Here,

A′ = ∑ ωi xi

m

B ′ = ∑ ωi xi2

,

i =1

m

,

i =1

C ′ = ∑ ωi yi i =1

(15) m

,

D′ = ∑ ωi yi

,

i =1

xi = ln(1 / ln(1 / pˆ i )) , yi = ln ti . k

ωi = ni ti / ∑ ni ti , i = 1, 2," , m . i =1

So the reliability assessment of the product is Rˆ (t ) = 1 − e

ηˆ − ( )mˆ t

(16)

5 Conclusion Concerning characters of failure data, the uniform distribution was taken as an prior distribution of the failure probability, in order to estimate the failure probability using the Bayes method. The least squares estimates of the distribution parameters were given, and Bayes estimation of failure probability was presented, so that the reliability assessment was obtained.

References 1. Han, M.: Estimation of Parameter in the Case of Zero-Failure Data. Journal of Systems Science and Systems Engineering 10(4), 450–456 (2001) 2. Liu, H.-t., Zhang, Z.-h.: Bayesian Analysis of Zero-failure Data Based on IFRA Distribution. Journal of China Ordnance 5(1), 65–69 (2009) 3. Keller, A.Z., Kamath, A.R.R.: Alternative reliability models for mechanical systems. Paper presented at 3rd International conference on Reliability and Maintainability, Toulouse, France (1982) 4. Calabria, R., Pulcini, G.: Confidence limits for reliability and tolerance limits in the inverse Weibull distribution. Reliability Engineering and System Safety 24, 77–85 (1989) 5. Calabria, R., Pulcini, G.: Bayes probability intervals in a load-strength model. Comm. Statistics –Theory and Methods 21, 3393–3405 (1992) 6. Calabria, R., Pulcini, G.: On the maximum likelihood and leastsquares estimation in the inverse Weibull distribution. Statistica Applicata 2, 53–66 (1990) 7. Calabria, R., Pulcini, G.: Bayes 2-sample prediction for the inverse Weibull distribution. Commun. Statist. – Theory Meth. 23(6), 1811–1824 (1994)

Improved Particle Swarm Optimization by Updating Constraints of PID Control for Real Time Linear Motor Positioning Ying-Hao Li1, Yi-Cheng Huang1, and Jen-Ai Chao2 1

National Changhua University of Education, Changhua County, Taiwan (R.O.C.) 2 Chienkuo Technology University, Changhua County, Taiwan (R.O.C.) [email protected]

Abstract. This paper proposes an Improved Particle Swarm Optimization (IPSO) technique for adjusting the gains of a PID controller. The new approach introduces particle space constraints so as to improve the performance of velocity updating and position updating capability. Numerical simulations and experimental results based on PID, PSO-PID and IPSO-PID control systems are compared. Real time experimental results show that the proposed IPSO algorithm has great computational convergence and ensure the stability of the controlled system without the strict constraints on the updating velocity. Tests on the linear synchronous motor (LSM) via the digital signal Microchip (dsPIC) processor demonstrate effectiveness and robustness in positioning with disturbance excitation. Keywords: Improved Particle Swarm Optimization, PID Controller, linear synchronous motor.

1 Introduction The conventional PID controller is widely applied in control systems because of its practicability and robustness. To obtain better dynamic performance, guarantee security and sustainably utilize equipments and plants, PID gains must be well tuned [1]. PSO was first introduced in 1995 by Kennedy and Eberhart [2]. It was developed through simulation of a simplified swarm social behaviour, and has been found to be robust in solving the continuous nonlinear optimization problem. There are many kinds of linear motors that driven by different kind of drivers, control and computation methods. Consequently, how to create a good motion control system with robust concern is very important. The Integrated Absolute Error (IAE) cost function is used for the minimization criterion of the transient error. PID parameters tuned by IPSO, PSO and PID from the Response Optimization toolbox of MATLAB/Simulink 7.7 ed. are compared both in numerical simulations and followed by real time experiments. An in-house single axis LSM is verified in real time via the dsPIC processor. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 515–523. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Transfer Function of LSM Fig. 1 illustrates a sketch diagram for single axis of an LSM. The transfer function of the moving coil is approximated by a first order lag system with resistance R25 and inductance L. A back electromotive force is generated by moving the coil. The open loop transfer function F with input voltage Va is approximated by a third order lag system [3].

Fig. 1. Simplified model of LSM

After inferential reasoning transfer function to the following equation: G (s ) =

(M + m )s(R

Kf + sL) + K f K v

25

,

(1)

where M is the payload mass on the forcer (moving part of the LSM) , m is the linear motor forcer mass, R 25 is the armature resistance, L is the inductance, K f is the thrust constant, K v is the contrary EMF constant, Va (s ) is the input voltage.

3 Particle Swarm Optimization In PSO, each particle adjusts its position according to its own experience and the experiences of neighbors, including the current velocity, position, and the best previous position experienced by itself and its neighbors. Moreover, the inertia weight is adopted to balance the local search ability and global search ability [2][4]. The algorithm of the PSO is derived as follows: Vid (t ) = w × Vid (t - 1) + c1 × Rand ( ) × (Pid - X id ) + c2 × Rand ( )× (Pgd - X id )

(2)

X id (t ) = X id (t - 1) + Vid (t ) ,

(3)

where Vid (t ) is the current velocity of i particle, i = 1,..., n , n is the population size; the superscript d is the dimension of the particle; Pid is the best previous position of the i th particle; Pgd is the best previous position among all the particles in the swarm; X id is the current position of the i th particle. The constants c1 and c 2 represent the weighting of the stochastic acceleration terms that pull each particle toward Pid and Pgd positions. The acceleration constants c1 and c 2 were often set to be 2.0 according to past references. Rand( ) represents the uniform random number between 0 to 1.

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By linearly decreasing the inertia weight( w ) from a relatively large value to a small value through the course of the PSO run, the PSO tends to have more global search ability at the beginning of the run while having more local search ability near the end of the run [5]. The inertia weight is set according to the following equation w = w max

w max w min × N iter N max ,

(4)

where N max is the maximum number of iterations, N iter and is the current number of iterations [6]. Equation [2] restricts the value w to the range [w max , w min ] . In this study, the maximum and minimum values of the inertia weights are set as 1 and 0.2, respectively.

4 Numerical Simulations The traditional PID controller has the following form in the time domain:

∫

t

u ( t ) = k p e ( t ) + k i e ( t ) dt + k d 0

de ( t ) dt ,

(5)

where e( t ) , u ( t ) , k p , k i , k d is the system error, control variable, proportional gain, integral gain, and derivative gain respectively. Each coefficient of the PID controller adds some special effects to the output response of the system. As mentioned above, using IPSO to tune the parameters of the PID controller is applied here. Nevertheless we must first establish the fitness function based on comprehensive evaluation performances to evaluate each particle, and take them as the basis for the individual optimal particle and global optimal particle to update, causing the initial solution to evolve towards the optimal solution. In this study, numerical simulations were carried out first. IPSO learning algorithm was applied to the PID control system as shown in Fig. 2.

Fig. 2. Control block diagram of the PID feedback control system based on IPSO.

The transfer function of LSM is approximated as

3.63 (6) 0.003588s + 11.316s + 72.6 At first, we use conventional PID control algorithm to do the simulation. In the algorithm, we choose K p = 15 , K i = 0.2 and K d = 3 . The unit step response simulation G (s) =

2

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results based on PID control algorithm within 10 sec are plotted in Fig. 3. The step responses of PSO-PID and IPSO-PID are also plotted in Fig. 3. Some performance measures, including settling-time TS , maximum overshoot ( M.O. ) and Integrated Absolute Error J IAE are shown in Table 1.

Fig. 3. Simulation results of the unit step response via PID, PSO-PID and IPSO-PID controllers. Table 1. Simulated performance comparisons of PID, PSO-PID and proposed IPSO-PID control systems in Fig. 3

KP PID PSO-PID IPSO-PID

15 27.5049 26.6288

Ki 0.2 0.3492 1.1737

Kd 3 3.0404 4.305

M.O. (%)

0.1753 0.3204 2.5416

TS (s) 1.8 1 0.65

J IAE 16.6906 14.8374 14.6693

Fig. 4 shows the step response of the system with a disturbance by adding extra load of 10 Kg to the plant. Key parameters and performance measures are in Table 2. These results clearly indicate that the designed IPSO-PID controller can achieve good robust performance and rapidly cope with inertia disturbances. In Fig. 5, when an injected disturbance with 30% of the step input is added to the plant at 10sec, the IPSO controller shows rapid adaptation and better robustness than the PID and PSO controllers. Table 2. Simulated performance comparisons of PID, PSO-PID and proposed IPSO-PID control systems in Fig. 4

KP PID PSO-PID IPSO-PID

15 9.2614 14.9138

Ki 0.2 0.0122 0.064

Kd 3 7.2751 8.8365

M.O. (%)

29.415 4.8464 8.146

TS (S) 6.2 4.5797 3.2

J IAE 38.9949 24.432 24.0018

Improved Particle Swarm Optimization by Updating Constraints of PID Control

Fig. 4. The step response of the system with a extra load of 10 Kg to the LSM

519

Fig. 5. Step response with injected disturbance at 10 sec.

5 Experiments In this study, the apparatus setup for the displacement motion of the LSM is depicted in Fig.6. In order to validate the proposed IPSO control systems in practical applications, a digital signal controller via Microchip is implemented. To name it in short we indicate it as dsPIC. All experimental computer programs are carried out using Turbo C language inserted into a dsPIC development module.

Fig. 6. Schematic illustration for the experiment configuration

In this study, searching PID parameters by Ziegler-Nichols (Z-N) method [7] is used experimentally first. Obtained values are K p = 0.125 , K i = 0.05 , K d = 0.9 . The manipulated controlled efforts on the LSM positioning will be compared with conventional PSO and proposed IPSO. From numerical simulation, there is no updating velocity and updating position constraints for searching the PID parameters via PSO technique. Running PSO can throw calculated PID parameters into the system for positioning compensation. However in actual platform positioning, there is a must for avoiding the occurrence of large control action signal, undesirable overshooting and preventing out of the maximum position limit. Since the PSO mechanism in updating velocity and updating position of particles sometimes is out of the optimal search, this may cause the system to be unstable in real time applications.

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For experiment implementation here, the parameters of the PSO are set as followings: The number of particles = 20. Dimension of particles = 3. The velocity extreme value: Vmax = 1 , Vmin = −1 . Learning factor: C1 = 2, C 2 = 2. The three position values of PID parameters are initialized randomly from 0 to 1. Figure 7 shows the real-time output positioning of the LSM. The displacement is out of range of maximum positioning limit and the onset of unstable oscillation is observed if values of Vmax and Vmin are set in +1 and -1. If the speed of the particles is confined and is relatively conservative by Vmax = 0.001 and Vmin = −0.001 in this work, aforementioned unstable problem of PSO application can be resolved. Nevertheless for real-time experiment concern, the Vmax and Vmin are not easy to be determined in advance. The parameters of the IPSO are set as below: The number of particles = 20. Dimension of particles = 3. The velocity extreme value: Vmax = 1 , Vmin = −1 . The position extreme values of three PID parameters are X p _ max = 1 , X p _ min = 0 , X i _ max = 0.1 , X i _ min = 0 , and X d _ max = 10 , X d _ min = 0 . Learning factor: C1 = 2, C 2 = 2. Experiment results for the response of PSO-PID and IPSO-PID are plotted in Fig. 8. Output performances are indicated in Table 3. Explicitly, the positioning response by PSO-PID control is improved and the oscillatory step response of Fig. 7 is alleviated. The overall performance of IPSO-PID control of LSM for indexes of settling time, overshooting and J IAE error is successful in real time application. Table 3. Experiment performance comparisons of PID, PSO-PID and proposed IPSO-PID control systems in Fig. 8

PID PSO-PID IPSO-PID

KP

Ki

Kd

M.O. (%)

0.125 0.0442 0.073

0.05 0.2842 0.0213

0.9 0.2602 1.0541

8.54 0 0

Fig. 7. LSM position response via real-time PSO-PID controller

TS (ms) 243 353 249

J IAE 1587163 1349979 1329294

Fig. 8. LSM position response via real-time PID, PSO-PID and IPSO-PID controllers

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A load of 0.256kg for piezoelectric (PZT) actuated stage is attached to the forcer of LSM. Figure 9 shows the picture of PZT stage on top of the LSM. Table 4 shows the experiment performance comparisons of the LSM positioning via PID, PSO-PID and IPSO-PID controllers. Figure 10 shows the real time system responses. Good transient response, lower overshoot and settling time can be observed by the IPSO-PID controller. In Table 4, it can be found that the settling time is almost as the same as Table 3 by IPSO-PID control when PZT stage is fixed to the LSM. The increased K p with decreased K d by IPSO-PID control consolidates the parameters tuning mechanism in real time under the consideration of stiffness and damping effects. Table 4. Experiment performance comparisons of the LSM by a load of PZT actuated stage with 0.256 Kg

PID PSO-PID IPSO-PID

KP

Ki

Kd

M.O. (%)

0.125 0.0553 0.0797

0.05 0.2871 0.0193

0.9 0.2508 0.8514

9.476 1.842 1.679

TS (ms) 338 378 225

J IAE 1589324 1271798 1252733

Fig. 9. Picture of the PZT actuated stage on top of the forcer of LSM

Fig. 10. LSM step response via PID, PSO-PID and IPSO-PID controllers

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To test the robustness of the PID, PSO-PID and IPSO-PID controllers, an injecting disturbance is generated by a shaker. The stinger of the shaker is controlled and given a sudden force to the LSM while the control parameters in Table 4 are kept the same. The LSM is disturbed at 0.5 second right after it reaches steady state. Figure 11 shows system response after the disturbance injection. Experimental results show the performance of the IPSO-PID control is better than the PID and PSO-PID ones. The PSO-PID control is not as good as the PID control during the transient response. This may be caused by that the updating velocity value and updating position are unconstrained and render the PSO-PID mechanism into local search and slow adaptation.

Fig. 11. System response for disturbance injection at 0.5 second

6

Conclusion

This paper proposes an IPSO technique for adjusting the gains of a PID controller. The new approach introduces particle position constraints so as to improve the performance of velocity updating and position updating capability. Numerical simulations and experimental tests of PID, PSO-PID and IPSO-PID control systems are compared. Experimental results show that the proposed IPSO algorithm has great speed convergence and robustness in real-time application. LSM via the dsPIC processor demonstrates the effectiveness in positioning with inertia and disturbance injection. Experiment results illustrate the proposed IPSO algorithm was shown to be effective and achieves bettering control in precision positioning. Acknowledgments. This work was supported by NSC99-2221-E-018-032 and in part by CSIST-800-V304 (100).

References 1. Fang, H., Chen, L.: Application of an enhanced PSO algorithm to optimal tuning of PID gains. In: Proceedings of 2009 Chinese Control and Decision Conference (CCDC 2009), Guilin, China, June 17-19, pp. 35–39 (2009) 2. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of IEEE Int. Conf. on Neural Networks, Perth, Australia, pp. 1942–1948 (1995)

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3. Yajima, H., Wakiwaka, H., Senoh, S., Minegishi, K., Fujiwara, N., Tamura, K.: Consideration on High-Response of a Linear DC Motor, vol. 33(5), pp. 3880–3882 (September 1997) 4. Shi, Y., Eberhart, R.: A modified Particle swarm optimizer. In: Proc.IEEE Int. Conf. Evol. Comput., Anchorage, AK, USA, pp. 69–73 (May 1998) 5. Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: Proc. IEEE Int. Conf.Evol. Comput., Washington, DC, USA, pp. 1945–1950 (July 1999) 6. Gaing, Z.-L.: A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans. Energy Convers. 19(2), 384–391 (2004) 7. Ziegler, J.G., Nichols, N.B.: Optimum Settings for Automatic Controllers. Trans. ASME 65, 759–768 (1942)

Small Form-Factor Driver for Power LEDs Powered with One Element Battery Garate Jose Ignacio, de Diego Jose Miguel, and Araujo Jose Angel UPV-EHU, School of Engineering, Alda. Urquijo. s/n, 48013 Bilbao, Spain {joseignacio.garate,jmdediego,joseangel.araujo}@ehu.es

Abstract. The present paper is focused on designing small form-factor drivers for high-power LEDs, which are fed with one battery element. Standard power supply architectures are not suitable due to the design requirements and technical restrains imposed. The study evaluates power supply systems to solve the design case analyzing their advantages and drawbacks. In doing so it is introduced new power supply architectures of constant current drivers for LEDs that improve the performance of standard ones. Keywords: Power supply, driver, LED, battery, Green Design.

1 Introduction In the last few years LED lighting technology is becoming more and more popular. LEDs lighting has provided to be more efficient that the incandescent lamps, and it also requires less complicated electronics than fluorescent ballasts. Nevertheless, LED technology has certain disadvantages, but the benefits it provides are far superior, that is the reason of its penetration in many industrial sectors, like consumer electronics, automotive industry and even in military applications. The key issue in any LED lighting system is its power driver. So far, the power supply system employed conditions system performance and its energy efficiency. There are two main lines to face this technical issue; linear regulation and switched regulation [1]. The first option is employed whenever a robust solution is required and when the available room for electronics is not a constraint; this is the case of the automotive industry. Nevertheless, manufactures are introducing switched regulator architectures, as they provide more features and better efficiency, which benefits, among other issues, the thermal compensation and handling, although reliability is reduced. On the other hand, unless switched regulation introduces electromagnetic interferences [2], it is suited whenever small size and efficiency are required over wide-range power source voltages. Based on the article reference framework, the present work is entitled to research, and design, switched power supply architectures for power LEDs that meet the following technical requirements: - High efficiency, (Green Design). - Small form-factor, around 20 mm diameter. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 525–532. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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- One Lithium element power source. - Constant LED drive current without large capacitor. - Constant current drain from battery without large capacitors. - Polarity reversal protection Fig. 1 illustrates the block diagram of target application that fulfils the enumerated requirements. The figure represents consumer electronics torch; the mechanical arrangement is only for didactic purposes, and it does not mean to be the best design approach but it is a widespread arrangement.

(a)

(b)

Fig. 1. (a) Block diagram of a power LED torch feed with a one battery element. (b) Detail of the electronics dimensions (courtesy of Barbolight S.A.).

2 State of the Art Before starting to describe our proposal, it is enlightened perform a briefly study of the following issues, for they help to understand and characterize the technical challenges designing small form factor drivers for power LEDs fed with one battery element: - Power LEDs characteristics. - Battery specifications and types. - Protection electronics and thermal balance. - Power converters architectures. Power LEDs. To illustrate the technical requirements that power LEDs impose, it has been taken as reference the Seoul LED model W724CO. Fig. 2 and Table. 1 summarizes its characteristics and parameters. The data that provides Fig. 2 and Table 1 show that LED forward voltage is a function of the forward current, consequently, to guaranty a uniform luminous flux it is required an average constant led current over time. This current is discontinuous or continuous, but not constant, for it has ripple, so filtering capacitors are required to smooth current discontinuities or to reduce the ripple range. Besides, many manufacturers include Zener diodes to protect the LED against ESD, (electrostatic discharge), which limits the range of feasible power supply architectures.

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Fig. 2. W724CO de Seoul, VF = 3,6 V@2,8 A, ILED nominal = 1,4 A, ILEDmax = 2,8 A [3]. Table 1. Electrical characteristics at IF=2,8 A and TA = 25ºC, and absolute maximum ratings of model W724CO of Seoul [3].

Parameter

Symbol

Forward Current

IF VF VF@IF=1400 mA Pd Rθ Tj Topr

Forward Voltage Power Dissipation Thermal Resistance Junction Temperature Operating Temperature

Min. -

-40

Value Typ. 2800 3,6 3,3 11,8 3 140

Max. 4,2 -

+85

Unit mA V V W ºC/W ºC ºC

Battery. Batteries could be primary or secondary, i.e., not rechargeable or rechargeable, respectively [4]. Due to the characteristics of the target application; voltage supply level, size and weight; the battery technologies more suitable, among others, are the following: - Niquel-Metal-Hydrite (NiMH) and Niquel-Cadmium (NiCd), both require fuse. - Ion-Lithium and Ion-Lithium-Polymer, both need a protection circuit and fuse. No matter the type, their basic equivalent circuit is made of its internal resistance, RIN, usually of low-value, and depends on the technology, tenths of milliohms for 1 Ahour capacity Ion-Lithium battery. It also includes a protection circuit, mandatory for safety requirements, which is made of the resistance of the fuse and a couple of Mosfets. Thus, batteries have a maximum current drain pulsed and discontinuous, overcoming these ratings or operating at discontinuous current will introduce unacceptable losses in their equivalent internal resistance, reducing the energetic efficiency of the electronics. To illustrate this behaviour battery manufacturers provide cell voltage as a function of the discharge capacity, Fig. 3.

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Fig. 3. Detail of the discharge capacity versus voltage of a LG Chem Ion-Lithium battery model ICR18650 with an internal resistance 80 mΩ and 45 g weight.

Protection circuits and thermal requirements. The family of battery cells used in the application conditions the amount and type of protection required. Unless, all the batteries require a fuse, (better if resettable), and some electronics to avoid polarity inversion; Lithium type requires an under-voltage protection circuit or low battery unplug circuit. Besides, as the power LED generates a substantial amount of heat it is mandatory to include a high temperature disconnection circuit, typically over 60ºC. Power converters. Being LED lighting technology widespread the manufactures of IC power converters have developed many architectures to help designers and application integrators. The key issues to search for are; high current drive to feed the LEDs properly, type of converter, voltage operation to manage low-battery levels, and high-switching frequency to reduce the size of inductors and capacitors. The following table provides selection of power converters that may be suitable for 1,4 A LED lighting system with a battery voltage range between 2,4 V and 6,6 V. The data show that there are not specific ICs that cover all the requirements; high power, low voltage and small size, not to mention temperature shutdown and polarity reversal protection. Table 2. Selection of commercial power converters for a 1,4 A LED lighting systems with a battery voltage range between 2,4 V and 6,6 V. Manufacturer ON semicon. Intersil Richtek Texas Instru. Intersil Zetex Linear MPS Analog Devices Texas Instru.

Model NCP3065 ISL97801 RT8271 TPS62110 EL7515 ZXLD1322 LT3757 MP1517 ADP1111-5 TPS63000

Type Buck Boost/buck Sept-down Sept-down Step-Up Step-Up SEPIC Boost Boost Buck-boost

IMAX (A) 1,5 1 2 1,5 0,65 0,7 2 4 1,5 1,2

Voltage range (V) Freq. (kHz) 3 - 40 250 2,7 - 16 4,75 -24 1200 3,1 - 17 1400 1,8 - 13,2 1200 2,5 - 15 2,9 - 40 1000 2,6 - 25 2- 30 72 1,2 - 5,5 -

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3 Objective The main objective of the research work is: present and describe new power supply architectures for high power LEDs feed with one battery element and reduced dimensions, which have constant input and output current. Doing so, it is guarantied maximum battery energetic efficiency and, at the same time, constant LED luminous flux.

4 Technical Alternatives Based on the characteristics of target applications it has been analysed four main power supply architectures and its variants [5]. Buck converter (VOUT
Fig. 4. Buck converter power supply alternatives with VOUT
Boost converter (VOUT>VIN). In order to manage supply input voltages lower than the output voltage, it is possible to employs Boost configuration. The basic architectures are represented in Fig. 5 The main drawback they present is that, in spite off the input current remains continuous, the current trough the LED does not. Also, the second circuit of Fig. 5 has the thermal contact in the positive pole of the battery. Buck-Boost or Inverter (|VOUT|>or<|VIN|). The reasoning line leads to the inverter configuration, Fig. 6, consequently; input voltage range is not a restraint as the output voltage could be greater or lower [6]. On the other hand, the input and output currents are discontinuous. Moreover, the first alternative requires a P-type switched, meanwhile; the second one, which has a N-type one, has its thermal contact attached to the positive pole of the battery.

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(a) VOUT
(b) |VOUT|> or <|VIN|

Fig. 5. Boost converter power supply alternatives with VOUT>VIN.

Fig. 6. Inverter power supply alternatives with |VOUT| > or < |VIN|.

Fig. 7. Bi-phase inverter and current waveforms.

Fig. 8. Bi-phase inverter with constant LED current and current waveforms.

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Bi-phase Inverter. All the previous architectures are feasible, but overcome their flaws lead to introduce the architecture represented in Fig. 7, a bi-phase inverter. With the bi-phase arrangement, the input and output currents are constant. The Fig. 7 represents the basic architecture and its current waveforms. This circuit could be further improved with the schematic represented in Fig. 8. The current waveforms show that the LED current becomes truly constant. The main drawback of the circuit is that this architecture requires two inductors, which occupy PCB area, unless it is possible to reduce inductors value and size increasing the switching frequency.

5 Results and Conclusions In order to verify the behaviour of the technical architectures proposed, the inverter has been tested. Fig. 9 shows an example of one simple inverter made of discrete components. The waveforms provided show that, in spite of the inductance current, I(L1), is continuous, the input and output currents, I(D1), are discontinuous. It is also provided the efficiency figure, approximately a 75%. This value of efficiency could be further improved replacing the Schotcky diode by a Mosfet. Consequently, it seems adequate to introduce a new driver architecture to cope with the technical issues that standard ones left unsolved: the bi-phase inverter.

Fig. 9. Discrete inverter architecture, top layer PCB implementation and waveforms at nominal battery voltage for CREE power LED model DXEWTH.

The Table 3 compares the small form factor power LED drives identified in the research work as alternatives. It also summarizes the research results that are summarized in the following conclusions.

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Despite Buck converter is commonly used in drivers for power LEDs battery powered, it has some drawbacks due to its basic architecture: the LED driver current is discontinuous, and, for input voltages lower than the output voltage, the LED luminosity starts to decay as the converter works out of regulation. Meanwhile, Boost converters allow managing input voltages below the output voltage range, at expenses of discontinuous led current and an eventual increase of the input voltage above the output that leads the driver out of regulation. Inverters, on the contrary, could manage input voltages higher or lower than the output, but have discontinuous input and output currents. As long as the basic converters architectures do not provide continuous currents, it is presented new architectures that meet all the requirements: the bi-phase inverter and its variant that, theoretically have a truly constant LED current, instead of simply continuous. Unless, it may appear that the bi-phase inverter is the best alternative, it requires two inductors, which, eventually, will increase the required PCB area to develop the driver. Furthermore, the bi-phase architecture evaluated, instead of standard PFM or PWM control schemes, requires a variable frequency control that will be describe in future works. Table 3. Summary of basic power LED driver architectures powered with one battery cell. Type Buck Buck variant Boost Boost variant Inverter Inverter variant Bi-phase inverter Bi-phase inverter alternative

Body connexion (-) (+) (-) (+) (-) (+) (-) (-)

Switch

IOUT

P N N N P N P

continuous continuous discontinuous discontinuous discontinuous discontinuous continuous

P

constant

IIN

VOUT / VIN

discontinuous VIN>VOUT discontinuous VIN>VOUT continuous VIN<|VOUT| discontinuous |VIN|><|VOUT| continuous |VIN|><|VOUT| continuous

|VIN|><|VOUT|

References 1. Arbetter, B., Erikson, R., Maksimovic, D.: DC-DC converter design for battery-operated systems. In: Proceeding of IEEE Power Electronic Specialist Conference, vol. 1, pp. 103–109 (1995) 2. Montrose, M.I.: Printed circuit Board Design techniques for EMC Compliance. IEEE Press, Piscataway (1996) 3. Seoul Semiconductors, W724C0 Datasheet, Rev. 03 (September 2008), http://www.ZLED.com 4. Saft Rechargeable Battery Systems, Rechargeable Battery Systems Handbook, Available from (2008), http://www.saftbatteries.com 5. Erikson, R.W.: Fundamentals of Power Electronics, 1st edn. Chapman and Hall, New York (1997) 6. Sahu, B., Rincon-mora, G.A.: A Low Voltage, Non-Inverting, Dynamic, Synchronous Buck-Boost Converter for Portable Applications. IEEE Transactions on Power Electronics 19(2), 443–452 (2004)

The Empirical Analysis of the Impact of Industrial Structure on Carbon Emissions Mei Liao and Tianhao Wu Economics and Management School, Beijing University of Technology Beijing, China [email protected], [email protected]

Abstract. China’s carbon emissions are not small for many reasons such as industrial structure, energy consumption structure, etc. First, this paper, from the perspective of industrial structure, applies cointegration test to analyze the factors of China’s long-term growth in carbon emissions. Then we carry out an empirical analysis of China’s carbon emissions and the three industries from 1987 to 2008 based on VAR model. The results show that there exists a longterm equilibrium relationship and short-term correction mechanism between the three industries and carbon emissions. China’s rapid development of three industries promotes of increased carbon emissions, where service industry is the key factor. We recommend that the characteristics of industrial stages of development should be considered when the industrial emission reduction policy is formulated. Keywords: industrial structure; carbon emission; co-integration test; VAR model; empirical analysis.

1 Introduction Carbon is one of the important mineral resources in human life. However, while blindly focusing on the rapid economic development, the degradation of the environment has been ignored. In recent years, people began to realize that changes in the environment, so they began to strive to enhance the economic development while not polluting the environment. In 2003, the first mention of a low carbon economy appeared in the release of former British Prime Minister Tony Blair's UK energy white paper Our energy future: creating a low carbon economy. In 2007, President Hu Jintao clearly stood for low carbon economy at the 15th APEC leaders meeting. With China's rapid economic development, China's greenhouse gas emissions, increase from 1.425 billion tons in 1978 to 6.731 billion tons in 2008, which is an increase of 4.72 times, and greenhouse gas emissions are still climbing, making China into the forefront of the world's greenhouse gas emissions. As a responsible big country, China has been proactively involved in international environmental protection cooperation, signing Framework Convention on Climate Change in 1992 and Kyoto Protocol in 1998; in the Copenhagen summit in 2009, although with no reduction obligations, China still committed herself to reducing carbon intensity by 40% to 45% in 2020 with 2005 as the base year. As one of the most important features in the M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 533–540. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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country's economic development , industrial structure includes the allocation and inter-constrain of national or regional labor, capital, natural resources and material resources in the various sectors of the national economy , reflecting the country's economic development level, internal dynamism and potential growth ;it is particularly important and urgent to analyze the impact of three industries on the carbon emissions and provide some ideas for the policies of industrial emissions reduction from the perspective of industrial structure.

2 The Review of Foreign and Domestic Research Literature Abroad, taking France, Germany, Japan, Britain and the United States as the study objects,Theodoros Zachariadis (2007) [1], using the VAR model, finds out that energy consumption is the Granger cause of economic growth ,and vice versa. Kojo Menyahaand Yemane WR (2010) [2] applies VAR model to analyze carbon emissions, renewable energy, nuclear energy and real GDP of the United States between 1960-2007; he finds out that nuclear energy is the one-way Granger cause of carbon emissions.Theodoros Zachariadis (2010) [3] establishes the autoregressive distributed lag model based on macroeconomic variables, prices, and weather to predict the electricity consumption in Cyprus. In China, Liu Lancui (2006) [4] applies LMDI to analyze the characteristics of changes in China's industrial carbon emission from 1999 to 2003 .The results show that changes in industrial structure play a facilitating role in slowing the growth rate of carbon emission. Wang Feng, Wu Lihua and Yang Chao (2010) [5], using logarithmic mean Divisia index decomposition method, conclude that the main positive drivers of China's CO2 emission growth from 1995 to 2007 are per capita GDP, economic structure, and the household’s average annual income; the negative drivers are energy intensity for the production sector, transport, and energy intensity of living. Ba Shusong and Wu Dayi (2010) [6], based on VAR model and impulse response function, establish CO2 emission reduction cost computing model and analyze the impact of energy consumption and 4 main kinds of energy on output, investment and employment, and conclude that the implementation of fuel conversion policy is a good reduction policy. Yan Han Xiang (2010) [7], using the VAR model, analyzes the relationship among carbon emissions, energy consumption, industrial structure, and economic growth in China’s eight integrated economic zones; study shows there exists a long-term equilibrium relationship among carbon emissions and energy consumption sources, transport, household consumption, corporate energy consumption respectively in China’s eight integrated economic zones. Based on the research above, we sum up that industrial structure is one of the important factors of influencing carbon emissions; the development of China's industrial structure plays a facilitating role on the carbon emissions; multivariate analysis based on VAR model is an important approach for the in-depth research of carbon emissions. Therefore, this paper, applies the VAR model to analyze the long-term and short-term relationship between three industries and carbon emissions, verifies the effect of China's industrial development on carbon emissions, and provides a theoretical basis for the formulation and revision of industrial emissions policies.

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3 Error Model Construction and Cointegration Test In the establishment of long-term model of the industrial structure and carbon emissions, we select carbon emissions growth rate as the explained variable, the primary industry growth rate, the secondary industry growth rate and tertiary industry growth rate as the explanatory variable. First, before the empirical analysis, we verify the correlation between variables. This is a simple and rough approach to verify the correlation between the variables. The final results are presented below: Table 1. Results of the correlation between variables. fgdp

sgdp

tgdp

ct

fgdp

1

sgdp

0.225624

1

tgdp

-0.39212

0.525839

1

ct

0.001453

0.77484

0.550957

1

By putting three independent variables into the right side of model for modeling, we find out that there is autocorrelation and fgdp variables can not pass through the variable’s t test. So by re-modeling and with several adjustments, the final model is presented as follows: ct t = −1.2831 + 0.2943ct t −1 + 0.4134 sgdp t + 0.0754tgdp t (3.3296)

(4.0484)

(0.0754)

The equation passed the F test, and DW = 1.9569, there is no autocorrelation, and the model of residuals are steady, so the model passed the test. In the following we establish error correction model to analyze short-term factors of carbon emissions growth rate .We make the residual series of long-run equilibrium model as the error correction model. The error correction model is established in the following:

Δctt = 0.2537 + 0.6209Δsgdpt − 0.6063ecmt (8.8951)

(-4.0859)

Equation passes the test and the autocorrelation does not exist. In equation, differential item reflects the influence of short-term fluctuations. Through short-term correction model, we can see that short-term changes of carbon emissions growth can be divided into two parts: the first part is a fluctuation of growth in the secondary industry in short-term and the second is the impact of deviation from the long-run equilibrium. The coefficient of the error correction term ecm reflects the adjustment of a deviation from the long-run equilibrium. That the estimate of the error correction term is -0.6063 in the model indicates when short-term fluctuations deviate from the longterm equilibrium, the adjustment will bring the non-equilibrium state back to equilibrium state at the rate of -0.6063 . In short-term correction model, differential item of the tertiary industry does not pass the test, which shows that in the short term fluctuations in carbon emissions are only related with the secondary industry, and has little to do with the tertiary industry .The tertiary industry only has small effects on the carbon emissions in the long term.

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4 VAR Model Construction and Empirical Analysis 4.1 VAR (p) Model Theory VAR (p) model is generally expressed as:

yt = φ1 yt −1 + " + φ p yt − p + θ xt + ε t t = 1, 2, " , T Where, yt is for the endogenous k-dimensional vector, xt is exogenous d-dimensional vector, p is the lag order,

φ1 ," , φ p is kxk dimensional matrix, θ is kxd dimensional

matrix are the coefficient matrix, ε t is for the k-dimensional random error vector, and T is the number of sample. The determination of the structure of VAR model: in the choice of lag order, on the one hand, make the lag periods of p large enough to eliminate the autocorrelation that exists in error item, making parameter estimation of noncompliance; on the other hand, the excessive lag periods will lead to a significant reduction in degrees of freedom, affecting the validity of model parameter estimators. 4.2 Model Stability Test The test of equations uses unit root test, usually the ADF unit root test method. After the test, 4 initial series are non-stationary, after the first difference series are stationary in Table 4, and then establish the VAR with non-stationary variables. Model test results showed all the roots are less than 1, and are located within the unit circle; it indicates VAR model is stable, and there exist long-term equilibrium relationship among variables. Table 2. Augment Dickey-Fuller unit root test results ADF

critical value

critical value

variable

statistic

˄1%˅

˄5%˅

ln(ct)

-3.07

-4.30

-3.57

(1,1,2)

non-stationary

D˄ln(ct)˅

-2.90*

-3.67

-2.97

(1,0,2)

stationary

Test form

conclusion

ln(fgdp)

-3.35

-4.30

-3.57

(1,1,2)

non-stationary

D˄ln(fgdp)˅

-5.30

-3.67

-2.97

(1,0,2)

stationary

ln(sgdp)

0.79

-4.30

-3.57

(1,1,2)

non-stationary

D˄ln(sgdp)˅

-3.69

-4.32

-3.58

(1,1,2)

stationary

ln(tgdp)

-2.46

-4.30

-3.57

(1, 1,2)

non-stationary

D˄ln(tgdp)˅

-3.73

-4.32

-3.58

(1, 1,2)

stationary

Note: In the test form (c, t, k), 1 and 1, respectively, represents with the constant term and with the trend term; 0 represents without the constant term and without the trend term, k represents lag periods, D represents the first difference operator, the item with * indicates a significant level of 10% .

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4.3 Impulse Response In this paper, the generalized impulse method (Generalized Impulses) is adopted to carry out the impulse the response function analysis to lnCT, lnfgdp, lnsgdp and lntgdp, to overcome the variable impulse results caused by the different order of variables in Cholesky impulse response method. The results are presented from Figure 2 to Figure 5. Among them, the horizontal axis indicates the lag period is in years, and the vertical axis indicates the rate of change.

Fig. 1. Response of carbon emission to the impact of the three industries

Figure 1 shows, when each of the three industries in the current period gives a positive impact of a standard deviation on carbon emissions, respectively, the carbon emissions show a significantly positive effect, indicating that industrial development promotes increased carbon emissions. Decompose various stages, in the period 1and 2, the three industries are on the rising stage , industry has the highest starting point ,whose line is above three lines , indicating that it is the major factor in the increase of carbon emissions; the amplitude and volatility of service industry are the largest , industry follows, the agriculture has small ups and downs, indicating that carbon emissions respond to service industry most sensitively so the carbon emissions of service industry should be regulated.

Fig. 2. Response of agriculture to the impact of carbon emissions, industry and service industry

Figure 2 shows, after carbon emissions, industry and services in the current period give an positive impact of one standard deviation to agriculture, the carbon emissions has been positively promoting agriculture; industry and services start to suppress agriculture, but gradually decrease, reaching the zero point in period 2 and 3 respectively,

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and the positive effects continued to increase to reach the highest point in period 6. The results show that the three industries compete for allocation of the resources of human, capital and technology in the initial phase, and there is a weakening effect, after the automatic adjustment of industrial development and policy adjustments the mutual promotion is achieved in the later stage; which, relatively speaking, response of agriculture to industry is sensitive than to service industry, indicating that agriculture has more closely links with industry.

Fig. 3. Response of industry to the impact of carbon emissions, agriculture and service

Figure 3 shows, after carbon emissions, agriculture and services; give the positive impact of one standard deviation respectively to industry in the current period, the carbon emissions has been positively promoting the industry. Among the three industries, from the period 1 to 1.5, agriculture has the reverse effect on industry, but gradually weakened ,after it passed the zero point in period 1.5, and gradually increased, showing a long-term positive effect; this indicates that industry respond to agriculture and is more sensitive to services; industry is inhibited by agriculture and promoted by service in the initial stage; agriculture and service jointly promote industrial development at a later stage, reflecting the coordinated development between industries.

Fig. 4. Response of service to the impact of carbon emissions, agriculture and industry

Figure 4 shows, after carbon emissions, agriculture and industry give the positive impact of one standard deviation respectively to service in the current period; the carbon emissions have been positively promoting the services. Among the three industries, agricultural reverse effect on industry weakens gradually, it passed the zero

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point in period 1.5 ,then a sharp rise appears, bringing the positive effects, showing an overall long-term positive effect on promoting the services; industry shows an overall long-term positive effect on promoting services ,indicating that services response more sensitively to agriculture; service is inhibited by agriculture and promoted by industry in the initial stage; agriculture and industry jointly promote service’s development at a later stage, reflecting the ordered development between industries.

5 Conclusions and Recommendations Studies above show that there exists a long-term equilibrium relationship and shortterm error correction mechanism among carbon emissions, agriculture, industry and services. Three industries have different effects on carbon emissions, based on which we build our conclusions and make appropriate policy recommendations. From the impulse response analysis, it suggests that industrial development contribute to carbon emissions increase. At first the industry is the main factor of carbon emission increase, later the service industry, indicating the management of industrial carbon emissions achieves good results, so the management of service’s carbon emissions should be given due attention; carbon emission is the long-term positive effect of agriculture, industry and services, reflecting the "high-carbon" features of China's economic development; there exists mutual interaction among the three industries, at first stage of agricultural and industrial, mutual inhibition among agriculture, industry and services, then the three industries develop coordinately in the late period, indicating the positive role of industrial self-regulation and policy control. According to the results of empirical analysis, the recommendations are made in the following.Firstly, China should develop new energy and low carbon emission energy. China’s energy structure has been featured as high-carbon structure, and the secondary industry is also the high energy consumption industry, and the coal mining, steeling, building materials cement, electricity industry are industries with high emissions of carbon, so promoting the development of new energy technologies and capital investment, and even the introduction of foreign technology are the necessary strategies to solve the carbon emissions from the root.Secondly,China should impose carbon tax to inhibit carbon emissions. The introduction of carbon tax to curb the emissions of heavy polluting enterprises should not be simply based on the poit-time carbon emissions, but should be based on an accumulative quantity. The heavier carbon emissions accumulated are, the more carbon tax should be levied on those enterprises per unit than enterprises with much lower emissions.Finally,China should be strict with the establishment of high carbon enterprises. Although foreign investment plays an important part in China’s rapid development, carbon leakage thus caused and the consequent high pollution and damage to the environment is the price we have to pay in the long term, so in the process of attracting foreign investment we should focus on the price/performance ratio between value and environment.

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References 1. Zachariadis, T.: Exploring the Relationship between Energy Use and Economic Growth with Simple Models: Evidence from Five OECD Countries. University of Cyprus. Working Paper (February 2007) 2. Menyaha, K.: Yemane WR.CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy 38(6), 2911–2915 (2010) 3. Zachariadis, T.: Forecast of electricity consumption in Cyprus up to the year 2030: The potential impact of climate change. Energy Policy 38(2), 744–750 (2010) 4. Liu, L.: Carbon emission reduction in China’s policy modeling and empirical research. Science and Technology University, Anhui (2006) 5. Wang, F., Wu, L., Yang, C.: China’s economic development drives the growth of carbon emissions factors. Economic Research (2), 123–136 (2010) 6. Ba, S., Wu, D.: Carbon emissions from energy consumption and economic growth. Economic and Management Research (6), 1–11 (2010) 7. Xiang, Y.H.: Carbon emissions in eight comprehensive economic zones in China. National Cheng Kung University, Taiwan (2010)

An Ant-Routing Algorithm for Wireless Sensor Networks Pengliu Tan1,2 and Xiaojun Deng 3 1

Institute of Internet of Things, Nanchang Hangkong University, china 2 School of Software, Nanchang Hangkong University, china 3 College of information technology, Nanchang Hangkong University, china [email protected]

Abstract. A wireless sensor network (WSN) has resource constraints, which include a limited amount of energy, short communication range, low bandwidth, and limited processing and storage in each node. In this paper, an antrouting algorithm is presented for wireless sensor networks. In the algorithm, ants are guided to choose different paths by adjusting heuristic factor. Meanwhile, the weights are dynamically coordinated in the probability function. The evaporation coefficient of pheromone is improved by utilizing the idea of simulated annealing algorithm. In order to prevent local optimization, it introduces a reward and punishment mechanism into the pheromone update process. The simulation results show that the algorithm is better than the other current typical routing algorithms. Keywords: ant colony algorithm, routing, wireless sensor networks.

1 Introduction With the development of wireless communications, integrated circuits, MEMS and sensor technology, WSN has aroused extensive attention [1]. WSN consists of sensor nodes with limited processing and computing resources. These sensor nodes can sense, measure, and gather information from the environment and, based on some local decision process, they can transmit the sensed data to the user. Routing protocols in WSNs differs from traditional routing protocols in several ways. The protocol should meet network resource constraints such as limited energy, communication bandwidth, memory, and computation capabilities. The traditional routing schemes are not suitable for WSN. In WSN, minimization of energy consumption is a major performance criterion to provide maximum network lifetime. To maximize network lifetime, the optimization of WSN routing is regarded as a combinatorial optimization problem. Many researchers have recently studied the collective behavior of biological species such as ants to provide a natural model for combinatorial optimization problems [2]. Ant colony optimization (ACO) algorithms which simulate the behavior of ant colony have been successfully applied in many optimization problems, such as the asymmetric traveling salesman [3], load balancing in telecommunications networks [4] and data aggregation in WSN [5]. At present, some ant-colony-based routing algorithms have been proposed for WSN. In [6], Singh applied ant colony algorithms for Steiner Trees to routing in

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 541–548. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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WSN. However, Singh did not consider the energy consumption. To meet the requirements of sensor networks, Zhang improved the basic ant-routing algorithm in [7]. Zhang proposed three ant-routing algorithms named SC, FF and FP. The ant algorithms worked quite well overall, with each having some advantages in terms of performance: SC is energy efficient, FF has shorter delays, and FP has the highest success rates. The authors in [8] presented an energy efficient ant based routing algorithm for WSN (EEABR), which was based on the Ant Colony Optimization metaheuristic, to maximize the lifetime of WSN. EEABR used lightweight ants to find routing paths between the sensor nodes and the sink nodes, which were optimized in terms of distance and energy levels. These special ants minimized communication loads and maximized energy savings, contributing to expand the lifetime of the wireless network. But the ant-routing algorithms in the above the literatures could result in the question of local optimization. In this paper, we present an ant-routing algorithm (Called ARA algorithm) for WSN. In ARA algorithm, ants are guided to choose different paths by adjusting heuristic factor. Meanwhile, the weights can be dynamically adjusted in the probability function. The evaporation coefficient of pheromone is improved by utilizing the idea of simulated annealing algorithm. In addition, it introduces a reward and punishment mechanism into the pheromone update process. The algorithm can not only prevent local optimization, but also balance the energy consumption and prolong the whole network lifetime.

2 ARA Algorithm In this paper, ants are divided into two kinds: forward ants and backward ants. Forward ants have life and can avoid searching the bad path and energy consumption. Forward ants are responsible to gather the nodes information, update the local pheromone between the nodes and deliver the messages to the backward ants. Backward ants go back following the original route and update the global optimal path according to the information left by forward ants. Ants have memory ability to hold the information of the passed nodes. The algorithm has the following essential points: 1) Adopt a set of ant individuals as human agents to search the optimal path from the source node to the destination node parallelly; 2) Combine local search with global search and adjust the evaporation coefficient of pheromone dynamically to avoid premature convergence; and 3) Forward ants and backward ants perform different update algorithms for pheromone respectively, and establish the routing collaboratively. In ARA algorithm, any ant sets out from the source node and completes a trip. The data is transmitted from the source node to the sink node along the optimal path. At time t, the k-ant`s transition probability from node i to node j as follows:

Ρ (t ) = t ij

τ ijα (t )η ijβ (t )

∑τ α (t )η β (t )

i∈Bk

is

ij

.

(1)

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Hereinto, Bk is the set of neighbor nodes of node i, τij is the intensity of the pheromone of the path ij, ηij is the heuristic factor between node i and j. ηij is determined by formulas (2)-(4).

η ij = ξ ij + ψ ij .

λ

ξ ij =

ψ ij =

.

d ij

∑

(2)

Ej n∈N i

En

(3)

.

(4)

Hereinto, λ is an adjustable coefficient, equation (3) denotes the distance heuristic factor, equation (4) is the energy level factor, Ej is the energy level of node j, and Ni is the set of the neighbor nodes of node i. In this way, the node with higher energy level will be selected as the next hop node more possibly, so the energy consumption of the whole network will be balanced better. The relative importance of the pheromone and the heuristic factor is determined by the parameter α and β respectively. In the traditional ant colony algorithm, the parameter α and β are constants, which has little impact upon path selection in the smallscale network. But the path optimization is very important for WSN with numerous nodes deployed randomly. The parameter α and β will have a significant effect on the results of path selection. In traditional ant colony algorithm, each path is initialized with the same pheromone, so the pheromone coefficient plays a secondary role and cannot distinguish which is the next hop node in the first cycle. But the distance factor plays a major role on the selection of the next hop node, and the node with shorter distance to the current node will be selected as the next hop node more probably. In ARA algorithm, the parameter α and β can be adjusted dynamically, and the formula is as follows:

⎧3i /100 ⎩ 2.5

α =⎨

⎧3 − 1.5i /100 ⎩2

β =⎨

0
(5) .

0
(6) .

Hereinto, i is the number of cycles, n is the critical point of i, and Nc is the largest number of cycles. Before nth cycle, the distance factor plays a major role, while the pheromone is not obvious. After nth cycle, both pheromone and distance factor play the same important roles. Before nth cycle, α is incremental linearly, but β is degressive linearly. After nth cycle, both α and β are constants.

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Pheromone evaporation coefficient ρ has some impact on the global path search. If a node has not been searched, its pheromone will decrease to zero, which will weaken the global search ability. If ρ is too small, the searched node will be chosen more probably again, which will also reduce the global search capability. If ρ is too large, the convergence speed will be dropped. If ρ keeps constant, this will weaken the search ability, and some nodes may be difficult to find, so that the solution may not be the global optimal solution. In this article, we introduce the idea of simulated annealing algorithm [9] to adjust the pheromone evaporation coefficient ρ dynamically. The evaporation coefficient ρ is adjusted according to formula (7).

⎧ ρ ( t )T

ρ ( t + 1) = ⎨

⎩ ρ m in

ρ ( t ) > ρ m in ρ ( t ) ≤ ρ m in

(7) .

Herein, T is a constant; ρ is a function of t, when ρ decrease to ρmin, ρ keeps constant. To resolve the problem of local optimization, we introduce the reward and punishment mechanism into the pheromone update. During the search process, if the path is better than the ever optimal solution, appropriate incentives will be offered, or punishment. Reward and punishment mechanism for the pheromone update is as follows:

τ ij (t + 1) = (1 − ρ )τ ij (t ) + Δτ ij (t , t + 1) + Δτ ij∗ (t , t + 1) .

(8)

m

Δτ ij (t , t + 1) = ∑ Δτ ijk (t , t + 1) . i =1

(9)

Where ρ is the pheromone evaporation coefficient, ∆τijk(t, t+1) is the pheromone left by the ant k when it goes through path (i, j) from time t to t+1. If ant k goes through path (i, j), ∆τijk(t, t+1) is determined by the formula (10), otherwise it is zero.

Δτ (t ,t + 1) = k

ij

Q . d

(10)

ij

Because the basic ant colony algorithm could cause a positive feedback phenomenon, local optimization and injustice will be result in. To overcome the shortcomings, ARA algorithm adopts the evaluation function of positive feedback and negative feedback. The evaluation function is shown as formula (11).

Δτ ij∗ (t , t +

1)

= ω

f (t + 1) − f max( b ) . f (t + 1) + f mav( b )

(11)

Where (b = 0, 1, ..., t), ω is the adjustable parameters, f(t) is evaluation function of a node, and it is determined by formula (12). f

(t )

=

1

c1 c2 [ Q1(t )] [ Q2(t )]

.

(12)

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Herein, c1 and c2 are the weights of the performance metric. Q1(t) and Q2(t) are the energy consumption and delay from the source to destination node respectively.

Q1(t

)

= a

∑d

n ij

.

(13)

ij

Q2(t )

=

m

HC. ∑ i 1

(14)

=

Thereinto, a is a constant, n is larger than 2 and less than 4, but n is usually close to 4, and dij is the path length. HC is the total number of hops from source node i to sink, the smaller the hops, the less the energy consumption and the shorter the time when the data is transmitted from the source node to the sink.

3 Routing Process Suppose there are n nodes in wireless sensor networks, node location is known, and destination node broadcasts its position in the network. The routing process from source to destination node is shown as follows: 1) Initialize the pheromone of all nodes, form the initial pheromone matrix, and put the source node into the taboo table. 2) Place M ants at the start point; the information carried by the forward ants is the current hop count and the id of nodes passed by. 3) Each ant uses roulette wheel selection method to choose the node according to the probability determined by the formula (1), and puts the chosen node into its taboo table. 4) Repeat step 3) until all ants reach the destination node, or until the number of cycles reaches the maximum number. If an ant does not reach destination node within the limited number of cycles, the ant is marked as death. 5) Backward ants calculate the length of the possible paths, and save the optimal path. 6) Update the local and global pheromone according to formula (8), (9) and (10). 7) Adjust the weight α and β according to formula (5) and (6), and adjust evaporation coefficient according to equation (7). 8) Repeat step 2) to step 7) until the optimal solution is found, or until the number of cycles reaches the maximum number.

4 Simulation For wireless sensor networks, there is no uniform standard to evaluate routing protocols. In general, the following three parameters are used. 1) Ec: the total energy consumption arising from routing establishment. Ec is determined by the formula (15).

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n

Ec = ∑ e j .

(15)

j =1

Herein, ej is the total energy consumption node j. 2) Average delay: It is the average delay when the packet is sent from the source to sink node. 3) Shortest path: The shortest path when ants traversal the whole network from source. We use OMNET++ 3.3 platform to assess the performance of ARA algorithm. In order to discover the relationship between the performance and network scale, we generate ten simulation scenarios with 10, 20, 30, …, and 100 nodes respectively. The nodes are randomly distributed in the region with size 200×200m2. Sink node is located at point (100m, 200m). Its initial energy is 0.2J. d0 is equal to 30 meters. The size of message packet is 64Byte. In our experiments, α, β, ρ, c1, c2, Q, Nc (the maximum number of cycles) and steplimit (the largest step limit) are set to 1.0, 0.8, 0.3, 0.1, 0.2, 100, 300 and 100 respectively. The simulation results are shown in Fig. 1, Fig. 2 and Fig. 3. Fig. 1 shows that the total energy consumption arising from path establishment when applying the three routing algorithms: ARA, EEABR and ACO respectively. We can see that ARA algorithm has the least energy consumption than the other algorithms. Fig. 2 shows that the average delay varies with the network size. We can see that ARA algorithm is better than the other two algorithms in average delay. We repeat 20 experiments for each of the three algorithms. It can be seen from Fig. 3 that ARA algorithm has the shortest path and it has the least fluctuation in the three algorithms. ARA algorithm is more stable than the others. The above Experiments show that ARA algorithm is very robust. It enhances the energy efficiency and maximizes the network lifetime.

Fig. 1. Energy consumption

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Fig. 2. Average delay

Fig. 3. Shortest path

5 Conclusion In this paper, an ant-routing algorithm (Called ARA algorithm) is presented for wireless sensor networks. In the algorithm, ants are guided to choose different paths by adjusting heuristic factor. Meanwhile, the weights can be dynamically coordinated in the probability function. The evaporation coefficient of pheromone is improved by utilizing the idea of simulated annealing algorithm. In addition, it introduces a reward and punishment mechanism into the pheromone update process. The algorithm can not only prevent local optimization, but also balance the energy consumption and prolong the whole network lifetime. The simulation results show that ARA algorithm is better than the other current typical routing algorithms. In the future work, we will realize the ant-routing mechanism in actual project. Acknowledgments. This work has been partially supported by National Science Foundation of Jiangxi Province under grant 2010GQS0177 and Doctor Research Foundation of Nanchang Hangkong University under grant EA200920178.

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References 1. Akyildiz, I.F., Su, W., Sankarasubramanian, Y., Cayirci, E.: A survey on sensor networks. IEEE Communications Magazine 40, 102–114 (2002) 2. Hussain, D.S., Islam, O.: Genetic Algorithm for Energy-Efficient Trees in Wireless Sensor Networks. In: Kameas, A.D., Callagan, V., Hagras, H., Weber, M., Minker, W. (eds.) Advanced Intelligent Environments, pp. 139–173. Springer, US (2009) 3. Liu, Z., Kwiatkowska, M.Z., Constantinou, C.C.: A biologically inspired QoS routing algorithm for mobile ad hoc networks. Int. J. Wire. Mob. Comput. 4, 64–75 (2010) 4. Schoonderwoerd, R., Bruten, J.L., Holland, O.E., Rothkrantz, L.J.M.: Ant-based load balancing in telecommunications networks. Adapt. Behav. 5, 169–207 (1996) 5. Liao, W., Kao, Y., Fan, C.: An Ant Colony Algorithm for Data Aggregation in Wireless Sensor Networks. In: Proceedings of the 2007 International Conference on Sensor Technologies and Applications, pp. 101–106. IEEE Computer Society, Los Alamitos (2007) 6. Singh, G., Das, S., Gosavi, S.V., Pujar, S.: Ant Colony Algorithms for Steiner Trees: An Application to Routing in Sensor Networks. In: Leandro, N.D.C., Fernando, J. (eds.) Recent Developments in Biologically Inspired Computing, pp. 181–206. IGI Global (2005) 7. Zhang, Y., Kuhn, L.D., Fromherz, M.P.J.: Improvements on ant routing for sensor networks. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 154–165. Springer, Heidelberg (2004) 8. Camilo, T., Carreto, C., Silva, J.S., Boavida, F.: An energy-efficient ant-based routing algorithm for wireless sensor networks. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 49–59. Springer, Heidelberg (2006) 9. Corana, A., Marchesi, M., Martini, C., Ridella, S.: Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm. ACM Trans. Math. Softw. 13, 262–280 (1987)

Transient Sensitivity Computations for Large-Scale MOSFET Circuits Using Waveform Relaxation and Adaptive Direct Approach Chen Chun-Jung Department of Computer Science, Chinese Culture University, #55 Hwa-Gang Road, Taipei, Taiwan [email protected]

Abstract. Transient sensitivity information is useful in circuit design community. This paper investigates transient sensitivity computations of large-scale MOSFET circuits using Waveform Relaxation (WR). A novel method for sensitivity subcircuit computation is proposed, which is called Adaptive Direct Approach. Other time-saving techniques are also proposed. All proposed methods have been implemented and tested, and the results well justify their outstanding performance. Keywords: Circuit simulation; Relaxation-based algorithms; Sensitivity computation; Waveform Relaxation.

1 Introduction Transient sensitivity information is very useful in many applications in the circuit design community. Due to the advance of electronic circuits, the scales of sensitivity simulations become bigger and bigger. So, it’s important to consider the efficiency of sensitivity computations. This paper adopts the relaxation-based algorithm, which is efficient in dealing with large-scale MOSFET circuits, to simulate transient sensitivities of large-scale MOSFET circuits. The well-known Waveform Relaxation (WR) [1] has outstanding simulation performance among relaxation-based algorithms. We choose it as the simulation algorithm. The sensitivity subcircuit calculations usually use Direct Approach [2, 3]. We find that there are many factors influencing the subcircuit computation efficiency, including the number design parameters, the sizes of subcircuits, positions of design parameters, and so on. To utilize these factors can enhance sensitivity computation efficiencies. So, we propose a new subcircuit solving technique called Adaptive Direct Approach (ADA) to take these factors into account. Beside, we also propose Selective-tracing (ST, only to simulate subcircuits affected by design parameters) and Simulation-on-demand (SOD, only to simulate subcircuits contributing to wanted outputs) to enhance the simulation speed. In timing simulation case, we utilize ITA-WR algorithm of [4]. This algorithm is a composition of ITA (Iterated Timing Analysis) [1] and WR. The principle of ITAWR is to identify feedback loops (including strongly coupled subcircuits and global feedback loops) in the signal flow graph of subcircuits and groups all subcircuits M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 549–555. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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inside adjacent feedback loops into macro subcircuits. A macro subcircuit is locally simulated by ITA (while other ordinary subcircuits are simulated by Direct Approach). ITA-WR eliminates troublesome feedback loops and raises the robustness of WR successfully. Besides ITA-WR, there exists other technique to enhance WR’s convergence property. Putting them altogether, we derive the Robust WR (RWR) [4] algorithm. RWR is just used to perform transient analysis. But we only use WR to perform sensitivity simulation. All proposed methods have been implemented to justify their advantages.

2 The Direct Approach for Sensitivity Subcircuit Calculation The traditional sensitivity subcircuit solver is Direct Approach [2, 3] (called Ordinary Direct Approach, ODA, in this paper). We will propose methods to improve ODA, so we describe it now. The simulated circuit can be represented as follows: .

(1)

F (Y (t ), Y (t ), t ) = 0 ,

where Y is the vector of circuit variables, t is the time, F is a continuous function and dot means differentiation with respect to time. In relaxation-based algorithm, (1) needs to be partitioned into subcircuits, where the ith one of which is: .

(2)

Fi (Yi (t ), Yi (t ), Ci (t ), Ci (t ), t ) = 0

One subcircuit, a, is rewritten as the abbreviated form: .

.

(3)

f ( y (t ), y (t ), w(t ), w(t ), t ) = 0 ,

where y (Yi, a sub-vector of Y) is the vector of n circuit variables in a, w (Ci, the decoupling vector) is the vector of circuit variables not in a, and f is a continuous function. Assume all (m) design parameters (circuit parameters to be adjusted in circuit design process) are in the vector D, (3) is rewritten as: .

.

(4)

f ( y (t ), y (t ), w(t ), w(t ), D, t ) = 0

Considering the transient sensitivity with respect to the jth design parameter, Dj (or d), we differentiate (4) with respect to d to have: .

∂y ∂y ∂w ∂w fy + f. + fw + f w + fd = 0 y ∂d ∂d ∂d ∂d

(5)

We apply the Trapezoidal Rule (TR) on (5) to have: . . 2 2 (6) J y sn+1 = − J w pn+1 + ( f sn + f s n + f pn + f p n ) − f d y y w w h h This is a nonlinear algebraic equation, in which, s = ∂y is the transient sensitivity ∂d ∂ w is the “input” transient sensitivity. Note that notation “n+1” means curand p = ∂d rent time point tn+1. Equation (6) is rewritten as: .

.

J y sn+1 = − J w pn+1 + On − f d

.

,

.

(7)

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in which On is the vector composed of “old” values at previous time point. This equation is used in sensitivity simulation to derive the sensitivity of a with respect to d, the jth design parameter. Considering entire sensitivities with respect to all design parameters, we have: (8) J y sˆn+1 = − J w pˆ n+1 + Oˆ n − f D , in which all symbols with hat are matrices for corresponding values with respect to all design parameters (e.g. sˆ = ∂y ∈ R n×m , is the matrix for sensitivities of a with respect ∂D to D). And, f D ∈ R n×m is a matrix, too. Following algorithm describe ODA: Algorithm 1 (ODA Sensitivity Subcircuit Computation): for(j = 1; j <= m; j++) { Calculate fDj, and Oj; Solve (7) for sn+1 with respect to Dj; }

3 The New Approach for Sensitivity Subcircuit Calculation Note that ODA computes the sensitivity matrix in the column-major fashion (Fig. 1 (a) shows this relation), which is not efficient when the number of design parameters (m) is big [5]. We present the Row-major Direct Approach (RDA) to cope this situation. For a subcircuit a, the sensitivity equation with respect to Dj can be written as: (9) J sˆ = − J pˆ + Oˆ − f y

j , n +1

j ,n +1

w

j

Dj

We move the Jacobian to the right-hand-side and extend it into matrix form: −1 sˆ = J (− J pˆ + Oˆ − f )

(10)

Assume row(A, i) takes the ith row of the A, we have: −1 row( sˆn+1 , i ) = row( J y , i )(− J w pˆ n+1 + Oˆ − f D )

(11)

n +1

y

w

n +1

D

Following algorithm represents RDA: Algorithm 2 (RDA Sensitivity Subcircuit Computation): Inverse Jy; for(j = 1; j <= m; j++) { Calculate fDj, and Oj; } for(all circuit variables, v) { i = v’s order (v is the ith circuit variable); Calculate (11) to derive a row of sensitivity; }

We compare the two Direct Approaches. ODA’s computation efforts formula is: m × ( L + R) ,

(12)

in which L is the effort for solving linear system, and R is the effort to compose the vector on the right-hand-side of (7). Also, we write the effort formula for RDA:

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(13) m× R + I + n× M , in which R is the same as in (12), I is the effort to inverse a matrix, and M is the effort to perform matrix multiplication (on right-hand-side of (11)). Equation (13) is more Table 1. Minimum Numbers of Design Parameters for RDA to be Chosen Node# Design Parameter# $

2 22

3 14

4 10

5 7

6 6

7 6

8 5

9 4

10 3

If n < 2, ODA is suggested. If n > 10, RDA is suggested.

advantageous than (12) when m is bigger. In fact, following equation states the condition that (13) is smaller: I (n) + nM (n) (14) L (n ) , in which n is the number of subcircuit nodes. Due to different implementations, effort variables are hard to state. Adaptive Direct Approach is the scheme to choose appropriate Direct Approaches. To judge whether to use RDA or ODA, we devise a heuristic method using real experimental data. Table 1 shows the criterions (number of design parameters) with respect to node numbers in subcircuits to use RDA in our simulator MOSTIME [3, 4]. We just implement ADA by using Table 1. We also use ST (Selective-tracing) and SOD (Simulation-on-demand) techniques. They check the signal flow graph of subcircuits, identify subcircuits “affected” by design parameters (ST), and subcircuits “contributing” to wanted outputs (SOD). Only “useful” subcircuits are allowed to be computed. m≥

4 The Experimental Results The proposed methods have been implemented in our circuit simulator MOSTIME [3, 4]. To reveal the algorithm’s effects more clearly, we use the simple analytic model for MOSFETs, pick meaning output nodes for circuits, and partition (some) circuits in bigger-subcircuit and smaller-subcircuit fashions respectively. The RWR algorithm is used in timing simulation in advance, and then the WR algorithm is used to simulate sensitivities. Timing and sensitivity simulations are separated. Design parameters are all width of MOSFET transistors. We simulate seven circuits whose circuit specifications, design parameters, and output nodes are listed in Table 2. The (sensitivity) running results, including subcircuit calculation counts and CPU times, are all recorded in Table 3. Four versions of algorithms have been performed. The ratios of subcircuit calculation counts (and used CPU time) compared with those of WR+ODA (the traditional work [3]) are shown for the convenience of observing. We check left part of Table 3 (subcircuit calculation counts). WR+ADA version just spends the same amount of subcircuit calculations. WR+ADA+ST, on the other hand, can bypass calculations on subcircuits not affected by design parameters. Note that all simulated circuits are all in the form of cascaded stages. We roughly assign design parameters in middle part of these cascaded stages for all circuits except for the 6th circuit (where design parameters are assigned to the first stage). Therefore, the

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saving ratios for all circuits are obvious except for the 6th circuit (which is zero). WR+ADA+ST+SOD version further reduces calculations for subcircuits not contributing to output nodes. In some circuit (2nd, 5th, and 7th circuits), there is no sensitivity computation undertaken. This is due to that the outputs of these circuits are located in front stages that have no sensitivity waveforms.

(a)

(b)

Fig. 1. (a) The entire sensitivity matrix of a subcircuit. (b) Sensitivity waveform (of the 1st stage) comparison for the 4-bit Synchronous Counter, where the design parameter is the width of a MOSFET in the 1st stage. Table 2. Specifications of Simulated Circuits Ckt 1 2 3 4 5 6 7

Name Node# MOSFET# Subckt# m Design Parameters Output Nodes 100-stage MOSFETs in 51st Outputs of 50th, 60th, 100 200 100 10 Inverters to 55th inverters and 70th inverters st 100-stage MOSFETs in 51 Output of 10th, 20th, 100 200 10 10 to 55th inverters and 30th inverters Inverters All “sum” outputs One MOSFET in 4-bit ALU 168 400 44 1 and “carry” output of rd the 3 stage last stage All “sum” outputs Twenty MOSFETs and “carry” output of 4-bit ALU 168 400 44 20 in the 3rd stage last stage First eight “sum” Twenty MOSFETs outputs and “carry” 64-bit ALU 2688 6400 704 20 in the 31st stage of last stage 4-bit Sync. Twenty MOSFETs Two outputs of most 88 176 12 20 Counter in the 1st stage significant stage 16-bit Shift Twenty MOSFETs Output nodes of 1st, 320 640 32 20 Register in the 8th stage 3rd, 5th, and 7th stages

The right side of Table 3 shows the used CPU times. We check the first two circuits at first. They are the same but have been partitioned into small and big subcircuits respectively. The 1st circuit has small subcircuits, which is not beneficial

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Table 3. Simulation Results: Subcircuit Calculation Counts and Used CPU Times

Ckt 1 2 3 4 5 6 7 $

Subckt. Calculate#(K) / Ratio to WR+ODA WR+ WR+ WR+ WR+ ADA+ST+ ODA ADA ADA+ST SOD 1018.9 1018/100% 502.34/49% 202.5/20% 349.08 349/100% 171.3/49% 0/0% 36.644 36.6/100% 7.492/20% 7.492/20% 74.854 74.8/100% 15.382/20% 15.38/20% 106.66 106/100% 13.896/13% 0/0% 36.91 36.9/100% 36.91/100% 36.9/100% 62.092 62.0/100% 32.914/53% 0/0%

Used CPU Times$ / Ratio to WR+ODA WR+ WR+ WR+ WR+ ADA+ST+ ODA ADA ADA+ST SOD 39.608 37.72/95% 24.08/61% 6.568/17% 97.25 60.63/62% 30.29/31% 0/0% 0.796 0.78/98% 0.203/26% 0.187/23% 12.137 9.126/75% 2.012/17% 1.95/16% 18.657 13.71/73% 2.013/11% 0.063/0.3% 64.631 21.07/33% 21.15/33% 20.95/32% 121.78 38.62/32% 20.93/17% 0.015/0%

CPU time is in Pentium 2.2G seconds.

to RDA, but the 2nd circuit is beneficial to RDA. ADA chooses appropriate Direct Approach for subcircuit calculations, so WR+ADA version performs better. Next, we check the 3rd and 4th circuits. They are the same but have different number of design parameters. As we explained previously, RDA performs better in dealing with more design parameters (4th circuit) and worse in dealing with fewer design parameters (3rd circuit). We can see that WR+ADA version always pick the appropriate direct approach to save computation time. We then check the big 5th circuit that has 20 design parameters. Obvious effect has been observed. Note the saving ratio would increase if the number of design parameters is raised. The 6th and 7th circuits are all feedback circuits, in which many transistors inside feedback loops have been grouped into same subcircuits and hence big subcircuits emerge. Due to these big subcircuits, the saving effects for WR+ADA version are more obvious. ST and SOD techniques also save more CPU times. We note that saving ratios for CPU time are just proportional to those for calculation counts. The simulated waveforms of new methods are just the same as those of classical method. Fig. 2 (b) shows comparisons for the sensitivity waveforms of the 6th circuit. The running results justify that ADA, ST, and SOD have successfully accomplished the designated goal of speeding up the sensitivity computation for large-scale MOSFET circuits.

5 Conclusion We consider detailed factors influencing transient sensitivity computations, and raise various techniques to speed up the sensitivity simulation. The major techniques are the novel Row-major Direct Approach, ST, and SOD techniques. Experimental results justify that these new methods are more efficient than traditional works in performing robust large-scale transient sensitivity simulations for MOSFET circuits.

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References 1. 2. 3. 4.

5.

Newton, A.R., Sangiovanni-Vincentelli, A.L.: Relaxation-based electrical simulation. IEEE Trans. on Computer-aided Design CAD-3, 308–331 (1984) Hocevar, D.A., Yang, P., Trick, T.N., Epler, B.D.: Transient sensitivity computation for MOSFET circuits. IEEE Trans., Computer-aided Design CAD-4, 609–620 (1985) Chen, C.J., Feng, W.S.: Relaxation-based transient sensitivity computations for MOSFET circuits. IEEE Trans. on Computer-aided Design 14(2), 173–185 (1995) Chen, C.J., Lee, C.J., Tsai, C.L.: Waveform Relaxation-based Large-scale Circuit Simulation for MOSFET and Lossy Coupled Transmission Line Circuits. In: The International Conference on Electrical Engineering and Automatic Control, Zibo China, November 26-28, vol. 1, pp. 311–315 (2010) Nguyen, T.V., Devgan, A., Nastov, O.J., Winston, D.W.: Transient sensitivity computation in controlled explicit piecewise linear simulation. IEEE Trans. on Computer-aided Design of Integrated Circuits and Systems 19(1), 98–110 (2000)

Incremental Circuit Simulation for Large-Scale MOSFET Circuits with Interconnects Using Iterated Timing Analysis Chen Chun-Jung Department of Computer Science, Chinese Culture University, #55 Hwa-Gang Road, Taipei, Taiwan [email protected] Abstract. The circuit level incremental simulation is a good strategy to reduce the circuit simulation time, since most circuit designers use circuit simulators to simulate incrementally modified circuits repeatedly. This paper researches the large-scale incremental circuit simulation using the well-known ITA (Iterated Timing Analysis) algorithm. The target circuits are popular MOSFET circuits containing interconnects that are modeled by transmission lines. This paper has proposed an efficient algorithm for incremental simulation, the method to handle transmission lines, and other time-saving techniques. Experimental results justify that proposed methods undertake large-scale incremental circuit simulation for MOSFET and transmission lines very well. Keywords: Circuit simulation; relaxation-based algorithms; incremental simulation; Iterated Timing Analysis; transmission line.

1 Introduction In circuit design process, circuit simulators are usually invoked repeatedly. Each simulation process usually simulates the circuit only been slightly modified. So, utilizing the waveforms of previous simulation is a suitable way to accelerate the simulation speed and maintain the waveform accuracy at the same time. This is the basic principle of incremental simulation [1]. In this paper, we discuss the incremental simulation in circuit simulation level for large-scale circuits with interconnects. Iterated Timing Analysis (ITA) is one of the relaxation-based algorithms [2] that have smaller time complexity in dealing with large-scale circuits. It has also been used in many fast commercial SPICE programs. So, this paper investigates undertaking incremental simulation by ITA. There exist several strategies for circuit-level incremental simulation, including Incremental-in-space (IIS), Incremental-in-time (IIT) [1, 3] and so on. This paper presents a technique called Portion-simulating Method (PS) to further enhance the efficiency of incremental simulation. Interconnects’ delays play an important role in modern circuits. To accurately simulate interconnects is important. This paper uses a transmission line model [4] to handle interconnects. Transmission lines need to be specially processed in the incremental simulation. To manipulate transmission lines well is also an important task in this paper. All methods proposed have been implemented and tested. Experimental results will be given to justify advantages of these proposed methods. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 557–562. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 The Circuit Level Incremental Simulation We describe the circuit simulation algorithm at first. The simulated circuit can be described as following time-varying differential equation: .

F (Y (t ), Y (t ), t ) = 0

(1)

Where Y is the vector of circuit variables, t is the time, F is a continuous function and “.” means differentiation with respect to time. The simulated circuit needs to be partitioned into subcircuits, and the ith subcircuit, say a, is: .

Fi (Yi (t ), Yi (t ), Ci (t ), Ci (t ), t ) = 0

(2)

Where Yi is the vector of circuit variables in a, Di (decoupling vector) is the vector of circuit variables not in a, and Fi is a continuous function. In this paper, a subcircuit calculation (used as performance index later) means to solve (2) for Yi(tn+1) (tn+1 is current time point), which include applying integral formula (such as Trapezoidal method) to (2), solving the derived nonlinear algebraic equations by Newton’s iteration, and so on. Algorithm 1 lists the pseudo code for ITA. Algorithm 1 (ITA Algorithm for Circuit Simulation): // E() is an priority queue, whose elements are ordinary queues Put subcircuits connected to primary input into E(Tbegin); while(E is not empty) { P: tn+1 = the smallest event-time in E, and retrieve E(tn+1); for(k = 1; E(tn+1) is not empty; k++) { // k is the relaxation index Clear TMP; // TMP is a queue for(each subcircuit a in E(tn+1)) { // E(tn+1) is a queue Solve a at tn+1 for transient responses; if(a has been converged) { // converged Estimate next solving time tnext, and add a into E(tnext); } else { // not converged Add a into TMP; ST: Add fan_out(a) into E(tn+1); // The Selective-tracing Scheme } } E(tn+1) = TMP; } }

We propose the Portion-simulation Method to further improve traditional incremental simulation algorithms. The basic concept of PS Method is to view the incremental-changing portion (subcircuits whose waveforms are different from that of previous simulation are incremental-changing) of the simulated circuit as the ordinary circuit and view the other portion (incremental-following portion) of the simulated circuit as nothing. The incremental-changing portion of the circuit is called Incremental Portion Circuit (IPC). IPC of the simulated circuit ckt at tn+1 is defined as: (3) IPC (t n+1 ) = {s | s ∈ Subckt (ckt ), s.cstate(t n+1 ) =" changing"} Where Subckt is the set of all subcircuits, tn+1 is the current time point, and s.cstate is the “varying” situation of a subcircuit (“changing” means that at time point tn+1 s is

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incremental-changing). Another value for s.cstate is “following” that means s is incremental-following. An IPC is composed of all incremental-changing subcircuits and is time-varying. Fig. 1 (a) shows examples of possible IPCs at two different time points. The principle of PS is to exactly simulate IPC well. The cstate of subcircuits need to be managed well. There exist management rules for them [3]. The simulation algorithms then refer cstate of subcircuits to simulate. In ITA, we just utilize Selective-tracing Scheme and the priority queue data structure (line ST and P of Algorithm 1). Only subcircuits whose cstate is “changing” can be put into E(tn+1) at line ST to be simulated. There exists a time-saving technique called Simulation-on-demand (SOD) that only allows subcircuits “contributing” to wanted outputs be calculated. SOD is effective, which can be seen in later section.

3 Transmission Line Calculation Since we use the transmission line to model interconnects, we should pick a method for transmission line calculations. We pick a full time-domain technique described in [4]. This method transforms a transmission line into a time-domain equivalent circuit to be simulated with ordinary circuit elements. We will describe it and explain how to use it in PS Method. Consider a coupled uniform transmission line (with N conductors and length is D) whose resistance, inductance, capacitance, and conductance per unit length are R, L, C, and G respectively. The electrical behaviors (voltages v, and currents i) are described by following Telegraph’s equation: ∂ ∂ (4) v( x, t ) = − L i ( x, t ) − Ri ( x, t ) ∂x ∂t ∂ ∂ (5) i( x, t ) = −C v( x, t ) − Gv( x, t ) ∂x ∂t where x denotes distance, t denotes time. These equations are partially decoupled (on the L and C sense only) by (6) to derive (7) and (8): (6) v( x, t ) = Xu( x, t ), i( x, t ) = ( X T ) −1 j ( x, t )

(a)

(b)

Fig. 1. (a) Example of IPCs at different time points. (b) The waveform comparison for incremental simulation, where the circuit is 4-bit Sync. Counter.

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∂ ∂ u ( x, t ) = − L j ( x, t ) − Rj ( x, t ) ∂x ∂t ∂ ∂ j ( x, t ) = −C u ( x, t ) − Gu ( x, t ) ∂x ∂t

(7) (8)

where X is the eigenvector matrix of LC, L = X −1 L( X T ) −1 , C = X T CX , R = X −1 R( X T ) −1 , and G = X T GX . Note that both L and C are diagonal matrices. We apply the Method of Characteristic to process them. The equations dx = γ and k dt dx −0.5 = −γ k (where γ k = ( Lk ,k C k ,k ) ) are used to define the characteristic lines in the x-t dt plane, and they are called the kth (for kth conductor) characteristic α and β lines respectively. Assuming all γ k are the same and differentiating (7) and (8) along charac-

teristic α lines, we have: −1 dα (9) [u ( x, t ) + Zj ( x, t )] = − RΓj ( x, t ) − Ck Gu ( x, t ) dt Where Z k = ( Lk ,k / Ck ,k ) 0.5 is the characteristic impedance of the kth conductor, and Γ is

a diagonal matrix made of γ of all lines. Similarly, along β characteristic lines, we have: −1 dβ (10) [u ( x, t ) − Zj ( x, t )] = RΓj ( x, t ) − C k Gu ( x, t ) dt Equation (10) is a simple 1st order differential equation, which can be solved by Forward Euler (FE) integral method: −1 (11) u (t n+1 ) − Zj (t n +1 ) = hRΓj (t n ) − hCk Gu (t n ) = V0 Note that both x-axis and t-axis have to be divided into segments (h is the segment length of t-axis). The x-t planes of all lines are divided into same Δt (h) and Δx, and Δt/Δx represents the slope (1/ k). In fact, each k might be different. We use 1/max{ k| k=1~N} as the slope in the x-t plane for computation convenience. We should note that, in using (11), each line has different k and, therefore, has different characteristic lines. Equation (9) is solved by FE similarly: −1 (12) u (t n+1 ) + Zj (t n+1 ) = −hRΓj (t n ) − hCk Gu (t n ) = VD

γ

γ

γ

γ

Using (11) and (12), we can obtain u/j values at new time point (tn+1). The u/j values at two ends (x=0 and x=D) of the transmission line are transformed by (8) to derive the v/i values (the time-domain equivalent element) to be used in circuit simulation [4]. Transmission lines differ from other circuit elements that they have states. Therefore, when a subcircuit containing transmission lines becomes incremental-changing, the u/j values (states) of transmission lines need to be assigned. In our implementation, we just use (8) to derive u/j values of two ends and use “linear distribution” to assign inside states. This method can generate correct waveforms. One accelerating technique is the Inner-skipping Scheme (IS) that “skips” (or bypass) “inner time points” (h) on which states transit slowly. If inner time point tn+1 has been skipped, stn+1 (state at time tn+1) would be equal to stn. IS has been implemented, too.

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4 Experimental Results The proposed methods have been implemented in our circuit simulator MOSTIME [3]. To emphasize proposed algorithms’ effects, we use the simple analytic model for MOSFET and partition circuits into smaller subcircuits (which will induce more global feedback loops but allows PS and SOD to perform better). There are two types of “design changes” (modifications in input decks), which are subcircuit design modifications and primary inputs modification. Five circuits have been tested, whose specifications are shown in Table 1. Since the efficiencies of PS and SOD are affected by positions of design changes and wanted outputs respectively, they are also listed. The waveforms of the 3rd circuit simulated by ITA and incremental simulation (PS) are compared in Fig. 1 (b), which justifies the accuracy of new methods. In our incremental simulation experiments, waveforms of previous simulation are stored in computer memory to eliminate the disc writing/reading time. Versions of algorithms include ITA, ITA+PS, ITA+PS+SOD, and ITA+PS+SOD+IS. The running results, subcircuit calculation counts and used CPU times, are shown in Table 2. Ratios of subcircuit calculation counts (and used CPU time) compared with those of ITA are shown for convenience. We check subcircuit calculation counts at first. Obviously, ITA+PS (the incremental simulation without time-saving techniques) performs very well. The calculation counts have been dramatically reduced. The WDR of each circuit, at the last column of Table 1, represent the degree of waveform difference between current and previous simulation. A good incremental simulation algorithm should only simulate the incrementalchanging portions and spend calculation counts proportional to the WDR. We can find that PS performs well in all circuits (except the 5th circuit that has higher count ratio) as WDR is concerned. Next we observe SOD. It has clear effect in the 2nd circuit whose wanted outputs are only the front-end eight “sum” outputs. We learn again that DOD’s function is related to wanted outputs. Next, we check IS that is Table 1. Specifications of Simulated Circuits

*

Ckt

Name

Node #

MOSFET#

1

100-stage Inverters

100

200

2

64-bit ALU 3,584

6,912

3

4-bit Sync. Counter

100

192

4

16-bit Shift 368 Register

704

5 64x1 SRAM 226

526

Subckt Tr. Design Changes # Line# The 99-staged 100 10 inverter is modified One inverter in 1,920 128 the 1st bit (LSB) is modified One inverter of 48 4 the 4th bit (MSB) is modified Two inverters of 176 16 the last bit are modified R/W control is 104 1 completely modified

Wanted Outputs WDR* Output of the last inverter

3.6%

First eight “sum” and last “carry” output

3.1%

All counter outputs

4.8%

All register outputs

1.4%

Data outputs of 9.4% SRAM

Waveform Differentiation Ratio is the ratio of different waveforms (time points) to total waveforms

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Ckt 1 2 3 4 5 $

Subckt. Calculate#(K) / Ratios to ITA ITA+PS+ ITA+PS+ ITA ITA+PS SOD SOD+IS 883.57 24.05/2.7% 24.05/2.7% 15.4/1.7% 11256 101.5/0.9% 81.72/0.7% 32.51/0.2% 1040.8 49.64/4.7% 49.64/4.7% 49.64/4.7% 1952.4 9.634/0.4% 9.634/0.4% 9.145/0.4% 493.52 113.4/22% 101.48/20% 101.4/20%

Used CPU Times$ / Ratios to ITA ITA+PS+ ITA+PS+ ITA ITA+PS SOD SOD+IS 26.676 2.683/10% 2.621/9% 0.796/2% 320.22 3.76/1% 2.48/0% 0.905/0% 23.93 3.728/15% 3.775/15% 3.323/13% 46.051 0.265/0% 0.249/0% 0.234/0% 12.652 7.004/55% 5.741/45% 5.772/45%

CPU time is in Pentium 2.2G seconds.

related to transmission line calculations. It shows great saving effect in the 1st and 2nd circuits that has high density of transmission lines (in the 2nd circuit, about 60% of calculation counts have been saved). In other circuits, IS also save calculation counts. Then, we check the used CPU times at the right side of Table 2. We mainly observe the ratios (to ITA). In the ITA+PS version, the used CPU time ratios are proportional to those of calculation counts but are mostly bigger. This is due to that PS exhausts computational overheads (such as those for waveform comparisons, and incrementalchanging/following statuses switching). The IS technique performs excellent for the 2nd circuit, in which the reduction effect is better than that for calculation counts. This is because that calculation effort for the subcircuit containing transmission lines is bigger than that for the ordinary subcircuits.

5 Conclusion We have proposed a new circuit level incremental simulation method called Portionsimulating Method and combine it with the popular ITA algorithm to simulate circuits composed of MOSFET and transmission lines successfully. Time-saving techniques, including Simulation-on-demand and Inner-skipping (for transmission lines), are also proposed to reduce simulation time. Experimental results simulated by real implementations of these methods justify their good performance. Various types of simulated circuits also prove that proposed methods are robust.

References 1. 2. 3.

4.

Newton, A.R., Sangiovanni-Vincentelli, A.L.: Relaxation-based electrical simulation. IEEE Trans. on Computer-aided Design CAD-3, 308–331 (1984) Ju, Y.C., Saleh, R.A.: Incremental circuit simulation using waveform relaxation. In: Proceeding of the ACM/IEEE DAC, pp. 8–11 (1992) Chen, C.J., Yu, J.L., Yang, T.N.: Selective-tracing waveform relaxation algorithm for Incremental circuit simulation. In: Proceeding of International Symposium on Intelligent Signal Processing and Communication Systems, Hong Kong, December 13-16, pp. 205–208 (2005) Mao, J.F., Kuh, E.S.: Fast simulation and sensitivity analysis of lossy transmission lines by the method of characteristics. IEEE Tran. on Computer-Aided Design 44(5), 391–401 (1997)

Inversion of Array Lateral-Logging Based on LSSVM Yu Kong1, Li Zhang2, Linwei Feng3, and Yueqin Dun2 1

Information Center of Shandong Medical College, Jinan , Shandong, China [email protected] 2 School of Electronic Information and Control Engineering, Shandong Polytechnic University, Jinan, Shandong, China [email protected] 3 Technology Center,China Petroleum Logging CO. LTD, Xi’an,Shanxi, China. [email protected]

Abstract. Based on introducing the working principle of the array sonde and the least squares support vector machine (LSSVM), the inversion of the array lateral-logging responses have been done by LSSVM in this paper. The kernel parameters of LSSVM are selected by 5 fold cross validation method, and the well inversion results has been obtained. By comparing the inversion results with that of an international well-known company, it is demonstrated that the inversion results based on LSSVM are more reasonable. Keywords: LSSVM; Array lateral-logging; Inversion.

1 Introduction To judge what layer or which depth of the earth is of petroleum or gas,a measurement sonde is put into a borehole and moved down to measure the physical characteristic of the soil, which is called physical geography logging[1]. The inversion problem of logging is the process getting the structure parameters of earth from the measured responses. The multi-parameters inversion is a nonlinear problem, and it also has multiple solutions. Fast inversion of multi-parameters can be realized by neural network [2], but the optimum value of network structure is very difficult to get at present, therefore its generalization performance is hard to be controlled. Recently LSSVM is a kind of popular classification tool which chooses some training points to produce a sparse estimation function. These training points are support vectors which can estimate output according to the input data. Although LSSVM is similar to neural networks, the neural network is based on empirical risk minimization principle. In comparison, LSSVM is the trade-offs between experience error (risk) and model complexity. It approximately realized Vapnik's structural risk minimization principle. Therefore, LSSVM can achieve global optimization, while neural network just realize a local optimum. In this paper, the correlation between the logging data and the earth resistivity has been analyzed by LSSVM, and the earth resistivity has been inversed basing on the real logging data. The inversion results based on LSSVM are more reasonable by comparing them with the results of an international company.

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2 Principle of the Array Lateral Sonde Fig. 1 gives the model of array lateral sonde, where the bias regions are the current electrodes (which are cylindrical-lamella). This sonde has six different working mode, the function and the current leaking into earth of each electrode are different under different working mode. Six curves of logging responses can be obtained simultaneously, that is, apparent resistivity Ra0-Ra5, which reflect the different measurement depth of the sonde [1,2]. These six different responses provide abundant information for the parametric inversion of the earth.

Fig. 1. An array lateral sonde model.

Fig. 2 shows the schematic diagram of the earth model. For the thick layers, the influence from the other adjacent layers can be ignored, thus, the two-dimensional problem (with horizontal layer) can be approximatively simplified to one-dimensional problem (without horizontal layer). Therefore, the earth parameters being inversed are the radius of invasion zone (ri), the resistivity of invasion zone (Rxo) and the real resistivity (Rt). In a general way, the three-parameters inversion refers to the inversion of ri , Rxo and Rt, but the most major objective is to get the real resistivity Rt of the earth, and to judge whether it is an oil layer or not according to Rt. So, the inversion of the earth resistivity, that is, Rxo and Rt, are mainly discussed in the paper. The array lateral sonde simultaneously measure six curves of logging responses, which has different measurement depth and reflect the combined action of the earth parameters. The relationship between the parameters being inversed and logging responses is nonlinear. To improve the inversion precision, the LSSVM are used to realize the nonlinear parameter inversion.

ρ

Fig. 2. Schematic diagram of the earth model

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3 Least Squares Support Vector Machine In the regression analysis models of LSSVM, for a given training data set, {x i , y i }, x i follows.

∈ R d , y i ∈ R, i = 1,2,", n , its regression target function is as

f(x) =w φ(x) + b. T

(1)

The optimization problem of LSSVM is to find a function, which can estimate the output almost close to the target y under the error ξ , and minimize w simultaneously to make it more generalization. The optimizing target is equivalence to a most basic convex quadratic programming, its form is as follows.

min = w, b,ξ

1

w

2

2

+γ

1 2

y i = w φ(x i ) + b + ξ i T

s.t.

n

∑ ξi

2

i =1

.

(2)

i = 1,2, " , n

An equivalent quadratic programming problem can be obtained by the method of Lagrange's multiplier, which has the follow form.

L=

1

w

2

2

+γ

1 2

n

n

∑ ξ i − ∑ α i (w φ(x i ) + b + ξ i − y i ) . 2

i =1

T

(3)

i =1

Where α i (i = 1,2,..., n ) is Lagrangian multiplier. In equation (3), the partial derivative

of w, ξ i , b, α i are as follows. n ⎧ ∂L = 0 → w = ∑ α i φ(x i ) ⎪ ∂w i =1 ⎪ ∂L n αi = 0 ⎪ =0→∑ i =1 ⎪ ∂b ⎨ ∂L ⎪ = 0 → α i = γξ i i = 1,2,..., n . ⎪ ∂ξ i ⎪ ∂L = 0 → w T φ(x ) + b + ξ − y = 0 i i i ⎪ ∂α ⎩ i

n

Due to w = ∑ α i φ(x i ) , ξ i = i =1

matrix form.

(4)

αi , equation (4) can be translated to the following γ

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⎢0 ⎥ ⎡b ⎤ ⎡0 ⎤ 1 = ⎢ T −1 ⎥ ⎢ ⎥ ⎢⎣ y ⎥⎦ . α 1 φ(x) φ(x) + γ I ⎣ ⎦ ⎣ ⎦ Where φ(x) = [φ(x 1 ) " φ(x n )] , y = [y1 ,..., y n ] , 1 = [1,...,1] , α = [α1 ,..., α n ] . T

(5)

For nonlinear problem, equation (5) can be translated as follows.

⎢0 ⎥ ⎡b⎤ ⎡0 ⎤ 1 = ⎢ −1 ⎥ ⎢ ⎥ ⎢⎣ y⎥⎦ α 1 K + γ I ⎣ ⎦ ⎣ ⎦ T

(6)

Where K = φ(x) φ(x) is kernel function of LSSVM. There are many kinds of kernel functions, the RBF kernel (seeing equation (7)) is selected in this paper, which can solved the complex and nonlinear problem quiet well. T

⎛ xi − x j K(x i , x j ) = exp⎜ − 2 ⎜ 2σ ⎝

2

⎞ ⎟ ⎟ ⎠

(7)

4 Resistivity Inversion The selection of the kernel parameters γ and σ is very important when the resistivity are inversed by LSSVR. The kernel parameters are selected by 5 fold cross validation method in the paper [3]. In 5-fold cross-validation the data is first partitioned into 5 equally (or nearly equally) sized segments or folds. Subsequently 5 iterations of training and validation are performed such that within each iteration a different fold of the data is held-out for validation while the remaining 4 folds are used for learning. The generalization error is estimates according to the average value of the mean squared error (MSE) getting of 5 iterations. Finally, the optimal parameters are selected. First, the responses Ra0-Ra5 of different models (Rxo and Rt take different sampling values) are calculated with the fast forward program [1]. To build the inversion model, Ra0-Ra5 are taken as input of LSSVR, Rxo and Rt as output. Then, the resistivity Rxo and Rt can be inversed by using the actual measurement Ra0-Ra5 as the inputs. To eliminate the influence of the scale, it is necessary to normalize all the parameters to the region [0,1] before training. The normalization equation is as follows.

xg =

x − x min x max − x min

(8)

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Fig. 3 gives the inversion results which are inversed with actual measurement data of foreign company by LSSVR. The thin dotted line is the inputs data for inversion, that is, Ra0-Ra5. Here, the curve Ra0 is not given due to it is small, and RLA1_UNCORR to RLA5_UNCORR stand for Ra1 to Ra5. The heavy line RT_JISUAN (Rxo_JISUAN) is the inversion results based on LSSVR, and the thick dotted line RT_O (Rxo_O) is the inversion results of foreign company. The inversion results of the two methods for the segment (from 685m to 715m) are close to each other. The actual measurement data in Fig. 3 is flat, which means that the layers are thick. The inversion results of this paper well coincide with that of foreign company for the flat layers.

(a) Inversion results of Rt

(b) Inversion results of Rxo

Fig. 3. Inversion results of Rt and Rxo for flat layers.

Fig. 4 gives the inversion results of Rt and Rxo for uneven layers. The results of foreign company will break, such as, from 838m to 840m for Rt, 751m and 758m for Rxo. This doesn’t agree with actual situation. While the inversion results of this paper has no saltation, and its change tendency is well accord with actual measurement data.

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(a) Inversion results of Rt

(b) Inversion results of Rxo

Fig. 4. Inversion results of Rt and Rxo for uneven layers.

5 Conclusion The array lateral sonde can simultaneously provide six curves of logging responses with different measurement depth, which give abundant information for the parametric inversion of the earth. The inversion of actual logging responses have been done by LSSVM in the paper. By comparing the inversion results of the paper with that of an international company, it is demonstrated that the inversion results can agree with each other very well for flat layers. While the results of this paper are superior to that of foreign company for uneven layers.

References 1.

2. 3.

Dun, Y., Tang, Z., et al.: Fast Calculation and Characteristic Analysis of Array LateralLogging Responses. International Journal of Applied Electromagnetics and Mechanics 33, 145–151 (2010) Dun, Y., Yuan, J.: Fast Inversion of Array Lateral Electric-logging. Journal of Tsinghua University(Science and Technology), 11, 156–160 (2009) Feng, G.: Parameter Optimizing for Support Vector Machines Classification. Computer Engineering and Applications 3, 123–124 (2011)

Voice Recognition Based on the Theory of Transmission Wave and LSSVM Yu Kong1, Yue Min2, and Jing Xiao3 1

Information Center of Shandong Medical College, Jinan, Shandong, China [email protected] 2 School of Electrical Engineering, Shandong University, Jinan, Shandong, China 3 Extra High Voltage Sub Company of SEPCO, Jinan, Shandong, China

Abstract. Based on introduced the principle of the one-dimensional voice transmission parameters and least squares support vector machine (LSSVM), one-dimensional transmission parameters are classified by LSSVM. γ and σ are important kernel parameters for LSSVM, and the kernel parameters of LSSVM are selected by 5-fold cross-validation method to obtainbetter recognition results. Finally, by comparing the identification results based on LSSVM with that based on BP neural network algorithm, it is found that the method of this paper is better than BP algorithm. Keywords: Transmission wave; LSSVM; Feature extraction; Classification.

1 Introduction The essence of the sound is sound waves produced by glottal vibration transmit along the channel. Transmission of sound waves in channel is a process of spectrum change of time and space channel changes.Different people or different sounds have different changes in the channel. Therefore, it is feasible in principle that the voice channel parameters can be extracted from the acoustic waves received in channel end (lip). According to modern wavelet theory, any wave function f (t) L2 (- ∞, + ∞) can be reconstructed by time and frequency spectrum [1]. Recently LSSVM is a kind of popular classification tool which choosing some training points to produce a sparse estimation function. These training points are support vectors which can estimate output data according to the input data. Although LSSVM is similar to neural networks, the neural network is based on empirical risk minimization principle. In comparison, LSSVM is the trade-offs between experience error (risk) and model complexity. It approximately realized Vapnik's structural risk minimization principle. Therefore, LSSVM is to achieve global optimization, while neural network just realize a local optimum. The recognition effect using the combination of transmission theory and LSSVM is better than only using BP neural networks. Since BP algorithm is relatively mature, the basic theory can consult the extensive literatures, it won’t be repeated in this paper.

∈

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2 Voice Transmission Theory and Calculation of Characteristic Variables The simplest sound channel model can be regarded as a system linked by a number of tubes with different diameter in series [2] (seeing Fig. 1). In the actual acoustic tube model, the air density of each tube’s section is different during wave propagation, therefore, the channel can be considered as a model linked together by different media. When the sound wave goes through different media, it has transmission and reflection wave. The ratios of wave source to transmission and reflection wave are called transmission coefficient and reflection coefficient, which are both functions of frequency [3].

Fig. 1. Simple model of sound channel.

Fig. 2 describes the process of transmission and reflection of sound wave when it goes through different media.

transmission wave reflection wave

Fig. 2. Transmission and reflection wave in model of voice channel.

Same sound from different person or different sound from same individual can be regarded as the same sound wave goes through different media. The frequency of sound wave will change when it goes through different media, therefore the parameters of medium should be complex function of frequency. Wave can be described by ternary complex function (instantaneous spectrum) of time, space and frequency. The

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instantaneous spectrum can be extracted from actual measured wave signal by using wavelet transform. Because the instantaneous value of transmission wave is relevant to the property of medium. It is different from the characteristics of reflection wave, so there has no recursion algorithm [3] to solve. In fact, it is impossible to identify medium of each layer only depend on transmission wave, and the action of multiple reflection wave must be considered. Sound wave is one-dimensional signal, assuming the sound channel is divided into two homogeneous layers shown in fig. 3. The s is discontinuity surface , horizontal ordinate denotes time t, vertical coordinate is distance d. The P(t,ω) is the instantaneous spectrum of signal p(t) produced by glottis vibration. The Q(t,ω) is the instantaneous spectrum of signal q(t) detected at lips. The t(s) is the transmission time for the voice transmitting from glottis to discontinuity surface s. If using travel time coordinates, then t(s)=s. The transmission line (characteristic line) of sound wave in space and time is a straight line.

P(t,ω) characteristic line s

t

d

Q(t,ω)

Fig. 3. Sound wave transmitting in two homogeneous layers along characteristic line.

The instantaneous spectrum Q(t,ω) can be obtained from signal q(t) by wavelet transform, it meets the follow relationship.

Q(t, ω) = W(s, ω)P(t - 2s, ω) .

(1)

Where P(t-2s,ω) is instantaneous spectrum of glottis signal at t-2s moment, s is the time for sound wave going through one layer (assume for the media with two layers), W(s,ω) is transmission coefficient of discontinuity surface s. If the media are more than two layers, only considering transmission coefficient is not enough, and the reflection coefficient of each layer must be considered. Assuming the medium has n layers and n-1 discontinuity surfaces, the characteristic parameters can be got based on transmission wave theory [4] as follows. D1 = D2 =

Qn P0 Q n+2 Qn

−

P2

.

P0

"" D 2n =

Q n + 2n Qn

−

P2n P0

+

P2n − 2 P0

D2 −

P2n − 4 P0

D4 + " −

P2 P0

D 2n − 2

(2)

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Where D1 = W1 W2 W3 " Wn is the direct transmission wave, D 2 is all waves with

square form, D i is all waves with power of i, Pi is the instantaneous spectrum of p(t) at t i moment, Q i is the instantaneous spectrum of q(t) at t i moment. The excitation signal from glottis can be simulated by the periodic triangle pulse sequence as follows [5].

，

π⋅t ⎧1 [1 − cos ] 0 ≤ t ≤ T1 ⎪2 T1 ⎪ t − T1 ⎪ )] T1 ≤ t ≤ T1 + T2 ⎨cos[π . 2T 2 ⎪ ⎪0 others ⎪⎩

，

（

，

(3)

3 Least Squares Support Vector Machine For a given a training data set {x i , y i }, x ∈ R , y ∈ {-1,1}i = 1,2," , n , the LSSVM optimal classifier hyperplane function is as follows. d

< w ⋅ φ(x) > +b = 0 .

(4)

Where < w ⋅ φ(x) > is inner product between W and φ(x) . The problem of LSSVM optimization can be expressed as an equality constraint, and the optimization objective function is as follows.

min = w,b,ξ

1 2

2

w +γ

1

n

∑ ξi

2 i =1

y i [< w ⋅ φ(x i ) > + b] = 1 − ξ i

s.t.

2

(5)

i = 1,2, " , n

.

A problem of equivalent quadratic programming can be obtained by the method of Lagrange's multiplier as follows.

L=

1 2

w

2

+γ

{

1 n 2 n ∑ ξ − ∑ α y i [ < w ⋅ φ(x ) > + b] − 1 + ξ i i 2 i=1 i i=1 i

(6)

Where α i (i = 1,2,..., n ) is Lagrange multiplier. In equation (6), the partial derivatives of w, ξ i , b, α i are as follows.

Voice Recognition Based on the Theory of Transmission Wave and LSSVM n ⎧ ∂L = 0 → w = ∑ α i y i φ(x i ) ⎪ ∂w i =1 ⎪ ∂L n α i yi = 0 ⎪ =0→∑ i =1 ⎪ ∂b ⎨ ∂L ⎪ = 0 → α i = γξ i i = 1,2,..., n . ∂ ξ i ⎪ ⎪ ∂L = 0 → y [w T φ(x ) + b] + ξ − 1 = 0 i i i ⎪ ∂α ⎩ i

n

∑α y x

Due to w =

i =1

i

i

i

, ξi =

573

(7)

αi , equation (7) can be translated to matrix form γ

as follows.

y ⎢0 ⎥ ⎡ b ⎤ ⎡ 0T ⎤ (8) ⎢⎣ y Ω + γ −1I ⎥⎦ ⎢⎣α T ⎥⎦ = ⎢⎣1 ⎥⎦ . Where x = [x 1 ,..., x n ] , y = [y1 ,..., y n ] , 1 = [1,...,1] , α = [α1 ,..., α n ] , n Ω = {y y φ(x ) φ(x ) }i, j=1 , K(x , x ) = φ(x ) φ(x ) is kernel function, the RBF T

T

i

j

T

j

T

i

i

j

i

j

kernel function (seeing equation (7)) is selected in this paper.

⎛ xi − x j K(x i , x j ) = exp⎜ − 2 ⎜ 2σ ⎝

2

⎞ ⎟ ⎟. ⎠

(9)

4 Experimental Results In this experiment, glottis excitation signal of sonant is simulated by periodic triangle pulse sequence. The sound samples are extracted from a Chinese male. Since the length of sound channel is about 17cm and the acoustic velocity is 360m/s, then the propagation time of voice in channel is about 0.5ms. The sampling rate of voice is 11.025 kHz and quantification precision is 12 bit. So there are 5 sampling points in 0.5ms. The experiment samples are “1,2,3,4,5,6,7,8,9,10” said in Chinese. 40 coefficients extracted from each sound are taken as classified data. If the number of coefficient is less than 40, the coefficient should be extended symmetrically. Fig.4 shows the transmission coefficient of sonant “1”. Fig.5 shows the transmission coefficient of sonant “7”. Fig. 6 gives the transmission coefficient of sonant “10”. It is clear that

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there have obvious differences between the transmission coefficients of sonant “10” and “1” or “7”. Though the transmission coefficients of “1” and “7” are similar, they still have tiny differences.

Fig. 4. Transmission coefficient of sonant “1”.

Fig. 5. Transmission coefficient of sonant “7”.

Fig. 6. Transmission coefficient of sonant “10”.

After extracting transmission coefficients, LSSVM is used to classify the coefficients. γ and σ are two kernel parameters which are very important for LSSVM. The kernel parameters are selected by 5-fold cross-validation method in the paper. In 5fold cross-validation the data is first partitioned into 5 equally (or nearly equally) sized segments or folds. Subsequently 5 iterations of training and validation are performed such that within each iteration a different fold of the data is held-out for validation while the remaining 4 folds are used for learning. The generalization error is estimated according to the average value of mean squared error (MSE) of 5 iterations. Finally, the optimal parameters are selected. If surd and sonant are both considered, the recognition rate of BP neural network is 84.5%, while that of LSSVM can reach to 87.8%, as shown in table 1. If only sonant is considered, the recognition rate of BP neural network is 73%, and that of LSSVM reaches to 77%, as shown in table 2.

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Table 1. Recognition rate of “1” to “10” considering both surd and sonant /6690 %3

Table 2. Recognition rate of “1 ,7 ,10” and “ 6 ,9” only considering sonant /6690 %3

5 Conclusion This paper mainly discussed the classification of voice parameters extracted from one-dimensional transmission wave based on the method of LSSVM, compared the identification results with that of BP neural network algorithm. LSSVM doesn’t need to determine the number of hidden nodes which is a hard problem for neural network. LSSVM avoids the local minimum trap and can achieve global optimization. LSSVM has better generalization effect. The experiment demonstrated that the recognition rate of LSSVM is better than BP neural network. LSSVM has better research value for the classification of voice. Making full use of the merits of LSSVM and neural network and combining the two methods to further improve recognition rate is the research direction in future.

References 1. 2. 3. 4. 5.

Mallat, S.: A Wavelet Tour of Signal Processing. China Machine Press, Beijing (2002) Rabiner, L.R.: Fundamentals of Speech Recognition. Prentice Hall, Inc., Englewood Cliffs (1999) Huang, G.: Seismic Reflection Wave Imaging New Method and Its Theoretical Basis. J. Scientia Sinica(D) 30(6), 650–655 (2000) Kong, Y., Huang, G., Ning, F.: Pilot Study of Extracting Voice Feature Parameters by Transmitted Wave. Journal of Shandong University(Natural Science) 39(2), 56–61 (2004) Rabiner, L.R., Schafer, R.W.: Digital Processing of Speech Signals. Prentice Hall, Inc., Englewood Cliffs (1978)

Design of a Two-Phase Adiabatic Content-Addressable Memory Meng-Chou Chang and Yen-Ting Kuo National Changhua University of Education, Department of Electronic Engineering, No.2, Shida Rd., Changhua 50074, Taiwan, R.O.C. [email protected]

Abstract. This paper presents the design of a two-phase adiabatic CAM, which achieves low-power dissipation by employing adiabatic operation and two-step data matching. The proposed adiabatic CAM can recycle the charge on match lines and keep the voltage drop between the power clocks and the output nodes close to zero during the charging/discharging process, leading to lower power dissipation. Also, the match-line in each CAM word is partitioned into two segments, and the second segment is selectively charged/discharged according to the match result of the first segment. If the match result of the first word segment is mismatch, the charging/discharging of the second segment of the match-line will be eliminated, further reducing the power dissipation. Simulation results show that the proposed two-phase adiabatic CAM with 64 words×144 bits can achieve a power reduction of 16.9% compared to the traditional single-phase adiabatic CAM at an operating frequency of 500 MHz. Keywords: Content-addressable memory (CAM), adiabatic logic, adiabatic CAM.

1 Introduction Content-addressable memory (CAM) compares input search data in parallel against a table of stored data, and returns the address of the matching data. CAMs can be used in a wide variety of applications requiring high-speed parallel search. These applications include pattern recognition, data compression, and network address translation [1]. However, the parallel search operations in CAMs cause high power consumption due to frequent switching of highly capacitive match lines (MLs). Traditional CAM power reduction techniques include low swing schemes, selective precharge schemes, and precharge low schemes. Recently, adiabatic switching principle has also been applied to the design of lowpower CAMs [2-6]. An adiabatic logic uses AC power sources instead of traditional DC sources to charge and discharge its output nodes. When an adiabatic logic charges/discharges its output nodes, the switch elements on the charging/discharging path consume almost zero power by keeping the voltage drop between the AC power M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 577–583. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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sources and the output nodes close to zero. In [6], Q. Xu et al. had implemented an adiabatic CAM using CPAL (Complementary Pass-transistor Adiabatic Logic) [7] and showed that the adiabatic CAM can achieve 86% energy saving compared to the conventional static CMOS implementation at 100 MHz. In this paper, we propose a two-phase adiabatic CAM that employs both the adiabatic switching techniques and match-line segmentation to reduce the energy consumption. In the proposed architecture, the match line in each CAM word is partitioned into two segments, and the second match-line segment is activated only if the corresponding first match-line is matched.

2 The Proposed Two-Phase Adiabatic CAM Figure 1 shows the block diagram of the proposed two-phase adiabatic CAM with a storage array of 64 words×144 bits. Each word in the CAM array is partitioned into two segments, the first word segment with 6 bits and the second word segment with 138 bits. That is, the CAM array is organized as two sub-arrays, the first sub-array with 64 words×6 bits and the second sub-array with 64 words×138 bits. As used in the adiabatic CAM proposed by Q. Xu et al. [6], CPAL (Complementary Pass-transistor Adiabatic Logic) [7] is also used to implement the circuits in our two-phase adiabatic CAM. Figure 2(a) shows the structure of the CPAL buffer/inverter. A CPAL gate is supplied by a single phase power clock, denoted by PC in Fig. 2(a). Cascaded CPAL gates, as shown in Fig. 2(b), can be driven by fourphase power-clocks, PC1, PC2, PC3, and PC4. The power clocks can be sinusoidal or trapezoidal, and there is a 90° phase lag between adjacent power clocks, as depicted in Fig. 2(c). D144

D7 D1

PC2

D6

bit-line drivers

PC1

PC2 PC3

bit-line drivers

PC1

PC2 PC3

PC1 BL1

BLb1

BL6

BL7

BLb6

BLb7

BL144

PC4

BLb144

SWL1

FWL1 Cell

Cell

FMDL1

Cell

FML1 ML recovery

Cell

SML1

Match

SMDL1

Sensor

ML recovery

Match Result Merge

Out1

Match Result Merge

Out64

SWL64

FWL64

Cell

Cell

Cell

FML64 FMDL64

ML recovery

1st storage sub-array

Cell

Match Sensor

SMDL64

2nd storage sub-array

Fig. 1. The proposed two-phase adiabatic CAM.

SML64 ML recovery

Design of a Two-Phase Adiabatic Content-Addressable Memory

PC1

PC D

N1

P2

P1

Db

PC4

OUT

IN

N2

PC3

PC2

579

INb

OUTb

(b) OUTb

OUT

t1

t2

t3

t4

t5

t6 t7

t8

N4

N3

PC1 PC2 IN

N5 INb

INb N8

N6 N7

IN

INb

IN

IN

PC3 PC4

INb

(c)

(a)

Fig. 2. (a) A CPAL inverter/buffer, (b) Cascaded CPAL buffers, (c) Waveforms of power clocks.

The operation of a CPAL gate is composed of four phases: wait, evaluate, hold, and recovery. During the wait phase, the power clock keeps low, and both the outputs of the CPAL gate remain low. During the evaluate phase, the power clock rises from 0V to the peak voltage, and one of the gate outputs is charged to high according to the input values. During the hold phase, the power clock remains high, and the gate outputs hold their values. During the recovery phase, the power clock falls from the peak voltage to 0V, and the charge on the charged output node is recycled to the power clock. Figure 3 (a) shows the structure of the CAM storage cell implemented with CPAL. If the stored bit in the CAM cell matches the search bit, the transistor N5 is turned off and the CAM cell does not provide a conducting path between the match-driving line (MDL) and the match line (ML); if the stored bit in the CAM cell mismatches the search bit, the transistor N5 is turned on and the CAM cell provides a conducting path between MDL and ML. That is, if the stored data in a CAM word mismatches the search word, there is at least a conducting path between the corresponding MDL and ML. The MDL is connected to a power clock, and thus the corresponding ML will be

BL

BLb

WL

PC VDD

N2

N1

MR

ML N3

X

N4 N5

ML

MDL

MDL (a)

(b)

Fig. 3. (a) CAM storage cell, (b) the match-line recovery circuit.

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PC3

PC2 N3 P1

N5

N7 P3

N4

N6 P2

SMDL N9

FML N1

N2

N8

Fig. 4. The circuit of the match sensor block.

PC4 SMDL P1

PC3

Out

SML N1

N2

N3

PC2

N4

PC1

Fig. 5. The circuit of the match result merge block.

driven to high by the power clock during the hold phase if the corresponding stored data mismatches the search data. On the contrary, the corresponding ML will be kept low during the hold phase if the corresponding stored data matches the search data. Figure 3(b) shows the match-line recovery circuit, which is used to provide a conducting path from ML to MDL during the recovery phase to recycle the charge on the match line. The CAM storage cell and the match-line recovery circuit used in our two-phase adiabatic CAM are the same as those used in [6]. Data matching in our two-phase adiabatic CAM is a two-step process. In the first step, the first 6-bit of the search data is compared with each CAM word of the first storage sub-array. Only those matching CAM words in the first step will activate the second-step data matching of the corresponding CAM words in the second storage sub-array. As shown in Fig. 1, in the proposed two-phase adiabatic CAM, each match line is composed of FML (first-stage match line) and SML (second-stage match line). Also, each match driving line is composed of FMDL (first-stage match driving line), driven by PC2, and SMDL (second-stage match driving line), driven by PC3. The match sensor block in Fig. 1 detects the match result on FML and determines whether power clock PC3 should drive SMDL. The circuit of the match sensor block is shown in Fig. 4. If the match result on FML is match, the voltage of FML will be low during the hold phase of PC2, resulting in a conducting path between PC3 and the corresponding SMDL, and thus the corresponding CAM word in the second storage subarray can participate the second-step data matching. On the contrary, if the match result on FML is mismatch, the voltage of FML will be high during the hold phase of PC2, blocking the conducting path between PC3 and the corresponding SMDL, and

Design of a Two-Phase Adiabatic Content-Addressable Memory

581

thus the corresponding CAM word in the second storage sub-array cannot participate the second-step data matching. The match result merge block in Fig. 1 detects the voltages on SMDL and SML to determine the final match result for the corresponding CAM word. Table 1 shows the three possible cases of data matching in the proposed two-phase adiabatic CAM. In case 1, the result of first-step matching is mismatch, and the second-step matching for the corresponding CAM word is cancelled. In this case, FML is high during the hold phase of PC2, and both SMDL and SML keep low during the hold phase of PC3. In case 2, the result of first-step matching is match, and the result of the second-step matching for the CAM word is mismatch. In this case, FML is low during the hold phase of PC2, and both SMDL and SML keep high during the hold phase of PC3. In case 3, the result of first-step matching is match, and the result of the second-step matching for the CAM word is also match. In this case, FML is low during the hold phase of PC2, and SMDL is high and SML is low during the hold phase of PC3. Figure 5 shows the circuit of match result merge block, which samples the voltage on SMDL and SML during the hold phase of PC3 and generates a valid match result Out during the hold phase of PC4. The match result Out goes high during the hold phase of PC4 only if SMDL is high and SML is low during the hold phase of PC3.

3 Simulation Results The Hspice simulations for the proposed two-phase adiabatic CAM with 64 words×144 bits were performed using BPTM (Berkeley Predictive Transistor Model) 45nm technology with a Vdd of 1.0V. Figure 6 shows the waveforms of the proposed two-phase adiabatic CAM for the three cases of data matching described in Table 1. In Fig. 6, trapezoidal power clocks with a frequency of 500 MHz were used for the adiabatic CAM. For comparison, the Hspice simulations for the traditional single-phase adiabatic CAM proposed by Q. Xu et al. [6] were also performed using the same CAM size and spice parameters. Table 2 lists the power consumption comparisons for the traditional single-phase adiabatic CAM and the proposed two-phase adiabatic CAM. Both CAMs use CPAL gates and the same structure of CAM storage cells. The difference between them is that data matching in the proposed CAM is a two-step process and most of the second-step comparison are never activated due to mismatch in the first step. From Table 2, it can be seen that the proposed two-phase adiabatic CAM can achieve a power reduction of 16.9% compared to the traditional single-phase adiabatic CAM when the frequency of power clocks is 500 MHz. Table 1. Three cases of data matching in the proposed two-phase adiabatic CAM.

Case 1 Case 2 Case 3

First-step comparison mismatch match match

Second-step comparison inactive mismatch match

FML

SMDL

SML

high low low

low high high

low high low

Match result mismatch mismatch match

582

M.-C. Chang and Y.-T. Kuo Table 2. Power consumption comparisons for two adiabatic CAMs.

Frequency of power clocks 100 MHz 200 MHz 300 MHz 400 MHz 500 MHz

Traditional singlephase adiabatic CAM [6] (64 words×144 bits) 125.7 uW 153.9 uW 189.4 uW 222.7 uW 262.0 uW

Case 3

The proposed twophase adiabatic CAM (64 words×144 bits) 114.7 uW 133.1 uW 159.0 uW 184.2 uW 217.5 uW

Case 2

Power saving 8.7% 14.2% 16.1% 18.8% 16.9%

Case 1

Fig. 6. Waveforms for the proposed two-phase adiabatic CAM.

4 Conclusions We have presented the design of the proposed two-phase adiabatic CAM. Adiabatic CAMs can recycle the charge on match lines and keep the voltage drop between the power clocks and the output nodes close to zero, leading to lower power dissipation. In our two-phase adiabatic CAM, the match line is further segmented into two segments, FML (first-stage match line) and SML (second-stage match line), and two-step data matching are employed. If the result of the first-step comparison is mismatch, the second-step comparison will be cancelled to further reduce energy dissipation. The circuit of the match sensor has been designed to detect the result on FML and to control the driving of SML. Simulation results have shown that the proposed two-phase adiabatic CAM can achieve a power saving of 16.9% compared to the traditional single-phase adiabatic CAM at an operating frequency of 500 MHz.

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References 1. Pagiamtzis, K., Sheikholeslami, A.: Content-Addressable Memory (CAM) Circuits and Architectures: A Tutorial and Survey. IEEE J. Solid-State Circuits 41, 712–727 (2006) 2. Natarajan, A., Jasinski, D., Burleson, W., Tessier, R.: A Hybrid Adiabatic Content Addressable Memory for Ultra Low-Power Applications. In: 2003 Great Lakes Symposium on VLSI, pp. 72–75 (2003) 3. Bala, G.J., Perinbam, J.R.P.: A Novel Low Power 16x16 Content Addressable Memory Using PAL. In: 18th IEEE International Conference on VLSI Design, pp. 791–794 (2005) 4. Wu, Y., Hu, J.: Low-Power Content Addressable Memory Using 2N-2N2P Circuits. In: IEEE 2006 International Conference on Communications, Circuits, and Systems, pp. 2657–2661 (2006) 5. Zhang, S., Hu, J., Zhou, D.: A low-Power Adiabatic Content-Addressable Memory. In: IEEE 2007 International Midwest Symposium on Circuits and Systems, pp. 1285–1288 (2007) 6. Xu, Q., Ye, L., Hu, J., Huang, L.: The Implementation of Low-Power CAM with Fully Adiabatic Driving for Large Node Capacitances. In: 2009 World Congress on Computer Science and Information Engineering, pp. 413–417 (2009) 7. Hu, J., Xu, T., Li, H.: A Low-Power Register File Based on Complementary Pass-Transistor Adiabatic Logic. IEICE Trans. Inf. & Syst., E88-D (7), 1479–1485 (2005)

Branch Importance Assessment under Cut-Off Power Flow Based on EM Clustering Feng Yuan, Wang Li-ming, Xia Li, Bu Le-ping, and Shao Ying College of Electric and Information Engineering, Naval University of Engineering, Wuhan, Hubei, 430033 [email protected]

Abstract. Assessment on branch importance under cut-off power flow helps to analyze the size of system disturbances caused by breaking of different branches. This article has proposed four factors which affect the branch importance based on cut-off power flow. After a branch importance assessment form is formed, according to the actual characteristics of the data sheet, this article has put forward an attribute weight assessment algorithm based on EM clustering, and has verified it with an example of a 38-node power system. It can be found from the weight vector of an attribute that branch importance is mainly determined by DifV, DifGenPower and DifBrPower. Through the comparison of the original score sheet and the assessment form, we can find that the ultimate branch importance sequencing is consistent with the above conclusion, which thus has proved the correctness of the algorithm. Keywords: power system, cut-off power flow, branch importance assessment, data mining, EM algorithm.

1 Introduction In power system operation, we may often encounter a variety of disturbances, and component failures may also occur, so as to lead to out of operation for some components. Studying the distribution of power flow in the system under a branch breaking, and inspecting whether a branch breaking may cause overload of other branches in the system, or out of range for the voltages of some load buses, will help to guide operators to take timely measures to eliminate overload or out of range[1-4]. Besides, research on changes in system parameters under a branch breaking will also help to analyze the size of system disturbances caused by breaking of different branches. Therefore, it is of great significance to study branch importance assessment under cutoff power flow in the power system.

2 The Basic Idea of the Algorithm Suppose

the

A = {a1 , a2

attribute

set

of

the

multi-attribute

am } ; the instance set is L = {l1 , l2 ,

assessment

form

is

ln } ; sik is the assessed

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 585–592. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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value under the attribute of

ak which corresponds to the instance of li . Then, each

element in L can generate m assessed values under the corresponding A, which constitute the assessed value matrix S as shown in Table 1. Suppose the weight vector corresponding to each attribute is vector of the instance set is

W = [ w1 , w2

D = [ d1 , d 2

D = [ d1 , d 2

wm ]T , and the final decision

d n ] . Then

dn ]

= S iW

(1)

⎛ a11 … a1n ⎞ ⎛ w1 ⎞ ⎟⎜ ⎟ = ⎜⎜ ⎟⎜ ⎟ ⎜a amn ⎟⎠ ⎜⎝ wn ⎟⎠ ⎝ m1 It is very easy to understand the above assessment criteria. That is, for a multiobjective (or multi-attribute, multi-factor) decision problem, we just need to assess a single objective of each instance, and give different weights to different objectives. At last, through the use of Formula (1), the final decision (assessment) results can be obtained. A variety of multi-objective optimization problems are all using this method or a variant of this method to establish assessment criteria. The criteria are very simple, but the difficulty lies in how to give weight to each attribute. With the method of analytic hierarchy process, decision and scoring can be made according to expert experiences, so as to obtain the weight vector of the attribute set. However, this method can only rely on expert experiences, so it is inevitably subject to personal preferences, and the conclusion may be somewhat biased. This article proposes a method that using the clustering algorithm to cluster such data like in Table 1, and obtain the weight vector of the attribute set. Assume that we have obtained the decision vector D of Table 1, and add it into Table 1 as the decision attribute. Then, we can use the EM clustering algorithm. The clustering results have nothing to do with the weight determining the assessment, but the performance evaluation indicators of the clustering results, such as log-likelihood, have objectively shown the clustering effects. The greater the log-likelihood, the better is the clustering effect. If we remove one attribute, the performance of EM clustering will definitely be affected, which is reflected in changes in log-likelihood. Thus, the importance of the attribute is beyond no doubt related to the increment of loglikelihood after this attribute is eliminated. The larger the increment, the more important the attribute, and the greater the corresponding weight value; vice versa. xm } through the Assume that the class obtained from an instance set {x1 , x2 , EM algorithm is

{C1 , C2 ,

Cn } , and the definition of its log-likelihood is as

follows:

⎛ m n ⎞ log-likelihood = log 2 ⎜ ∏ ∑ P(C j ) P( xi | C j ) ⎟ ⎝ i =1 j =1 ⎠

(2)

Branch Importance Assessment under Cut-Off Power Flow Based on EM Clustering

Among them,

587

P (C j ) represents the probability of the class C j , and P ( xi | C j )

represents the probability of the instance

xi that belongs to the class C j .

Therefore, attribute weights can be determined by EM clustering, so as to assess branch importance of Table 1. The flow chart of the algorithm is shown in Figure 1.

Fig. 1. The flow chart of the clustering-based attribute weight assessment algorithm

Among the weight vectors obtained based on the above method, each weight value determines the importance of each attribute. Therefore, in the attribute set A, the length of each attribute interval must be consistent. It is not feasible to directly put the assessed value matrix in Table 1 into Formula (1), so we must normalize each column vector in Table 1 before calling Formula (1).

3 Generation and Pre-treatment of the Branch Importance Assessment Form In the process of power system running, when a branch breaking occurs, the system changes caused can be measured by the following four indicators: (1) DifV which represents the degree of changes in voltage magnitude of each node (bus). Assume that the node set of a power system is {n1 , n2 , nm } , and the voltage magnitude vector of the node at

t0 is

V (t 0 ) = ( v1 (t 0 ), v2 (t 0 ),

vm (t 0 ) ) . Suppose

the voltage magnitude vector of the node after a branch breaking occurs at

t1 is

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F. Yuan et al.

V (t1 ) = ( v1 (t1 ), v2 (t1 ),

vm (t1 ) ) , and then DifV that is the degree of changes

in voltage magnitude of each node can be defined by the following formula:

DifV =

sum

(( V

(t1 ) − V (t 0 ) )

2

)

=

m

∑ ( v (t ) − v (t ) ) i

i

1

2

0

(3)

i =1

Similarly, the other three indicators can be defined as: (2) DifAngle which represents the change in voltage phase difference at both ends of each branch:

(

DifAngle = sum ( d Θ(t1 ) − d Θ(t0 ) )

2

) ∑( dθ (t ) − dθ (t )) =

s

i

1

2

0

i

(4)

i =1

(3) DifBrPower which represents the change in power flow of each branch:

(

DifBrPower = sum ( dP(t1 ) − dP(t0 ) ) + ( dQ(t1 ) − dQ(t0 ) ) 2

s

∑( dp (t ) − dp (t )) + ( dq (t ) − dq (t ))

=

2

i

1

0

i

1

i

i

2

) (5)

2

0

i =1

(4) DifGenPower which represents the change in throughput of each electric generator:

(

DifGenPower = sum ( dPG(t1 ) − dPG(t0 )) + ( dQG(t1) − dQG(t0 )) =

2

r

∑( dpg (t ) − dpg (t )) + ( dqg (t ) − dqg (t )) 2

i

1

i

0

i

1

i =1

Fig. 2. Network topology of a power system

i

0

2

) (6)

2

Branch Importance Assessment under Cut-Off Power Flow Based on EM Clustering

589

The above four indicators can be obtained by changes in cut-off power flow before and after a branch breaking occurs. Taking a 38-node power system in Figure 2 as an example, we verify the algorithm in Section 1. In order to assess the importance of each branch in the power system as shown in Figure 2, we cut off each branch once, and then calculate the values of the above four indicators of this branch. In this way, the assessment form formed has 37 instances, each standing for a branch. The attribute set of instances is the set of the above four indicators, namely DifV DifAngle DifBrPower DifGenPower , thus, the branch importance assessment form formed at last is shown in Table 1. Discretization schemes of DifV DifAngle DifBrPower and DifGenPower are shown in Table 2-5.

｛

，

，

，

，

，

｝

Table 1. Branch importance assessment form of the 38-node example No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

DifV 0.00340432600125688 8.35695074179583E-5 5.79922563950256E-6 4.52266483535953E-7 4.70369428579647E-6 1.63826242767072E-6 8.4937409851007E-4 0.00127406114776511 0.00397347345954063 0.00524554884118672 0.00581685269274162 5.25050784172493E-4 2.14962039715691E-6 7.54057324361026E-6 2.29661214432493E-5 0.00544167874110104 0.00581215174258109 0.00608113567356488 0.00528995353171892 0.00340040174878768 8.34222458096973E-5 5.80113727376007E-6 4.54207889779117E-7 4.66150168089895E-6 1.57755124248942E-6 4.39409655938969E-4 0.00425203376119709 0.00535237865824362 0.00500876843243892 5.2388962991251E-4 2.23104061824839E-6 7.17878215082896E-6 2.31425659015219E-5 0.0051846872567289 0.00511754748673742 0.00574470018280519 0.00608058093239142

DifAngle 0.225061204314816 0.00175500756847545 1.21788255305471E-4 9.48868580944875E-6 1.05221282594555E-4 3.918663462142E-5 0.00724489758680611 0.0160705565193942 0.2849713667938 0.255524998164968 0.247852380591799 0.00597767204820271 3.32611539859744E-4 0.00132706353555697 0.00422876127676194 0.274829966591299 0.241066856607496 0.279314886385203 0.290905053314283 0.230132233688245 0.244469622137357 0.0169930520004376 0.00134492582399308 0.0134701961779425 0.00527617707997256 0.0133921304328285 0.290105456338376 0.254233157641671 0.246636267768716 0.0514066542511433 0.00145445802603794 0.00560294186581023 0.0182117928423102 0.235708773084351 0.279479943427911 0.245544129957989 0.265668195528536

DifBrPower 0.182368646034108 0.00176621523158616 2.09065388086655E-4 1.67574692252627E-5 1.81867998524467E-4 6.88491458626745E-5 8.8298148461953E-4 0.00141277037539125 0.0306387974169379 0.0087547183967317 0.00892276050498212 0.00125494564166862 2.86774184654777E-5 1.29354798877109E-4 5.53700607705125E-4 0.00935134841663105 0.00907613031447038 0.0373839870242216 0.036358974806495 0.181524120640651 0.196050928331789 0.00124950765565013 3.33934117103194E-5 8.41713237232106E-4 1.93844728179513E-4 3.85213613052834E-4 0.0308657343411399 0.00844027082940741 0.00819027052682039 0.00751048775465395 4.33707853158693E-5 2.32243852117149E-4 0.0014421898896895 0.00826738179897265 0.00882394266644837 0.0345394717320888 0.0368089637468814

DifGenPower 81.9581968284197 44.0992484601377 3.06015258092036 0.238419053323693 2.64663900361579 0.984654611149533 2.33169762488723 3.81472368639318 0.530793499162651 0.487844307193314 0.5011026542472 11.4781453362922 0.34985442882718 1.42652531449544 4.46068375514486 0.739382602508517 0.69198848196906 0.761174785552933 0.751143181512689 82.5066180433843 69.1421012136608 4.80700796346065 0.378141330327426 4.00965511443702 1.50034814197802 2.50087120345261 0.861611087799057 0.768432301551797 0.736458398036631 15.1739420068954 0.379329253476743 1.45037847231635 4.72610557633655 0.685668552534714 0.769166073278181 0.716357544597963 0.769467493329999

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before discretization after discretization

DifV≤1

1
3
DifV>5

1

2

3

4

Table 3. DifAngle attribute discretization scheme

before discretization after discretization

DifAngle≤0.1

0.1
1
DifAngle>5

1

2

3

4

Table 4. DifBrPower attribute discretization scheme

before discretization

DifBrPower≤max (DifBrPower)/10

max(DifBrPower)/10< DifBrPower≤5max(Dif BrPower)

5max(DifBrPower)< DifBrPower≤10max (DifBrPower)

after discretization

1

2

3

Table 5. DifGenPower attribute discretization scheme

max(DifGenPower)/10 5max(DifGenPower)< before DifGenPower≤max
4 Comparison and Analysis of Assessment Results According to the algorithm as shown in Figure 1, assess the weight of each attribute in the attribute set after discretization in Table 1. According to algorithm requirements, we need to use the EM algorithm to do clustering five times, and the corresponding log-likelihood values are shown in Table 6. Table 6. Log-likelihood values in five clusterings

attribute range loglikelihood

all attributes

eliminate DifV

eliminate DifAngle

eliminate DifBrPower

eliminate DifGenPower

-2.15059

-1.67665

-2.11715

-1.79912

-1.75015

，

，

，

Thus, the weight vectors of all attributes are (0.4739 0.0334 0.3515 0.4004), and the obtained branch importance sequencing of the 38-node example based on cut-off power flow is shown in Table 7.

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From the weight vector of an attribute, we know that branch importance is mainly determined by DifV, DifGenPower and DifBrPower. Through the comparison of the original score sheet 1 and the assessment form 7 , we can find that the final branch importance sequencing is consistent with the above conclusion, which thus proves the correctness of the algorithm. Table 7. Branch importance sequencing of the 38-node example

Branch Number

Assessed value of branch importance

20 1 21 18 37 36 19 17 11 16 28 10 35 34 29 27 9 2 30 8 12 7 26 33 22 15 24 3 5 32 25 14 6 31 23 13 4

1.01731721367712 1.01588484924231 0.721427125401349 0.575587928711358 0.572985335568080 0.540165295375911 0.513367468382353 0.499136026773862 0.499078347302102 0.474866037820130 0.464046033362561 0.455066031817065 0.449336556866262 0.448133545269075 0.435787238660239 0.423066976253150 0.398748138501264 0.223307920618338 0.132840176543426 0.121024354670244 0.0985035329464798 0.0787404032431944 0.0474246567292034 0.0282604264937813 0.0268170334869193 0.0237543366723871 0.0217109911625610 0.0145092229886538 0.0123603934558930 0.00745282635211794 0.00715307366430159 0.00668891489531057 0.00382154538579692 0.00103834265319858 0.000863590968056526 0.000733222049597563 0

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5 Conclusion This article has proposed a clustering-based attribute weight assessment algorithm, and then put forward four factors which may affect the branch importance based on cut-off power flow. After the branch importance assessment form has been formed, this article has verified this algorithm with an example. From the weight vector of an attribute, we find that branch importance is mainly determined by DifV, DifGenPower and DifBrPower. Through the comparison of the original score sheet 1 and the assessment form 7, we know that the final branch importance sequencing is consistent with the above conclusion, which thus has proved the correctness of the algorithm. Branch importance assessment results help to analyze the size of system disturbances caused by breaking of different branches, which may provide decision support for static security control of a power system.

References 1. Grainger, J.J., Stevenson, W.D.: Power system analysis. McGraw-Hill, New York (1994) 2. Wang, X., Fang, W., Du, Z.: Modern Power System Analysis, pp. 20–100. Science Press, Beijing (2003) 3. Xiaomeng, B.: Work on improving the performance of power system state estimation based on PMU, pp. 1–20. Zhejiang University (2008) 4. Zhang, B., Chen, S., Yan, Z.: Advanced power network analysis, pp. 241–277. Tsinghua University Press, Beijing (2007) 5. Yan, X., Song, Z., Bi, C.: Mathematical modeling and experiments, pp. 9–40. Science Press, Beijing (2009) 6. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn., pp. 1–324. Morgan Kaufmann Publishers, Amsterdam (2005) 7. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)

Knowledge Discovery of Energy Management System Based on Prism, FURIA and J48 Feng Yuan, Xia Li, Wang Li-ming, Pu Le-ping, and Shao Ying Electric Power and Information Engineering College, Naval University of Engineering ,Wuhan, Hubei, China [email protected]

，，

Abstract. Facing mountains of data in modern energy management system Related operators need to use machine learning to derive corresponding knowledge to support its decision-making. In view of the above question Based on Prism, FURIA and the J48 classifier this paper used 10 fold cross validation on the energy management system for training a data table TP rate respectively were: 92%, 88% and 84%, Prism classifier produced 5 rules, FURIA classifier produced 4 rules decision tree generated by J48 had 5 valid braches Rules generated by classifiers can provide decision-making guidance for energy management system, and accelerate decision-making response performance.

，

。

，

Keywords: Power System Supervisory and Control, Data Mining, Classifier.

1 Introduction With increasing demand of modern power system monitoring, energy management system (EMS), which is the brain of power system, has more and more data type. At the same time, the data will accumulate exponentially over time. As a result, it is difficult for power system operator to make a correct operation. If power monitoring platform is not so “clever”, the operator must have a high professional ability to deal with variety of system abnormal in a correctly, quickly and effectively way, when facing huge data and information. One of the most important task of power system operator is to determine the current state of the system. Facing variety of analog value(e.g. bus voltage, branch power flow, et al) and digital value(e.g. switch position and state), the operator must construct the running state of the system quickly according to the raw data, so as to make a correct operation in the next step. For an instance, if the secure state of power system has three levels: normal, abnormal and emergency, operator has to confirm what state the system running in according to the current state of generators, loads and branches, and decision whether the energy need to be scheduled or optimized and how to do them. However, the problem is that, it is too difficult to class the data and get knowledge, when the quantity of history data is huge, or the number of state parameter is excessive, or the refresh frequency of real-time data is over quick. The technology of data mining, also called knowledge discovery, can provide theory and technique support for solutions of the problem[1-3]. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 593–600. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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To solving this problem, reference [4] made some beneficial attempt using rough set. Based on WEKA[5], this paper mines the same data in [4] by using Prism[6] FURIA[7] and J48[8] classifier, and produces some helpful rules.

、

2 Prism, FURIA and J48 Classifier 2.1 Prism Prism is a kind of covering algorithm to produce rules[6]. A covering algorithm is to take each class in turn and seek a way of covering all instances in it, at the same time excluding instances not in the class. By its very nature, this covering approach leads to a set of rules rather than to a decision tree. Prism generates only correct or “perfect” rules. It measures the success of a rule by the accuracy formula. Any rule with accuracy less than 100% is “incorrect” . 2.2 FURIA FURIA classifier extends the well-known RIPPER classifier [7]. It not only retains the advantage of RIPPER, but also can produce fuzzy rules. RIPPER divides the training data set to the growing set and pruning set. In the first step, the rule will be specialized by adding antecedents which were learned using the growing set. Afterward, the rule will be generalized by removing antecedents using the pruning set. The criterion used by the growing set to generate a single rule is:

⎛

⎛

⎞ ⎛ p ⎞⎞ − log 2 ⎜ ⎟⎟ ⎟ ⎝ p + n ⎠⎠ ⎝ pr + nr ⎠

IGr = pr × ⎜ log 2 ⎜

⎝

pr

(1)

where pr and nr denote, respectively, the number of positive and negative instances covered by the rule; likewise, p and n denote the number of positive and negative instances covered by the default rule. The criterion used by the pruning set to generate a single rule is: p − nr V (r ) = r (2) pr + nr FURIA replace pr and nr in the formula (1) and (2) by fuzzy membership function. For every single attribute, the membership function is presented by the formula (3): c1 ≤ x ≤ c2 ⎧ 1

⎪ x−c 0 ⎪ ⎪ c1 − c0 F I ( x) = ⎨ ⎪ c3 − x ⎪ c3 − c2 ⎪ ⎩ 0

c0 ≤ x ≤ c1

(3) c2 ≤ x ≤ c3 else

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Then the fuzzy membership of an instance which owns k attributes can be presented as: k

μr ( x) = ∏ I i ( x )

(4)

i =1

2.3 J48

J48 classifier adopts the famous C4.5 decision tree algorithm. C4.5 inherits the advantages of ID3, and makes improvement in the following respects: (1) It use information gain ratio to select attributes, for overcoming the disadvantage of tendency to select the attributes having more instances when using information gain. (2) It prunes the tree during its constructing process. (3) It can change the continuous attributes to discrete attributes. (4) It can deal with missing values. In C4.5 decision trees, every node contains information which can compute the entropy (E) of attributes. The information is compose of the counting of positive instances and negative instances, for determining which attribute can be used to splitting by the minimum E. The smaller is E, the more-organized is the attributes in the data set. The entropy of set S can be presented as: k

Info ( S ) = − ∑ ( freq ( Ci , S ) / S )i i =1

log 2 ( freq ( Ci , S ) / S where

)

(5)

freq ( Ci , S ) is the counting value of instances belonging to class Ci in S,

S is the counting value of S. The formula of weighted sum of subset entropy is: Infox (T ) = −

∑ (( T

i

/ T )i Info (Ti ))

(6)

where T is the split set according to attribute x. The selected node corresponds to the maximum entropy gain which can be obtained by the entropy difference between the split set and the original set.

3 EMS Dataset and Its Visualization Here the validating dataset is same to reference [4], and be presented as Tab.1. In Tab.1, No. is the ID of an instance, PL is the ratio of actual capacity of branch flow to its rated capacity, V is bus voltage per unit. CB is breaker status, D is the decision. S means secure. U1 means abnormal. U2 means emergency.

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PL1 N L N N L L L L N L L L N L N N L L L N L H L H L

PL2 N N N L H N N H L H N N H N L H N N N N N N N N H

PL3 L N L L N N L N L N L L N N L L N N N L N N N N N

V1 N H N N H H H N N H H H L H N N H H H N H L H N N

V2 H L H H N L N N H N N N L L H H L N L H L H L H N

V3 N H N N H H H N N H H H L H N N H H H N H L H N N

Fig. 1. Visualization of Tab.1

CB1 ON OFF ON ON OFF ON OFF OFF ON OFF OFF OFF ON OFF ON ON ON OFF ON ON ON ON ON ON OFF

CB2 ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON

D S U2 S S U1 U1 U2 S S U1 U2 U2 U1 U2 S S U1 U2 U1 S U1 U2 U1 S S

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The histograms shown in Fig.1 can visualize the instance set. In each histogram, every pole presents the counting value of instances for certain attribute value. Different colors in the pole present different counting value of instances for certain attribute value in the decision attribute.

4 Results and Performance of Prism The results and evaluation of Prism are shown in Tab.2-4. Table 2. Overview of Evaluation of Prism Correctly Classified Instances Incorrectly Classified Instances Kappa Statistic Mean Absolute Error Root Mean Squared Error Relative Absolute Error Root Relative Squared Error Coverage of Cases(0.95 level) Mean Rel. Region Size(0.95 level)

92% 8% 0.8786 0.0533 0.2309 12.037% 48.9008% 92% 33.333%

Table 3. Detailed Accuracy by Class of Prism Class S U2 U1 Avg.

TP Rate 1 0.857 0.875 0.92

FP Rate 0 0.056 0.059 0.034

Precision 1 0.857 0.875 0.92

Recall 1 0.857 0.875 0.92

F-Measure 1 0.857 0.875 0.92

ROC Area 1 0.901 0.908 0.943

Table 4. Confusion Matrix of Prism Class S U2 U1

S 10 0 0

U1 0 6 1

U2 0 1 7

From above tables, it can be included that the classing performance of Prism is quite well for the validating dataset. The TP rate is up to 92%. There is only 2 fault classing instances in the instance set. Besides the evaluation index, the output of Prism classifier can be expressed as ifthen rules, which is the basis of EMS expert system. These rules are shown as: If V1 = NORMAL then S If CB1 = OFF and PL2 = NORMAL then U2 If PL1 = HIGH and V1 = LOW then U2 If V2 = LOW and CB1 = ON then U1 If PL2 = HIGH and V1 = HIGH then U1

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5 Results and Performance of FURIA The results and evaluation of FURIA are shown in Tab.5-7. Table 5. Overview of Evaluation of FURIA Correctly Classified Instances Incorrectly Classified Instances Kappa Statistic Mean Absolute Error Root Mean Squared Error Relative Absolute Error Root Relative Squared Error Coverage of Cases(0.95 level) Mean Rel. Region Size(0.95 level)

88% 12% 0.8175 0.073 0.2535 16.4855% 53.6819% 92% 36%

Table 6. Detailed Accuracy by Class of FURIA Class S U2 U1 Avg.

TP Rate 1 0.857 0.75 0.88

FP Rate 0.067 0.111 0 0.058

Precision 0.909 0.75 1 0.894

Recall 1 0.857 0.75 0.88

F-Measure 0.952 0.8 0.857 0.879

ROC Area 1 0.937 0.868 0.94

Table 7. Confusion Matrix of FURIA Class S U2 U1

S 10 1 0

U1 0 6 2

U2 0 0 6

The rules generated by FURIA are shown as: (V1 = NORMAL) => Decision=S (CF = 0.9) (CB1 = OFF) and (PL2 = NORMAL) => Decision=U2 (CF = 0.82) (V2 = LOW) and (CB1 = ON) => Decision=U1 (CF = 0.83) (PL2 = HIGH) and (V1 = HIGH) => Decision=U1 (CF = 0.66) It can be included that the result of FURIA is a little different from that of Prism.

6 Results and Performance of J48 The results and evaluation of J48 are shown in Tab.8-10.

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Table 8. Overview of Evaluation of J48 Correctly Classified Instances Incorrectly Classified Instances Kappa Statistic Mean Absolute Error Root Mean Squared Error Relative Absolute Error Root Relative Squared Error Coverage of Cases(0.95 level) Mean Rel. Region Size(0.95 level)

84% 16% 0.7585 0.1029 0.3036 23.2134% 64.2903% 92% 37.3333%

Table 9. Detailed Accuracy by Class of J48 Class S U2 U1 Weighted Average

TP Rate 1 0.857 0.625 0.84

FP Rate 0 0.167 0.059 0.065

Precision 1 0.667 0.833 0.853

Recall 1 0.857 0.625 0.84

F-Measure 1 0.75 0.714 0.839

ROC Area 1 0.877 0.886 0.929

Table 10. Confusion Matrix of J48 Class S U2 U1

S 10 0 0

U1 0 6 3

U2 0 1 5

J48 can generate decision tree when the algorithm is finished. Thus the corresponding decision tree is shown as Fig.2.

Fig. 2. Decision Tree Generated by J48

In Fig.2, the non-leaf nodes of decision tree present conditional attribute, the leaf nodes present class value, numbers in the bracket after the class value present the number of supported instances. The branches present conditional attribute value.

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7 Conclusion No matter whether table 1 is real power system state parameter set or simulation data set, the results of classifier in section 3-5 make quite guiding significance on EMS. If the if-then rules generated by Prism and FURIA, or the decision trees generated by J48 can form a intelligent module of EMS by way of an expertise database, it can not only provide decision support and reference for EMS to improve response performance of EMS, but also apply in 3D power system simulation training. When certain characteristic of power system is hard to be generated by real-time simulation, the expertise rules generated by offline history data or assumptive contingency data can be helpful to generate the responding characteristic. Therefore, it can provide another feasible solution for power system simulation training.

References 1. Madan, S., Son, W.K., Bollinger, K.E.: Applications of Data Mining for Power Systems. In: Proceedings of 1997 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE 1997), Canada, pp. 403–406 (1997) 2. Ordieres Mere, J., Ortega, F., Bello, A., et al.: Operational Information System in a Power Plant. In: Proeeedings of the IEEE International Conference on Systems, Man and Cybernetics, Computational Cybernetics and Simulation, Orlando, USA, pp. 3285–3288 (1997) 3. Steele, J.A., McDonald, J.R., D’Arcy, C.: Knowledge Discovery in Databases: Applications in the Electrical Power Engineering Domain. IEE Colloquium(Digest.) 340, 33–38 (1997) 4. Lambert-Torres, G.: Application of Rough Sets in Power System Control Center Data Mining. IEEE Transaction on Power Delivery 17(3), 1368–1373 (2002) 5. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn., pp. 1–324. Morgan Kaufmann Publishers, Amsterdam (2005) 6. Cendrowska, J.: PRISM An algorithm for inducing modular rules. Man-Machine Studies 27(2), 349–370 (1987) 7. Huehn, J., Huellermeier, E.: FURIA: An Algorithm for Unordered Fuzzy Rule Induction. Data Mining and Knowledge Discovery 47(19), 293–319 (2009) 8. Quinlan, R.: C4.5: Programs for Machine Learning, pp. 1–100. Morgan Kaufmann, San Francisco (1993)

Study on the Medium-Term and Long-Term Forecast Technology of Wind Farm Power Generation Yang Gao1, Li Liu1, Guoyan Liang1, Shihai Ma1, and Chenwei Tian2 1 Shenyang Institute of Engineering, Shenyang 110136, Liaoning Province, China 2 Beijing Institute of Technology, Haidian District, Beijing 100081, China

Abstract. In view of analyzing forecast method of long-term forecast of generating capacity of wind power, this paper puts forward the long-term forecast model of wind power generation, which is based on the grey systems theory. Considering that wind power generation depends on wind speed, first we may establish wind - power function through the existing wind speed and wind power of the pre-installed wind turbine, resulting in years of wind power generation data. And then, a grey information renewal GM (1 ,1) model for predicting calculated capacity of annual wind power generation is established by using the calculated annual wind power in this paper. Moreover, the model is applied to predict the calculated capacity of annual output of a wind turbine generator system in FuJin wind farm. Keywords: Grey Forecast; Grey Model; Forecast of Wind Power Generation; Mean Absolute Error.

1 Introduction In recent years, wind power generation has made an immensely rapid headway. Developing wind power, a king of energy without carbon, is of great significance to carrying out a strategic plan of energy in our country, ensuring Energy Safety, developing low-carbon economy and dealing with climate change. In order to make the invest benefit evaluation on the large-scale wind farm, the feasibility study on the design of a wind power plant, the choice of the address, the arrangement of overhaul and the management examination, the long-term forecast of the generating capacity of wind power will be needed. In this way (one month to one year), we can provide the evidence for the management of wind farm. By predicting the generating capacity of wind power in a year, we may work out the assessing method on the production of the wind farm and meanwhile make an effective evaluation about the effect after the establishing of the farm. By forecasting the generating capacity of wind power (one month to a quarter), we can also supply the evidence for the arrangement of the overhaul and maintenance the equipment in the wind farm. In a word, the medium-term and long-term forecast of wind power generation is one of the problems which needs to be solved immediately in the operation management nowadays[1,2]. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 601–607. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 The Medium-Term and Long-Term Forecast Method of Wind Power Generation Capacity The cycle of long-term forecast of wind power generation needs a period of time which is as long as more than one year, but the numerical weather forecast can not make the forecast in such long period of time[3-4]. To solve the problem of the long-term forecast of wind power generation, this paper brings forward the long-term forecast model of wind power generation which based on the Rough Set Theory. The wind-driven generating unit mostly adopts the maximum power point tracking (MPPT) control method so as to obtain the clean energy resources farthest. Therefore, there are direct and certain relation to the generating capacity the wind-driven generating unit and speed. To get the long-term capacity forecast of wind power generation, the best way is to make the long-term forecast on average wind speed and then we can get the long term generating capacity forecast of wind power by making use of the fundamental characteristics curve of wind-mill generator. The common medium-term and long-term predicting methods are Support Vector Machine, Neural Networks, Time Series Analysis, Grey Forecast etc, of which Grey Forecast is the basic thinking and method based on the grey systems theory[5-6]. The subject investigated of Grey Systems Theory is the grey system of which part of the information is known, while the other is unknown. The factors influencing the change of generating capacity of wind power are uncertain, and it seems that they are irregular, so the system of wind power generation can be regarded as a grey system. Because the grey forecast model has the advantages of having less historical data needed, simple model, high precision of forecast, easily calculating, no need to take the distributing order etc., thus it has got the extensive application. The routine greater precise data of forecast model GM 1,1 are only the latest several data, and in order to cover the shortage, this paper introduces a Metabolic Grey Model –GM (1, 1) which makes forecast and verification of the annual generating capacity of some wind-driven generating units in Fujin wind farm.

（）

3 Metabolic Grey Model –GM (1,1) 3.1 The Principles Of Metabolic Grey Model –GM (1,1) The Grey Systems Theory accumulates the seemingly irregular historical data serial 1-AGO , this make it have the exponential growth feature. Because the solutions of differential equation has the feature of exponential growth Consequently, by using the differential equation model, it is quite natural to get the regular historical data serial with the growth of exponential growth after accumulating the data serial (1-IAGO). Making use of the solutions of differential equation, we can easily make forecast on the regular historical data serial of the generating capacity of wind power and then make the reverse forecast value, that is, we can get the actual capacity forecast value by descending the regular historical data serial (1-IAGO). The most common model of grey forecast is GM 1,1 model, but as a medium-term and long-term forecast method, it has less precision. For the sake of high precision, this paper introduces Metabolic Grey Model –GM (1,1). On the one hand the model keeps the advantages of the routine

(

)

，

（）

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GM (1,1) model, on the other hand it can also take the disturbance factors coming into the system into account promptly. To use the Metabolic Grey Model to make forecast is not to use keep forecast with one model, it is to predict a value according to the model GM(1,1) established on the basis of the known serials, and then add the value to the known serials, meanwhile delete the oldest datum so as to keep the serials invariable. In this way, we can predict one by one and fill vacancies in the proper order until we finish the forecast target. This method overcomes the shortage of the regular GM(1,1) model[7]. 3.2 Grey Model GM

（1,1）

（）

GM represents Grey Model, In the bracket of GM 1,1 , the first 1 stands for First Order Equations, and the second 1 for a variable. Suppose that the known historical annual generating capacity is X, the data serial is as the following:

X (0) = [ x (0) (1), x (0) (2), …, x (0) (n)]

(1)

Making the accumulation of the serial, we get 1-AGO, and the new serial is:

X (1) = [ x (1) (1), x (1) (2),…,x (1) (n)]

(2)

among which k

x (1) (k ) = ∑ x (1) (i ) i =1

and from this new serial we got the close average value serial

Z（1）

Z (1) = [ z (1) (1), z (1) (2),…, z (1) (n)] among which

z (1) (k ) = 0.5 x (1) (k )+0.5x (1) (k -1) , k = 2,3, …, n

(3)

(4)

(5)

To set up the grey model GM (1, 1), the first degree Whitening differential equation is

dx (1) + ax (1) = b dt

(6) in which a and b stand for parameter. The least square in the parameter serial A = [a

b]T is A = [a b]T = ( BT B) −1 BT Y

among which

⎡ − z (1) (2) ⎢ (1) − z (3) B=⎢ ⎢ # ⎢ (1) ⎣ − z ( n)

⎡ x (0) (2) ⎤ 1⎤ ⎢ (0) ⎥ ⎥ x (3) ⎥ 1⎥ Y =⎢ ⎢ # ⎥ #⎥ ⎢ (0) ⎥ ⎥ 1⎦ ⎣ x ( n) ⎦

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Put the calculated parameter a, b into equation (6), assume

x (1) (0) = x (0) (0) , we

solve the differential equation, and then get the equation for model GM(1,1) Λ (1)

b b x (k + 1) = [ x (0) (0) − ]e − ak + a a

(7)

After that we make the first degree decreasing restoration to the equation and we mark it as 1-IAGO and then we can get the grey forecast model GM(1,1)for the original serial X

，

Λ (0)

Λ (1)

Λ (1)

x (k ) = x (k ) − x (k − 1) ,

k = 1, 2,…, n

(8)

3.3 Metabolic Grey Model –GM (1, 1) [8]

In the original data serial, we insert new information information x

(0)

x (0) (n + 1) and delete the oldest

（）

(1) , and then we can set up the grey model GM 1,1 according to the (0) steps mentioned in section 2.2 by taking X = [ x (0) (2),…, x (0) (n), x (0) (n + 1)] as the original serial. We can do it again and again and then fill vacancies in the proper order until we finish the forecast target, that is, the Metabolic Grey Model –GM (1,1). On the forecast side, the metabolic model GM (1, 1) is an ideal model. With the development of the systems, the old information is becoming less important, so it is natural to delete the old information while the new information is constantly supplied. The establishing of the serial model can reflect the characteristic of the present system, especially when the system changes beyond the past with the accumulation of the quantitative change comparing with the old-timely system, so it is clearly reasonable to delete the old information which can not reflect the current characteristic of the system. Besides, by constant metabolism we can avoid the expanding continuously of computer memory because of the increase of information, meanwhile we can also avoid the difficulty of the increasing forecast budget in establishing model[9].

4 Analysis of the Case of Long-Term Generating Capacity Forecast of Wind Power Let’s take some wind farm in Fujin area as an example. Here we have the time serial of daily mean wind speed from 1954 to 2009 in this area. See Fig.1. The long-term generating capacity forecast of wind power is the forecast of the energy capture process, it has direct connection with the forecast of mean wind speed. When we get the changing forecast data of wind speed, we can calculate the actual generating capacity by the curve of design feature of wind-driven generating units. We can also find that the output power of wind power generation has certain relation with wind speed. This mainly depends on the MPPT control strategy of the wind power generating.

605

daily mean wind speed(m/s)

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Time(day)

Fig. 1. daily mean wind speed from 1954 to 2009 in Fujin Area

By five-time polynomial combining the measured data of the relation between the output power of wind power generation and wind speed, we obtain the feature of the output power of wind-driven generating unit, see formula 9. Putting the data of the daily mean wind speed in Fujin Area from National Meteorological Information Centre to formula 9 and then integrating in one year cycle, we can obtain the calculated values of the generating capacity of some wind-driven unit from 1954 to 2009 in Jinjiang Area. Therefore, in view of the establishing of wind-driven farm in Jinjiang in a short time, we may take the calculated values as the sample for the study on the forecast methods of, instead of the actual generating capacity.

P = 6.61925 −3.54685v − 3.16953v2 + 2.40084v3 −0.19352v4 + 0.00436v5

(9) In the formula, V stands for speed, the unit is m/s, P for power and the unit is kw. First of all, we take the calculated values of the annual generating capacity from 1954 to 1961 as the sample data to set up the grey model GM(1,1), making the forecast on the annual generating capacity in 1962 of the unit. Then we establish the grey model GM (1,1) by using the calculated values of the annual generating capacity from 1955 to 1962 as the sample data, making the forecast on the annual generating capacity in 1963 of the unit. Every time when insert the new data of the generating capacity and delete the old data, we update the data in turn until we get the forecast generating capacity in 2009 of the unit. The comparing figure of the predicting curve and the curve of the original sample is shown as Fig.2.

power generation (MW h)

3400

calculate values 计算值预测值 forecast values

3200 3000 2800 2600 2400 2200 2000 1800 1600 1400

1955

1965

1975

1985

1995

2005

2015

sample point(year)

Fig. 2. medium and long-term wind power generation forecast

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The evaluation criterion of model is the precision of forecast. This paper chiefly applies the generalized mean absolute error NMAE to evaluate the precision of forecast of the model. It is defined as installed capacity

（

（

MAE =

1 N

NMAE =

））

N

∑x −x i =1

i

(10)

ip

MAE CAP

(11)

The generalized mean absolute error of the generating capacity of some wind-driven generating units in Fujin Area from 1962 to 2009 is 7.8806%. It is obvious that metabolic grey model –GM (1, 1) have the better combining precision, we can come to the conclusion that the model can be applied to the medium-term and long-term forecast of the generating capacity of wind power generation. By using the model to calculate, We can obtain the stimulant value of the generating capacity of the wind-driven generating units from 2001 to 2008 in Fujin Area and comparing with its original serial X, the relative mean error is 2.99976% a =0.03001<0.3 so it can more prove that the model can be applied to the medium-term and long-term forecast. The predicting value of the generating capacity of the generating units in 2009 is 2296.518MWh and the calculated value is 2464.958MWh the relative error between the predicting value and the calculated value is 6.833%

，

，。

，

，

5 Conclusion In order to solve the problem of medium-term and long-term forecast of the generating capacity of wind power generation, this paper puts forward the long-term forecast model of generating capacity of wind power generation, which based the grey systems theory. Adopting grey predicting method to analyze the actual affecting factors to the generating capacity of wind power, setting up the condition attribute standing for various affecting factors and the decision table standing for the decision attribute of wind power generation, making use of the grey systems theory to discretize and briefly analyze the concatenation attributes of the decision table, we finally obtain the main factors influencing the capacity of wind power generation, which are used as the input of the medium-term and long-term predicting model of the capacity of wing power generation. In the end, we apply the Metabolic Grey Model to predict and verify the generating capacity of the wind-driven units in Fujin Area.

References 1. Yu, J.-m., Yan, F., Yang, W.-y., et al.: Grey Variable Weight Combination Model for Middle and long term Load Forecasting. Power System Technology 29(17), 26–29 (2005) 2. Cai, G.-w., Du, Y., Li, C.-s., et al.: Middle and long-Term Daily Load Curve Forecasting Based on Support Vector Machine. Power System Technology 30(23), 56–60 (2006)

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3. Han, S.: Study of Short-term Wind Power Forecast Method. North China Electric Power University, Hebei (2008) 4. Huang, Z., Chalabi, Z.S.: Use of Time Series Analysis to Model and Forecast Wind Speed. Joural of Wind Engineering and Industrial Aerodynamics 56, 311–312 (1995) 5. Zhang, F.-s., Liu, F., Zhao, W.-b., et al.: Application of Grey Verhulst Model in Middle and long term Load Forecasting. Power System Technology 27(5), 37–81 (2003) 6. Liu, Y.-q., Han, S., Hu, Y.-s.: Review on Short-term Wind Power Forecast. Modern Electric Power 24(90), 6–11 (2007) 7. Deng, J.-l.: Grey Forecast and Decision. Huazhong University of Science and Technology Press, Wuhan (1989) 8. Liu, S.-f., Guo, T.-b.: Grey System Theory and Its Application. Henan University Press, Henan (1991) 9. Liu, S.-f., Xie, N.-m., et al.: Grey System Theory and Its Application. Science Press, Beijing (2008)

Optimal Space Vector Modulation Control for Three-Phase Inverter Su Xiaofang1, Rao Binbin2,*, Li Shijie2, and Zheng Ruilan2 1

Hubei University of Education, China 2 School of Electrical Engineering, Wuhan University,Wu Han, China [email protected]

Abstract. This paper proposes a method for optimization of the harmonic performance of three-Phase inverter under Space Vector Modulation(SVM) control. It is shown that SVM provides a number of degrees of freedom, which make it suitable for optimization, subject to desired constraints. An immune algorithm (IA) optimizer is used to solve the optimization problem. Simulation results are shown to result in significant reduction of dominant harmonics compared with the traditional control strategies. Keywords: Immune Algorithm; space vector modulation; degrees of freedom; Three-Phase Inverter; harmonic analysis.

1 Introduction With the rapid development of power industry, The application of inverter is more and more widely. High power electronic devices are being used increasingly to control and facilitate flow of electric power while meeting stringent operating conditions of today’s heavily loaded networks. One of such devices is voltage-sourced converter (VSC) that acts as a controlled voltage source, converting a dc voltage to an ac voltage with desired frequency, phase, and magnitude [1-7]. Fig. 1 shows the schematic diagram of a two-level VSC. The output waveform of the VSC contains harmonics, largest of which occurs around the switching frequency. Sinusoidal PWM (SPWM) has a relatively robust harmonic spectrum, i.e., the harmonic spectrum of the resulting waveform is tied to the selected switching frequency. In contrast, space vector modulation (SVM), which is a relatively new approach to waveform synthesis using a VSC, offers several degrees of freedom that can be used effectively to design an improved harmonic spectrum and obtain the desired waveform quality. SVM has found numerous applications in both power system schemes, such as static compensator (STATCOM) [8] and high voltage direct current converter systems [9], and electrical drive applications [10]. In [3], An optimized space vector modulation sequence for improved harmonic performance is presented. *

Corresponding author.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 609–616. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Fig. 1. Schematic diagram of a two-level VSC

This paper aims to optimize space vector sequences through exploiting the degrees of freedom available in SVM. The objective of the optimization is to relieve the filtering requirement by distributing the energy of the waveform more uniformly among harmonic components. An overview of the SVM methods is provided in the next section, which also describes the parameters that are used in the optimization. As will be described later, the problem involves both real and integer variables. Therefore, Immune Algorithms (IA), which can handle such mixed-integer problems, are used for its optimization. IA have been previously applied to find the filter parameters in electrical drives [3-7], as well as to a two level SPWM converter to minimize the harmonic content of its output waveform.

2 SVM Methods and the Degrees of Freedom In conventional SPWM, which is an analog domain method, the duration of each pulse is found through comparison of a sinusoidal reference waveform and a triangular carrier waveform. A digital domain variation of PWM, which is the SVM, on the other hand, directly computes the duration of voltage pulses using the amplitude and angular location of the reference vector. In a three-phase two level VSC, the output voltage of a leg can be either +Vdc/2 or−Vdc/2 (see Fig. 1), and as such, the VSC can be placed in eight states, depending on the ON/OFF status of its six switches (switches on the same leg have complementary states). The voltages corresponding to each state can be transformed to a space vector using six of these space vectors, denoted by active vectors V1 to V6 in Fig. 2, point to the vertices of a hexagon; two of the states translate to space vectors with zero length and are denoted by zero vectors V0 and V7 located at the origin[3-4]. Similarly, any set of balanced reference phase voltages va,vb and vc can be represented as a corresponding reference space vector V in two dimensions using function (1).

2 V = (va + vb e j 2π /3 + vc e − j 2π /3 ) 3

(1)

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Any reference vector that lies entirely within the hexagon can be decomposed to space vectors V0 to V7, A reference vector can be decomposed using virtually any subset of the eight space vectors. Nonetheless, decomposition is done typically using only the two adjacent active vectors. as shown in Fig. 2, for a Vref in the first sector of the hexagon. This is achieved by constructing voltages that average to the reference vector , which is sampled at a rate determined by a given sampling frequency Fs.

Fig. 2. Space vectors and decomposition of a reference vector

Fig. 3. Conventional SVM sequence

The voltages are synthesized by placing the converter in respective states for designated time shares within each sampling period. The time shares are proportional to the length of the projected vectors. The rest of the sampling period is filled with zero space vectors V0 and/or V7. Fig.3 shows the conventional SVM sequence. 0 For example, if ∆θ=2 , one period is divided into 180, i.e., it has 180 periods of vectors, so the sample period is

Ts = 0.02 s / 180 ≈ 111us

(2)

In sector I, time shares are calculated as

T4 = 3 2 mTs sin(π 3 − θ ) T6 = 3 2 mTs sin θ Tzero = T0 + T7 = Ts − T4 − T6

(3) (4) (5)

where T4, T6, T0, and T7 are the time shares of the respective voltage vectors (T1 for the first active vector V4, T6 for V6, T0 for V0, and T7 for V7), Ts is the sampling period and is equal to 1/Fs, θ is the angle between the reference vector and the space vector V4, and m is the modulation index, which is defined as

m = Vref (Vdc 2)

(6)

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Note that the SVM strategy offers several degrees of freedom, they are as follows . 1) It determines the time shares of space vector, but the individual time shares of T0 and T7 are not specified. 2) It does not identify the order in which they are applied. For example, the order of vector can be V0V4V6V7, but it also can be V4V0V6V7. this can be exploited as a degree of freedom for crafting waveforms with given properties. 3) In different period, the individual time shares of T0 and T7 can be different. For example, if each time of T0 and T7 is applied for half of the total zero-vector time , then it can be applied as that T0-share is 80% of the total zero-vector time for the next period. In different period, the order in which they are applied also can be different. For example, it can be V0V4V6V7, but it can be V4V0V6V7 in the next period.

3 SVM Optimization Based on IA Immune Algorithm (IA) belong to a group of evolutionary algorithms that are inspired by natural processes. Such methods seek to maximize the fitness of a population or, as in the common notion, minimize the associated cost (or objective) function[5-7]. In IA, the solution space is represented by a generation of candidate solutions, called chromosomes. An objective function (OF) is calculated to reflect the fitness of each chromosome. By exploiting the degrees of freedom built-in to SVM, appropriate switching sequences can be devised to obtain the desired waveform quality, through the definition of an appropriate OF. A. Objective Function This paper is desired to lower the harmonic content of the output current, so it use weighted THD (WTHD) as the OF. The WTHD, defined as follows, is calculated in the same way as THD, but each harmonic component is divided by its order, so that higher order harmonics receive lower weight and contribute less in this figure of merit. N

WTHD =

∑

(

n = 2,3,"

Vn 2 ) n

(7)

V1

B. Flow Of IA The Implementation of the IA contains the following steps, the flowchart of which is shown in Fig.4. a) Step 1. Individual encoding and initial populations. This paper presents an ameliorative genetic algorithm based on integer encoding strategy for optimization problems because of the shortages of binary encoding . To demonstrate how sequences are encoded, consider the conventionally used sequence V0V4V6V7. Encoding results in a string of 0(V0(000)), 4(V4(100)), 6(V6(110)), 7(V7(111)),so the conventional SVM sequence(0-π/6) can be encoded as Figure 5.

Optimal Space Vector Modulation Control for Three-Phase Inverter

Fig. 4. Flow chart of immune algorithm

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Fig. 5. Individual encoding

b) Sstep2. Vaccine inoculate. It can be likely to increase affinity of the antibody. According to the practical experience or normal control strategies, some feasible solutions of the practical problems can be gain. Those solutions are called as vaccine. And those vaccines are injected into the initial generation, which can greatly quicken convergence of the algorithm. c) Step 3. Fitness calculation. IA seek to maximize the fitness of a population, and the fitness is associated with Objective Function (OF). The definition of the OF in this paper is to minimize the THD of the output waveform under the method of SVM. d) Step 4. Immune selection. IA uses selection operator to simulate natural evolution. Some antibodies of high affinity should be inherited to the next generation with greater probability. e) Step 5. Crossover. Members of the antibodies are paired together. The process of crossover is that various antibodies exchange their substrings sections to generate new antibodies. Fig.6 shows the crossover option.

ChromA 046704670467...

046747606407...

Crossoverpoint ChromB 406747606407...

406704670467...

Crossoverpoint

Fig. 6. Crossover option

Fig. 7. Inversion option

f) Step 6. Mutation. Mutation operation is a process of randomly changing one or some alleles of an antibody. Based on the predetermined mutation rate, some antibodies randomly selected are mutated. In this paper mutation option is for the individual time shares of T0. g) step 7. Inversion. Inversion operation is that vectors exchange each other during the same period. Fig.7 shows the inversion option.

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h) Step 8. Random addition and New generation. Some antibodies randomly created will be injected into every generation, which is called as random addition in this paper. This process can avoid falling into the trap of local optima. After IA carries out above 8 operations, it will achieve the latest generation, If the termination criterion was satisfied, the procedures end, otherwise, it will continue from the beginning.

4 Discussion of Results The IA optimizer is run with the aforementioned OF. Although mutation is implemented in the IA solver as a means to reduce the likelihood of converging to a local minimum, the stochastic nature of IA necessitates running the optimizer several times to further reduce this possibility. For each run, the best sequence of vectors and the share of V0 are recorded. The main parameters used by IA in simulations were set as Table 1. The modulation index is 0.8 and the normalized sampling Period is 111us. The DC voltage is 25V, and the inductance is 63.7mH, the output current is 0.72A in theory. Table 2 shows the optimized SVM sequence (0-π/3).The T0-share in different period has also been optimized. Both the conventional and optimized SVM sequences are forward, however, the optimized SVM positions the vectors in a different order and allots different inactive time interval to the zero vector Z0. Table 1. Value of the IA parameters IA PARAMETERS

VALUE

Maximum iteration times

2000

Population Size

100

Population Length

240

Crossover probability Mutation probability

0.7 0.005

Inversion probability

0.015

Immune selection probability

0.01

Table 2. The optimized Svm sequence 047604760476046704674607640 740670467046704674067406740 764067406740674067406740670 467047640674067406740674607 460746076047602760276027062 702760267026702670267206702 672067206720672067206720672 607607260276027602760276027 602760276027620720672067

Fig.8 shows the harmonic spectrum of the voltage waveform of the conventional SVM, and Fig.9 shows the harmonic spectrum of the voltage waveform of the optimized SVM. Through the Fig 8. and Fig 9, for both conventional SVM and conventional SVM, harmonic components 3th, 5 th and 7th are the largest contributors to the harmonic distortion. However, optimization has reduced the 3th, 5th and 7th harmonics. Optimization has reduced the harmonics from 0.46% and for the conventional SVM sequence to 0.39% for the optimized sequence. What’s more, the optimization also reduces the DC component of the output current.Such significant reductions will result in smaller harmonic currents in the filter and thus allow the use of smaller filters.

Optimal Space Vector Modulation Control for Three-Phase Inverter

Fig. 8. Harmonic spectrum of the conventional SVM

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Fig. 9. Harmonic spectrum of the optimal SVM

5 Conclusion a) The possibility of applying IA to obtain optimized SVM sequences has been invest IAted in this paper. The OF has been defined with the goal of lowering most significant harmonics. b) The optimized sequence obtained using IA has been provided in this paper. This sequence distributes the waveform energy more evenly throughout the spectrum, resulting in less harmonic pollution. Since the aim of this paper is to optimize the harmonic performance of the SVM based output waveform, the associated switching losses are not included. Although this paper focuses on two level SVM, the developed method can be extended to multilevel schemes by devising proper formulation.

Acknowledgment The authors wish to express their gratitude to the National Foundation of China, school of electrical engineering of Wuhan University, and Hubei University of Education, for support of this research effort (National Natural Science Foundation of China under Grant 50807041, foundation of Key subject of Hubei University of Education, foundation of the master-degree program of Hubei University of Education, foundation of the key project of Hubei University of Education).

References 1. Holtz, J.: Pulsewidth modulation—A survey. IEEE Trans. Ind. Electron. 39(5), 410–420 (1992) 2. Schutten, M.: Genetic Algorithms for Control of Power Converters. In: Proceedings of 26th Annual IEEE of Power Electronic SpecialistsConference.PESC 1995 Record, Atlanta, GA, vol. 2, pp. 1321–1326 (1995) 3. Mehrizi-Sani, A., Filizadeh, S.: An Optimized Space Vector Modulation Sequence for Improved Harmonic Performance. IEEE Trans.Ind. Electron. 56(8), 2894–2903 (2009)

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4. Yuan, Yuan, J., Su, X., Chen, B., Tian, C.: Optimum Vector PWM Strategy for Three-Phase Inverter Based on Immune Algorithm. Transactions of China Electrotechnical Society 24(9), 114–119 (2009) 5. Yuan, J., Chen, B., Tian, C., Jia, J.: The research of Inverter’s control based on Immune Algorithm. In: Proceedings of the CSEE, vol. 26(5), pp. 110–117 (2006) 6. Yuan, J., Chen, B., Jia, J.: Gentic Algorithm based approach for invertor control. Automation of Electric PowerSystems 28(24), 32–35 (2004) 7. Yuan, J., Chen, B.: Research on Optimum Control Strategy of Three-Level Single-Phase Full-Bridge Inverter. Transactions of China Electrotechnical Society 21(3), 42–46 (2006) 8. Saeedifard, M., Nikkhajoei, H., Iravani, R.: A space vector modulated STATCOM based on a three-level neutral point clamped converter. IEEE Trans. Power Del. 22(2), 1029–1039 (2007) 9. Saeedifard, M., Nikkhajoei, H., Iravani, R., Bakhshai, A.: A space vector modulation approach for a multimodule HVDC converter system. IEEE Trans. Power Del. 22(3), 1643–1654 (2007) 10. Somasekhar, V.T., Gopakumar, K., Baiju, M.R., Mohapatra, K.K., Umanand, L.: A multilevel inverter system for an induction motor with open-end windings. IEEE Trans. Ind. Electron. 52(3), 824–836 (2005) 11. Lopez, O., Alvarez, J., Doval-Gandoy, J., Freijedo, F.D.: Multilevel multiphase space vector PWM algorithm. IEEE Trans. Ind. Electron. 55(5), 1933–1942 (2008) 12. Jiao, L., Wanlei: A novel Genetic algorithm Based on Immunity. IEEE Transactions on System,Man,and Cybernetics-Part A:Systems and humans 30(5), 552–561 (2000) 13. Bowes, S.R., Holliday, D.: Optimal regular-sampled PWM inverter control techniques. IEEE Trans. Ind. Electron. 54(3), 1547–1559 (2007) 14. Maswood, A.I., Wei, S.: Genetic-algorithm-based solution in PWM converter switching. In: IEE Proceedings Electric Power Applications,, May 6, vol. 152(3), pp. 473–478 (2005) 15. Gupta, A.K., Khambadkone, A.M.: A space vector PWM scheme for multilevel inverters based on two-level space vector PWM. IEEE Trans.Ind. Electron. 53(5), 1631–1639 (2006) 16. Lai, Y.S., Shyu, F.S.: Optimal common-mode voltage reduction PWM technique for inverter control with consideration of the dead-time effects—Part I: Basic development. IEEE Trans. Ind. Appl. 40(6), 1605–1612 (2004) 17. Beig, A.R., Narayanan, G., Ranganathan, V.T.: Modified SVPWM algorithm for three level VSI with synchronized and symmetrical waveforms. IEEE Trans. Ind. Electron. 54(1), 486–494 (2007) 18. Chen, B.-Y., Lai., Y.-S.: Switching Control technique of phase-shift- controlled full-bridge converter to improve efficiency under light-load and stand by conditions without additional auxiliary components. IEEE Trans. Power Electron 25(4), 1001–1012 (2009) 19. Oggier, G.G., Garcıa, G.O., Oliva, A.R.: Switching control strategy to minimize dual active bridge converter losses. IEEE Trans. Power Electron 24(7), 1826–1837 (2009) 20. Mao, X., Ayyanar, R., Krishnamurthy, H.K.: Optimal variable switching frequency scheme for reducing switching loss in single-phase inverters based on time-domain ripple analysis. IEEE Trans. Power Electron 24(4), 991–1001 (2009) 21. Lai, R., Wang, F., Burgos, R., Pei, Y., Boroyevich, D.: A systematic topology evaluation methodology for high-density Three-phase pwm AC-AC converters. IEEE Trans. Power Electron 23(6), 2665–2680 (2008)

Optimal Dead-Time Elimination for Voltage Source Inverters Su Xiaofang1, Rao Binbin2,*, and Zeng Yongsheng2 2

1 Hubei University of Education, China School of Electrical Engineering, Wuhan University,Wu Han, China [email protected]

Abstract. The dead-time effects of an inverter is analyzed, and a no-dead-time control scheme is presented for the Single-Phase bridge inverter. According to current polarity, a phase-leg can be decomposed into two switching cells without dead-time. Using Immune algorithm (IA) to find the optimal no-dead-time control sequence for the Single-Phase inverter . A simulation based on the no-dead-time control is designed based on matlab / Simulink, and the experiment is done with a full-bridge inverter. The simulation and experimental results verify that the output waveform is improved significantly DC voltage utilization ratio is increased.

，

Keywords: Immune algorithm; dead-time elimination; Inverter.

1

Introduction

In a pulse width modulation (PWM) controlled voltage source inverter (VSI), dead-time is used to avoid “shoot-through”. During dead time, a small interval during which both the upper and lower switches in a phase-leg are off. However, such a dead-time can cause problems such as output voltage loss and current distortion [1]. To overcome dead-time effects, several methods have been presented, these methods include dead-time compensation [2–6], and dead-time minimization [7–8] and dead-time elimination [9–11]. The dead-time varies with the gate drive path propagation delay, device characteristics and output current, as well as temperature, which makes the compensation less effective, especially at low output current, low frequency, and zero current crossing. For the dead-time minimization method, the dead-time effect cannot be completely removed under this circumstance, because dead time is still required when the current is around zero-crossing points in which current polarity detection is difficult and not accurate. Dead-time elimination method is based on decomposing of a generic phase-leg into two basic switching cells, which are configured with a controllable switch in series with an uncontrollable diode. Therefore, dead-time is not needed. In [9], current polarity is determined by detecting the terminal voltages of the anti parallel diode of a power device. In [10], present a dead-time elimination scheme without a Separate Power Sources for current polarity detection circuit. All the dead-time elimination methods are based on two-level PWM, and the PWM sequences are not optimized. *

Corresponding author.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 617–624. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Fig. 1. Single-phase full-bridge inverter

This paper will present an optimal dead-time elimination PWM sequences based on three-level for Single phase inverter. The separate power Sources for current polarity detection circuit is not needed in this method. Immune algorithm (IA) is used to optimize the PWM sequence, The objective of the optimization is to reduce the THD of the waveform on the basis of dead-time elimination.

2 Dead-Time Effect and Dead-Time Elimination A.

Dead-Time Effect

Fig. 1 shows a Single-phase full-bridge inverter. Fig. 2 shows the dead-time effect on output voltage in the voltage source inverter. S1 and S2 are the gating pulse for IGBT V1 and V3. Fig. 2(a) and Fig. 2(b) show the gating pulse in the ideal condition, and Fig. 2(c) shows the ideal output u0. Due to the delay of drive circuit and turn-off delay of power devices, these control signals are modified by adding a dead time in each rising pulse, Fig. 2(c) and Fig. 2(d) show the gating pulse (S1d ,S2d) with dead time. This additional dead time results in output voltage distortion which is defined as the difference between “u0” and “u0d” as shown in Fig. 2(c) and Fig. 2(f). Therefore, the real output voltage is greater (smaller) than its command as current is negative (positive). Fig. 3 shows the phase voltage and current of the inverter output. As shown in Fig. 3, the output voltage v0(t) is with distortion as compared to that without dead time (see waveforms A and B). However, waveforms A and B cross each other at points P1 and P2. The output current i0(t) is also distorted at these two zero-crossing points, as shown in Fig. 3. With conventional PWM control, the output voltage distortion is inevitable since dead-time must be added to avoid a shoot-through circuit in VSI. In next section, the dead-time elimination method is proposed. This method is based on decomposing of a generic phase-leg into two basic switching cells, which are configured with a controllable switch in series with an uncontrollable diode. Therefore, dead-time is not needed in PWM control.

Optimal Dead-Time Elimination for Voltage Source Inverters

i>0

P1

i<0

A（

V 0 (t)

i

619

id e a l ）

B（

w ith d e a d t im e ）

0

(a)

S1

(b)

S2

P2

i 0 (t) 0

(c)

u0

(d)

S1d

(e)

S2d

(f)

u0d td

(g)

Deadtime

?

u0

Fig. 2. Voltage distortion caused by dead time

B.

Fig. 3. Current distortion caused by dead time

Fig. 4. Decomposing of a generic phase-leg

Dead-Time Elimination

To explain the principle of the proposed dead-time elimination method, a generic phase-leg of VSI, as shown in Fig. 4, is considered. V1 is the upper side switch and V3 is the lower side switch. VD1and VD3 are anti-parallel diodes to switches V1 and V3, respectively. As shown in Fig. 4, an anti parallel diode is connected with a power device. As the power device is off while the current conduction continues, the anti parallel diode of its opposite power device provides the current path. For example, when power device “V1” is turned off and the current direction retains, “VD3” will provide the current path when power device “V1” is turned off. Similar facts occur to power device “V3” and diode “VD1 ”. Therefore, there is no need to turn on the opposite power device during this turn-off period. Once no switching occurs to its opposite power device, dead time is no more needed. As shown in Fig. 5, S1 and S3 are the gating pulse for IGBT V1 and V3. Once current is positive, P-cell control is retained. Meanwhile, there is no PWM control signal for N-cell control. Similarly, when current is negative, a PWM control signal is applied to N cell only. Therefore, dead time is no longer required while guaranteeing no short through between positive and negative dc links.

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Fig. 5. Signals for PWM generator.

3

Fig. 6. Flow chart of immune algorithm

Optimal Dead-Time Elimination

Immune Algorithm (IA) belong to a group of evolutionary algorithms that are inspired by natural processes. Such methods seek to maximize the fitness of a population or, as in the common notion, minimize the associated cost (or objective) function. An important feature of IA is their ability to deal with real and integer (or binary-coded) optimization variables simultaneously. This proves to be essential in voltage source inverter harmonic spectrum optimization. In [13-18], IA is used to optimize the switching control of inverters. In this paper, IA is used to optimize the switching control of dead-time elimination. A. Objective Function The objective of the optimization is to reduce the THD of the waveform on the basis of dead-time elimination. so it use weighted THD (WTHD) as the OF. The WTHD, defined as follows, is calculated in the same way as THD, but each harmonic component is divided by its order, so that higher order harmonics receive lower weight and contribute less in this figure of merit. N

WTHD =

∑

(

n = 2,3,L

Vn 2 ) n

(1)

V1

B. Flow Of IA The Implementation of the IA contains the following steps, the flowchart of which is shown in Fig.6. a) Step 1. Individual encoding and initial populations. Fig. 1 shows a Single-phase full-bridge inverter, this inverter can work on three level. In application of dead-time elimination, the switching control can be

Optimal Dead-Time Elimination for Voltage Source Inverters

u0

⎧ ⎪+ E ⎪ ⎪⎪ = ⎨ −E ⎪ ⎪ ⎪ 0 ⎪⎩

X

= 1

i

X

i

= 2

X

i

= 0

⎧ V 1、 V 4 ⎨ ⎩ V D 1、 V D

⎧ V D 2、 V D ⎨ ⎩ V 2、 V 3 ⎧ V 1、 V D 2 ⎨ ⎩ V 3、 V D 4

4 3

on

i >0

on

i <0

on

i >0

on

i <0

on

i >0

on

i <0

621

(2)

So the sequences of switching control can be defined as the following:

Chrom = X 1 , X 2 , X 3 , L X n

(3)

b) Sstep2. Vaccine inoculate. It can be likely to increase affinity of the antibody. According to the practical experience or normal control strategies, some feasible solutions of the practical problems can be gain. c) Step 3. Fitness calculation. IA seek to maximize the fitness of a population, and the fitness is associated with Objective Function (OF). The definition of the OF in this paper is to optimize the harmonic performance of the output current. d) Step 4. Immune selection. IA uses selection operator to simulate natural evolution. Some antibodies of high affinity should be inherited to the next generation with greater probability. e) Step 5. Crossover. Antibodies are paired together. The process of crossover is that various antibodies exchange their substrings sections to generate new antibodies.

Fig. 7. Mutation operation

Fig. 8. Inversion option

f) Step 6. Mutation. Mutation operation is a process of randomly changing one or some alleles of an antibody. Based on the predetermined mutation rate, some antibodies randomly selected are mutated. Fig.7 shows the inversion option. g) step 7. Inversion. Inversion operation is that vectors exchange each other during the same period. Fig.8 shows the inversion option.

4

Experimental Results

The IA optimizer is run with the aforementioned OF. For each run, the best sequence of vectors and the share of V0 are recorded. The main parameters used by IA in simulations were set as TABLE I. The modulation index is 0.8 and the sampling frequency is 10kHz. The DC voltage is 88V, and the inductance is 0.28H.

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Table 1. Value of the IA parameters IA PARAMETERS

VALUE

Maximum iteration times

2000

Population Size

100

Population Length

200

Crossover probability

0.7

Mutation probability

0.005

Inversion probability

0.015

Immune selection probability

0.01

Table 2. The optimized sequence

111111111111110111110111011 101010101010010000121000002 120002002020202020220222022 222202222222222222222222222 222222022222022202220202020 202002000021200000121000100 101010101011011101111110111 11111111111

TABLE.2 shows the optimum control sequence (one period) of dead-time elimination method searched by IA. The sequence can be translated into 4 gating pulses for switch V1,V2,V3 and V4, respectively. The diagram of experiment system is consists of a single-phase-full-bridge inverter, DSP TMSLF2812 controller, gate driver circuits and computer. The electronic switches of the inverter are implemented with four metal–oxide–semiconductor field-effect transistors (MOSFET) switches with anti-parallel diodes (IRF640N). Four high-voltage high-speed IGBT drivers (EXB841) were used to provide proper and conditioned gate signals to these power switches. Low-cost high speed Texas Instruments TMS320F2812 digital signal processor (DSP) was used to send digital PWM control signals to control the inverter. The optically coupled isolators 4N25 are used to realize an electrical isolation between the DSP controller and the main circuit.

Fig. 9. Output of conventional PWM control

Fig. 10. Output of optimal control

Fig.9 shows the results of the conventional PWM control with a dead-time of 3us, it obvious that the output current has a distortion. Fig.10 shows the results of the optimal dead-time elimination control elimination method. clearly shows that the output current is very close to a sinusoidal waveform. Compared to the conventional PWM control with dead-time, this method significantly reduces the output distortion.

Optimal Dead-Time Elimination for Voltage Source Inverters

5

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Conclusion

Dead-time elimination method for voltage source inverter based on three level is presented in this paper. A novel method employing IA for the dead-time elimination control sequences of the inverter is presented in this paper. The optimal switch control sequences is achieved ahead of time. According to the results of the experiments, there are obvious reductions in the total harmonic distortion of the waveform by applying IA rather than conventional PWM control. With the method of dead-time elimination, the inverter also reduces gate drive power and minimizes switching loss since switches are only active for half of each fundamental cycle.

Acknowledgment The authors wish to express their gratitude to the National Foundation of China, school of electrical engineering of Wuhan University, and Hubei University of Education, for support of this research effort (National Natural Science Foundation of China under Grant 50807041, foundation of Key subject of Hubei University of Education, foundation of the master-degree program of Hubei University of Education, foundation of the key project of Hubei University of Education).

References 1. Summers, T.J., Betz, R.E.: Dead-time issues in predictive current control. IEEE Trans. Ind. Appl. 40(3), 835–844 (2004) 2. Cichowski, Nieznanski, J.: Self-tuning dead-time compensation method for voltage-source inverters. IEEE Power Electron. Lett. 3(2), 72–75 (2005) 3. Lai, Y.S., Shyu, F.S.: Optimal common-mode voltage reduction PWM technique for inverter control with consideration of the dead-time effects—Part I: Basic development. IEEE Trans. Ind. Appl. 40(6), 1605–1612 (2004) 4. Kim, H., Moon, H., Youn, M.: On-line dead-time compensation method using disturbance observer. IEEE Trans. Power Electron. 18(6), 1336–1345 (2003) 5. Urasaki, N., Senjyu, T., Uezatoand, K., Funabashi, T.: Adaptive deadtime compensation strategy for permanent magnet synchronous motor drive. IEEE Trans. Energy Convers. 22(2), 271–280 (2007) 6. Lin, J.L.: A New Approach of Dead-time Compensation for PWM Voltage Inverters. IEEE Trans.on Circuits and Systems 49(4), 476–483 (2002) 7. Choi, J.S., Yoo, J.Y., Lim, S.W., Kim, Y.S.: A novel dead time minimization algorithm of the PWM inverter. In: Conference Record of the IEEE IAS, vol. 4, pp. 2188–2193 (1999) 8. Attaianese, V., Attaianese, Nardi, V., Tomasso, G.: A novel SVM strategy for VSI dead-time-effect reduction. IEEE Trans. Ind. Appl. 41(6), 1667–1674 (2005) 9. Chen, L., Peng, F.Z.: Dead-time elimination for voltage source inverters. IEEE Trans. Power Electron. 23(2), 574–580 (2008) 10. Lin, Y.K., Lai, Y.S.: Dead-Time Elimination of PWM-Controlled Inverter/Converter Without Separate Power Sources for Current Polarity Detection Circuit. IEEE Trans. Power Electron. 56(6), 2121–2127 (2009)

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11. Zhang, A., Huang, Q., Chen, B.: A novel IGBT gate driver to eliminate the dead-time effect. In: Conference Record of the IAS, vol. 2, pp. 913–917 (2005) 12. Mehrizi-Sani, A., Filizadeh, S.: An Optimized Space Vector Modulation Sequence for Improved Harmonic Performance. IEEE Trans.Ind. Electron. 56(8), 2894–2903 (2009) 13. Yuan, J., Su, X., Chen, B., Tian, C., Optimum Vector, P.W.M.: Strategy for Three-Phase Inverter Based on Immune Algorithm. Transactions of China Electrotechnical Society 24(9), 114–119 (2009) 14. Yuan, J., Chen, B., Tian, C., Jia, J.: The research of Inverter’s control based on Immune Algorithm[J]. Proceedings of the CSEE 26(5), 110–117 (2006) 15. Yuan, J., Chen, B., Jia, J.: Gentic Algorithm based approach for invertor control. Automation of Electric PowerSystems (24), 32–35 (2004) 16. Yuan, J., Chen, B.: Research on Optimum Control Strategy of Three-Level Single-Phase Full-Bridge Inverter. Transactions of China Electrotechnical Society 21(3), 42–46 (2006) 17. Lopez, J., Alvarez, J., Doval-Gandoy, Freijedo, F.D.: Multilevel multiphase space vector PWM algorithm. IEEE Trans. Ind. Electron 55(5), 1933–1942 (2008) 18. Jiao, L., Wanlei: A novel Genetic algorithm Based on Immunity. IEEE Transactions on System,Man,and Cybernetics-Part A:Systems and humans 30(5), 552–561 (2000) 19. Jiao, L., Wanlei: A novel Genetic algorithm Based on Immunity. IEEE Transactions on System,Man,and Cybernetics-Part A:Systems and humans 30(5), 552–561 (2000) 20. Bowes, S.R., Holliday, D.: Optimal regular-sampled PWM inverter control techniques. IEEE Trans. Ind. Electron. 54(3), 1547–1559 (2007) 21. Chen, B.-Y., Lai, Y.-S.: Switching Control technique of phase-shift- controlled full-bridge converter to improve efficiency under light-load and stand by conditions without additional auxiliary components. IEEE Trans. Power Electron 25(4), 1001–1012 (2009) 22. Oggier, G.G., García, G.O., Oliva, A.R.: Switching control strategy to minimize dual active bridge converter losses. IEEE Trans. Power Electron 24(7), 1826–1837 (2009) 23. Mao, X., Ayyanar, R., Krishnamurthy, H.K.: Optimal variable switching frequency scheme for reducing switching loss in single-phase inverters based on time-domain ripple analysis. IEEE Trans. Power Electron 24(4), 991–1001 (2009) 24. Lai, R., Wang, F.F., Burgos, R., Pei, Y., Boroyevich, D., Wang, B., Lipo, T.A., Immanuel, V.D., Karimi, K.J.: A systematic topology evaluation methodology for high-density Three-phase pwm AC-AC converters. IEEE Trans. Power Electron 23(6), 2665–2680 (2008) 25. Maswood, A.I., Wei, S.: Genetic-algorithm-based solution in PWM converter switching. In: IEE Proceedings Electric Power Applications, May 6, vol. 152(3), pp. 473–478 (2005)

Research of the Influence Factors in Single-Phase Inverter Output Xiaofang Su1, Binbin Rao2,*, Chong Chen2, and Junbo Liu2 1

Hubei University of Education, China 2 School of Electrical Engineering, Wuhan University,Wu Han, China [email protected]

Abstract. Immune Genetic Algorithm has been used in PWM control of inverter. This paper analyses the relationship among the switching frequency of power electronic devices the current modulation and the merits extent of the single-phase inverter’s current waveform. In this paper, the computer programs of the immune genetic algorithm are written in programming C language. The optimal control sequences are computed in different switching frequency and current modulation. According to the Simulations, if the switching frequency is up to a certain extent, even further increasing the switching frequency, it can not effectively reduce the THD of output current waveform. When the switching frequency is fixed, selecting a certain current modulation can make the minimum THD. At last, this paper uses TMS320F2407 digital signal processor and builds an experimental platform for a large number of experiments .The data also shows the conclusions are correct.

，

Keywords: Immune algorithm; 0ptimal waveform; Switching frequency; Current modulation; DSP.

1 Introduction The main purpose of inverter control technique is to make the inverter current (or voltage) output closed to the ideal reference waveform by controlling the switching devices’ opening and off. Pulse width modulation (PWM) such as sampling, space vector and selective harmonic elimination method has become a power electronics conversion system. It’s an leading switching technology to converse the voltage, current, frequency. But there are many shortcomings of analog control, for example, the structure is complex, the consistency and reliability are poor. Article [1] proposed a new digital control strategy for inverters’ optimal control commands based on genetic algorithm. Article [2][3] analyzed and studied the multi-objective optimization control strategy by immune genetic algorithm based on article[1], but the DC voltage and load were fixed , for the inverter, the load is usually shifty, and the DC voltage may fluctuate during the operation, therefore, single-phase inverter output current waveform should consider the merits of the two factors. *

Corresponding author.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 625–632. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Based on digital control strategy, the aim of this article is to search for the condition of the single-phase inverter’s optimal output, and it also analyses the influence of switching frequency and current modulation ratio on the THD of the inverter’s current output. It also obtains optimal control sequences based on immune genetic algorithm when the two factors select different value, according to the sequences, it can get the output current waveform of the inverter, then it analyses the proximity between the output current waveform and the ideal reference wave. Through a large number of simulation and experimental data, it shows that: when the current modulation ratio is fixed and the switching frequency is increased to a certain extent, it can not significantly reduce the output current THD even further improving the switching frequency, and at a certain switching frequency, selecting some of the current modulation ratio can make the minimum THD of the output current waveform. Therefore, in order to obtain the output current waveform and make the waveform closed to ideal reference waveform and make the THD lowest, it should select the appropriate ratio of switching frequency and current modulation.

2 The Inverter Control Strategy Based on IA 2.1 The Mathematical Model of Inverter Control The equivalent circuit of single-phase full-bridge two-level voltage inverter with pure inductive is shown in Figure 1.

Fig. 1. The equivalent circuit of two levels Single-phase inverter with perceptual load

Each of the inverter switching devices in accordance to the working state can be divided into on (1) and off (0), and in this article 0,1 are named as the states of switching devices, corresponding to the immune genetic algorithm applied to single-phase full-bridge voltage Inverter circuits, states variables are:

⎧1, V1 V4 is on , V2 V3 is off xi = ⎨ ⎩ 0, V2 V3 is on, V1 V4 is off When the output voltage is E, the inductor current increases. When the output voltage is -E, the inductor current decreases.

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In a frequency cycle, the working states of V1 ~ V4 are different at different time. A series of V1 ~ V4 working state variables xi at different time constitutes a space vector x = ( x1 , x2 , x3 ...xN ) , so it maps between the state vectors and IGA control sequences, N is the gene length, constituting chromosomes. By the immune genetic algorithm, the optimal control sequences 0,1of the inverter can be to find. When the control sequence is 1, the corresponding moment of current value increases. When a certain control sequence is 0, the corresponding moment of the current value decreases, so it can achieve correspondence between the control sequences and the inverter current waveform. 2.2 The Problem of the Inverter Output Current Optimization In the voltage source inverter system, the output voltage is usually chose as the controlled variable, as the output voltage has nothing with the load and it is more easy to control, but because the current is more direct with the loss, output power correlation than the voltage, so controlling the output current is more meaningful. Article [4] used THD as the assessment factor of voltage harmonic content. It analyzed the output current harmonic content of the inverter with a pure inductive load, but it also can use WTHD as an evaluation factor. The definitions of the WTHD and THD are similar, namely the amplitude of harmonic voltage divided by the corresponding harmonic frequency and sum these data, then extract a root and at last it is divided by amplitude of fundamental voltage, then the high harmonics of harmonic content of the weight is relatively small. N

WTHD =

∑

n = 2,3,"

(

Vn 2 ) n

(1)

V1

2.3 Applications of IGA in the Single-Phase Inverter Control Immune genetic algorithm is a global optimization algorithm. It simulates biological adaptive process of antibody concentration based on genetic algorithm. It draws on biology immune system to recognize foreign material stimulus, and it is able to make accurate response, it also can retain the unique immune system memory. The main features of the algorithm are [3]: it injects therapeutic vaccine(line solution or better solution) into the initial sequence, so it can greatly speed up the convergence and reflects the characteristics of the secondary rapid immune response in the immune response. It adds the immune operation on the basis of selection, crossover and mutation which are the three major steps in the conventional genetic algorithm, it means randomly selecting part of antibody to vaccinate preventive vaccine and the aim is to restrain degradation in crossover and mutation process, so it can increase possibilities of the low fitness antibody evolved to a higher one. This paper describes the instantaneous output current of the inverter used piecewise function. With a pure inductive load for a single-phase full-bridge inverter, a cycle time of 0.02 second is divided into equal parts, each time is Δt =

0.02 s 4* N

(N is number of the control

sequence in a quarter One cycle), and it is the action time of each state variables. A role in

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each time period, the inverter is seen as linear systems, so L equivalents to L

Δi Δt

di dt

can be approximately

. So L

Δi E = ± E ⇒ Δi = ± Δt Δt L

(2)

In the time t = (mΔt , (m + 1)Δt ) , the output current function is: i(t ) = i (mΔt ) + km

⎧1, km = ⎨ ⎩ −1

E (t − mΔt ) L

m=0,1,…N

(3)

is the inverter output current waveform with the time changing when the inverter control sequences is antibody x, i f (t ) is the reference current waveform. the affinity evaluation function can be written as: ix (t )

Fitness ( xi ) =

1 T

∫0 [i x (t ) − i f (t )]

2

= dt

1 N

( m + 1) Δ t

∑ ∫m Δ t

m=o

(4)

[i x (t ) − i f (t )]2 dt

The greater affinity function is, the greater affinity between the inverter output waveform and the reference current waveform is, and the THD is smaller. 2.4 The Analysis of the Factors of the Inverter Optimal Output Waveform For single-phase two-level full-bridge inverter with pure inductive load, the following equation holds: ±E=L

di di ± E ⇒ = L dt dt

(5)

Ideal reference current is i(t ) = A sin( wt )

(6)

di ± E = = A cos( wt ) dt wL

(7)

AwL 1 = ±E cos( wt )

(8)

In the equation, AwL is the voltage amplitude of the inverter output. In this article, the modulation ratio defines as the inverter output voltage amplitude divides by the ratio of the DC voltage

k=

AwL E

.

3 Simulation Because of the Symmetry of the object itself, it just needs to find the 1/4 cycles of switch control signals, through the symmetry transformation, the entire cycle control

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signals can be got, so it can ensure the strict symmetry of the output waveform and eliminate the even harmonics and harmonic in cosine term, thereby the total harmonic content is reduced. In addition, reducing the dimension of the state space from 4N to N, it can greatly shorten the computation time of immune genetic algorithm and ensure the rapid convergence of computing process. The main circuit simulation used in this paper is shown in Figure 1, the parameters are set as follows: the frequency f is 50HZ, current modulation ratio ranges from 0.2 to 1.2, the voltage across the capacitor E is 88V, inductance L is 0.28H. Parameter setting: the number of each generation is 100 antibodies, according to the needs of a quarter of a cycle, the number of genes N contains in each antibody is 50, 80, 100, 125, 160, 200, 250, 400, 500, and the role of each control command corresponding to the time of Δt =

0.02 4* N

, crossover pc is 0.55, mutation rate pm is 0.002, inversion rate Pcon is 0.005,

a and b is in the selection function. In this paper a is 0.5 and b is equivalent to a. 3.1 The Relationship between the Switching Frequency and THD with a Particular Current Modulation When the modulation ratio is determined and the switching frequency is different, the optimal control sequences calculate by the immune genetic algorithm, the THD of the output current waveform is obtained by simulation which is shown in Figure 2. 2.5

2

THD(%)

1.5

1

0.5

0 50

100

150

200

250

300

350

400

450

500

N

Fig. 2. The relationship between switching frequency and harmonic content When modulation ratio is fixed

When k is 1.027and N is 50,the THD is1.611%.when k is 1.027and N is 250, the THD is 0.367%, the THD reduces 77.2%. When k is 1.027 and N is 400 ,the THD is 0.269%, the THD reduces by 26.7% compared with N is 250. When k is 0.889 and N is 50,the THD is 2.488%.When k is 0.889 and N is 250 when THD is 0.466%, the THD reduces 81.3%.When k is 0.889 and N is 400,the THD is 0.282%, the THD reduces by 39.3% compared with N is 250. These two sets of data show that if the current modulation is in a certain ratio, the fourth cycle of the switching control sequences number from 50 to 250 can significantly reduce the THD of inverter output waveform, switching control of a quarter cycle sequence number increasing from 250 to 400 can

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reduce the inverter output current waveform of the THD, but the effect is not so significant. 3.2 Modulation Ratio and the Relationship between THD with a Particular Switching Frequency N was selected as250, the optimal control sequence calculated by the immune genetic algorithm, the THD of the output current waveform was obtained by simulation which was shown in Figure 3. This article took different current modulation ratio and switching frequencies and calculated the optimal control sequence by the immune genetic, then the output current waveform THD was gotten by simulation which was shown in Figure 4. For Figure 3, when N was 250 and K was 1.026, the THD was 0.364%, when N was 400 and K was 1.026, the THD was 0.236%. The data suggested that: if a quarter cycle of the switching frequency was fixed, the output current waveform THD could be minimized when the current modulation was near 1.026. N=400 3

2.5

2.5

2

2 THD(%)

THD(%)

N=250 3

1.5

1.5

1

1

0.5

0.5

0 0.2

0.3

0.4

0.5

0.6

0.7 k

0.8

0.9

1

1.1

1.2

0 0.2

0.3

0.4

0.5

0.6

0.7 K

0.8

0.9

1

1.1

1.

Fig. 3. The relationship between different modulation ratio and switching frequency when N is 250 and 400

7 6

THD(%)

5 4 3 2 0 1 0 0.2

100 200 300 0.4

0.6

400

0.8

1

1.2

500

N

K

Fig. 4. The relationship between switching frequency and harmonic content When modulation ratio is fixed

Fig. 5. The relationship among different modulation ratio, switching frequency and harmonic content

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It can be seen from the above three figures: the current modulation ratio k changed from 1 to 1.143, the output current harmonic content increased rapidly, corresponding to each different switching frequency, when the current modulation ratio of the output was near 1.026 ,the minimum current harmonic content can be gotten; the current modulation ratio k changed from 0.5 to 1, the greater the switching frequency was, the smaller the change of the output current THD was, and compared with the change of k ,the change of THD was smooth; current modulation ratio k changed from 0.2 to 0.5 , the output current THD decreased and the amplitude was larger. The above analysis shows that: when the switching frequency was fixed, in order to get the excellent output current, you can choose the right current modulation ratio ,it means that you can select the appropriate modulation current amplitude.

4 Conclusion (1) When the current modulation ratio was fixed, the larger the switching frequency was, the smaller the output current’s THD was. For the pure inductive load with a single-phase full-bridge inverter, when the switching frequency was 50K, the current THD of the inverter output can be the smaller, if the switching frequency was increased to 80K or more, it could not significantly reduce the harmonic content of the output current waveform, but the inverter switching losses would increase because of increased frequency of the inverter switching. (2) According to the simulation, when the inverter’s switching frequency was fixed and the current modulation ratio k changed from 1 to 1.143, the harmonic content of the current output increased dramatically, corresponding to each of the different switching frequency, the lowest harmonic content could be gotten when the current modulation ratio was 1.026; when the current modulation ratio k changed from0.5 to 1, the output current’s THD was large and changed gentle with the great switching frequency; current modulation ratio k changed from 0.2 to 0.5, the output current’s THD decreased and changed gently, so the load value of the inverter can be chosen to allow the current modulation ratio in the interval. In the case of the Switching frequency is fixed, in order to get the excellent output current we can chose right current modulation ratio which is near 1.026.

Acknowledgment The authors wish to express their gratitude to the National Foundation of China, school of electrical engineering of Wuhan University, and Hubei University of Education, for support of this research effort (National Natural Science Foundation of China under Grant 50807041, foundation of Key subject of Hubei University of Education, foundation of the master-degree program of Hubei University of Education, foundation of the key project of Hubei University of Education).

References 1. Schutten, M.: Genetic Algorithms for Control of Power Converters. In: Proceedings of 26th Annual IEEE of Power Electronic Specialists Conference. PESC 1995 Record, Atlanta, GA, vol. 2, pp. 1321–1326 (1995) 2. Mehrizi-Sani, A., Filizadeh, S.: An Optimized Space Vector Modulation Sequence for Improved Harmonic Performance. IEEE Transactions on Industrial Electronics 56(8), 2894–2903 (2009)

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3. Chun, J.S., Kim, M.K., Jung, H.K., et al.: Shape optimization of electronic devices using immune algorithm. IEEE Trans.On Magnetics 33(2), 1876–1879 (1997) 4. Shenyu, Chen, B., Yuan, J., Zhangnan: IGA-based control strategy for inverter and its realization on DSP platform. Electric Power Automation Equipment 26(6), 40–43 (2006) 5. Wang, L., Pan, J., Jiao, L.: The Immune Algorithm. ACTA ELECTRONICA SINICA 28(7), 74–78 (2000) 6. Lai, R., Wang, F.F., Burgos, R., Pei, Y., Boroyevich, D., Wang, B., Lipo, T.A., Immanuel, V.D., Karimi, K.J.: A systematic topology evaluation methodology for high-density Three-phase pwm AC-AC converters. IEEE Trans. Power Electron 23(6), 2665–2680 (2008) 7. Mao, X., Ayyanar, R., Krishnamurthy, H.K.: Optimal variable switching frequency scheme for reducing switching loss in single-phase inverters based on time-domain ripple analysis. IEEE Trans. Power Electron 24(4), 991–1001 (2009) 8. Oggier, G.G., García, G.O., Oliva, A.R.: Switching control strategy to minimize dual active bridge converter losses. IEEE Trans. Power Electron 24(7), 1826–1837 (2009) 9. Chen, B.-Y., Lai, Y.-S.: Switching Control technique of phase-shift- controlled full-bridge converter to improve efficiency under light-load and stand by conditions without additional auxiliary components. IEEE Trans. Power Electron 25(4), 1001–1012 (2009) 10. Holtz, J.: Pulse width modulation—A survey. IEEE Trans. Ind. Electron 39(5), 410–420 (1992) 11. Bowes, S.R., Holliday, D.: Optimal regular-sampled PWM inverter control techniques. IEEE Trans. Ind. Electron. 54(3), 1547–1559 (2007) 12. Maswood, A.I., Wei, S.: Genetic-algorithm-based solution in PWM converter switching. In: IEE Proceedings Electric Power Applications, May 6, vol. 152(3), pp. 473–478 (2005) 13. Ozpineci, B., Tolbert, L.M., Chiasson, J.N.: Harmonic Optimization of Multilevel Converters Using Genetic Algorithms. IEEE Electronics Letters 3(3) (2005) 14. Schutten, M.J.: Genetic algorithms for control of power converters. In: 26th Annual IEEE Power Electronics Specialists Conference , PESC 1995 Record, vol. 2, pp. 18–22 (1995) 15. Beig, A.R., Narayanan, G., Ranganathan, V.T.: Modified SVPWM algorithm for three level VSI with synchronized and symmetrical waveforms. IEEE Trans. Ind. Electron. 54(1), 486–494 (2007) 16. Mehrizi-Sani, A., Filizadeh, S.: An Optimized Space Vector Modulation Sequence for Improved Harmonic Performance. IEEE Trans.Ind. Electron. 56(8), 2894–2903 (2009) 17. Jiao, L., Wanlei: A novel Genetic algorithm Based on Immunity. IEEE Transactions on System,Man,and Cybernetics-Part A:Systems and humans 30(5), 552–561 (2000) 18. Holtz, J.: Pulse width modulation-a survey. In: 23rd Annual IEEE Power Electronics Specialists Conference, PESC 1992 Record, 29 June-3 July, vol. 1, pp. 11–18 (1992) 19. Kazmierkowski, M.P., Malesani, L.: Current control techniques for three-phase voltage-source PWM converter: a survey. IEEE Trans.Ind. Electron. 45(5), 691–703 (1998) 20. Habetler, T.G., Divan, D.M.: Acoustic noise reduction in sinusoidal PWM drives using a randomly modulated carrier. IEEE Trans. Power Electron. 6(3), 356–363 (1991) 21. Chen, L., Peng, F.Z.: Dead-Time Elimination for Voltage Source Inverters. IEEE Trans. Power Electron 23(2), 574–580 (2008) 22. Ho, C.N.-M., Cheung, V.S.P., Chung, H.S.-H.: Constant-Frequency Hysteresis Current Control of Grid-Connected VSI Without Bandwidth Control. IEEE Trans. Power Electron 24(11), 2484–2495 (2009) 23. Liu, F., Maswood, A.I.: A Novel Variable Hysteresis Band Current Control of Three-Phase Three-Level Unity PF Rectifier With Constant Switching Frequency. IEEE Trans. Power Electron 21(6), 1727–1734 (2006)

The Inspiration of the Economic Restructuring in Ruhr of Germany to the Sustainable Development of Mining Cities in Henan Province of China Wang Liping School of Economics and Management Henan Polytechnic University Jiaozuo 454000, P.R. China [email protected]

Abstract. Mining city is a special type of city which is developing on the basis of resource exploitation. Since the founding of the People’s Republic of China, mining cities have made great contributions to Chinese economic development. However, with the depletion of resources, the economic development of mining city is getting in trouble. Hence, the article firstly analyzed the main obstacles to economic restructuring of mining cities in Henan province, and then summarized the successful experiences of economic restructuring of Dortmund in the Ruhr area in Germany. Finally the article put forward some advices for the sustainable development of mining cities in Henan province of China. Keywords: Ruhr area; Mining cities; Economic restructuring; Sustainable development.

1 Introduction The industry structure of many cities in Henan province is based on resource development, and their industries are mostly the mining industry or primary processing industries. With the increasing depletion of mineral resources, the mining city's economic development is faced with the problem of unsustainable growth, coupled with other contradictions in the process of economic development, industrial structure restructuring of the cities has become an inevitable choice.

2 The Obstacles to Industrial Structure Restructuring of Henan Province’s Mining Cities 2.1 Excessively Depending on the Mineral Resources Many cities in Henan Province were formed based on the mineral resource, so their main industries were the mining and processing of mineral resources. The number of people engaged in mining and mineral production value is in a dominant position in M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 633–638. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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urban population structure and economic structure. The development of mining cities showed a clear phase characteristics [1]. According to the proved reserves of mineral resources, the annual output, the total urban population, the city's status and role in regional areas and other indicators, the development of the mining city is divided into three stages: young, middle-aged and older three stages. In the young stage: the typical characteristics are only the primary processing of mineral exploration, and the mineral reserves is huge and could be mined for a long time, the urban population is small, the city has great development potential; In the middle age: the leading industry promotes the development of other industries, urban population is sharply increasing, the mining industry, as the leading industry, has the larger proportion; In the old age: with the development of mining industries, mineral resources will continue to reduce to the depletion, most of mines become crisis ones. Because of long-term mining, many mineral cities in Henan Province have stepped into middle and old-age stages, especially Jiaozuo, Hebi, Puyang, Yima cities. The available resource reserves are deceasing and the life span of mining development is shortened, as a result resource is becoming the bottleneck constraining economic growth (Table 1). Table 1. Mineral resources and exploitation ability of mining cities in Henan Province Reserve Exploitation ability Exploitation (billion ton) (ten thousand ton/year) life(year) Pingdingshan Coal 1.244 2089 35.7 Jiaozuo Coal 0.732 427 102.86 Hebi Coal 0.423 737 34.44 Yima Coal 0.309 1105 16.78 Yongcheng Coal 0.741 540 82.33 Puyang Petroleum 0.055 380 14 Lingbao Gold 4.452 9.63 4.6 Wugang Iron 0.13 370 25 Data resource: Mine Resource Program in “Tenth Five-Year Plan” of Henan Province. Mining city

Ore type

2.2 Serious Environmental Pollution Because of the extensive economic growth pattern and deficient treatment investment, environmental pollution of mining city in Henan Province is quite serious. Major environmental problems are waste water, waste rock pile, ground subsidence, soil erosion, etc. (Table 2). However, the high-tech industries, such as IC (integrated circuit design), software design, biotechnology, and new materials, strictly require good environment, particularly air and water, so does tourism industry and ecological agriculture, they all belong to typical environmental protection industry. Therefore, bad environment restricts the development of these sunrise industries, and it is very difficult to attract talented people and upgrade the industrial structure.

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Table 2. The environmental condition of main mining cities in Henan Province

City

Waste discharge (ten thousand ton or ten thousand m3/Year)

Secondary geological disasters (km2)

Ground subsidence 2.64 km2 Hebi Ground fissures appeared year after year Gangue 1016 Ground subsidence 73.38 km2 Jiaozuo Waste water 6314 Ground fissure occur, soil erosion Gangue 10000 Ground subsidence 112 km2 Pingdingshan Waste water 4620 Ground fissures occurred in 35 places Ground subsidence 7km2 Yongcheng Waste water 1614 Ground fissures occur Data resource: Mine Resource Program in “Tenth Five-Year Plan” of Henan Province. Gangue 1124.93 Waste water 315

2.3 Intensifying System Contradictions State-owned large and medium enterprises are the subject of mining cities, the mining sector and city government departments implement unification management system of government and enterprise, which would result in the deficit of city's comprehensive service, the low efficiency of enterprises and society, the slow development of other economic sectors and SME, difficultly absorbing surplus workers, little contribution to the urban economy [2]. 2.4 Talent Shortage In the early stage of company’s establishment, some managers and technological personnel were introduced into the mining cities in Henan Province. However, they usually had the poor education experiences and reasonable knowledge structure. In addition, the education foundation of many new mineral cities was too weak to cultivate all kinds of talents who were required by the society (Table 3). Compared with the developed coastal regions, the mineral cities could neither keep talents from leaving nor abstract outsider talents because of bad environment and low development potential. Therefore, the mineral cities of Henan Province seriously lack in high qualified talents. Table 3. The development stages of major mining cities in Henan Province

City

The proportion of mineral workers

Pingdingshan Puyang Jiaozuo Hebi Yima Yongcheng

0.23 13 9 7 54 12

The proportion of mineral output 10 34 22 18 41 33

The development stage Middle-aged Elderly Elderly Elderly Elderly Youth

Mining types Coal Oil and gas Coal Coal Coal Coal

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3 The Successful Experience from Ruhr’s Economic Restructuring The German Ruhr industrial area is the apotheosis of the old industrial areas sustainable development in Germany and in the world [3]. Dortmund located in the east of Ruhr area, is an industrial center of iron and steel. After the seventies of the 20th century, with coal, steel and other traditional industries declining, and the westward movement of the Ruhr iron and steel industry, Dortmund faced a structural crisis. But through a series of reforms Dortmund quickly got back its former prosperity, the successful experiences from Ruhr’s economic restructuring could be summarized into the following three aspects. (1) Strengthen technological reconstruction of coal pillar industry From the internal industrial structure, mining and raw material industries in the eastern Ruhr area had been dominated, and the total number of employees and sales amount respectively accounted for 58% and 60% of the whole industrial area [4]. For example, Westphalia state, in North Rhine, only Ruhr Area East exploited 1/5 coal and 1/3 coke of the whole state [5]. Hence, enterprises adopted a series of new technologies and rational production and finally made a breakthrough in the competition. The latest modern technologies which were researched and developed by East Ruhr Area are becoming popular in the international market, and the coal conversion process here has reached a new level. Traditional coal industry again shows the new vitality. (2) Vigorously develop the tertiary industry Vigorously develop the tertiary industry, particularly commercial, transportation, tourism, the proportion of employee in these industries is increasing while the proportion of secondary industry and construction industry is decreasing (Table 4). In the tertiary industry, the wholesale industry played a key role in promoting the economic restructuring of Dortmund. At present, the Dortmund city gathers nearly 1,700 companies [6]. More importantly, their sales range has been extended to the whole state and the German border. For example, the steel trade with international significance and food wholesale with regional significance mostly locates in Dortmund and Hamm city, and Una is not only the trade center of automatic high-bay warehouse in Germany, but also the sale center of the state's personal car and truck [7]. In a word, the industrial clustering effect of the area is very obvious. Table 4. The change of employment structure in Dortmund (1978 - 1984)

Economic sector Agriculture Mining, energy Processing industry Construction industry Business, transportation Services and others

Percentage of employees (%) 1978 1984 1.5 0.7 12.7 13.2

Change -0.8 +0.5

33.6

29.7

-3.9

8.3

7.2

-1.1

19.6

19.8

+0.2

25.3

29.4

+4.1

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(3) Emphasize staff training Slowing economic growth and technological progress led some sectors have to reduce labor jobs, which required some laborers must transfer to new posts, which required continuous vocational training [8]. In fact, the Dortmund City, in the economic restructuring process, established a number of vocational training institutions, which played a very important role in staff’s reorientation in the region.

4 Sustainable Development Strategy of Mining City in Henan Province 4.1 Change the Management System and Improve the Relationship between Local Government and Enterprises It is very necessary to have unified planning agencies for both a single city and cities. Establishing an agency like Ruhr coal management association is conducive to the coordination and cooperation between departments and the implementation of policies and regulations. Government should implement unified planning for the sustainable development of resource-based cities, and put the revitalization of resource-based cities into the national macro decision-making system. Give transfer payments and other financial supports to some cities which are in serious trouble. Establish the special funds of national mining shift in production. Change the resource development model from single national exploitative subject to diversified exploitative subject that the national and local government participate in together, and formulate some supporting policies as soon as possible. Rationalize the relationship of local government and enterprises and solve some conflicts that exist in mining cities, and establish the new co-existence, co-aid and co-prosperity relationship between local government and enterprises. 4.2 Emphasize the Improvement of Investment Environment, and Play Regional Comparative Advantage Attracting foreign investment is an important way of industrial transformation. Although the location of resource-based cities is devious, abundant natural resources and skilled labor, a large number of existing plant and infrastructure are the important comparative advantage for attracting outside investment. However, prevalent problems of mining cities are too many outstanding debts on municipal building. Only depending on the local government is difficult to improve the hard environment of urban development. According to law, levy resource tax and mineral resources compensation fee, leave these funds in the city in proportion, as the resources development fund and the compensation fund after the exploitation; and emphasize the market-oriented use of resources, based on the law of value, appropriately increase the retention rate to make the place have the ability to improve the urban environment. Improving the soft environment is even more important, which include fair and transparent policies, clean and efficient government, and good social and cultural environment.

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4.3 Promoting the Diversified Ownership Structure Compared with the other cities, the resource-based cities usually have high proportion of state-owned economy. The single ownership structure seriously hampered the smooth flow of stock elements and it is difficult to guarantee the optimization of industrial structure. Small and medium enterprises must establish the ownership structure with various economic sectors and diverse interest subjects. The experience of resource-based cities in Germany shows that promoting the diversified development of small and medium enterprises can mobilize a wide range of production factors, and promote the production and optimal allocation of production factors. Especially for many old industrial cities in Henan province, it is important that it can absorb a large number of surplus workers, greatly ease the pressure on the urban employment. 4.4 Improving Social Security, Following the Road of Sustainable Development With the urban industrial structure adjustment, there will be a large number of workers being laid off, increasing social instability, which will seriously affect the process of urban economic structure. How to deal with efficiency and fairness troubles the government in a long term. We can follow the example of the German Federal Government making some special policies, giving incentives in taking loans and training to encourage job creation, avoiding the widening gap between the rich and the poor, promoting social equity. In the process of changing the industrial structure, the government should adhere to the principles of sustainable development and promote the coordinated development of resources, environment, and population.

Acknowledgement This paper is supported by PhD Funds of Henan Polytechnic University (No: B200960), here with heartfelt thanks.

References 1. van Guoping, L.: Mining City: Adjust Life Cycle and Promote Sustainable Development. China Mining Economy 5, 16–17 (2002) 2. Qiu, Y.: The Stable Development of the Federal German Economy. Hunan People’s Publishing House, Changsha (1988) 3. Li, S.: Mining Cities - A Case Study of the German Ruhr Area. China Population Resources and Environment 13(4), 94–97 (2003) 4. Guo, J.: The Ruhr Group Development Research and Prospects for the Development of Coal Enterprises in China. Technology and Economy 1, 10–11 (2002) 5. Yuan, Z.: The Inspiration of Ruhr Model to the Sustainable Development in Henan Mining Cities. Mining Conservation and Utilization 2, 11–15 (2006) 6. Liao, H., Zhu, T.: Research on the Implementing Means of the Environmental Policy in Germany. Shanghai Environment and Science 12, 748–750 (2002) 7. Wang, Z.: The Development of German Green Environment Technology Industry and Policy Mechanism. Global Technology Economic Outlook 3, 14–19 (2010) 8. Chang, L.: The Role of Government in the Old Industrial Base - the German Ruhr Area Practice and Enlightenment. Science Technology and Industry 5(2), 8–12 (2005)

Design of Metal Pipe Defect Detection Device Based on Electromagnetic Guided Wave Shuwang Chen1,2, Deliang Li1, and Zhangsui Xu1 1

Department of Electric Engineering Mechanical Engineering College, Shijiazhuang 050003, China 2 Hebei university of science and technology, Shijiazhuang, 050018, China [email protected] Abstract. An experimental device of electromagnetic guided wave generator is designed. The generator is stimulated by high power pulse, so it is cheaper than ultrasonic guided wave generator. The method of emission stimulation and receive signal in short metal pipes is presented in the paper. In terms of the transmit characteristic of electromagnetic guided wave in pipes, as the stimulate pulse, a special single frequency pulse signal is chosen. The pulse has very high power in short time, so it is suitable for detection as the transmit signal. The defect information can be detected according to the receive signals. Keywords: electromagnetic guided wave, metal pipe, defect detection.

1 Introduction In the industrial production of steel rolling, there has strict requirements for the smallest defect detection, thus the testing equipment must have high precision and high reliability. In order to increase the detection speed and production speed, synchronous detection equipment should have higher detection speed. In recent years, the method of defect detection technology common have ultrasound, ray, magnetic power, osmosis, eddy current and so on. These methods have each characteristic in the tiny crack detection seamless steel pipe size and the shape of positioning, but they have obvious flaw in the detecting precision, price, use convenient, ect[1]. Some methods aren’t able to detect dozens of meters or hundreds of meters pipeline, such as ultrasonic, eddy current and magnetic particle. It is difficult to test the complicated pipe that is fluid-filled pipe[2].Therefore, as a new kind of method in the steel nondestructive testing, the electromagnetic guided wave has good direction, it can completely cover the steel tube body and effectively avoid the missed cases of other methods. The electromagnetic guided wave signal that is produced by using pulse incentive is strong, so the output signal that is in the internal defects of metal pipes is conspicuous, and it can carry out the long distance detection for the metal pipe.

2 Design of Electromagnetic Guided Wave Inspired Device 2.1 Analysis of Electromagnetic Guided Wave Propagation Properties If the electromagnetic guided wave that spreads in the homogeneous medium meets the discontinuous defects of medium surface, such as hole, crack, it will occurs the M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 639–643. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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scattering phenomenon and the mode conversion. Because there are reluctance differential between the two different medium boundary surface, it decided the electromagnetic guided wave energy to have different reflex value. Reluctance is the product of wave velocity and the density of medium. When the propagation speed of guided wave changes, the reflection and refraction phenomenon will also appear in the guided wave. The reluctance difference is bigger, the reflective energy is greater[3]. When the electromagnetic guided wave spreads in the measured pipes, if it meets the discontinuous place in the pipe or the geometry of pipeline changes, these will change the propagation speed of electromagnetic guided wave. When the thickness of wall changes, such as thinning or thickening, these will also change the propagation speed of electromagnetic guided wave, thus the produced output signal will carry the defects information of steel tubular structure[4], then the collected signal is analyzed and processed, it can detect the defects features in the steel pipes. 2.2 Design of Electromagnetic Guided Wave Inspire Device The electromagnetic guided wave generator provides the needed excitation signal of system work for the entire detection system. According to the actual test objects, the signal frequency and amplitude are chose. It uses the principle of LC resonant circuit, and the strong voltage inspires LC oscillation circuit, the electromagnetic guided wave is eventually generated by the LC oscillation circuit, and it spreads along the certain direction in the measured steel pipes, the speed of propagation and the properties are different in the different medium[5]. The principle diagram is shown in figure 1. E is high-voltage DC power supply, the switch K is IGBT switch module, capacitance C is high voltage capacitance, the inductance coil L is surrounded by the narrow copper.

Fig. 1. Theory of electromagnetic guided wave generator

At first, the switch K is hit the place of 1, it chooses a higher voltage E to charge for the capacitance C, when C is full, the switch is hit the place of 2, then capacitance C and inductance L form a traditional resonant circuit, the inductance L generates corresponding electromagnetic guided wave, and the electromagnetic guided wave may spread around the medium. The wave frequency is controlled by capacitance C and inductance L. According to the changes of LC, it can adjust the frequency of electromagnetic guided wave. The corresponding formula is shown in equation 1.

Design of Metal Pipe Defect Detection Device Based on Electromagnetic Guided Wave

f0 =

1 2π LC

641

(1)

2.3 Device Selection of Components Parameter 2.3.1 Inductance Signal attenuation coefficient meets the equation 2.

α=

R 2L

(2)

Among the formula, R is the conductor resistance in circuit. Therefore, when the size of inductor L increase appropriately, it is slow the signal attenuation. However, with the increase of inductance in the circuit, the time that pulse current rises from zero value to the ninety percent of peak is delayed, and it decreases the maximum rate of rise in the pulse current. 2.3.2 Capacitance The signal frequency is inversely proportional to the capacitance, so lower capacitance value can improve the signal frequency in order to facilitate testing. 2.3.3 Power Supply Voltage Supply voltage is proportional to loop current, so the initial voltage must try to improve, but it is considered the pressure situations of components that must meet the safety requirements. To sum up and to acquire the ideal excitation signal, the value of resistance must be as low as possible in the circuit, the value of inductance is moderate, the power supply voltage should be as large as possible[6].

3 Flaw Detection Experiments of Metal Pipe The transmitters of electromagnetic guided wave will launch the excitation signal along the direction of measured steel pipe, frequency and bandwidth are adjusted accordingly by controlling the LC oscillating circuit in the signal. After spreading in the steel pipes the signal is received by the receiving circuit, and after the signal is processed, it will be displayed in the digital storage oscilloscope, and it is stored directly in the hard disk storage, then we will analysis the signal data in order to judge the situation of defects in the steel pipes. In actual application of testing pipes, we certainly hope the longer the electromagnetic guided wave detects the length scale of defective pipes, the better it is. The study used the method the receiver and the transmitter are located on the both sides in the pipeline. According to receive the signal, we can judge whether the pipeline is flawed. In the scheme, the analysis of signal is simple, and we needn’t consider the superposition of echo. In the process of experiment, we choose 20# steel pipe for experiment. Its diameter is 109mm, its wall thickness is 10 mm, its length is 5m. The receive mode of signal is the type of straight-through, so it can better detect the blemish

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signal between the transmitter and the coil of receiver, and it reduces the interference of superposition in the reflection-receiving type of signal. The experimental devices are shown in figure 2.

Fig. 2. Experimental device diagram of pipe test

The transmission of electromagnetic guided wave exits certain degree of attenuation in the steel pipe, and with the increase of steel pipe length, the electromagnetic guided wave gradually decays, when it defect the flaw, the amplitude of electromagnetic guided wave is gradually reduced. In the distance measurement, the amplitude of electromagnetic guided wave will be reduced to zero, that will impact the effect of detection. Because of the restrict of experimental condition, the attenuation parameters is also impossible to determine, the maximum length of measured steel pipe remains to be further research. In the surface of steel pipe, a crack is artificially made by us, the width is 2mm, the length is 40mm, and the deepest is 8mm. The output signal that the receiving coil gets is shown in figure 3.

(a) Receiving waveform without defect

(b) Receiving waveform with defect

Fig. 3. Receive wave signal within defect or not

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From the view of experimental results, we can see the output waveform has obvious regional of phase transition. So it can prove the design of experiment is correct, it can get the defect of waveform. The size and location of signal can be determined by signal analysis.

4 Conclusion A pulse signal is injected into the long-straight conductor. Through analyzing the characteristics of polarity and amplitude in the received pulse, we can determine the impedance discrete points of position, character and size in the transmission system. The experiment device can prove the existence of electromagnetic guided wave, and the incentive and receiving is very convenient in the defect of steel pipe. When the electromagnetic guided wave meets the flaw in the steel pipe, it will generate obvious fluctuations, so we can determine the position of defect by the parameters of amplitude and time.

Acknowledgement The paper is gained financial support from China Postdoctoral Science Foundation, No.20100471815.

References 1. Tian, G., Liu, Z.: Study on guided wave in a step plate. Non Destructive Testing 25(7), 333–336 (2003) 2. Xu, S., Wang, W., Zhao, P., Song, M.: Application of magnetostrictive ultrasonic guided wave technique for the inspection of defects in pipes. Non Destructive Testing 30(7), 434–437 (2008) 3. Dixon, S., Palmer, S.B.: Wideband low frequency generation and detection of Lamb and Rayleigh waves using electromagnetic acoustic transducers. Ultrasonics 42, 1129–1136 (2004) 4. Cawley, P., Lowe, M.J.S., Simonetti, F.: The variation of the reflection coefficient of guided waves in pipes from defects as a function of defect depth, axial extent, circumferential extent and frequency. Journal of Mechanical Engineering Science 216(11), 1131–1143 (2002) 5. Wang, Y., Zeng, F., Sun, F.: Study on a method of echo signal processing for guided waves nondestructive testing of pipes. Journal of Naval University of Engineering 20(5), 18–22 (2008) 6. Feng, W., He, Y., Fan, J.: Study of the Method of Steel and Iron ENDT Based on Digital Signal Progressing. Journal of Harbin university of science and technology 9(2), 114–117 (2004)

Implement of Explosion-Proof and Intrinsic Safe Model Mult-protocol Converter Ming-san Ouyang, Cheng-jie Zhu, and Zhe Liang School of Electric and Information Engineering, Anhui University of Science and Technology, Huainan 232001

Abstract. Aiming at the variety of communication fieldbus underground coal mine, a mult-protocol converter based on embedded system is proposed. It can make the equipments and monitoring systems with different protocols (include RS485, ModBus, ProfiBus, Ethernet) interconnect and form the automation system. This paper gives the use of 32 bit micro-controller S3C2410 ， the hardware interface circuit design which realized the detailed above protocols， and software design of mult-tasks processing using embedded real-time operating system μC/OS-II. The converter achieves mutual conversion between the above protocols. It makes data transmission of those devices and monitoring systems based on those protocols fieldbus real-time and transparent. Keyword: Mult-protocol, Communication-interface, Embedded System, Protocol converter.

1 Introduction At present, in order to improve the coal production ability and realize safe production, the coal in1tegrated automation system begins to universal application [1] [2] [3]. But most of monitoring and intelligent facilities underground coal mining are being used traditional RS232/485 serial communication interface or the CAN bus or Modbus or Profibus and the communication protocols of site instruments are much various. Because these monitoring systems are basically a closed system, which distribute in coal mining each department or region, they form respectively information islands. This hinders the coal industry informatization and automation process. In order to solve these problems, it needs a multipurpose protocol converter to integrate these different protocol subsystems and realize comprehensive integrated automation [1]. Therefore, this paper, based on software and hardware design development, presents a multi-protocol converter which implements to convert each other between the current mainstream protocol RS232 and RS485, TCP/IP, MODBUS, CAN, PROFIBUS. Considering the different equipment hardware’s compatibility, the physical interface of these protocols are designed to intrinsic safety and explosion-proof. Using embedded microcontroller and μC/OS-II embedded real-time operating system, it realizes multiple field communication protocol conversion and data transmission between the monitoring systems. Thus they can directly access automation ring network in coal 1

Supported by The foundation of department of education of AnHui Province (KJ2009A087).

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mine. It realizes distributed control and centralized management, and has wide application prospects.

2 Controller Structure 2.1 Hardware Structure To achieve protocol reliable and high-speed conversion, the controller hardware consists of the power supply, each site protocol interfaces module, micro-controller and reset and keyboard display interface etc. This system hardware principle block diagram is shown as figure 1. Because the controller must finish communications protocol conversion between different fieldbus devices, process very big data capacity, and be able to timely handle a large real-time dynamic data, Samsung's ARM9 series S3C2410 chip is used as the main controller. This microprocessor can run on the highest 266MHz. It integrates ARM company’s ARM920T processor core 32-bit micro controller and has rich resources, independent 16KB instruction Cache and 16KB data Cache, and FDU for the virtual storage, LCD controller, parallel IO ports, 2 channels SPI, etc.

System control and drive External RAM or Flash

RJ45 interface

CAN Bus interface

PROFIBUS interface Power and clock

Fig. 1. System hardware block diagram

Keyboard display task CAN module

Ethernet module ModBus module

Data exchange and buffer

Reset circuit

ARM S3C2410

RS485/232 interface

Keyboard display

PROFIBUS module RS232/485 module Man-machine interface module

Fig. 2. Software structure frame

2.2 Software Structure The main functions of the protocol converter are completing mutual data conversion among RS485 protocol, ModBus protocol, ProfiBus protocol, CAN protocol and TCP/IP protocol and realizing the seamless transparent transmission between the data packets. μC/OS-II embedded real-time operating system is used in the software design in the system. The μC/OS-II opens its source code derived from Linux2.0/ 2.4 kernel and is a highly optimized and code compact embedded Linux specifically targeted at the CPU without MMU. Also the μC/OS-II operating system is based on priority, the real-time kernel with wonderful type source open, easy to operate, the characteristics of strong of portability. The controller adopt modularization design program and is easy to upgrade and maintenance. The software design structure diagram is shown in figure 2.

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3 Interface Circuit Design In view of the particularity of underground coal mine production, the controller must satisfy explosion-proof or intrinsic-safety requirements. So, the interface circuitry of RS232 and RS485, TCP/IP, MODBUS, PROFIBUS and CANBUS can connect with the explosion-proof equipments and intrinsic-safety equipments, and also consider the issue of anti-jamming. The following is the design of the interface circuitry. 3.1 Ethernet Interface Circuit Ethernet support is the first consideration question in this system design. RTL8019AS is used as the Ethernet interface chip which is Realtek company production by Taiwan. It accords with the Ethernet controller with Ethernet II IEEE802.3 standards, full-duplex mode, transceiver at the same time 10Mbps, internal integration a DMA controller, the ISA bus controller and 16KRAM, network PHY transceiver, etc. Through interruption drive the RTL8019AS realize the Ethernet connection. The Ethernet interface is shown in figure 3. Through RJ45 connection to Ethernet, S3C2410 finishes initialized configuration of RTL8019AS, all kinds of necessary control, and realizes the data network transmission and other functions. DATA BUS SPC3

ADDR BUS IOCHRDY

TXD

TPTX-

SRADD0~2

RXD23 MAX232* 2

TXD23

TPTX+

MEMW SBHE

MAX485* 2

RXD45 TXD45

STADD0~2

S3C2410

TXD-

TXD1-

GM8125

TXD+

TPRX-

RJ45

INT0

RXD-

HR901170

IORB

RTL8019

LPCS3C2410

IOWB

RXD

TPRX+

RXD+

AEN

RXD1

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Fig. 3. Ethernet interface circuit

Fig. 4. RS232 RS485 and ProfiBus interface

To prevent interference and realize hot plug and unplug function, RTL8019AS send and receive data to network through an integrated magnetic filter element HR901170 which can separate RTL8019A from external line. 3.2 Rs232 and RS485 Interface Circuit Because of ProfiBus and ModBus agreement also using serial interface circuit work style, the system needs to expand serial ports. Here the multi-serial port expansion chip GM8125 connects to UART interface, which can extended to five RS232 serial UART

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for the S3C2410. Among them there are 2 channels for RS232 communication, one for PROFIBUS interface, the rest for RS485 extension MODBUS bus interface and RS485 bus respectively. Hardware diagram is shown as in figure 4. System chooses MAX485 and MAX232 to deal with level conversion for 3.3V S3C2410 and external circuit. 3.3 ProfiBus and ModBus Interface Circuit Currently profibus-DP protocol is adopt in most of mining equipments. Because it is open and without the protection of intellectual property rights. PROFIBUS protocol standard can achieve in any microprocessors through programming. For implementing high-speed transmission, the SPC3 chip of Siemens company is chosen. It integrates complete profibus-DP protocol in chip and can complete all profibus-DP communication function independently, then accelerate the performance of communication protocols. SPC3 provides the formatting user data interface. User can access these interfaces easily by its solid source program. Profibus-DP have 3 types equipments (the master, the second master and slave). The bus access agreement of master-slave mode is taken between master and slave station equipment. The master station can configure 32 slave stations. This profibus interface is shown as in figure4. MODBUS protocol also is completely open and relatively simple and need not use agreement chip. The system adopts serial asynchronous communication mode and MAX485 as interface chip. A bus can communicate between one host station and eight slave stations. 3.4 CAN Interface Circuit Because there is not CAN controller in S3C2410 microprocessor, CAN network controller interface circuit adopts MCP2510 controller which is the latest production in the American microchip technology company. It supports the agreement of version able to send and receive information frame of standard and the expansion. The controller contains three send-buffer and two receive-buffer and can exchange data with MCU by SPI interface. Its SPI interface data transfer rate can reach 5 MB/s. Bus interface CTM1050

CANL R1

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Fig. 5. S3C2410 microprocessor and CAN network controller interface circuit

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transceiver drive unit adopts high-speed isolation driver module type transceiver CTM1050. The main function of the module is to convert the logic level of CAN controller to its difference level and can with isolate with DC 2500V. Figure 5 is shown.

4 Soft Designing of Controller The μC/OS-II operating system is adopted in the system. System’s procedures include operating system transplantation software and communications agreement conversion program. In order to make whole design simple and easy to debug, the whole program according to the real-time and importance of each task, are divided into 8 different priority application software design task. These division tasks are shown such as figure 6.

Start up mult-task Run

Processing web task

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task

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Fig. 6. Mult-tasks processing sketch map

Using μC/OS-II operating system the controller creates multiple process for function call. These process modules are written with C language. The modularization design method is adopted in the program of communication agreement conversion. According to hardware configuration operating system enable corresponding device drivers. The requirement of protocol conversion calls protocol conversion module. The data conversion process reads the data from different reading and writing process and converts into the required format through the automatic identification, then transfers out by the corresponding writing process.

5 Conclusion The proposed mult-protocol converter chooses as far as possible more fieldbus protocols to design. The communication configuration parameters of system can be setup by the keyboard or Web browser. By operation commissioning the controller can realize

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protocols mutual conversion and data real-time transparent transmission among in ModBus, CANBus, ProfiBus and TCP/IP protocol. As a result of using μC/OS-II embedded real-time operating system, it makes the operation of the system stability, faster, bit-error lower, and conversion efficiency higher. It can satisfy the needs of mine automation reconstruction and the construction. Because the realization of protocol data conversion is mainly used by software, the software real-time and module optimization is focus in the future study.

References 1. Zhu, C.-j., Wang, R.-p., Ouyang, M.-s.: Research of Explosion-proof and Nature-safe Model Mine Mul-protocol Converter Based on LPC2292. Coal Mine Machinery 31, 145–147 (2010) 2. Shi, L., Wang, P.,: Research and development of multi-protocol Ethernet gateway in coalmine comprehensive automatic system. Relay 35(24), 43–47 (2007) 3. Bing, B., Wu, B., Qian, L.: Design and Realization of protocol Converter Based on Embedded for Coal Mine. Industry and mining automation, 79–81 (2008) 4. Wei, D., wei, P.: Design and implementation of the gateway for transfer protocol between CAN Bus and Ethernet based on ARM. Mechatronics, 28–33 (2007) 5. Li, H.-l., Ouyang, M.-s., Lu, J.: Design of the monitoring substation with protocol convert function of the belt conveyor centralized control system. Mining machinery 38, 26–30 (2010) 6. Wang, Q., Ma, W.: Embedded Ethernet-CAN Converter Based on LPC2292. Nanjing University of Aeronautics and Astronautics Journal 39(5), 607–611 (2007) 7. Xu, J., Fang, Y.: Development of embedded fieldbus protocol conversion gatway. Computer Engineering 32(12), 255–257 (2006) 8. Scharbarg, J.L., Boyer, M., Fraboul, C.: CAN-ethernet architectures for real-time applications. In: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 245–252. IEEE Computer Press, Catania (2005) 9. Guan, J.: Design and Realization of Embedded Modbus-TCP/IP Protocol Convert Gateway Based on μC/OS-[D]. Chongqing University (2005) 10. Zhou, L.: ARM Embedded system foundation course. Beijing University of Aeronautics and Astronautics press, Beijing (2005)

The Application in Mobile Computing of Spatial Association Rules Mining Algorithm Based on Separating Support Items Gang Fang School of Computer Science & Engineering, Chongqing Three Gorges University, Chongqing 404000, P.R. China [email protected]

Abstract. In mobile computing many data expressing spatial locations are very important to extract spatial association rules, which can provide potential and useful information for mobile clients. In order to fast mine these spatial association rules and improve efficiency of mobile intelligent system, this paper proposes an algorithm of spatial association rules mining based on separating support items, which can extract location association of spatial object in mobile intelligent system according to mobile client demand. The algorithm firstly uses the way of spatial buffer analysis to extract spatial predicate values among these given spatial predicate, target object and non-target object, and then uses every target object to create a transaction and turns these transactions into transaction vector, finally uses the down search method of separating support item to extract spatial association rules of meeting mobile client demand. Neither does it need create candidate frequent itemsets nor compute support to extract spatial association rules, and so the algorithm avoid to create redundant candidate and reduces repeated computing to improve its efficiency. The result of simulate experiment indicates that the algorithm is faster and more efficient than present mining algorithms when extracting spatial association rules of meeting mobile client demand in mobile computing. Keywords: spatial association rules; mobile computing; separating support items; down search; mobile client demand.

1 Introduction Spatial data mining mainly extract latent and useful knowledge, spatial association and pattern from spatial database written in [1]. Mining spatial association rules is an important task of spatial data mining. At present, people mainly focuses on studying two kinds of spatial association written in [2], namely, lengthways spatial association and transverse spatial association. Lengthways spatial association is among these attributes of the same spatial target objects under the same spatial pattern, transverse spatial association has two types, and one expresses this association among these different objects under the same spatial pattern, the other expresses this association among these different target objects under the different spatial patterns. Now in spatial data mining, there are mainly three kinds of mining spatial association rules M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 651–656. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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written in [3], namely, layer covered based on clustering written in [3], mining method based on spatial transaction written in [4 and 5] and mining method based on non-spatial transaction written in [3]. In order to shorten response time of mobile intelligent system, and improve spatial utilization ratio and time efficiency of system, we use mining method based on spatial transaction to extract transverse spatial association of meeting mobile client demand among these different objects under the same spatial pattern in mobile intelligent system. Since present algorithms such as BApriori written in [6], have some redundant candidate and repeated computing when mining spatial association, hence facing to mobile computing, this paper proposes an algorithm of spatial association rules mining based on separating support items, named by SARMSSI, which may improve efficiency of mobile intelligent system.

2 Creating Spatial Mining Database by Buffer Analysis Facing to mobile computing of spatial target object close to non-target object in mobile intelligent system, we regard “close_to” as spatial predicate to create mining database. The process of creating spatial mining database has three steps. The first step is using circle buffer analysis to extract spatial predicate values; the second is forming a spatial transaction of each target object, the third is turning spatial transaction into transaction vector, denoted by TV. The process of extracting spatial predicate value by circle buffer analysis is expressed as follows: Step 1: We use the point-entity express each spatial object, let (xio, yio) be coordinates of target object as Oi, let (x, y) be coordinates of non-target object as NOk. Step 2: Let r be radii of buffer, and make each target object be the centre of a circle to create each buffer, which is defined as (x-xio)2+(y-yio)2≤r2. Step 3: If coordinates of some a non-target object is expressed as (x, y) and (x-xio)2 + (y-yio)2 ≤ r2, we will extract spatial predicate value as close_to(Oi, NOk). The process of creating spatial transaction and turning transaction into transaction vector is expressed as table 1. Suppose spatial class set of non-target object is expressed as T = {A, B, C, D, E and F}, if V (V T ) is a kind of spatial class, Vx is a spatial instance of V.

∈

Table 1. Forming mining database ID

List of spatial class items as{A, B, C, D, E and F}

Transaction vector TV

O1

close_to(O1, C3), close_to(O1, D4), close_to(O1, E2), close_to(O1, F1)

(23, 22, 21, 20)

O2

close_to(O2, A3), close_to(O2, B4), close_to(O2, E5), close_to(O2, F2)

(25, 24, 21, 20)

…

...

…

On

close_to(On, An), close_to(On, Bn-4), close_to(On, Cn-1), close_to(On, Dn-3), close_to(On, En+2)

(25, 24, 23, 22, 21)

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According to this table, we will use these transaction vectors to make up spatial mining database.

3 Extracting Spatial Association by Separating Support Items 3.1 The Method of Separating Support Items In order to avoid creating redundant candidates and reduce repeated computing, we present the method of separating support items, which is used to separating all items or itemsets supported by a spatial transaction to find frequent itemsets. Definition 1. Transaction Identification, denoted by TI, it is an integer, whose value is the sum of element value in transaction vector. Example, a spatial transaction is {close_to (O2, A3), close_to (O2, B4), close_to (O2, E5), close_to (O2, F2)}, whose vector is TV = (25, 24, 21, 20), TI = 51. Definition 2. Transaction Length, denoted by TL, it is an integer, whose value is the number of element in transaction vector. Example, transaction length of above spatial transaction is 4, i.e. TL=4. The process of spatial transaction is separated support item is expressed as follows: Step1, we compute length of spatial transaction according to definition 2. Step2, we regard every itemsets supported by the spatial transaction as a new spatial transaction, and build an interval as [1, 2TL-1], finally, we use this interval to compute transaction identification of these separated itemset. Step3, we compute transaction identification of every support itemsets as TIx = B x ⋅ TV T , x ∈ [1,2TL − 1] , B x is vector whose form is TL binary number of integer 2TL-x. Example, a spatial transaction is {close_to (O2, B4), close_to (O2, E5), close_to (O2, F2)}, whose vector is TV = (24, 21, 20). The process is expressed as follows: Step1, we compute length of spatial transaction, namely, TL=3. Step2, we build an interval as [1,23 − 1] , namely, [1, 7]. Step3, we compute transaction identification of every support itemsets as follows: TI1= B1 ⋅ TV T = (1, 1, 1) · (24, 21, 20) T =19, it expresses support itemsets = {close_to (O2, B4), close_to (O2, E5), close_to (O2, F2)}. TI2 = B 2 ⋅ TV T = (1, 1, 0) · (24, 21, 20) T =18, it expresses support itemsets = {close_to (O2, B4), close_to (O2, E5)}. TI3 = B3 ⋅ TV T = (1, 0, 1) · (24, 21, 20) T =17, it expresses support itemsets = {close_to (O2, B4), close_to (O2, F2)}. TI4 = B4 ⋅ TV T = (1, 0, 0) · (24, 21, 20) T =16, it expresses support itemsets = {close_to (O2, B4)}. TI5 = B5 ⋅ TV T = (0, 1, 1) · (24, 21, 20) T =3, it expresses support itemsets = {close_to (O2, E5), close_to (O2, F2)}. TI6 = B6 ⋅ TV T = (0, 1, 0) · (24, 21, 20) T =2, it expresses support itemsets = {close_to (O2, E5)}. TI1 = B1 ⋅ TV T = (0, 0, 1) · (24, 21, 20) T =1, it expresses support itemsets = {close_to (O2, F2)}.

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Obviously, the algorithm may gain all itemsets supported by this spatial transaction {close_to (O2, B4), close_to (O2, E5), close_to (O2, F2)}. 3.2 Mining Spatial Association Rules of Meeting Mobile Client Demand Input: (1) Spatial class items as {T1, T2… Tm}. (2) Minimal support as S. (3) Minimal confidence C. (4) Mobile client demand MCD, which may be regarded as spatial transaction. Output: Spatial association rules of meeting mobile client demand. The algorithm uses these data structure as follows: Structure spatial transaction { Vector[m] integer; //Saving element value of transaction vector Count integer; //Saving the number of the same transaction vector } ST Array [2m-1]; // saving the number of the same itemsets, i.e. support of itemsets. F [2m-l-1]; // saving frequent itemsets, l is length of mobile client demand. Step1, we use circle of buffer analysis to extract spatial predicate value and to create spatial transaction which make up spatial database. Step2, the algorithm will turn these spatial transaction into transaction vector according to spatial class items and compute the number of same transaction vector, namely, a transaction of mining spatial database has two attributes vector and count. Step3, Scanning ST[k] (k=0), according to ST[k].Vector, we compute Transaction Length according to definition 2. Step4, the algorithm uses separating support items in above chapter to compute transaction identification TIx of separated itemsets from this transaction ST[k], and then finishes the operation as follows: Array [TIx -1] = Array [TIx -1] + ST[k].count. Step5: k = k + 1, and repeated executing these operations form step3 to step4. Step6: Scanning Array [i - 1] by down search, namely, i is descending, if Array [i 1] > S and i MCD = MCD ( is logical “and” operation), and writing i to F after deleting j (i j = j, j ∈ F ). Step7: Computing and outputting spatial association rules from F according to minimal confidence.

∧ ∧

∧

4 The Analysis and Comparing of Capability If we use B-Apriori written in [6] to extraction association of spatial location in mobile intelligence system, it will has some redundant candidate and repeated computing because it adopts iterative search to create candidate frequent itemset. The algorithm is suitable for extracts short frequent itemset, but it is not fast and efficient. This paper proposes SARMSSI, which need not create candidate to extract frequent itemsets of meeting mobile client demand. Not only is the algorithm suitable for mining long frequent itemsets of meeting mobile client demand, it is also able to reduce some candidate frequent itemsets and more repeated computing to improve the efficiency.

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4.1 The Capability Analysis of the Algorithm Suppose spatial class items are expressed as {T1, T2… Tm}, the number of different spatial transaction is n and the length of mobile client demand is l. Time complexity. Computing of SARMSSI mainly includes three parts which are expressed as extracting spatial predicates values, separating support items of spatial transaction and mining spatial association rules of meeting mobile client demand. Time complexity is expressed as follows: l × n × (n + 1) × (2 l −1 − 1 / 2) + 2 m−l −1 (2 m −l + 1) .

Space complexity. Space complexity of SARMSSI is O ( α ⋅ 2 m ), α is parameter about support and length of mobile client demand. If l becomes more and more big, the space utilization rate of algorithm will become worse. 4.2 The Comparing of Experimental Result Now we use result of simulate experiment to prove above analyses. Two algorithms BApriori and SARMSSI are used to create frequent itemsets of meeting mobile client demand from 12267 spatial transactions, whose transaction identifications are from 3 to 8191, these transactions does not include any simple spatial predicate value, the number of spatial class items is 13, transaction identification of mobile client demand is 10, the number of spatial transaction observe the discipline which is expressed as follows: Transaction identification is 8191, which has one spatial transaction. Transaction identification is 8190, which has two spatial transactions. Transaction identification is 8189, which has one spatial transaction. Transaction identification is 8188, which has two spatial transactions. ... Our experimental circumstances are as follow: Intel(R) Celeron(R) M CPU 420 1.60 GHz, 1.24G, language of the procedure is Visual C# 2005.NET, OS is Windows XP Professional. The runtime of two algorithms is expressed as Figure 1 as support of frequent itemsets changes, and the runtime of two algorithms is expressed as Figure 2 as length of frequent itemsets changes. According to these two figures, we knew that SARMSSI is faster and more efficient than B-Apriori when mining frequent itemsets of meeting mobile client demand in large spatial database.

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) 50000 d n o c 40000 e s i l 30000 l i M 20000 ( e m i 10000 t n u 0 R

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5 Conclusion This paper proposes an algorithm of spatial association rules mining based on separating support items, which can extract location association of spatial object in mobile intelligent system according to mobile client demand. And then, we need further discuss how to improve space utilization ratio. Acknowledgment. This work was fully supported by science and technology research projects of Chongqing Education Commission (Project No. KJ091108), and it was also supported by science and technology research projects of Wanzhou District Science and Technology Committee (Project No. 2010-23-01) and Chongqing Three Gorges University (Project No. 10QN-22).

References 1. Koperski, K., Han, J.: Discovery of Spatial Association Rules in Geographic Information Databases. LNCS, vol. 951, pp. 47–66. Springer, Berlin (1995) 2. Fang, G., Wei, Z.K., Yin, Q.: Extraction of Spatial Association Rules Based on Binary Mining Algorithm in Mobile Computing. In: IEEE Information Conference on Information and Automation, pp. 1571–1575. IEEE press, Los Alamitos (2008) 3. Zhang, X.W., Su, F.Z., Shi, Y.S., Zhang, D.D.: Research on Progress of Spatial Association Rule Mining. Journal of Progress in Geography 26(6), 119–128 (2007) 4. Liu, Y.L., Fang, G.: The research and application of a transaction complement mining algorithm. Journal of Computer Engineering and Applications 44(35), 168–170 (2008) 5. Fang, G., Liu, Y.L.: Application of Binary System Based Spatial Mining Algorithm in Mobile Intelligent Systems. Journal of Southwest University (Natural Science Edition), 31(1), 95–99 (2009) 6. Chen, G., Zhu, Y.Q., Yang, H.B.: Study of Some Key Techniques in Mining Association Rule. Journal of Computer Research and Development 42(10), 1785–1789 (2005)

Automatic Test Case Generation for Web Applications Testing Lulu Sun, Junyi Li, and Shenglan Liu College of Information Science and Engineering, Hunan University, Changsha 410082, China [email protected] Abstract. The quality of Web applications is becoming more and more important. Web applications testing is a challenging work owing to its dynamic behaviors and complex dependences. In this paper, we present an automatic test case generation algorithm by exploiting DUCC expression and constructing transformed dependence diagram. We propose the two-phase approach to generate test cases automatically and apply the approach into intra-and inter-page levels to decrease the complexity of test case generation. To enlarge the generated test cases, we also exploit a predicate negating method to obtain more test cases. The experimental result shows our approach is applicable for real world testing and can generate test cases effectively. Keywords: Web applications testing; test case generation; automatic testing; software testing.

1 Introduction As the spread trend of Web-based software systems continue to grow rapidly, it is becoming critical to assure the quality and reliability of Web applications. Due to the character of open standard platform, Web applications have certain unique characters, such as dynamic behavior and complex dependencies, compared to traditional software system, and that features bring great challenges to researchers. Test case generation[1][2][3] is an important step to fulfill Web applications testing, aiming to generate test cases automatically to detect the defects hidden in system structure or functional application courses. To date, all existing test approaches[1][2][4][6]have not been practical in the domain of Web applications, for certain issues existing in test case generation need more practical approaches and still be ad hoc. There are some concerned issues as follows[3]. (1) How to model Web applications to describe it reasonably and to support the automatic test case generation in a cost-effective way? (2) With the model of Web applications, how to automatically generate targeted test cases to perform testing requirements with the maximum fault-detection coverage? This paper focuses on the issues illustrated above and presents an automatic technique for generating test cases for Web applications testing on the structural test level. Our technique defines the dependence relationships intra/inter pages and applies them to construct relative dependence diagrams. Considering the features of future Web applications, such as rich client (turn to [2] for more details ), we propose the two-phase approach to generate test cases automatically based on analyzing the structure of Web M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 657–666. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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applications. It contains the Web application analysis in phase 1 and test case generation in phase 2. We improve the existing dependence diagrams in phase 1 and introduce DUCC expression to generate test cases in phase 2. In order to enlarge the set of test cases, a negating method is introduced to cover most execution paths. In the experiment, the presented approach is applied to generate test cases to perform software test on Web applications. The experimental results show that our approach can deploy the dependence diagrams to automatically generate test cases effectively. The remainder of this paper is organized as follows. Section 2 presents an analysis of dependence relationships of the program, discuss the disadvantage of the previous work and proposes the DUCC expression to transform the dependence diagram. Section 3 presents the algorithm and illustrates it on an example program. Section 4 discusses structural testing for Web applications in two phases. Section 5 presents our experimental evaluation of our approach on Web applications . Section 6 presents conclusion and the future work.

2 Dependences Analysis and DUCC Expression 2.1 Analysis of Web Applications Dependences Although several server side languages are available for structural testing study, we will take PHP script language as a analysis example, because the chosen languages offer all the basic features available in the others and the source-open character of PHP support our research to implement experiments on several websites. Dependence relationships can be extracted from source code to assist test case generation. Knowing these dependences in Web applications, we could propose certain algorithms or approaches to it with correlated input attributes and execution paths. a. php a.php 1 $b) 5 $c=$a+2*$b; 2 3 4 6 6 else $c=$b; 7 if( $c+$b<0){ 8 $a=$a-2; 5 8 9 10 9 $b=$b+$c; 10 echo $b;} 11 elseif ($a2-4*$c>=0){ b.php 12 echo $b; 13 $d=”a”; 20 14?> 15
. 21 16
17 ”>
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Straight line---control dependence Curve line---data dependence Dash curve line---call dependence

Fig. 1. Fragment of PHP/HTML code and dependence relationships

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Control dependence: control dependence connects the predicate tested at a conditional or loop statement to the instructions the execution of which directly depends on the true value of the predicate[2]. This kind of dependence continues to hold for the server/client side code. The HTML control dependence is a special kind of dependence relationship between the nest tags and the HTML codes included in it. As shown in Fig 1. The straight lines all represent the special control dependences. Data dependence: In traditional programs data dependence holds between the statement which defines the value of a variable and the other statement which uses the defined variable[2]. Such a situation can be found in server/client code, data flow may occur in a Web application from server/client side statements to the HTM code. On the other side, a variable definition at an HTML statement can reach a server/client instruction through a procedure invocations, as a submission parameter, or through a DOM element access. In Fig 1, variable $d is defined in line 13 and propagated to line 17 and line 18. Call dependence: When a server side program is invoked from an HTML statement(e.g. via from submission or HREF), control of execution is transferred to the server and never returns back to the Web page issuing the call[2]. This happens to the dynamic Web applications a lot. In Fig 1, Line 15 submits a call from the client side to invoke b.php page and consequently the program in page b.php starts running and the call relationship is constructed, as the dash curve line shown in Fig 1. Definition 1 dependence: A dependence holds between any statement and server/client side program or procedure invoked, or two statements. It can be simply represented as (p, q, Ai), p and q means two statements and Ai represents the dependence defined above, such as a transit variable. Our analysis of dependence relationship is an extension of Ricca’s previous work[1][2] . In his research, he reports three different kinds of dependences existing in the system code. But the dependence diagrams are constructed without considering the usage of guiding test cases generation. For example, relationships between HTML code and PHP code described in Fig 1 still needs further discussion. In this study, we reconstruct the dependence relationships of code fragment from control dependence, data dependence and call dependence, respectively, in order to support the test case generation algorithm proposed in the next section. 2.2 Transformed Dependence HTML code may contain buttons that –when pressed by users—result in the loading and execution of additional PHP source files. We simulate user input by transforming the HTML code contained in PHP pages. We add additional input parameter bi to the PHP program to represent buttons from 1 to n, and the input values are transformed to variables in accordance with PHP code.

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The example of the transformed HTML code in Fig 1 are shown in Fig 2:

Fig. 2. Example of transformed HTML code

2.3 DUCC Expression Definition 2 DUCC: DUCC expression is used to represent the dependence relationships of program. Specifically, DUCC=(p:r:d:u:c:c); p represents the page the program belongs to; r represents the PHP page and the row id of each node; d represents the variable defined by node r (if the node is a predicate/html nest, output statement, regard the node define a new variable named p, ht, o, respectively); U presents the set of variables used by node r; the first c means the directly control dependence of node r; the second c means the call dependence of node r. Here, we present a DUCC expression to label each node of the reconstructed dependence diagrams.

Fig. 3. The code fragment and its DUCC expression

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3 Automatic Test Case Generation Algorithm 3.1 Predicate Path Constraint A predicate path constraint is a conjunction of predicate conditions on the program’s input parameters. Program is executed along the predicate path constraints. Hence, using DUCC expression to extract the predicate path constrains is an effective way to generate test cases for structural testing. Predicate Path constraint can be described as n i=1Pi and it reflects the path that will be executed.

∧

Definition 3 Control Node: For node R0 (p0:r0:d0:U0:c0:c0), if R0.r0 is represented by p, R0 will be considered as the self-control node; Otherwise, if exists R1 (p1:r1:d1:c1:c1), and R1.d1=R0.c0, we define R1 as the control node of R0.

∈

Definition 4 Definition Node: In node R0 (p0:r0:d0:U0:c0:c0), if exists R0.u0 R0.U0, ∀ R0.u0 R1.d1, and R0.r0>R1.r1, we define R1 as the definition node of R0.u0.

∈

∈

Definition 5 K-distance Control-Node : For node R0 (p0:r0:d0:U0:c0:c0), ∃ R0.u0 R0. U0: All the control nodes of definition nodes of Variable R0.u0 will be called the 1-distance control node; If R is the 1-distance control node of R0.u0, and the DUCC expression is p:r:d:U:c:c, variables used by node R(p:r:d:U:c:c) meet R.u R.U, we define all the control nodes mapped by the definition nodes of R..u are the 2-distance control nodes of R0.u0. Assume R is R0.u0’s (k-1)-distance control node, and the DUCC expression is p:r:d:U:c:c. The relevance node R(p:r:d:U:c:c) , R.u R.U, all the control nodes reflected by definition nodes of variable R.u are called the k-distance control node of R0.u0.

∈

∈

Definition 6 Control Path: For node R0 (p0:r0:d0:U0:c0:c0), assume the control set is {

tm }, and tj pkt11 , pkt 22 ,...., pkm

∈

{-1,1},

⎧ pki : t j = 1 pkjtj = ⎨ ,for any ⎩~ pki : t j = −1

tm t1t2t3 ...tm combination, pkt11 , pkt 22 ,...., pkm are called the control path of node R0.

Through analyzing the DUCC relationships among programs, we can transform the extended dependence diagrams (seen Fig1) into a diagram called TC diagram, as shown in Fig 4, which is similar to a reversed binary tree, but with an extra exit node. Then, deduce the predicate path constraints starting from the leaf node, so as to get the predicate path constraints (the execution path).

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exit leaf nodes

Fig. 4. The transformed diagram: TC diagram

3.2 Algorithm Based on the transformed TC diagram from extended dependence diagrams, we can apply a test case generation algorithm to the constructed diagram. The leaf node of TC diagram can be seen as the basic node and the algorithm deduces the control path by reversing it. Fig 5 shows the description of our algorithm. The input to the algorithm is : a program p. The output of the algorithm is a set of test cases T’ for the program p. From the below algorithm illustrated in Fig 5 and Fig 6, we can deduce the predicate path constraints from the basic node (leaf node) to the k-distance control node. In order to generate more test cases from the output T’, we can modify versions of the predicate control path of test cases. And the input data will be propagated to variables in the generated test cases from user session, log files and other specification documents. 3.3 Example Let us now consider how the algorithms of Fig 5 and Fig 6 generate the test cases in the example of Fig 1. Through analyzing the program code of page a.php and page b.php, get the DUCC expression as shown in Fig 3. Then take node 10 for example: The DUCC expression of node 10 is : a:12:o1:$b:p2; The initial set of T’ equals φ . It’s obvious that the a.12.p2≠ φ ,we can get the directly control node of node 10 is node 7 and the a.7.U ={$b, $c}. According to the algorithm in Fig 5, the definition node set of variable $c is {node 5, node 6}, meanwhile, the control node set of variable $c is {p1,~p1}; Apply the same algorithm to variable $b, obtain the definition node set {node 3} and the control node set is φ . By using the Search_DRNS algorithm in Fig 6, get the control node set DRNS={p1,~p1}, consequently the control path of node 7 is p1,~p1; The generated test case of basic node 10 is {{node 2,node 3, node 4, node 5, node 7, node 9, node 10}, {node 2, node 3, node 4, node 6, node 7, node 9, node 10}}, or represented as {p1 ∧ p2, ~p1 ∧ p2}.

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Aiming to generate more test cases, we can negate the last predicate constraint for each prefix of the control path and mutate the predication along the test path. In this instance, new test cases can be generated by systematically traversing subsequences of the output control path constraint, and negating the last conjunct. Hence, from {p1 ∧ p2,~p1 ∧ p2}, other test cases can be derived as follows: { p1 ∧ ~p2 ; ~p1 ∧ ~p2 ; p1; ~p1} Input

StringSearch_DRNS(C.vi)

Program P

{

Output

//outputthe setofrelevantcontrolnodesforC.vi

Test suit T’

D=Search_DN(C.vi)

Method

//getthedefinitionnodesetDofC.vi

1. Analyze program p and get the DUCC expression of each node in the program fragment 2. Collect the basic nodes and their DUCC expressions. Take basic node S for example.

FORanyDjęD {//foranynodeDjęD Cj=Search_CN(Dj) //getcontrolnodeCjforDj IFCjĮ

3. Initialize T’= ;

4. If S.c0= ,terminate the algorithm and output p;

{//ifDjexistscontrolnodes

DRNS=DRNS+{Cj} //recordthecontrolnodeCj

5. If S.c0Į ,get the control node C for S; 6. If the DUCC expression of C is r:d:U:c:cˈassume

r:d:U:c:c=DUCC˄Cj˅ //gettheDUCCexpressionofCj

C.U={C.v1, C.v2,ĂĂ,C.vm};

ForanyCj.vięCj.U

7. For any C.vięC.U, i=1,2,ĂĂ,m: firstly initialize

{//outputthesetofcontrolnodesforCj.vi

DRNS= , DRNSi= , then apply Search_DNS(C.vi) algorithm to get the DRNS set.

Search_DRNS(Cj.Vi) }

8. The DRNS=ĤiDRNSi, get all the control paths of t1

t2

}

tm

node C, T’=T’+{ pk 1 , pk 2 ,...., pkm } 9. Return T’

} RETURN(DRNS) }

Fig. 5. Test suite generation algorithm

Fig. 6. Control path algorithm

4 Structural Testing for Web Applications 4.1 Architecture of Automatic Test Case Generation We have illustrated about the algorithm of automatic test case generation. In order to assure the algorithm is suitable for the web applications testing in practice, we take the characteristics of Web applications into account and decide to divide the whole test case generation course into two phases. As shown in Fig 7, phase 1finishs the relationship detection work to analysis program code and construct the TC diagram. Phase 2 takes advantage of TC diagram and apply the presented algorithm to it to generate test cases.

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Fig. 7. Test case generation course for Web applications structural testing

4.2 Two Testing Levels With consideration of the future characters of Web applications and the efficiency of test case generation , we apply the proposed test case generation algorithm to intra-program and inter-program levels. • Intra-program means to analyze and generate test cases for one web page. With the development of Web applications, more and more computational load will be moved to the client. Hence, we should both generate test cases for client side pages and server side pages. • Inter-program means to construct test cases for two or more pages which hold call dependences among each other. The merits of this methodology are :1) Dynamic web applications have become a irresistible trend,. It is necessary to test the structure of a single page, for hidden errors may lay in it. This method will generate suitable test cases for testing single page; 2) The scale of Web applications may be large with many web pages. The test cases for one single page can be connected together or used for the generation of test cases for inter-page testing. This reduces the complexity of generating test cases for systematic testing.

5 Experiment In this study, we implement the prototype system based on the reported approach to generate test cases. The prototype system is implemented in JavaScript, PHP, and MYSQL. We emulate execution on Web applications ”Bookstore” and “Portal”, which can be accessed on the Web site: http://www.gotocode.com/. That is OpenSource online Web site available for every researcher. The test case generator takes TC diagrams and produces a set of test cases based on our analysis and algorithm. At the same time, we identified the dependences in Web application and generated test paths based

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on evaluated results. The experiments are designed and results are evaluated based on the test case generator. In the experiment, we will evaluate the numbers of test paths and numbers of test cases by negating the last conjunction . In our observation, the complex system contains more dependence relationships intra and inter pages. We detect the dependence from two Web applications. The statistics of dependences of Web applications are shown in Table 1. Table 1. Statistics of dependence types of Web applications

Dependence Type Data dependence Control dependence

BookStore 78 10

Portal 79 12

Call dependence

77

101

To evaluate the proposed algorithm, we generate predicate control path and test cases with dependences. In Table 2, the predicate control path means the test case generation by considering the dependence relationships from data dependence, control dependence and call dependence aspects. The third column in Table 2 means the final test cases generated by enlarging the numbers of test cases with the last predication negation. The experiment result shows that using the proposed approach can effectively generate test control paths and test cases intra or inter programs of pages. Table 2. Test path and test case generation

BookStore Portal

Test control path 15 15

Test cases 546 2256

6 Conclusion In this paper, we propose a automatic test case generation approach for Web applications testing and implement a prototype tool with the approach. Firstly, we extend our research by Ricca’s work to analysis the dependences among programs, and then propose to transform the HTML code to support the construction of dependence diagram. The DUCC expression helps to build the TC diagrams for test case generation and the experimental results shows that the dependences of Web applications with DUCC expression can instruct the generation of test cases. And with the consideration of enlarging the numbers of test cases, more test cases can be derived through the generated test control path by negating last conjunction method. In the future, we will evaluate the effectiveness of defect reveal and search for new approach to minimize the test cases in case of the situation of state explosion. Besides, the whole testing process is partially automatic, we aim to develop some tools or method to enhance the automatic level of the testing approach.

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References 1. Ricca, F., Tonella, P.: Analysis and Testing of Web Applications. In: Proceedings of the International Conference on Software Engineering, pp. 25–34 (2001) 2. Ricaa, F., Tonella, P.: Construction of the System Dependence Graph for Web Application Slicing. In: Proceedings of the Second IEEE International Workshop on Source Code Analysis and Manipulation(SCAM 2002)., pp. 148–157 (2003) 3. Artzi, S., Kiezun, A., Dolby, J.: Finding Bugs in Dynamic Web Applications., pp. 54–65. ACM, Washington (2008) 4. Tung, Y.-H., Tseng, S.-S.: A Novel Approach to Automatic Test Case Generation for Web Applications, pp. 399–404. IEEE, Los Alamitos (2001) 5. Conallen, J.: Modeling Web Application Architectures with UML. Communications of the ACM 42(I), 63–71 (1990) 6. Liu, C.H., Kung, D.C., Hsia, P., Hsu, C.T.: Object-based data flow testing of Web Applications. In: Proceedings of the First Asia-Pacific Conference on Quality Software (APAQS 2000), Hong Kong, China, pp. 7–16 (2000)

Design of Holmium Laser Treatment Instrument Control System Based on STM32 Youjie Zhou*, Chunhua Xiong, and Changbo Lu Beijing POL Institute, beijing 102300, china [email protected]

Abstract. Based on current marke tof the holmium laser lithotripsy device power by manual control, the image recognition technology are presented by which are used in the stone therapy. , in this paper STM32 is adopted in our control system design research.the core circuit, the external circuit and image recognition part of the circuit and software are discussed, and the corresponding Design ideas and programs is given. The system which can not only generate reliably, practically, high intelligently, but also can be widely applied to the treatment of ureteral stones, bladder stones and kidney stone.

，

Keywords: holmium laser, STM32, image processing, control system.

1 Introduction Stones are common urological diseases, in recent years its incidence is increasing. Which treatment is generally taken to its traditional surgery, the pain of traditional surgery is obvious [1]. With the development of minimally invasive and laser technology, there has been a lot of new ways avoiding surgical treatment and become a reality [2]. In the current market holmium, the rubble powe of holmium laser treatment instrument often only is selected by a doctor experience to bring some suits to the risk of surgery and the pain. In this paper,STM32 is adopted in our control system design research. The core circuit, the external circuit and image recognition part of the circuit and software are discussed, and the corresponding Design ideas and programs is given.The image recognition technology are presented to be used in the stone therapy so as to automatically identify stone and automatically adjust the rate according to the rate of breaking to improve the efficiency of gravel. The results can be widely applied to the treatment of ureteral stones, bladder stones and kidney stone.

2 Holmium Laser Treatment System Holmium laser treatment can be divided into 4 parts, lasers, control systems, cooling systems, and image acquisition, the relationship between them as shown in Figure 1. *

Corresponding author.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 667–672. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Shown in Figure 1, the above four parts together is a complete holmium laser treatment, the control system is equivalent to treatment for the brain, it can control the laser excitation as needed, as well as the size of the optical power.The laser can send the appropriate laser power to the Focus site through the optical fiber.In the light of the process. While the laser cavity will produce a great heat, it is cooled throught cool system, which make sure the laser cavity contain a relatively low temperature working environment (in this case is 3 °C to 25 °C) . Image acquisition part of the equivalent as the human eye, is mainly responsible for the size of stones ,in the same time ,which collecte and analysis feedback to the control system, and then there is a decision-making by the control system is to increase or decrease the laser energy.

Fig. 1. structure of holmium laser treatment

3 Holmium Laser Treatment Control System Design There are holmium laser treatment and various parts of the control system block diagramshown in Figure 2.According to the chart, the hardware of the Holmium laser treatment control system consists of at least three elements besides the core circuit, the external circuit and image recognition circuit.

Fig. 2. holmium laser treatment control system

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3.1 Core Circuit Design ST company has introduced the Cortex-M3 core-based STM32 family of processors, Some consider it to be advanced Cortex-M3 core structure, superior and innovative peripherals, low cost, good power control and low power consumption in one. Moreover, STM32 processors has a full range of feet on the foot, peripheral and software compatibility, you can not modify the source code and software under the conditions of the framework,it will be applied to upgrade to more storage space, or use different packages Specifications of the chip, very portable. In addition, ST offers a lot of development information, application notes from the software to make use of STM32 very easy to develop [3],[4]. With the system's functional requirements, the design uses SD card to control circuit, On these bases, the choice of CPU is STM32F103VCT6 whose main frequency is 72 MHz, with 256K FLASH, 48K RAM, 16×12 bit A / D, 3 channels of SPI, 2 I2C, 3 channels of UART. In a word , core circuit system fully meet the design requirements. 3.2 Image Recognition Circuit Design Stone is composed of crystal and protein matrix, the specific form for many reasons, specific to each piece of stone, there are material differences between its specific to each person's treatment is the difference between the size and hardness. This requires a processing time of stone can not be generalized, in order to achieve the intelligent handling of stone, the introduction of image-recognition technology, shown in Figure 3. Its basic principle is to use image recognition technology, to identify stones and then calculate the rate of gravel, and threshold comparison decision is to increase or decrease power.

Fig. 3. image recognition module

ADS7843 has two auxiliary inputs IN3, IN4 can be set to 8-bit or 16-bit mode. Its external connection circuit shown in Figure 4, the circuit's reference voltage to determine the converter's input range, output data representative of each digital bit analog voltage equal to the reference voltage divided by 4096.

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3.3V U16

X+ Y+ XYGND IN3 IN4

1 2 3 4 5 6 7 8

VCC DCLK X+ CS Y+ DIN XBUSY YDOUT GND PEN IN3 VCC IN4 Vref

TS

16 15 14 13 12 11 10 9

ADS7843_SCK ADS7843_CS ADS7843_DIN ADS7843_BUSY ADS7843_OUT ADS7843_PEN

3.3V R63 10K

3.3V 3.3V

ADS7843 3.3V C58 10uF

C56 104

.

.

Fig. 4. ADS7843 circuit

3.3 Control System Software Design According to their workflow , holmium laser work can be divided into five states, namely, the start state, a wait state, error state, ready state and a light state, the process are shown in Figure 5.

Design of Holmium Laser Treatment Instrument Control System Based on STM32

Fig. 5. Holmium laser treatment processes

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4 Conclusion The image recognition technology are presented to be used in the stone therapy so as to automatically identify stone and automatically adjust the rate according to the rate of breaking to improve the efficiency of gravel.The real time intelligently control of the laser treatment instrument is realized with image recognition technology. The system is reliable, practical, high intelligent.The results can be widely applied to the treatment of ureteral stones, bladder stones and kidney stone.

References 1. Yip, K.H., Lee, F., Tam, P.C.: Holmiun laser lithotripsy for ureteral calculi:an outpatient procedure. J. Endourol. 12, 241–246 (1998) 2. Geng, J.J.: Endoscopic treatment of urinary calculi with holmium laser Nursing. Journal of Nursing 38(3) (2003) 3. Li, N.: Processor-based STM32 MDK, development and application, vol. 10. Beijing Aeronautics and Astronautics Press, Beijing (2008) 4. Wang, H., Wang, X.z.: On the 4-wire resistive touch screen and the interface STM32. High-tech enterprise in China (21), 46–47 (2009)

Research on Attribute Granular Computing and Its Fuzzification Method Based on Qualitative Mapping* Ru Qi Zhou, Yi Lin Wu, and Yi Qun Chen Department of Computer Science Guangdong University of Education, Guangzhou, Guangdong Province, China

Abstract. A novel attribute fuzzy granule cluster based on qualitative transformation mechanism of attribute and the computing model thereof was put forward. This paper first establishes information granules belonging to the attribute topological space by qualitative mapping, then figures out expression of attribute cluster operators under various granularities to form a hierarchical attribute granule simplex, studies how to determine granularity for fuzzy information system and finds out a method for generating fuzzy attribute granule. The result proves that the method can better model overall attribute analysis ability of human by formalization, so that the computer will better simulate such ability, providing a new research approach of granular computing method for artificial intelligence. Keywords: qualitative mapping, granular computing, attribute granule, fuzzy granule, qualitative standard.

1 Introduction Granular Computing is a new concept and computing paradigm for information processing, mainly applied to processing massive, inaccurate, fuzzy, uncertain and partially true information. In 1979, Zadeh came up with the concept of information granularity for the first time and held that Fuzzy Set Theory has great potential in granular computing. In 1997, he developed the fuzzy information granulation into Theory of Computing with Words[1]. The information granules are applied to Rough set, Fuzzy set, evidence theory, ect. The granular computing is getting more and more important in soft computing, knowledge discovery and data mining and has make wonderful achievements[2]. Main theoretical models for granular computing include: word computing model based on fuzzy set, rough set model, granular computing model based on quotient space and mixed model of various granularities. The granular computing covers researches on theories, methods, techniques and tools of granularity and has become one of the most attractive subject in the field of intelligent information processing. *

This work is supported by Natural Science Foundation of Guangdong Province(2009170004203010),Foundation for Distinguished Young Talents in Higher Education of Guangdong(LYM09137).

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 673–680. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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This paper puts forward a novel attribute fuzzy granule cluster based on qualitative transformation mechanism of attribute and the computation model thereof. This paper first establishes information granules belonging to the attribute topological space by qualitative mapping, then figures out expression of attribute cluster operators under various granularities to form a hierarchical attribute granule simplex, studies how to determine granularity for fuzzy information system and finally the method to generate fuzzy attribute granule. The result proves that the method can better model overall attribute analysis ability of human by formalization, so that the computer will better simulate such ability, providing a new research approach of granular computing method for artificial intelligence.

2 Qualitative Mapping and Attribute Granule In psychology, it is believed that human feeling reflects the attributes of things and human consciousness is a process for making simple attributes of things into a synthesized attribute. Let a(x) be a sensation attribute of thing x, then it will have qualitative attribute and quantitative attribute. Let da(x) be the quantitative attribute and Pa(x) be the qualitative attribute. Then when x has the qualitative attribute Pa(x), it can be described that x has the attribute represented by Pa(x). For example, let x be water and a(x) be water temperature. When the actual temperature da(x) is 100 which means the water is boiling, the Pa(x) stands for boiling water and represents the attribute of hotness. Usually, we consider 100 as the boiling point of water, so 100 is the standard for determining whether the water is boiling and is called the qualitative standard. The qualitative standard of an attribute may be a number, a set, an interval on a number axis or a domain of the multidimensional space.

℃

℃

Definition 1: Let [αi,βi] and [αj,βj] be the qualitative standards of attributes pi(x) and pj(x) respectively, then q(x) = pi(x) pj(x) and r(x) = pi(x) pj(x) are conjunction attribute and disjunction attribute of the pi(x) and pj(x) respectively and the qualitative standards are [αi,βi]∩[αj,βj] and [αi,βi] [αj,βj]. Let qualitative attribute numerical range of attribute a(x) of thing x be da(x), and standadrd domain cluster Γ of a(x) be {[αi,βi]⏐i∈I} . It is easy to figure out that da(x) and Γ will form a topological space, so that the following conclusion is obtained:

∧ ∪

Proposition 1: T(da(x),Γ) is a standard topological space. Definition 2: Let Γ={[αi,βi]⏐i∈I} be the cluster of all qualitative standards of proposition p, then mapping T:Γ→Γ is the standard mapping of the proposition p. So, following equation is true in [αi,βi],[αj,βj]∈Γ: T ([αi,βi])=[αj,βj]

(1)

And T is the qualitative standard transformation from [αi,βi] to [αj,βj]. Definition 3: An attribute granule is a binary tuple defined in the attribute topological space. (P, Γ) =(pi(x),[αi,β i]) or (P, Γ) = (da(x), [αi,β i])

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The a(x) is a sensation attribute of thing x, pi(x) is property of the a(x), [αi,β i] is a qualitative standard domain of pi(x) and d a(x) is a quantitative attribute value of a(x). Definition 4[3]: Let Da={d(x)}⊆ R be the magnitude set of attribute a(x), Px={pi(x)| i I} and Γ={cpi(x)=(α,β)i |(α,β)i ⊆Da} be the feature (or property) set of thing x and qualitative standard cluster of attribute pi(x). τ:Da×Γn→Pz is the mapping from Da×Γn to property set Px. If a pi(x) in Px and its qualitative standard (α,β)i Γ exist for any da(x)∈Dx, and make the following matrix true:

∈

∈

⎧(α , β )1 ⎫ ⎧da ( x) ⊥ (α , β )1 ⎫ ⎧∂p1( x) ⎪ ... ⎪ ⎪... ⎪ ⎪... ⎪⎪ ⎪⎪ ⎪⎪ ⎪⎪ ⎪⎪ τ (da( x), ⎨(α , β )i ⎬) = ⎨da ( x) ⊥ (α , β )i ⎬ = ⎨∂pi( x) ⎪ ... ⎪ ⎪... ⎪ ⎪... ⎪ ⎪ ⎪ ⎪ ⎪ ⎩⎪(α , β )n⎭⎪ ⎩⎪da ( x) ⊥ (α , β )n⎭⎪ ⎩⎪∂pn( x) ⎧1 iff da( x ) ∈ (α , β )i , ∂=⎨ ⎩0 iff otherwise

(2)

τ:Da×Γ→Px is the qualitative mapping to transform the mag-

⊥

nitude da(x) of a(x) to corresponding feature pi(x) and is quantitative-qualitative feature transformation operator. The following proposition is true according to Definition 2 and Definition 3: Proposition 2: the binary tuple (P, Γ) =( pi(x),τ(a(x)∈ [αi,βi])) means information granule under the qualitative mapping. Attributes of things contain massive information. For instance, the temperature attribute of water may show states of water such as liquid and steam and its properties such as hot and cold, etc. Actually, an attribute is an information complex formed by integrating many basic attributes through proper operation. Therefore, attribute integration plays an important role in the attribute granular computing. The basic operation is defined below. Definition 5: Let (pi(x),[αi,β i]) and (pj(x),[αj,βj]) be two attribute granules, then the integration operation of attribute granule is defined as follow:

∧ p (x), [α ,β ] ∩[α ,β ] ) (3) Δ is the integration operator of granule and ∧ is the attribute conjunction operation (pi(x),[αi,βi])Δ (pj(x),[αj,βj] )=( pi(x)

j

i

i

j

j

in Definition 1. Integration operation of granule will generate a brand-new granule with brand-new qualitative standard sometimes. This is called dissimilation or evolution of granule or sometimes granule reasoning. Such complex granular computing operation is defined below. Definition 6: Dissimilation operation or evolution operation of two attribute granules (pi(x),[αi,βi]) and (pj(x),[αj,βj]) are defined as: (pi(x),[αi,β i])→(pj(x),[αj,βj] )=( pi(x)⇒pj(x), [αj,βj]=T([αi,βi] ) )

(4)

The ⇒ means attribute transformation or attribute reasoning and T is the standard transformation in Definition 2.

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The (pi(x), [αi,βi] )→ (pj(x), [αj,βj]) at the left is granule information reasoning. The operation will generate a new granule or original granule. When the result is the original granule, the dissimilation is considered reflexive. Obviously, the reflexive relation is logically true in the above definition. When a new granule is generated, the transformation can be described by the quantitative-qualitative feature transformation operator in Definition 4.

3 Computing and Structure of Attribute Granule Granule has structure. One problem space may have different granule layers due to different granulation degree. Internal structure of granule layer means relationship between the granules in the layer. When solving problems, it is vital to choose the optimum granule layer because different granule layers hold different complexities for solving the same problem. One granulation principle corresponds to one granule layer and different granulation principles correspond to many layers, reflecting different angles from which human look at, understand and solve problems. Relationship between the layers forms a relational structure called granule structure[4]. Let (X, Mx,P x, Γ ) be a attribute granule set in which Mx={a i(x)|i∈I} is a data attribute set of thing X, and P x ={pi(x)| i I} and Γ={cpi(x)=(α,β)i |(α,β)i ⊆Da} are feature (or property) set and qualitative standard cluster of attribute pi(x) respectively. If in Δ operation, any a(x), b(x)∈M x has a c(x)∈ Mx that makes (d c(x), [αi,βi])= (d a(x), [αj,βj]) Δ (d b(x), [αk,β k]) true, Mx is encapsulated in the integration operation Δ and the following proposition is consequently true:

∈

Proposition 3: The integration operation of the Mx and attribute granule forms a semigroup of attribute granule S(Mx, Δ). If a suitable unit element Ix can be assigned to the semigroup of attribute granule S(Mx, Δ) to make the following equation true: (pi(x),[αi,βi])ΔIx =Ix Δ (pi(x),[αi,βi])=(pi(x),[αi,βi]) the attribute granule set forms a monoid of attribute granule M(Mx, Δ, I x). If an attribute a(x) of thing X exists in the monoid of attribute granule M(Mx, Δ, I x) and the conjunction operation of the a(x) and another attribute result in a(x), namely a(x)∧b(x)=a(x), a reasoning relation (d a(x), [αj,β j]) → (d b(x), [αk,β k]) is true. Definition 7: (d a(x), [αj,βj])Δ(d b(x), [αk,βk])= (d a(x), [αj,βj]) is true if and only if a(x)=a(x)∧b(x). The two attribute granules are related by integration absorption (or extraction absorption), attribute granule (d a(x), [αj,βj]) is the integration absorption element of attribute granule (d b(x), [ αk, β k]) and (d b(x), [αk, β k]) is the extraction absorption element of (d a(x), [αj,β j]). Proposition 4: The reasoning relation (d a(x), [αj,β j]) → (d b(x), [αk,β k]) in the monoid of attribute granule M(M x, Δ, I x ) is true if and only if (d a(x), [αj,βj]) and (d b(x), [αk,βk]) are related by integration absorption (or extraction absorption).

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Based on above definitions and propositions, structure of the attribute granule can be formed by establishing an attribute granule simplex K(x) and dissected complex K(m)(x) of its gravity center. Let basic attribute granule set Mx* in the attribute granule set (X, Mx,P x, Γ ) have n+1 basic attribute granules, namely Mx*={ej(x)},j=0,…, n} in which e0=Ix is unit element. Definition 8: Let a n-dimension simplex K=(e0,…, en) whose peak is n+1 basic attribute granules ej(x) of the basic attribute granule set Mx*. K is the attribute granule simplex of thing X. Operation method of the attribute granule simplex dissects the attribute granule simplex K to the number m layer K(m)(x) until there is no attribute granule in the semigroup of attribute granule S(Mx, Δ) or monoid of attribute granule M(M x, Δ, I x) corresponding to any new gravity centers of number m+1 dissection K(m+1)(x). Proposition 5: The hierarchy m of dissected complex K(m)(x) of simplex gravity center of the attribute granule semigroup S(Mx, Δ) or attribute granule monoid M(M x, Δ, Ix ) of thing X is a limited natural number if and only if integration absorption relation exists between attribute granules of (X, Mx,Px, Γ ). Theorem 1: Reasoning relation exists between the peak attribute pil(x) and of any simplex sir(x)=(pi1(x), …,pir(x)) and its gravity center attribute p(sir(x)) in the dissected complex K (m)(x) of simplex gravity center of attribute granule:

(1) p(s (x))→p (x),l=0,…,r r i

il

(2) ∧pil(x)→p(sir(x))

Theorem 2: The dissected complex K(m)(x) of simplex gravity center of attribute is an isomorphic representation of the attribute granule monoid M(Mx, Δ, I x). Definition 9: Attribute granule coordinate system K(m) (x) is a linear coordinate system whose origin is e0(x), axis is basic attribute ei(x) and scale is weight d i(x) of basic attribute. Theorem 3: The attribute granule monoid M(Mx,∧) and attribute granule coordinate system K(m)(x) are isomorphic.

4 Fuzzification of Attribute Granular Computing Zadeh applied fuzzy logic to the information granulation and he thus created fuzzy information granulation theory. According to human cognition, fuzziness of granule directly derives from fuzziness of indistinction, similarity, proximity and function. In 1997, Zadeh developed the fuzzy information granulation subject into theory of computing with words[1]. In the same year, T.Y.Lin defined the granular computing officially and put forward a granular computing model[5] in the neighborhood system which is a good reference for attribute granular computing based on standard domain. The attribute granular computing means to solve problems of Fuzzy Set Theory like limitation of numeralization membership function expression, irrelevant expressed

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concepts and complex logical expression and operator realization, and to make them more applicable to thinking mode of human. Definition 10: Mapping η:X×Γ→[-1,1]i is the conversion degree function that makes pi(ξi) show its quality features if following equation is true for ∀(x,N(ξi,δi))∈X×Γ,∃ηi(x)∈[-1,1]:

η(x,ξi,δi)=|x-ξi|⊥δi=ηi(x)

(10)

The N(ξi,δi) is the qualitative standard domain, δI is radius of the domain, ξI is core of the domain, ηi(x) is diversity factor of |x -ξi| and δi as to the mathematic nature. In 1979, L.A.Zadeh studied size of divided class for granule and defined the information granularity as a proposition, specially speaking, value of x belongs to the fuzzy subset G⊆U by membership λ. The x is a variable of U and the value of x is an entity of U, represented by g = x is G is λ. Such relation is described as g = {value of u U:x (v(x) = u, v is an assignment symbol of U) is calculated based on membership of λ in the fuzzy set G⊆U. } in form. Obviously, 0≤λ≤1 in the formulation above. Based on fuzzy set theory, the λ is a fuzzy membership function. Based on logic, the λ is the fuzzy truth value or probability of the proposition[11]. It can be easily figured out by comparing that in the qualitative mapping τ with transformation program function, a information granule (pi(x),[αi,β i]) or (d a(x),[αi,βi]) in Definition 5 shows that the fact attribute a(x) having the property pi(x) is calculated based on the membership of the attribute value d a(x) in the standard [αi,β i]. They are the same in essence.

∈

Definition 11: Let the standard domain Ni(oi,ri) be the core of a fuzzy set A, namely Ni(oi,ri)=A1 = {x│μA(x)=1 }. The μA(x) is membership of x∈A. λ cut set of A is Aλ={x | μA(x)≥λ},λ∈(0,1], consisting of element x of membership not less than λ. Τλ is the λ standard transformation of the fuzzy set A whose core is Ni(oi,ri) if it meets following equation:

Τλ(Νi)=Αλ

(11)

Definition 12: Let A and B be two fuzzy sets with standard domains [αi,β i] and [αk,β k] as cores respectively. Tα is the standard transformation from [αi,β i] to [αk,βk] if it meets following equation:

Τα([αi,βi])=Ν(Τα(oi),Τα(ri))=Να(ok,rk)

(12)

Following theorem can be obtained immediately: Theorem 4: After composite standard transformation Τα˙λ=Τλ˙Τα which begins with the core Ni of fuzzy set A, λ cut set Bλ=Νλ(oα,rλ)={x│μA(x)≥λ} of fuzzy set B with Να as core can be obtained. Definition 13: Let (ak,bk)= Nik and (am,bm)= Nim in the standard cluster τi={(ai,bi)⊆X|(ai,bi) is the qualitative standard of pi(x)} of property pi(x). If Nik⊆Nim, Nik is a more strict qualitative standard compared with Nim. Proposition 6: (τi,⊆)forms a grid and is a linear ordered set.

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Definition 14: In the grid(τi,⊆), Let Ni1=strictest{Nik} and Ni0+=light{Nik} be the most strict and most tolerant qualitative standards in τi and make following equation true: (1)hλ(Ni0+)=0+∈(0,1), hλ(Ni1)=1 (2) hλ(Nik)≥hλ(Nim)⇔Nik⊆Nim. hλ is strict degree mapping of the standard cluster τi and qualitative standards Ni0+ and Ni1 are the shell and core of τi. Proposition 7: Mapping hλ is a order homomorphism from(τi,⊆)to((0,1],≤). Proposition 8: Let Hλ be the one-to-one correspondence between τi and ℜ. Hλ: τi →ℜ is a bijection. Let H be the mapping from ℜ to Nim, namely H:ℜ→Nim, then the standard transformation Tij can be defined as the composite mapping Tij=H⋅ Hλ in practice. Since the Hλ is a bijection, the composite mapping can be expressed by H= Tij⋅ Hλ−1. Substantially, H is the mapping from (0,1] to Nim, and above mapping H can be expressed as H:(0,1]→ Nim. Standard judgment domain changes in size with the adjustment of disturbance factor mapping Hλ of the standard transformation, i.e. if λ1<λ2 in ∀λ1,λ2∈(0,1], λ1,λ2∈ℜ, Η(λ1)⊇Η(λ2). Therefore, H is the nested set on the standard domain cluster and induced by standard topological field. Based on representation theorem of fuzzy set, H expresses a fuzzy set on τi: A= λ∈(0,1]λΗ(λ) or A= λ∈(0,1]λ•Τij•Ηλ−1(λ). So, a fuzzy set is determined based on the standard transformation, its disturbance factor mapping and coefficient value.

∪

∪

Eduction: An information granule with accurate qualitative standard can be transformed into an information granule with fuzzy qualitative standard with help of the degree function ηi(x), which means an accurate property granule cluster can be transformed into a fuzzy property granule cluster.

5 Conclusion It is known to all that sense organs of human only response to the sensation attributes they sensitive to and the information received by the brain are nothing more than sensation attributes, things reflect their states, motions and change processes by attributes and change processes and the cognitive process of human begins when the attribute information is received. In philosophy, attribute represents not only the quality property, but also the quantity property which should be defined and standardized by the attribute. Based on such theory, this paper put forward a novel attribute fuzzy granule cluster and computing model thereof on the basis of qualitative transformation mechanism of attribute. The result demonstrates that the method can better model overall attribute analysis ability of human by formalization, so that the computer will better simulate such ability. This paper also preliminarily discusses basic issues on

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non-monotonicity, uncertainty and fuzziness in the cognitive process of human based on such model and has obtained significant outcomes.

References 1. Zadeh, L.A.: Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 19, 111–127 (1997) 2. Cheng, Y., Qian, M.d., Rong, F.Q.: Granular computation based on fuzzy rough sets. Computer Scicnces 34(7), 142–145 (2007) 3. Feng, J.L., Zhao, T., Huang, W.J.: A kdd model based on conversion of quantity-quality features of attributes. Journal of Computer Research & Development 37(9), 1114–1119 (2000) 4. Yao, Y.Y.: Granular computing for data mining. In: Proceedings of SPIE Conference on Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security, Kissimmee, USA (2006) 5. Lin, T.Y.: RSFDGrC 2003. LNCS, vol. 2639. Springer, Heidelberg (2003)

A CT Image Denoise Method Using Curvelet Transform Junmin Deng1, Haiyun Li1, and Hao Wu2 1

School of Biomedical Engineering, Capital Medical University, Beijing China 2 Xuan Wu Hospital affiliated to Capital Medical University

Abstract. In this paper, we proposed a CT image denoising approach. This method combines Curvelet transformation with Monte-Carlo algorithm, firstly CT image’s Curvelet decomposition is processed. Secondly, Monte-Carlo algorithm is used to estimate high frequency coefficients. Finally, the coefficients are shrunk according to threshold function. The results show proposed approach can obtain better visual effect and de-noise effect. Keywords: Curvelet translation, Monte-Carlo, image de-noise, PSNR.

1 Introduction Image signals are often subjected to various noise interference in the generation, transmission and recording process. Image denoising by a appropriate method is very important before the high-level process of edge detection, image segmentation, feature extraction, pattern recognition and on. The image denoising method can be divided into two categories: one is the spatial domain methods, in which image smoothed by convolution of smoothing template and, in order to suppress or eliminating noise. The other is frequency domain methods, in which the image is filtered by a appropriate frequency band-pass filter after transformation of the image, and then get the de-noised image by the inverse transform. Spatial domain methods includes mean filtering, median filtering, adaptive noise filtering and morphological filtering, frequency filter includes Fourier transform and image de-noising using wavelet and ultra-wavelet. In recent years, wavelet theory has developed rapidly and because of its capability of good time-frequency localization and multi-resolution analysis, it has a very wide range of applications in various fields of image processing. In the field de-noising, wavelet theory is attached any importance by many scholars, who denoised signals using wavelet transform, and obtained very good results [1] [2]. Because of advantages and potential in denoising ,wavelet analysis has been a research focus, and get some achievements. At present, the wavelet denoising method can be broadly classified into three categories. The first method proposed by Mallat is wavelet transform modulus maxima denoising. The second category is correlation denoising method based on wavelet transform and the third method is threshold method proposed by Donoho [3] [4] [5] [6]. Reconstruction filter of modulus maximum is to reconstruct the signal by using the modulus maxima of wavelet coefficients at all scales of the signal. The modulus maxima of wavelet coefficients of signal contains the peak variability and singularity.

，

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 681–687. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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，

If these signals can be reconstructed from the maximum value and the singularity of signal can be changed by dealing with the modulus maxima of wavelet coefficients, so the corresponding singularity can be removed by inhabiting the certain maxima of coefficients above is the basic idea of modulus maximareconstruction filter. Modulus maxima reconstruction filter is based on signal and noise presented various properties in wavelet transform with the different scale, it has a good theoretical basis and more stable filtering performance, it is less depend on noise and do not need to know the noise variance, so it has superiority especially for low SNR when the signal filtering. However, it has a fundamental drawback, that is, there is a problem comes from the reconstruction of wavelet coefficients by modulus maxima in filtering process, which makes the calculation of this method is greatly increased, while the actual filtering effect is not very satisfactory. Wavelet denoising based on signal correlation and noise is constructing the corresponding rules on different expression of wavelet transform at different scales, then the wavelet coefficients of signal and noise are processed, as well as the substance is completely removed the reduced coefficient of the noise , maximize the effective signal to retain the wavelet coefficients at the same time. After the wavelet transform of the signal, the wavelet coefficients of signal at each scale have obvious correlation, particularly near the edge of the signal, the relevance is more obvious, but the the wavelet coefficients corresponding to scale of noise is no such obvious correlation. Wavelet threshold denoising method is able to concentrate signal energy to a small number of wavelet coefficients, while the transformations of white noise in any orthogonal basis is still the white noise, and have the same magnitude. In contrast, the wavelet coefficients magnitude of signal are inevitable larger than those of noise whose energy scattered and smaller. To select an appropriate threshold and remove the wavelet coefficients of the noise by thresholding can achieved the purpose of retaining useful signals. Wavelet threshold denoising method steps are: (1) By wavelet transform signal

，

f (t ) with noise , to get a group of wavelet coefficients w j ,k ; (2) Dealing with wavelet ˆ j ,k ,and coefficients w j , k ,to obtain estimated wavelet coefficients w

wˆ j ,k − u j , k as small as possible ( u j ,k is the non-noise signal wavelet coeffiˆ j ,k , to get cients); (3) Using the estimated reconstruction of wavelet coefficients w estimated signal fˆ (t ) , that is, the signal after being denoised [3] [4] [5] [ 6].

made

Later came the development of Curvelet on wavelet basis and the last two years Shearlet proposed to denoise is also based on this idea, but in the process of image decomposition, an increase of orientation, resulting in the coefficient associated with the direction of some linear singular point play better noise reduction effect. We call the method including wavelet and wavelet multiresolution analysis method (MRA)[10] [11]. In recent years, a variety of multi-scale analysis in the field of image fusion has been widely applied. The wavelet transform has properties of good multi-scale and time-frequency localization, the integration base on it achieved good results, however, wavelet analysis can only represent the singularity of zero signal, and the

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two-dimensional isotropic wavelet is difficult to effectively express the edge of the image information and other details. For the defects of Wavelet transform , Candes and Donoho proposed Curvelet that more suitable for analyzing signals in the high singularity. Curvelet transform has a good selection and identification of the direction to effectively approximate the image details of the edge of and texture, Curvelet transform in the field of image fusion can better describe the characteristics of the original image, thereby improving the quality of fusion, now the second generation of Curvelet transform is mainly used for image processing [10] [11].

2 Curvelet 2.1 Continuous Curvelet Transform Continuous Curvelet transform signal represent as inner product of basis functions and signal function (or function) to denote the sparse representation [7] [8] [9], expressed as:

c ( j , l , k ) := 〈 f , ϕ

Where, ϕ

j ,l ,k

j ,l , k

〉

(1)

is Curvelet function, j, l, k, respectively, is scale, orientation, location

parameters. Curvelet transform implements in the frequency domain. To define a window function W (r ), r (1 / 2, 2):and a angle function V (t), t [1,1].

∈

∞

∑W

∈

(2 j r ) = 1, r ∈ (3 / 4,3 / 2)

(2)

(t − 1) = 1, t ∈ (−1 / 2,1 / 2)

(3)

2

j = −∞ ∞

∑V

2

l = −∞

⎛ 2[ j / 2 ] θ ⎞ ⎟⎟ U j (r ,θ ) = 2 −3 j / 4 W (2 − j r )V ⎜⎜ ⎝ 2π ⎠

(4)

Where [j / 2] is the integer part of j / 2, The support area Uj is a wedge-shaped area come from support area W and V , wedge-shaped shaded area in Figure 1. To define the curvelet at the scale 2-j, the direction of θl, translation parameter (k1, k2) is.

ϕ

j ,l , k

(x) = ϕ j (R ( x − x ( j , k ) )) θ k l

x ( j,l) = R − 1 (k • 2 − j , k • 2 − j / 2 ) θ 1 2 k l

(5)

(6)

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Curvelet transform can be expressed as:

c ( j , l , k ) := 〈 f , ϕ

j ,l , k

〉 = ∫ R 2 f ( x )ϕ ( x ) dx j,l, k

(7)

2.2 Discrete Curvelet The input is f [t1, t2] (0 ≤ t1, t2 ≤ n) in the Cartesian coordinates corresponding to the local window , The discrete form of Curvelet transform is

c D ( j , l , k ) :=

∑

0≤t t ≤n 1, 2

[t , t ] f [ t , t ]ϕ D 1 2 j, l, k 1 2

(8)

Aided by a band-pass function

ψ (ω1 ) = (ω1 / 2) 2 − (ω1 ) 2 to define ψ Where sθ

j

(9)

(ω1 ) = ψ (2 − j ω1 )

(10)

⎛ 1 ⎜ = : s A is a shear matrix, θ ⎜ ⎜ − tan θ l l 1 ⎝

0 ⎞⎟

1 ⎟⎟ (11), It is not equally ⎠

spaced, but the slope is equal intervals. Figure 2. illustrates the whole image represented in spectral domain in the form of rectangular frequency tiling by combining all frequency responses of curvelets at different scales and orientations. It can be seen that curvelets are needle like elements at higher scale.

Fig. 1. Curvelet tiling of space and frequency. The induced tiling of the frequency plane (left).The spatial Cartesian grid associated with a given scale and orientation (right).

Fig. 2. 5-level curvelet digital tiling of an image.

A CT Image Denoise Method Using Curvelet Transform

~

Define U

685

j ( ω ) := ψ j ( ω 1 )V j ( ω )

(12)

~ ~ U j ( ω ) := ψ j ( ω ) V j ( s ω ) = U j ( s ω ) 1 θ θ ,l l l

(13)

In 2005, Candès propose two implementation methods based on the second generation theories of fast discrete Curvelet transform : 1) Two-dimensional FFT algorithm of non-uniform spatial sampling of the (Unequally-Spaced Fast Fourier Transform, USFFT); 2) Wrap Algorithm (Wrapping- Based Transform). USFFT algorithm is no longer used in the current toolkit because of too many parameters in the process, the most important reason is that doing ridgelet transform, in order to reduce blocking effects, we need a block overlap method. Now wrap algorithm is used more often generally [7] [8] [9]. The following are the steps of wrap algorithm based FDCT algorithm: Step 1: Apply the 2D FFT to an image to obtain Fourier samples:

∧ f

∧ f

[ n , n ], − n / 2 ≤ n , n ≤ n / 2 1 2 1 2

Step 2: In the frequency domain ,for each pair (j, l) (scale, angle), to re-sample ∧

[ n , n ] , obtained samples. f [ n1 , n 2 − n 2 tan θ l ], (n1, n2) 1 2

∈

Pj,

where Pj is the support are of Uj, l , length is 2j, width is 2j / 2. Step 3: To multiply

∧ interpolated with the window function Uj, obtain f

~

∧

j , l [ n1 , n 2 ] = f [ n1 , n 2 − n 2 tan θ l ]U j , l [ n1 , n 2 ] ∧ Step 4: f [ n , n ] is wraped around the origin. 1 2 f

~

Step 5: Apply IFFT to f

j , l ,obtain c D ( j , l , k ) .

3 Methods The following are steps of image denoise using Cuevelet generally: Step 1: preprocessing the image; Step 2: Curvelet transform the image;. Step 3: calculate the threshold of transform coefficients for each offspring, deal with coefficient ; Step 4: To do Curvelet inverse transform to oabain denoised image. The following are steps of CT image denoise using Cuevelet: Spep 1: CT images with noise decomposed into low frequency coefficients and high frequency coefficients at each scale by Curvelet transform;

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Step 2: To estimate the noise variance using Monte-Carlo method with the scale of sub-band , and then process high-frequency coefficients at all scales , the hard threshold function is defined as: ⎧ x, f x > λ T ( x ) = ⎪⎨ ⎪⎩ 0 , f x ≤ λ Where λ = 3σ Step 3: Apply IFFT to high-frequency coefficients and low-frequency coefficients, get the the denoised image. The CT images of lumbar vertebra noised by Gaussian noise denoised by the proposed method of this paper ,the result shows in Figure 3.

(a)（PSNR=20.3）

(b)(PSNR=42.5

(c)(PSNR=46.3)

Fig. 3. Denoising effect contrast (a) image noised by the Gaussian, (b) Wavelet denoising, (c) the method denoising.

According to the results, we can see Curvelet combined with Monte-Carlo method can get better denoising effect and visual effects.than Wavelet does.

4 Conclusion In this paper, we propose a method of image denoise combining the Monte-Carlo with Curvelet transform , this method brought better denoising effect and visual effects than that of method using Wavelet . Curvelet transform can accurately express very little edge using very less non-zero coefficient to guarantee the better ability of image representation than wavelet does on lower mean square error in the noisy environment.

Acknowledgement We would like to thank the institutions to provide research funds. This work is funded by the National Natural Science Foundation of China, No.30670576; Scientific

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Research Key Program of Beijing Municipal Commission of Education, No.kz200810025011; Medicine and Clinical Cooperative Research Program of Capital Medical University. Grant No. 10JL24.

References 1. Fourati, W., Kammoun, F., Bouhlel, M.S.: Medical image denoising using wavelet thresholding, J. Test. Eval. 33(5) (2005) 2. Mulcahy, C.: Image compression using the Haar wavelet transform, Spelman Sci. Math J. 1, 2231 (1997) 3. Daubechies, I.: Wavelet transforms and orthonormal wavelet bases, Different Perspectives on Wavelets. In: Proceedings of Symposia in Applied Mathematics, San Antonio, Tex, vol. 47, pp. 1–33. American Mathematical Society, Providence (1993) 4. Mallat, S.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7), 674–693 (1989) 5. Rajpoot, K., Rajpoot, N.: Hyperspectral colon tissue cell classiffication. In: Proceedings of the SPIE Medical Imaging (2004) 6. Uzun, I., Amira, A.: Design and FPGA Implementation of Finite Ridgelet Transform. In: IEEE International Symposium on Circuits and Systems (ISCAS), vol. 6, pp. 5826–5829 (2005) 7. Alzu’bi, S., Amira, A.: 3D Medical Volume Segmentation Using Hybrid Multiresolution Statistical Approaches. Advances in Artificial Intelligence (2010), http://www.hindawi.com/journals/aai/2010/520427.html (accessed Online on: October 8, 2010) 8. Boubchir, L., Fadili, J.M.: Multivariate statistical modelling of images with the curvelet transform. In: Image processing group, GREYC UMR CNRS 6072, France, pp. 747–750 (2005) 9. Candes, E., Demanet, L., Donoho, D., Ying, L.: Fast Discrete Curvelet Transform. In: Applied and Computational Mathematics, Caltech, Pasadena, CA, Department of Statistics, Stanford University, Stanford, CA (2006) 10. Do, M.N., Vetterli, M.: The finite ridgelet transform for image representation. IEEE Trans. Image Process 12, 1628 (2003) 11. Do, M., Vetterli, M.: Image denoising using orthonormal finite ridgelet transform. In: Proceedings of SPIE: Wavelet Applications in Signal and Image Processing, vol. 4119, pp. 831–842 (2003)

Research System Optimization of the Wireless Mesh Networks Based on WLAN Jin Wen Chongqing University of Science & Technology, Chongqing 401331, China [email protected]

Abstract. This essay introduce a wireless mesh network is a distribution network of mesh topology, in which the communications between nodes rely on mutual collaboration in wireless mufti-hop manner to provide wireless access service for users, extend the coverage of wireless access networks and reduce the deployment cost. Analyze hidden terminal, mixed distribution of different transmission rate terminals and overlap way between had been coverage districts are major factors of effect system throughput. In multi-cell wireless mesh network based on WLAN, the core of network planning is MR placement and channel configuration. Consider maximizing system throughput and fairness of resource allocation, so put out objective function and system optimization Method of wireless mesh network based on WLAN. Keywords: wireless mesh networks; co-channel interference; system throughput; objective function; optimization method.

1 Introduction These years, with need of user to wireless network become more and larger, wireless network technology got quick development. Tradition wireless local area network is single hop wireless access network, but the distance of single hop wireless transmission is limited, the coverage range of WLAN is smaller, WLAN strongly depends on wired network, and the cost of deployment is still too expensive. A wireless mesh network (WMN) is a distribution network of mesh topology, WMNs have advantages of high capacity, high speed, low cost and good scalability, in which the communications between nodes in wireless mufti-hop manner, to provide internet access service for users, It could through wireless multi-hop connect cable network that expand coverage of wireless access network. It has very wide application foreground. With wireless transmission equipment cost reduce, wireless transmission speed advance and portable terminal become more and more popular, more and more park and building start achieve large mode wireless coverage by wireless mesh network based on WLAN, build multi-cell wireless network. In multi-cell wireless network, many mesh router had been placed, between of them has co-channel interference problem is very serious. Make sure achieve seamless coverage and higher system throughput that need to research system optimization method wireless mesh network based on WLAN. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 689–695. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Wireless Mesh Networks Based on WLAN 2.1 Wireless Mesh Network Wireless Mesh Network is a kind of new multi-hop wireless broadband networks. Deployed in mesh topology[1], internet access is provided by wireless mufti-hop relay in WMNs. A WNIN is composed of Mesh Routers, mesh terminals, and mesh gateways. Mesh Gateways are the WMN-and-Internet joints; mesh routers with fixed locations and stable power supply, Mesh terminals access WMN by connecting to mesh routers. In the wireless mesh network, every node of the network can send, accept and relay data, can good work in high speed, high frequency and topological transformation condition, every node in the network could send and accept signal, every node could direct connect one or many nodes, don’t need through access point (AP) transfer send. In wireless mesh network, every equipment have more than one transmission paths could use, network could base on every node communications load condition dynamic allocation communications route that effectively avoid communication block of nodes[2]. Between nodes of wireless mesh network don’t need cable network connect, it could through wireless multi-hop connect cable network that expand coverage of wireless access network. 2.2 IEEE 802.11 Protocol IEEE802.11 protocol current includes IEEE802.11 a / b / g / n and so on. IEEE 802.11a standard use 5 GHz frequency transmission that its speed could reach 54Mb / s, 802.11n will use MIMO technology to let the rate more than 100Mb / s[3], IEEE 802.11 framework of security(IEEE 802.11i) and service quality assurance (IEEE 802.lle) and other standards have been introduced, forming a more perfect system[4]. For IEEE802.11 defines two modes of the terminal- Infrastructure mode and Ad hoc mode[5]. Working in a Infrastructure mode, the network includes at least one wireless access point and a series of wireless terminals. Wireless terminals directly communicate with the AP, by the AP access to the wired network. The two terminals communicate must through the AP forward. In the Ad hoc mode, between wirelesses terminals could achieve multi-hop interconnection, between the wirelesses terminals is reciprocal relationship, be able to communicate directly, don’t need through AP forward. IEEE802.11 defines the physical layer and digital link layer communications standards, using a Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) mechanism[5], communications equipment composed of wireless mobile terminal and wireless access points. The wireless terminal is a user of communications equipment such as notebook computers, mobile phones and so on. They are basic WLAN units; AP is WLAN and the wired network connection point. 2.3 Wireless Mesh Networks Based on WLAN WMN is a kind of network structure, do not limit the use of communications technology, the underlying communication technology can be used Wi-Fi, WiMax, UWB and other wireless technologies. With the WLAN popularizing, IEEE802.11

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technology has been well known; it provides access to the Ad hoc mode enables wireless mesh connectivity between nodes for the establishment of WMN. Wireless Mesh Networking capabilities of all routing and transfer send by the reticular interconnected Mesh Router and Mesh Gateway completed, mesh terminals directly access to the WMN by mesh routers. In wireless mesh network based on WLAN, each node through multi-hop wireless link form mesh topology, mesh routers form a self-configuring and self-healing network. Mesh Router (MR) is a one or more wireless transceivers, constitute the backbone of WMN network, is responsible for Mesh Terminal (MT) access and data transfer send. Mesh Gateways (MG) is the WMN and the wired network connection point, providing routing and gateway functionality, WMN can have multiple gateways, data streams can choose the most suitable gateway through to get with wired networks communication. The structure shown is Figure 1. Mesh Terminal can be an ordinary PC, notebook PC, PDA, Wi-Fi mobile phones, projectors, RFID readers and wireless sensors, the user directly to the equipment used, these mesh terminals device through the mesh router access to the upper layer Mesh network structure to realize network nodes interconnection.

Fig. 1. Wireless mesh network

3 Impact on System throughput Performance of the Main Factors 3.1 Hidden Terminals In the wireless LAN, hidden terminal is wireless terminal cannot detect other one or more other had been linked network wireless terminal case. In the wireless mesh networks based on WLAN, "hidden terminal" is multi terminals use a central access point to connect, the multi terminals able to listen to the center of the existence of access points, but in the same cell (basic service area) from the terminals may be due to distance and obstacle reason that not always listening to each signal of terminal, as in Figure 2. Mesh Router (MR) with the terminal A or terminal B communications, but the distance of terminal A and terminal B too far, cannot be directly listening to

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each other's signals, terminal A sends data to the MR, the terminal B cannot have knowledge transfer is ongoing, at this time if the terminal B to the MR to send data, MR will receive the two signals interfere with each other, in MR the collision increase from mobile terminals. In the wireless LAN, hidden terminals often lead to the wireless LAN reflection slow [6], the system throughput come down.

Fig. 2. Hidden phenomenon terminal

3.2 Mixed Distribution of Different Transmission Rate Terminals In Wireless Mesh Network based on WLAN actual environment, because the distance from every terminal to MR is different, the received signal strength of different power, wireless terminal will work on a different transmission rate. Since all terminals occupy the channel capacity is the same, nothing to do with the transfer rate. High transfer rate terminals after get channel could quickly finish transmission and release channel; but low transfer rate terminals after get channel need take a long time to complete the data packet transmission, high transfer rate terminals only could patient waiting in the process. A large number system channel time had been used by low transfer rate terminals, making a high transfer rate terminals throughput has been greatly inhibited. When the a part of terminals working rate is lower state, the throughput of all terminals are falling sharply, and can only achieve a low transmission rate the level of the terminals throughput. With the number of mobile terminal increase in the same cell, the phenomenon of hidden terminals will highlight, collision between the mobile terminals to increase, the system throughput with the increase in the number of terminal decline gradually. 3.3

Overlapping Way of between Cells (Basic Service Area)

At present, more and more park began to use wireless mesh network based on WLAN achieve large-scale wireless coverage, the formation of multi-cell wireless mesh network. In multi-cell wireless mesh networks, in order to ensure seamless coverage and higher system throughput, a large number of MR will be placed. Between the cells more or less the exists overlapping area situation, as figure3. The methods of overlap between cells will also affect the system throughput[7].

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Fig. 3. Multi-state area coverage overlap

When two MR had been assigned the same channel, if the distance of between the two MR is greater, not direct interference, that the two MR can work at full capacity, high throughput system; if the distance of between the two MR gradually reduce, the co-channel interference between MR will gradually increase, the system throughput will gradually reduce; when distance of between two MR less than the distance of carrier sense that the two MR has each other serious co-channel interference, One MR send data, then the another MR will sense the carrier busy, and the pause data transmit and wait for idle channel, the system is low throughput. Two MR had been assign adjacent channels, if the distance of between the two MR is greater, not direct interference, the two MR can work at full capacity, high throughput system; if the distance of between the two MR gradually reduce, between MR weak co-channel interference also slowly gradually increasing, the system throughput slow reduce.

4 System Optimization Method of the Wireless Mesh Networks Based on WLAN 4.1 The Objective Function In a complex physical environment, with put more and more MR that get higher system throughput, the co-channel interference almost can’t avoid, the number of terminal which connected to the MR will serious impact system performance, if terminal register to every MR number imbalances, some MR will service a large number of terminal that congestion, every terminal had been assigned channel resources is extremely limited; but some MR business less that the channel resources have been a large number of idleness. Load balancing want to operations load been evenly spread to each MR, every MR can ensure to guarantee the definite channel utilization, which helps to alleviate the pressure of the regional hot spots, improve the system throughput. The objective function will be considered to maximize system throughput and the fairness of the allocation of resources, the objective function, see type (1). N

OF = ∑ THRi × i =1

M N

694

J. Wen

THRi is a terminal throughput measurements in the wireless mesh networks sysN

tem,

∑ THR i =1

i

is system throughput; N is the total number of terminals of wireless

mesh networks system service area; M is the number of wireless mesh networks system terminal, these terminals throughput measurement design figure of meet users to terminals throughput need. As the network bandwidth capacity is based on user requirements to design, when the wireless mesh networks system every terminal throughput meet the user needs, the number of terminals which meet user throughput needs and the total number of terminals proportion M =1, indicating that the terminals throughput balance at this time;

≥

N

when the terminals throughput is not balance, serious imbalance,

M N

M N

﹤1; when the terminals throughput

near 1 / N.

The assumption that per-terminal business volume send queues have data to send at any time, if only consider maximization of system throughput and neglect meeting users to terminals throughput need, it could lead to a lot of system resources been provided high transmission speed terminals, low transmission speed terminals only get very few system resources. On the other hand, if only consider meeting user to terminal throughput needs could lead to system resources been unreasonably provided low transmission speed terminals, high transmission speed terminals throughput been forced suppression. At this time, the throughput of all terminals is lower; overall system throughput is very poor. So the objective function needs considered the system throughput and meeting user to terminal throughput needs is reasonable. 4.2 System Optimization Method The core of network planning is MR placement and channel configuration[8]. MR arrangement objective is to determine the location of MR placement, with the number of MR as little as possible for the service area users to provide the best possible coverage service. Channel configuration objective is to determine how allocation limited channel to had been placed MR, achieve reduced co-channel interference between MR that sure user business needs. Avoid between MR direct interference could through adjust the MR location, adjusting the frequency allocation to resolve. How to put wireless mesh routers location and configure the channel to the MR the problems become more and more protruding. So network planning need a systematic method to work out the problems. 1) Wireless Transmission and Access Channel For IEEE802.11a network, two adjacent physical channel center frequency is 20MHz apart, the general indoor regional select 5.15 - 5.25GHz , 5.25 - 5 .35 GHz band total 200MHz radio frequency; outdoor regional select 5.725-5.825GHz band 100MHz radio frequency. For the IEEE802.11b network, ETSI provides optional 13-channel, FCC provides for optional 11-channel. Effective channel bandwidth is 22MHz, adjacent channel spacing is 5MHz. Due to adjacent channel exist spectrum overlap, so in order to avoid spectrum overlapping interference, general network only use three nonoverlapping channels. Wireless transmission and access could choose different band. 5.8GHz-band have 8 channel in indoor areas (or 4 Channel in outdoor areas) could choose, 2.4GHz has

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three channel could choose, usually done using 5.8GHz band as wireless transmission, choose 2.4GHz band as wireless access. 2) System Optimization Method In had been setting network coverage area, one by one put MR on could choose location of MR every time, the algorithm select a could choose location of MR and had been putting MR together provide the maximum objective function, until finish putting had been designed the number of MR. a) If in network coverage area have L could choose locations, all L could choose locations make up could comprise could choose set (MR1, MR2, , MRL); b) If only allows an MR work, in the L could choose locations select a location, N which could provide maximum objective function OF = ∑ THR × M , and permai N i =1 nent put a MR in the location, and delete had been sure choice location from the could choose set; c) Algorithm will choose and sure the second MR putting location from the remaining could choose set and channel configuration. Now have L－1 choose location, indoor area wireless transmission eight could-choice channels (or outdoor area wireless options 4 could-choice channel) and wireless access 3 could-choice channels could choose. Algorithm will calculate the second MR could put every location and channel configurations for every location MR, with had been put MR together provide N the maximum objective function OF = ∑ THR × M , and keep the solution which i N i =1 could provide the maximum OF. This is the new MR location and channel configuration will been sure. d) And delete the new-sure could-choice location from could choose set, the next round MR putting and channel configuration sound out search will repeat going until finish design quantity of MR.

，

…

References 1. Akyidiz, I.F., Wang, X., Wang, W.: Wireless mesh networks:a survey. Conputer Networks 47(4), 445–487 (2005) 2. Bruno, R., Conti, M., Gregori, E.: Mesh Networks: Commodity Multichip Ad Hoc networks. IEEE Communications Magazine, 123–131 (March 2005) 3. Foschinig, J.: On limits of wireless communications in fading environments when using multiple antennas. Wireless Personal communication 6(3), 311–335 (1998) 4. Tang, X.: Broadband Wireless Access Technology and Apply. Publishing House of Electronics Industry, Beijing (2006) 5. Li, L.: Wireleess Local Area Networks (WLAN) — principle, Technique and Application. Xidian University Press (2004) 6. Ziouva, E., Antonakopoulos, T.: The effect of finite population on EEE802.11wireless LANs throughput / delay performance. In: MELECON (2002) 7. Zhang, Y., Guo, D.: Wireless Mesh Network theory and technology. Publishing House of Electronics Industry, Beijing 8. Lee, Y., Kim, K., Choi, Y.: Optimization of AP Placement and Channel Assignment in Wireless LANs. In: Proceedings of the 27th Annual IEEE Conference on L,

Stabilization Control of Chaotic System Based on LaSalle Invariant Principle Chunchao Shi1 , Yanqiu Che2, , Jiang Wang3 , Xile Wei3 , Bin Deng3 , and Chunxiao Han2 1

School of Electrical Engineering, Zhejiang Industry Polytechnic College, Shaoxing 312000, P.R. China 2 Tianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, P.R. China [email protected] 3 School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, P.R. China

Abstract. In this paper, we propose an adaptive control scheme for the stabilization of chaotic systems, which is based on LaSalle invariant principle. The controller can asymptotically stabilize unstable ﬁxed points of chaotic systems without explicit knowledge of the desired steady-state position. Three nonlinear chaotic systems are chosen as our examples, the well known Chua’s circuit, L¨ u system and Gyro system. The simulation results demonstrate the eﬀectiveness of the proposed control scheme. Keywords: chaos, adaptive control, steady state control, LaSalle invariant principle.

1

Introduction

Chaos, which is complex and important in a variety of physical, chemical, and biological systems [1,2,3,4,5], has been developed and studied extensively in the last few years [6,7,8]. Stability, bifurcation and chaos have been analyzed in electrical power systems [6], DC-DC boost converter[7], duﬃng oscillator in mechanical system[8], phase-looked loop (PLL) in communication systems[9], and a permanent magnet reluctance machine [10]. Because it is sensitive to initial conditions and usually diﬃcult to be predicted, chaos could lead systems to undesirable. Therefore chaos should be eliminated in many cases. In the past 10 years controlling chaos has attracted much attention [11], which means to design a controller that is able to bringing the chaotic state to a ﬁxed point or a limit cycle. Since the pioneering work of Ott et al. [12], many methods have been proposed for controlling chaos, such as non-feedback control [13], linear or nonlinear feedback controllers [14,15], delayed feedback [16], and multiple delay feedback control [17].

Corresponding author.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 697–704. c Springer-Verlag Berlin Heidelberg 2011 springerlink.com

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In this paper, based on LaSalle’s invariant principle [18], we propose an adaptive control scheme for the stabilization of chaotic systems. The adaptive gain of our method improves the performance in comparison with the same control methodology that uses a ﬁxed gain. The overall scheme is proven to be stable in term of Lyapunov theory. Moreover, the controller is model-independent. Simulation results on the application of the proposed method for the task of stabilization of three chaotic system, Chua’s circuit [19], L¨ u system [20] and Groy system [21], are provided to demonstrate the feasibility of the method. The rest of this paper is organized as follows: Sec. 2 presents the main theory, including system description and design of the proposed controller. Simulation examples are given in Sec. 3. The results demonstrate the eﬀectiveness of the proposed control scheme. Finally, conclusions are given in Sec. 4.

2

Theory

We consider the uncertain chaotic system under control: x˙ = F(x) + u

(1)

where x = (x1 , x2 , . . . , xn )T ∈ Rn is the state vector of the dynamical system. F(x) = (f1 (x), f2 (x), . . . , fn (x))T ∈ Rn . u is the control input. Assumption 1: There exists an unknown unstable ﬁxed point xs in the controlfree system x˙ = F(x). Assumption 2: For all i = 1, . . . , n, fi (x) are locally Lipschiz, i.e. given any system states x and y, there exists a constant parameter Li > 0 such that fi (x) − fi (y) ≤ Li x − y, where · denotes the Euclidean norm. The main goal is to design u to stabilize the chaotic dynamics to the steady state xs . Since the steady state xs is unknown, conventional proportional control cannot be obtained. Here we estimate xs by the ﬁrst-order estimator ˆ˙ s = γ(x − x ˆs ) x

(2)

ˆ s = (x1s , x2s , . . . , xns )T ∈ Rn is the estimation of xs , γ is the gain where x constant. The adaptive controller u is designed as: ui = − ki (x − x ˆis ) ˙ki = d||x − x ˆ s ||2 , i = 1, . . . , n

(3)

where k = (k1 , k2 , . . . , kn )T ∈ Rn is feedback gain vector, d > 0 such that ki (t) is a nondecreasing positive function. When the system reaches its desired state, ki (t) tends monotonically to its maximum value with zero initial ki (0) = 0. Theorem 1. Consider the dynamics (1) with uncertain nonlinear function F(x), suppose Assumption 1 and Assumption 2 are satisﬁed and choose the control

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699

input as Eq. (3), and adaptive estimator as in Eq. (2). Then the system dynamics converges to the ﬁxed point xs . Proof. Consider the following Lyapunov function candidate n

V =

1 1 ˆ s )T (x − x ˆs) + (x − x (K − ki )2 2 2d i=1

(4)

where K is a positive constant parameter. Diﬀerentiating Eq. (4) with respect to time and noting Eqs. (1)-(3), we obtain ˆ s )T (F(x) + u) − V˙ = (x − x =

n

n

ˆ s 2 (K − ki )x − x

i=1

ˆs (xi − x ˆis )fi (x) − nKx − x

(5) 2

i=1

ˆ s . Deﬁne L = Since fi (x) is locally Lipschitz, we have fi (x) ≤ Li x − x and choose K > L/n, one can obtain ˆ s 2 V˙ ≤ − (nK − L)x − x ≤0

n

i=1

Li

(6)

According to LaSalle’s invariance principle [18], the system under control asymptotically stabilizes at the equilibrium point xs . This completes the proof.

3

Numerical Simulation Examples

In this section, three representative examples are given to verify the eﬀectiveness of the proposed control approach in Section 2. 3.1

Chua’s Circuit System

Consider the modiﬁed Chua’s circuit system [19] x˙ =p(y − (2x3 − x)/7) y˙ =x − y + z

(7)

z˙ = − qy + u where u represents a voltage source in series with the inductor, x and y are voltages across two capacitors, and z is the current through the inductor. The system parameters are set p = 10, q = 100/7 in the chaotic region without the control input u. Fig. 1 shows an example of responses of this system with initial conditions of (x(0), y(0), z(0)) = (0.65, 0, 0). Fig. 1 (a) gives the time series of system state, and Fig. 1 (b) gives the corresponding phase portrait on the y − z plane.

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(b)

x

2

2

0 1 −2 0.5

z

0

z

y

0 −0.5 2

−1

0 −2 0

20

40

60

80

−2 −0.4

100

−0.2

0 y

t

0.2

0.4

Fig. 1. Responses of chaotic Chua’s circuit system without control: (a) time courses of states x, y and z; (b) the corresponding phase portrait on the y − z plane.

x

2 0 −2 0.5 y

0

z

−0.5 2 0 −2 0

20

40

60

80

100

t

Fig. 2. Time courses of states x, y and z of chaotic Chua’s circuit system. The control input is switched on at time t = 50.

The controller is designed as u = − k(z − zˆs ) ˙zˆs =γ(z − zˆs ) k˙ =dz − zˆs 2

(8)

The control parameters are chosen as γ = 1, d = 2. We switch on the controller at time t = 50. The simulation results are shown in Fig. 2. Before the control is implemented, the system exhibits chaotic dynamical behaviors. After the controller is applied, the system state converges to a very small neighborhood of a ﬁxed point and the system is stable. 3.2

L¨ u System

Consider the well-known L¨ u system [20] x˙ =λ1 (y − x) y˙ = − xy + λ2 y z˙ =xy − λ3 z + u

(9)

where system parameters are λ1 = 36, λ2 = 20, λ3 = 3. The L¨ u system is known to generate a chaotic phenomenon when the system parameters are set at values speciﬁed above.

Stabilization Control Based on LaSalle Invariant Principle (b)

(a) 20 x

−20

30

20

25 y

0

y

40 35

0

20

40

15

20

10

z

−20

0 0

701

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60

80

5 −30

100

t

−20

−10

0 x

10

20

30

Fig. 3. Responses of chaotic L¨ u system without control: (a) time courses of states x, y and z; (b) the corresponding phase portrait on the x − y plane 20 x

0 −20 20

y

0

z

−20 40 20 0 0

20

60

40

80

100

t

Fig. 4. Time courses of states x, y and z of chaotic L¨ u system. The control input is switched on at time t = 50.

Fig. 3 shows an example of chaotic responses of this system with initial conditions of (x(0), y(0), z(0)) = (0, 5, 10). Fig. 3 (a) gives the time series of system state, and Fig. 3 (b) gives the corresponding phase portrait on the y − z plane. The controller is the same as Eq. (8) except that the control parameters are chosen as γ = 5, d = 0.5. We also switch on the controller at time t = 50. The simulation results are shown in Fig. 4 similar to that in Fig. 2. After the controller is applied, the system asymptotically stabilize at a ﬁxed point. 3.3

Gyro System

The normalized equations for dynamics of a symmetrical gyro with linear-pluscubic damping of the angle θ can be expressed as [21]: x˙ =y y˙ = − α2

(1 − cos x)2 − c1 y − c2 y 3 + β sin x + f sin(wt) sin x + u sin3 x

(10)

where x = θ and y = θ˙ are the system states and u is a control input. This gyro system exhibits complex dynamics [21] for values of f in the range of 32 < f < 36 and constant values of α2 = 100, β = 1, c1 = 0.5, c2 = 0.05, and

C. Shi et al. (b)

(a)

4

x

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0

−2 5

−2

0

−4 −1.5

y

y

2

2

−1

−0.5

0 x

0.5

1

−5 0

1.5

20

40

60

80

100

t

Fig. 5. Responses of chaotic Gyro system without control: (a) time courses of states x and y; (b) the corresponding phase portrait on the x − y plane.

x

2

0

y

−2 5

0

−5 0

20

40

60

80

100

t

Fig. 6. Time courses of states x and y of chaotic Gyro system. The control input is switched on at time t = 50.

ω = 2. Figs. 5 shows an example of chaotic responses of this system for f = 35.5 and initial conditions of (x(0), y(0)) = (1, −0.5). The controller is designed as u = − k(y − yˆs ) ˙yˆs =γ(y − yˆs ) k˙ =dy − yˆs 2

(11)

The control parameters are chosen as γ = 0.5, d = 5. We again switch on the controller at time t = 50. The simulation results are shown in Fig. 6. The controller eﬀectively drives the states of the system from chaotic motion to a stable ﬁxed point.

4

Conclusion

This paper has addressed the problem of stabilization of chaos without knowing the position of the unstable ﬁxed point. Based on the LaSalle invariant principle, a model-independent adaptive steady state control scheme has proposed to

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stabilize the unstable ﬁxed point of the chaotic system. Three nonlinear chaotic systems have been chosen to be our cases study, the modiﬁed Chua’s circuit, L¨ u and Groy systems. The simulation results have demonstrated the eﬀectiveness of the proposed control scheme. Acknowledgment. This work is supported by The National Natural Science Foundation of China (Grant No. 50907044, 60901035 and 61072012), the National Science Foundation for Post-doctoral Scientists of China (Grant No. 200902275) and the. We would also acknowledge the support of Tianjin University of Technology and Education (Grant No. KYQD10009).

References 1. Sch¨ oll, E., Schuster, H.G. (eds.): Handbook of Chaos Control. Wiley-VCH, Weinheim (2007) 2. Cross, M.C., Hohenberg, P.C.: Pattern Formation Outside of Equilibrium. Rev. Mod. Phys. 65, 851–1112 (1993) 3. Frisch, T., Rica, S., Coullet, P., Gilli, J.M.: Spiral Waves in Liquid Crystal. Phys. Rev. Lett. 72, 1471–1474 (1994) 4. Fenton, F.H., Cherry, E.M., Hastings, H.M., Evans, S.J.: Multiple Mechanisms of Spiral Wave Breakup in a Model of Cardiac Electrical Activity. Chaos 12(3), 852–892 (2002) 5. Varela, H., Beta, C., Bonnefont, A., Krischer, K.: Transitions to Electrochemical Turbulence. Phys. Rev. Lett. 94, 174104 (2005) 6. Abed, E.H., Varaiya, P.P.: Nonlinear Oscillations in Power Systems. Int J. Electric Power Energy Syst. 6, 37–43 (1989) 7. Harb, A., Al-Mothafar, M.R.D., Natsheh, A.: Application of Theory to Current Mode Controlled Parallel-Connected DC-DC Boost Converters. Int. J. Model. Simul. 25(1) (2005) 8. Nayfeh, A.H., Balachandran, B.: Applied Nonlinear Dynamics: Analytical, Computational, and Experimental Methods. John Wiley & Sons, Chichester (1995) 9. Endo, T., Chua, L.O.: Chaos From Phase-Locked Loops. IEEE Trans. Circ. Syst. 35, 987–1003 (1988) 10. Harb, A.: Nonlinear Chaos Control in a Permanent Magnet Reluctance Machine. Chaos, Solitons & Fractals 19(5), 1217–1224 (2004) 11. Mikhailov, A.S., Showalter, K.: Control of Waves, Patterns and Turbulence in Chemical Systems. Phys. Rep. 425, 79–194 (2006) 12. Ott, E., Grebogi, C., Yorke, J.A.: Controlling Chaos. Phys. Rev. Lett. 64, 1196–1199 (1990) 13. Braiman, Y., Goldhirsch, I.: Taming Chaotic Dynamics with Weak Periodic Perturbations. Phys. Rev. Lett. 66, 2545–2548 (1991) 14. Chen, C.T.: Linear System Theory and Design. Rinehart & Winston, New York (1984) 15. Slotine, J.E., Li, W.: Applied Nonlinear Control. Prentice-Hall, London (1991) 16. Pyragas, K.: Continuous Control of Chaos by Self-controlling Feedback. Phys. Lett. A 170, 421–428 (1992)

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17. Masoller, C., Marti, A.C.: Random Delays and the Synchronization of Chaotic Maps. Phys. Rev. Lett. 94, 134102 (2005) 18. LaSalle, J.P.: The Extent of Asymptotic Stability. Proc. Natl. Acad. Sci. USA 46(3), 363–365 (1960) 19. Hartley, T.T.: In: Proceedings of the American Control Conference, Pittsburgh, PA, p. 419 (1989) 20. Lv, J., Chen, G.: A New Chaotic Attractor Coined. Int. J. Bifurcat. Chaos 12(3), 659–661 (2002) 21. Chen, H.K.: Chaos and Chaos Synchronization of a Symmetric Gyro with Linearplus-cubic Damping. Journal of Sound and Vibration 255, 719–740 (2002)

Parameter Estimation in a Class of Chaotic System via Adaptive Steady State Control Chunchao Shi1 , Yanqiu Che2, , Chunxiao Han2 , Jiang Wang3 , Bin Deng3 , and Xile Wei3 1

School of Electrical Engineering, Zhejiang Industry Polytechnic College, Shaoxing 312000, P.R. China 2 Tianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, P.R. China [email protected] 3 School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, P.R. China

Abstract. In this paper, we propose an adaptive steady state control scheme for parameter estimation of chaotic systems, which is based on the LaSalle invariant principle. By adaptive stabilizing the chaotic motion to a steady state, one can obtain the unknown parameters, assuming that independent variables and the structure of underlying dynamical evolution equations for the chaotic system are available. Two nonlinear chaotic systems, the well-known Chua’s circuit and L¨ u system, are chosen as our examples. The simulation results demonstrate the eﬀectiveness of the proposed scheme. Keywords: parameter estimation, chaos, steady state control.

1

Introduction

Chaos is a peculiar behavior in deterministic systems and sensitive to the initial state values. Due to chaotic system’s property, it has potential applications in many ﬁelds such as secure communications, biological systems, chemical reactions, and information processing, etc. [1]. Unfortunately, it is diﬃcult to obtain the exact values of the parameters for practical chaotic systems. Promoted by various practical applications, parameters identiﬁcation of chaotic systems has received considerable attentions. Many methods have been proposed to estimate the unknown parameters of chaos systems[2,3,4,5,6,7,8,9,10,11], such as autosynchronization [2], adaptive control approach [3], a diﬀerential evolution approach [4], statistical method [5], and chaotic ant swarm [6]. In this paper, assuming that system states and the structure of underlying dynamical evolution equations for a chaotic system are available, we address the problem of determining the values of the parameters. Based on the LaSalle

Corresponding author.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 705–711. c Springer-Verlag Berlin Heidelberg 2011 springerlink.com

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invariant principle [12], an adaptive feedback technique is investigated in this context to force a chaotic system to be stable at a steady state. Then the values of the parameters can be solved at the steady state according to the dynamical evolution equations. The rest of the paper is organized as follows. Section 2 formulates the problem and gives the main results. Section 3 provides simulation results, and Section 4 concludes the paper.

2

Theory

We consider a chaotic system described as x˙ = F(x, λ, t)

(1)

where x = (x1 , x2 , . . . , xn )T ∈ Rn is the state vector, F(x, λ, t) = (F1 (x, λ, t), F2 (x, λ, t), . . . , Fn (x, λ, t))T

(2)

is a nonlinear vector function with Fi (x, λ, t) = fi (x, t) +

n

gij (x, t)λi ,

i = 1, 2, . . . , n

(3)

j=1

where fi (x, t) and gij (x, t) are nonlinear functions, and λ = (λ1 , λ2 , . . . , λn )T ∈ Rn is the unknown system parameter vector of the dynamical system. The main goal is to identify λ. Assumption 1: For all i = 1, . . . , n, Fi (x, λ, t) are locally Lipschiz, i.e. given any system states x and y, there exist a constant parameter Li > 0 such that Fi (x, λ, t) − Fi (y, λ, t) ≤ Li x − y, where · denotes the Euclidean norm. Rewrite system (1) into the following form: x˙ = f (x) + G(x)λ

(4)

where f (x) = (f1 (x, t), f2 (x, t), . . . , fn (x, t))T and G(x) = {gij (x, t)} ∈ Rn×n . Assumption 2: There exists a constant vector C = (c1 , c2 , . . . , cn )T ∈ Rn such that the extended system x˙ = f (x) + G(x)λ + C

(5)

has an equilibrium xs = (x1s , x2s , . . . , xns )T and F(xs ) + C = 0, G(xs ) is invertible. Under Assumption 2, we have f (xs ) + G(xs )λ + C = 0

(6)

If we know the equilibrium xs , then we can obtain the parameter vector λ as: λ = −G−1 (xs )(f (xs ) + C)

(7)

Parameter Estimation via Adaptive Steady State Control

707

Since xs is usually unknown, the above result cannot be obtained. To overcome this, we ﬁrst estimate the equilibrium xs by the ﬁrst-order ﬁlter ˆ˙ s = γ(x − x x ˆs )

(8)

ˆ s = (ˆ where x x1s , x ˆ2s , . . . , x ˆns )T ∈ Rn is the estimation of xs , γ is the gain constant. Then we design a controller us to force the dynamics of system (5) to ˆ s . Here the adaptive controller us = (u1s , u2s , . . . , uns ) is designed be stable at x as: uis = − ki (x − x ˆis ) (9) ˆ s ||2 , i = 1, . . . , n k˙ i = d||x − x where k = (k1 (t), k2 (t), . . . , kn (t))T ∈ Rn is feedback gain vector, d > 0 such that gain ki (t) is a nondecreasing positive function which tends monotonically to its maximum value when the desired state is reached with zero initial ki (0) = 0. Theorem 1: Consider the chaotic system (1), suppose the Assumption 1-2 are satisﬁed, and choose the adaptive controller u = us + C and the estimator as ˆs. (8), then the system (1) is asymptotically stable at x Proof. Consider the following Lyapunov function candidate n

V =

1 1 ˆ s )T (x − x ˆs) + (x − x (K − ki )2 2 2d i=1

(10)

where K is a positive constant parameter. Diﬀerentiating Eq. (10) with respect to time and noting Eq. (8) and (9), we obtain ˆ s )T (F(x, λ, t) + C + us ) − V˙ = (x − x =

n

n

ˆ s 2 (K − ki )x − x

i=1

ˆs (xi − x ˆis )(Fi (x, λ, t) + ci ) − nKx − x

(11) 2

i=1

ˆ s . Deﬁne Since Fi (x, λ, t) is locally Lipschitz, we have Fi (x, λ, t) + ci ≤ Li x − x n L = i=1 Li and choose K > L/n, one can obtain ˆ s 2 V˙ ≤ − (nK − L)x − x ≤0

(12)

According to LaSalle’s invariance principle[12], the system under control asympˆ s . This completes the proof. totically stabilizes at the equilibrium point x According to Theorem 1, we have f (ˆ xs ) + G(ˆ xs )λ + C = 0

(13)

Thus we can obtain the parameter λ as: λ = −G−1 (ˆ xs )(f (ˆ xs ) + C)

(14)

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Numerical Simulation Examples

In this section, two representative examples are given to verify the eﬀectiveness of the proposed control approach in Section 2. 3.1

Chua’s Circuit System

Consider the modiﬁed Chua’s circuit system [13] x˙ =p(y − (2x3 − x)/7) y˙ =x − y + z

(15)

z˙ = − qy where x and y are voltages across two capacitors, and z is the current through the inductor. The system parameters are set p = 10, q = 100/7 in the chaotic region without the control input u.

(a)

(b)

x

2

2

0 1

z

−2 0.5 y

0

z

−0.5 2

0

−1

0 −2 0

20

40

60 t

80

100

−2 −0.4

−0.2

0 y

0.2

0.4

Fig. 1. Responses of chaotic Chua’s circuit system without control: (a) time courses of states x, y and z; (b) the corresponding phase portrait on the y − z plane.

Fig. 1 shows an example of responses of this system with initial conditions of (x(0), y(0), z(0)) = (0.65, 0, 0). Fig. 1 (a) gives the time series of system state, and Fig. 1 (b) gives the corresponding phase portrait on the y − z plane. We apply the proposed method to estimate the unknown parameters p and q. The control parameters are chosen as C = (10, 0, 10)T , γ = 2 and d = 0.5. The simulation results are shown in Fig. 2. Fig. 2 (a) shows the state of Chua’s circuit system under control, which has been stabilized at an equilibrium point. Fig. 2 (b) shows the parameter estimation result. pest and qest are the estimated values of parameters p and q, respectively. We can see that, the parameters are estimated with high accuracy.

Parameter Estimation via Adaptive Steady State Control (b)

3 x y z

2

x, y, z

1 0 −1

30 pest q 20

estimation of p, q

(a)

709

est

10

0

−2 −3 0

20

40

60

80

100

−10 0

20

40

60

80

100

t

t

Fig. 2. (a) Time courses of states x, y and z of chaotic Chua’s circuit system under control. (b) The estimated values pest and qest for parameters p and q, respectively

3.2

L¨ u System

Consider the well known L¨ u system [14] x˙ =a(y − x) y˙ = − xy + by z˙ =xy − cz

(16)

where system parameters are a = 36, b = 20, c = 3. The L¨ u system is known to generate a chaotic phenomenon when the system parameters are set at values speciﬁed above. Fig. 3 shows an example of chaotic responses of this system with initial conditions of (x(0), y(0), z(0)) = (0, 5, 10). Fig. 3 (a) gives the time series of system state, and Fig. 3 (b) gives the corresponding phase portrait on the x − y plane. Similar to the above example, we apply the proposed method to estimate the unknown parameters a, b and c. The control parameters are chosen as C = (30, 0, 0)T , γ = 2 and d = 0.5. The control results are shown in Fig. 4. Fig. 4 (a) shows the state of L¨ u system under control, which has been stabilized at an equilibrium point. Fig. 4 (b) shows the parameter estimation result. aest , best (b)

(a) 20 x

−20

30

20

25 y

0

y

40 35

0

20

40

15

20

10

z

−20

0 0

20

40

60 t

80

100

5 −30

−20

−10

0 x

10

20

30

Fig. 3. Responses of chaotic L¨ u system without control: (a) time courses of states x, y and z; (b) the corresponding phase portrait on the x − y plane

C. Shi et al. (a)

(b) 30 x y z

x, y, z

25 20 15 10 5 0

estimation of a, b, c

710

60

aest

50

best c

est

40 30 20 10

20

40

60 t

80

100

0 0

20

40

60

80

100

t

Fig. 4. (a) Time courses of states x, y and z of chaotic L¨ u system under control. (b) The estimated values aest , best and cest for parameters a, b and c, respectively

and cest are the estimated values of parameters a, b and c, respectively. We can see that, the parameters are estimated with high accuracy.

4

Conclusion

This paper has addressed the problem of parameter estimation for chaotic systems with known system states and structure. Based on the LaSalle invariant principle, a adaptive steady state control scheme has proposed to stabilize the chaotic system ﬁrst. Then the unknown parameters have been solved at the stable equilibrium. The well-known Chua’s circuit and L¨ u system are used to be the study examples. The simulation results has demonstrated the eﬀectiveness of the proposed scheme.

Acknowledgment This work is supported by The National Natural Science Foundation of China (Grant No. 50907044, 60901035 and 61072012), the National Science Foundation for Post-doctoral Scientists of China (Grant No. 200902275) and the. We would also acknowledge the support of Tianjin University of Technology and Education (Grant No. KYQD10009).

References 1. Chen, G., Dong, X.: From Chaos to Order. World Scientiﬁc, Singapore (1998) 2. Parlitz, U.: Estimating Model Parameters from Time Series by Autosynchronization. Phys. Rev. Lett. 76, 1232–1235 (1996) 3. Maybhate, A., Amritkar, R.E.: Use of Synchronization and Adaptive Control in Parameter Estimation from a Time Deries. Phys. Rev. E 59, 284–293 (1999) 4. Chang, W.D.: Parameter Identiﬁcation of Chen and L Systems: a Diﬀerential Evolution Approach. Chaos, Solitons & Fractals 32, 1469–1476 (2007) 5. Pisarenko, V.F., Sornette, D.: Statistical Methods of Parameter Estimation for Deterministically Chaotic Time Series. Phys. Rev. E 69, 36122 (2004)

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6. Li, L.X., Yang, Y., Peng, H.P., Wang, X.D.: Parameters Identiﬁcation of Chaotic Systems via Chaotic Ant Swam. Chaos, Solitons & Fractals 28, 1204–1211 (2006) 7. Annan, J.D., Hargreaves, J.C.: Eﬃcient Parameter Estimation for a Highly Chaotic System. Tellus, 56A, p. 520 (2004) 8. Chen, S.H., Hu, J., Wang, C.H., Lv, J.H.: Adaptive synchronization of uncertain rossler hyperchaotic system based on parameter identiﬁcation. Phys. Lett. A 321, 50–55 (2004) 9. Lv, J.H., Zhang, S.H.: Controlling Chen’s chaotic attractor using backstepping design based on parameters identiﬁcation. Phys. Lett. A 286, 148–152 (2001) 10. Lie, J., Chen, S., Xie, J.: Parameter Identiﬁcation and Control of Uncertain Uniﬁed Chaotic system via Adaptive Extending Equilibrium Manifold. Chaos, Solitons & Fractals 19, 533–540 (2004) 11. Alvarez, G., Montoya, F., Romera, M., Pastor, G.: Cryptanalysis of an Ergodic Chaotic Cipher. Phys. Lett. A 311, 172–179 (2003) 12. LaSalle, J.P.: The Extent of Asymptotic Stability. Proc. Natl. Acad. Sci. USA 46(3), 363–365 (1960) 13. Hartley, T.T.: In: Proceedings of the American Control Conference, Pittsburgh, PA, p.419 (1989) 14. Lv, J., Chen, G.: A New Chaotic Attractor Coined. Int. J. Bifurcat. Chaos 12(3), 659–661 (2002)

Algorithms of 3D Segmentation and Reconstruction Based on Teeth CBCT Images Wenjun Zhang Institute of Applied Computing Technology, China Women’s University, No.1 Yuhuidonglu, 100101 Beijing, China [email protected]

Abstract. 3D CBCT imaging plays an important role in diagnosis and treatment planning in orthodontics. The 3D tooth segmentation and reconstruction are a vital precondition. In the article, we propose two algorithms: (1). a novel image segmentation algorithm with two thresholds to separate teeth from soft tissue; (2). a local region-growing algorithm for 3D tooth extraction is created by using the local maximum gray-scale value taken as the seed point. The resulting object is defined as a single connected component containing both the seed point and voxels with gray-values lying between the local seed point and the threshold. The 3D tooth is reconstructed from the stack of the segmented image slices. Then we use VC++, OpenGL, VTK and ITK to implement the algorithms, automatic rearrangement of teeth, and 3D virtual simulation of treatment planning. It has been shown that the algorithms are robust and effective and the 3D teeth visualization software can fit into dentist’s diagnosis and treatment. Keywords: Orthodontics, Image segmentation, Local seeded region-growing, CBCT, 3D tooth visualization.

1 Introduction Orthodontics is the branch of dentistry concerned with the study of growth of the craniofacial complex, and the practice of manipulating a patient’s teeth to provide better function and appearance. The detection and correction of malocclusion and other dental abnormalities is one of the most important and critical phases of orthodontic diagnosis [1]. Typically, brackets are bonded to a patient’s teeth and coupled together with an arched wire as shown in Figure 1(a). The combination of the brackets and wire provides a force on the teeth causing them to move. Once the teeth have moved to a desired location and are held in a place for a certain period of time, the body adapts bone and the surrounding soft-tissue to maintain the teeth in the desired location. Conventionally, orthodontists rely on two important sources [2] to acquire and assess necessary orthodontic information in diagnosis and treatment planning, namely the plaster model and lateral cephalogram, as shown in Figure 1 (b, c) respectively. With the help of the cephalogram, orthodontists became more aware of the underlying jaw disproportion in the etiology of malocclusion. Also, the analyses and M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 713–720. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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treatment methods designed to address both the dental and facial skeletal components of orthodontic problems became more universal. However, it is the orthodontist’s expertise that plays the major role in the treatment process.

(a). Brackets & archwire

(b). Plaster model

(c). Cephalogram

Fig. 1. Traditional orthodontic methods and diagnosis.

The use of various imaging techniques, such as X-rays and plaster models, are instrumental to the treatment planning process in orthodontics. They give information on the patients’ dentition as well as their skeletal structure. However, these investigations are 2D and static. They lack the ability to provide 3D information of the teeth and bone and most importantly they lack the ability for orthodontists to manipulate and predict. So we use to Visual C++, OpenGL, VTK (Visualization Toolkit) [3] and [4] and ITK (Insight ToolKit) [5] libraries to develop a 3D visualization system, which is capable of presenting a 3D virtual patient’s teeth to help orthodontists in planning of orthodontic clinical treatments, placing brackets on the patient’s teeth and designing the shape of the patient’s archwire only by clicking-mouse. By taking in data generated from various medical imaging techniques, such as 3D laser scanning plaster imprint, and cone beam computerized tomography (CBCT) [6], the visualization system helps orthodontists in predicting and simulating the outcome of possible treatment plans and hence to decide which one should be taken in practice. In any medical data analysis a good visualization of specific parts or tissues are fundamental in order to perform accurate diagnosis and treatments. For a better understanding of the data, a segmentation process of the images to isolate the area or region of interest is important to be applied beforehand any visualization step. In this paper we present methods and algorithms based on Otsu and local region growing for 3D teeth surface segmentation, extraction and reconstruction from CBCT-data images. Proposed methods are implemented by using open source image processing libraries such as ITK and VTK, and are tested. Experimental results indicate the 3D orthodontic treatment simulation based on the methods and algorithms can be applied in orthodontic treatment planning.

2 3D Dental Imaging Orthodontists need to digitally replicate their patients’ mouths for many reasons. For 3D data acquisition of patient’s teeth, we research and analyze almost all medical imaging technologies such as 3D Laser Scanning Plaster Imprint Model(3DLSPIM),

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Computerized Tomography (CT), Cone Beam Computerized Tomography (CBCT), Magnetic Resonance Imaging (MRI) and Lateral Cephalogram X-ray, and so on. By comparing their accuracy, ease of use, radiant doses and costs in the orthodontic applications, finally we use two kinds of imaging techniques to acquire 3D data from patients’ teeth: 3DLSPIM and CBCT. 2.1 3D Laser Scanning Plaster Imprint Model Depending on what work is to be done, the first step is to make an impression of a patient's teeth and jaw set. From that, a physical plaster model, or impression is created. The model in orthodontics is taken to record the initial malocclusion of the patient. It may be used to allow a more accurate assessment of the malocclusion and to facilitate measurement. Once the physical model is made, 3D laser scans it thoroughly to bring it into the 3D digital world in computer. The process as shown in the Figure 2 is automated and a whole mouth scan takes only a few minutes. The scan data is extremely accurate and can be used in many dental applications. From the 3D laser scans of an orthodontic patient’s teeth, 3D digital models can be manipulated to simulate the individual’s progress from ongoing corrective treatments, position brackets on the tooth and design the archwire shape, and to bend the archwire to correct tooth alignment at every stage. The patient only needs to have one set of impressions made. The subsequent device fitting is done through progressive 3D models of the patient’s teeth that digitally show the various stages of corrections.

Fig. 2. Process of 3D laser scanning plaster dental model.

Various uses of 3D dental models have been envisaged (ranging from computerized model analysis to helping in bracket placement and archwire designing). It is postulated that these will overtake their plaster counterparts as shown in Figure 2. The main disadvantages of the above mentioned methods are that the roots of teeth cannot be visualized, and different tooth can not be segmented from another one in the 3D model and moved independently. 2.2 Cone Beam Computerized Tomography (CBCT) Recently CBCT has become an important image technique for dental-maxilla facial applications [7] on orthodontics. Compared with the standard medical CT, CBCT can generate three-dimensional (3D) data at lower cost and absorbed doses than conventional CT. Data from the craniofacial region are often collected at higher

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resolution in the axial plane than those from conventional CT systems. In addition, these systems do not require a large amount of space and can easily fit into most dental practices today. CBCT Scanner firstly scans patient, acquires CBCT dataset and exports and saves the CBCT datasets in a raw DICOM dataset format [8]. They are then processed by the 3D visualization software, and a final 3D-generated model of the teeth is produced and analyzed. With CBCT, orthodontists can view virtual patients from multiple perspectives in three planes of space, pinpointing the precise shape, location and morphology of unerupted teeth and dental roots within the bone.

3 Segmentation Algorithm of 3D Teeth 3D anatomical structure information of patients’ teeth in 3D CBCT can display individual crowns and roots and craniofacial structures, and help the orthodontists in diagnosis and treatment planning to determine various treatment options, monitor changes over time, predict and display final treatment results, and evaluate treatment outcomes accurately. The accurate virtual 3D model can also be used for bracket positioning, especially for lingual appliances, wire bending, and surgical simulation. To implement the above functions, segmentation of anatomic structures from imaging data is very important precondition. So in the following section the segmentation algorithms are researched and proposed.

Fig. 3. Threshold based on Otsu algorithm.

3.1 Image Segmentation Based on Otsu Algorithm After the CBCT images of patient’s teeth are acquired, the multiple threshold method by Otsu [9] is applied to compute appropriate threshold values (Gray-Scale Value) for segmentation process. Otsu method aims at selecting the thresholds by maximizing the between-class variance and minimizing the inter-class variance. Best results were achieved by using three threshold classes shown as Figure 3, one of them representing teeth & tone, soft tissue and background. That is to say, the threshold 1 is used to segment teeth & tone from soft tissue, and the threshold 2 for segmenting soft tissue from background.

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3.2 3D Tooth Extraction Based on Local Seeded Region Growing Algorithm For the extraction process of teeth and tone, a local seeded region growing (LSRG) method is proposed. Segmentation is performed on consecutive 2D slices from CBCT datasets in a DICOM dataset format, using the local brightest point (maximum grayscale value in local area of image) which is taken as the seed point of the local seeded region growing algorithm [10], [11] and [12].

Fig. 4. LSRG growing flow chart.

After local seed points are selected, the algorithm operates by assigning the pixel coordinates of the local seed points as starting points of the segmentation procedure and expanding the region of interest (ROI) by checking their neighboring pixels on CBCT image. A growing criteria (GC) will be defined as shown in Equation (1) so that at each step boundary, neighboring pixels that fall in this GC will be added to the region. The growing process will continue until there is no other neighboring pixel of the ROI that falls within this GC. The whole LSRG procedure is shown in Figure 4.

GC = {P( x, y) | Threshold 1 ≤ GVP( x, y) ≤ GVLS }

(1)

Where GVLS is the gray-value of the local seeded pixel; GVp(x,y) is the grayvalue of the pixel P(x,y); the Threshold 1 is used to segment teeth & tone from soft tissue. At each step, pixels with the value within this GC will be added to the ROI. Growing process stops when there is no neighboring pixel that satisfies this criterion. The resulting object is defined as a single connected component containing the brightest points, which contains all voxels with gray values lying between the local

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brightest point and the threshold 1, see Figure 5, 6. 3D Teeth Extraction is the largest connected component of the segmented image volume. After the segmentation, the software automatically computes and renders each tooth’s 3D model from the stack of segmented 2D slices. A smoothing function is also used after the calculation of segmentation. After thresholding and local seeded region growing are executed, more than teeth structures can be generated as seen in Figure 7.

(a). Original images

(b). Local seed point

(c). After LSRG growing

Fig. 5. Image segmentation process based on local seeded region growing.

Fig. 6. Teeth image segmentation based on LSRG.

Fig. 7. 3D segmentation and reconstruct of teeth.

4 Software Design The 3D teeth visualization software programmed by VC++, OpenGL, ITK and VTK, reads CBCT imaging datasets in a DICOM dataset format, computes the thresholds

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based on the Otsu algorithm, segments consecutive 2D slices, extracts individual tooth based on the local seeded region-growing algorithm, and finally reconstructs the 3D teeth; Then according to the segmented teeth, the position of the teeth is rearranged, and orthodontic treatment planning is designed and simulated in 3D virtual mode, which displays the alignment of each tooth from the beginning to the final outcome. Finally the results and the patient’s information are stored into MS SQL Server database through the ODBC component. The software modules are described in Figure 8.

Fig. 8. Software framework of 3D teeth visualization for orthodontics.

5 Conclusions This paper studied the segmentation of teeth for 3D reconstruction, visualization, rearrangement and treatment planning simulation for orthodontics by using automated algorithms applied to CBCT datasets. A novel image segmentation algorithm with multiple thresholds is proposed to process raw CBCT datasets in order to separate teeth from soft tissue; a local seeded region-growing algorithm for 3D tooth extraction is created by using the local maximum gray-scale value taken as the seed point. The resulting object is defined as a single connected component containing both the seed point and voxels with grayvalues lying between the local seed point and the threshold. The 3D tooth is the connected component of the segmented image volume and each one is reconstructed from the stack of the segmented 2D image slices. A program is written in VC++, VTK and ITK to implement the algorithms, automatic rearrangement of teeth, and 3D virtual simulation of treatment planning. The developed segmentation algorithms successfully separate each tooth and it is proved that the algorithms are robust and efficient. The software can rearrange effectively malocclusion teeth and simulate orthodontic treatment planning. The 3D virtual tooth simulation can be widely applied into orthodontics. In the future we can further research the 3D virtual design and manufacturing of orthodontic archwire and appliances on teeth. Acknowledgments. This research project has been sponsored by Department of Orthodontics under Peking University School of Stomatology. We would like to express our heartfelt thanks to the sponsors.

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References 1. Alcañiz, M., Chinesta, F., Monserrat, C., Grau, V., Ramón, A.: An Advanced System for the Simulation and Planning of Orthodontic Treatments. In: Höhne, K.H., Kikinis, R. (eds.) VBC 1996. LNCS, vol. 1131, pp. 511–520. Springer, Heidelberg (1996) 2. Leifert, M., Leifert, M., Efstratiadis, S., Cangialosi, T.: Comparison of Space Analysis Evaluations with Digital Models and Plaster Dental Casts. Am. J. Orthod. Dentofacial Orthop. 7, 136–152 (2009) 3. Visualization Toolkit (VTK) Information, http://www.vtk.org 4. Avila, L.S., Barré, S., Blue, R., Cole, D.: The VTK User Guide Updated for Version 5, Colombia, Kitware (2006) 5. Insight Toolkit (ITK) Information, http://www.itk.org 6. Hilgers, M.L., Scarfe, W.C., Scheetz, J.P., Farman, A.G.: Accuracy of Linear Temporomandibular Joint Measurements with Cone Beam Computed Tomography and Digital Cephalometric Radiography. Am. J. Orthod Dentofacial Orthop. 128, 803–811 (2005) 7. Pasini, A., Casali, F., Biaconi, D.: A New Cone-Beam Computed Tomography System for Dental Applications with Innovative 3D Software. International Journal of Computer Assisted Radiology and Surgery 1, 265–273 (2006) 8. DICOM Information, http://medical.nema.org 9. Otsu, N.: A Threshold Selection Method from Gray-Level Histogram. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979) 10. Hojjatoleslami, S.A., Kittler, J.: Region Growing: A New Approach. IEEE Transactions on Image Processing 7, 1079–1084 (1998) 11. Li, G., Wan, Y.C.: Adaptive Seeded Region Growing for Image Segmentation Based on Edge Detection, Texture Extraction and Cloud Model. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds.) ICICA 2010. LNCS, vol. 6377, pp. 285–292. Springer, Heidelberg (2010) 12. Alazab, M., Islam, M., Venkatraman, S.: Towards automatic image segmentation using optimised region growing technique. In: Nicholson, A., Li, X. (eds.) AI 2009. LNCS, vol. 5866, pp. 131–139. Springer, Heidelberg (2009)

A Calculation Method for Series Lagging Correction Based on Root Locus Li Wang Information Engineering school, Nanchang University, Nanchang, Jiangxi, China [email protected]

Abstract. Traditionally, corrections for linear control systems are always based on precise drawing of root locus or Bode plot of the linear systems, requiring fussy trying again and again, but not necessarily giving satisfying correction results. Combining analytic geometry with root locus method, this paper introduced a calculation method for series lagging correction. Without precise drawing of root locus of the system and fussy trying, this method gives precise and satisfying correction results, providing a simple but effective route to the designing of linear control systems. It also establishes another groundwork for simulating the design of linear control systems using computers. Keywords: Root Locus, Series Lagging Correction, Calculation Method.

1 Introduction Based on years of teaching experience and the study on the present situation of automatic control theory, there are two basic methods to correct a linear control system: root locus method and frequency domain method [1]. In frequency domain, precise Bode plot of the linear control system and a large amount of fussy trying and checking computation are always required to get much satisfying correction results. Though Ref [3] put forward a calculation method for frequency domain correction based on roughly drawing Bode plot, some of its data still need to be estimated and tried. About root locus method, Ref [4] mentioned that an open-loop dipole set near the origin of coordinates may improve system’s static performances, but it only discusses the principle in terms of PID control, not presenting a systematic correction method. Especially on the problem of how to judge if a dipole is near enough to the origin of coordinates, it still employs an random selecting method. Combining analytic geometry with root locus method, this paper introduced a calculation method for series lagging correction. Without precise drawing of root locus of the system and fussy trying, this method gives precise and satisfying correction results, providing a simple but effective route to the designing of linear control systems It also establishes another groundwork for simulating the design of linear control systems using computers.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 721–727. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Lagging Correction Unit and Its Properties The transfer function of the typical lagging correction unit can be written as

Gc (s) =

1 + aTs 1 + Ts

（a <1）

where a is separation factor, and T is time constant. Its frequency response is

G c ( jω ) =

1 + jaTω 1 + jTω

magnitude-frequency characteristic

Ac (ω ) =

1 + a 2T 2ω 2 1 + T 2ω 2

phase angle-frequency characteristic

ϕ c (ω ) = tg −1 aTω − tg −1Tω = tg −1

(a − 1)Tω 1 + aT 2 ω 2

Because a < 1 , ϕ c (ω ) is always less than zero, i.e. lagging correction unit always provides angle of phase lag, as shown in Figure 1. Obviously, lagging correction unit produces integral action on input signals whose frequency is between 1 T and 1 aT , thus reducing static errors of the system.

Fig. 1. Bode plot of lagging correction unit

Fig. 2. Zero and pole distribution of lagging correction unit

From the aspect of root locus analysis, zero and pole distribution of lagging correction unit is shown in Figure 2. Because a < 1 , its negative real pole p c = − 1 T is always at the right side of its negative real zero z c = − 1 aT . The distance between

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them is determined by separation factor a . Changing parameters a and T , z c and p c can be at anywhere on negative real axis of s plane.

3 Principle of Series Lagging Correction Based on Root Locus When performance indices of the control system are proposed in time domain, system corrections are always done using root locus method. The idea of this method of correction is based on the fact that system’s dynamic performances can be denoted by a couple of conjugate closed-loop dominant poles near imaginary axis. Thus performance indices proposed in time domain can be converted to a couple of expected closed-loop dominant poles. After positioning this couple of closed-loop dominant poles, we first judge if they are on the root loci of the original system according to angle condition of plotting root loci. If this couple of closed-loop dominant poles are just on the root loci of the original system, which means that dynamic performance indices of the original system are satisfied, then we calculate K c ---- the value of the open loop gain at the position of this couple of closed-loop dominant poles. If K c doesn’t satisfy system’s static performance index (i.e. static error), we cascade a lagging correction 1 + aTs unit G c ( s ) = a < 1 to the original system. By introducing an open loop 1 + Ts dipole z c = − 1 aT and p c = − 1 T , which are close to the origin of coordinates, the open loop gain at the position of the closed-loop dominant poles can be increased, thus reducing the static errors of the system. If this couple of closed-loop dominant poles is not on the original root loci, series leading correction can be employed to change the loci of the root to make them through the expected closed-loop dominant poles. Please refer to Ref [5] for details.

（

）

4 Parameter Calculation of Lagging Correction Unit In general, the open loop transfer function of the system to be corrected is as below m

Go ( s) =

K Π (s − z j ) j =1 n

Π ( s − pi )

i =1

where z

j

are system’s open loop zeroes, p i are system’s open loop poles, and K is

system’s open loop gain. Series lagging correction is mainly used to increase system’s open loop gain, thus improving static performance of the system. Nevertheless the system’s original dynamic performances which already satisfy the proposed performance indices are hoped not to be affected as far as possible. Based on this principle, the steps taken to calculate the parameters a and T of series lagging correction are as follows:

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Step 1: Determine the position of system’s expected closed-loop dominant poles s1, 2 according to the proposed dynamic performance indices in time domain s1, 2 = −ξω n ± jω n 1 − ξ 2

Step 2: Take one pole, say s1 to calculate its phase angle m

n

j =1

i =1

∠G o ( s1 ) = ∑ ∠( s1 − z j ) − ∑ ∠( s1 − p i ) According to the angle condition of plotting system’s root loci, if ∠G o ( s1 ) = ±180 D ± k ⋅ 360 D ( k =0,1,2…), which means that s1 is on the root loci of the original system, then we calculate the value of K at the location of s1 , and denote it as K c .

From magnitude condition of plotting root loci G o ( s) = 1 , it is derived that n

K=

∏ s − pi i −1 m

∏ s−zj j =1

hence, the corresponding value of K at s1 is n

Kc =

∏ s1 − p i i =1 m

∏ s1 − z j j =1

Step 3: Compute expected value of K at system’s closed-loop dominant pole s1 according to required static error e ss , and denote it as K * . K c is always much less than K * ,which means that although system’s closed-loop dominant pole s1 satisfies proposed dynamic performance indices, it doesn’t fulfill the requirement of static performance. In order to increase system’s open loop gain at s1 to reduce system’s static error, a lagging correction unit is cascaded to the original open loop system. This way, the value of system’s open loop gain at s1 can be increased about 1 a times as described below:

The open loop transfer function of the corrected system is G o' ( s ) = G o ( s ) ⋅

s + 1 aT 1 + aTs = Go (s) ⋅ a ⋅ a + Ts s +1 T

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let z c = − 1 aT , p c = − 1 T ,then G o' ( s ) = G o ( s) ⋅ a ⋅

s − zc s − pc

Root loci of the corrected system must also satisfy the magnitude condition G o' ( s) = 1 , that is m

K ⋅∏ s − z j j =1

n

∏ s − pi

⋅a⋅

s − zc s − pc

=1

i =1

from which we get the formula of calculating the value of open loop gain at s1 of the corrected system n

K c' =

∏ s1 − p i i =1 m

∏ s1 − z j

1 s1 − p c 1 s1 − p c ⋅ ⋅ = Kc ⋅ ⋅ a s1 − z c a s1 − z c

(1)

j =1

Properly positioning z c and p c (i.e. selecting value of parameter a and T ) to let them close enough to each other and to the origin of coordinates can ensure that 1.

s1 − p c s1 − z c

≈ 1 , from formula (1) we deduce that K c' = K c ⋅

1 , this is to say the value a

of open loop gain at s1 of the corrected system is in creased about 1 a times comparing to the original system, which means the reducing of static errors. 2. Because z c and p c are close enough to each other, they can be taken as a dipole, which almost not affect the loci of the roots, hence not affecting the dynamic performances of the original system. 3. Setting z c and p c close to the origin of coordinates can also ensue that 1 a is great enough. Then how to judge if z c and p c are close enough to each other? As a rule of thumb, we choose to let

ϕ = ∠( s1 − p c ) − ∠( s1 − z c ) < 5 D ~ 10 D as shown in Figure 3.

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Fig. 3. Closed-loop dominant pole s1 and dipole z c and p c

Step 4: Calculate parameters a and T . From a=

1 K* = we derive a Kc

Kc K*

Next we calculate parameter T . For calculating convenience, ϕ < 5 D ~ 10 D is converted to ∠os1 z c < 5 D ~ 10 D , here we take 10 D . Applying sine theorem in Δos1 z c

zc sin 10

D

=

ωn sin(δ + 10 D )

We deduce

zc =

1 sin 10 D = ωn ⋅ aT sin(δ + 10 D )

Then

T=

sin(δ + 10 D ) 1 = a ⋅ zc aω n sin 10 D

The results of simulation in MATLAB verify that all performances of the corrected system satisfy the proposed performance indices.

5 Conclusion The calculation method introduced in this paper provides a theory basis for computer simulation of system correction based on root locus, and is of great practical significance in engineering applications.

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References 1. 2. 3. 4. 5.

Li, Y.: Automatic Control Theory. National Defense Industrial Press, Beijing (1989) Hu, S.: Automatic Control Theory, 4th edn. Science Press, Beijing (2001) Xia, D., Weng, Y.: Automatic Control Theory. Machine Press, Beijing (2006) Wu, Q.: Automatic Control Theory. Tsinghua University Press, Beijing (1990) Li, W.: A Calculation Method for Series Leading Correction Based on Root Locus. Journal of Nanchang University (Natural Science), 610–615 (2007) 6. Wu, W.: Study on root Locus Theory. Journal of Panzhihua University, 65–67 (1996) 7. Lei, Y., Yang, J., Zhong, J.: Study on root Locus Simulation. Journal of Hubei Institute for Nationalities (Natural Science), 29–31 (1998)

The Computer Simulation and Real-Time Control for the Inverted Pendulum System Based on PID Yong Xin1, Bo Xu1, Hui Xin2, Jian Xu1, and Lingyan Hu3 1

School of Sciences, Nanchang University, Nanchang 330031, China 2 JiangXi Bluesky University, Nanchang 330098, China 3 School of Information Engineering, Nanchang University, Nanchang 330031, China [email protected]

Abstract. The paper established mathematical models of inverted pendulum system, based on the mechanical analysis on the inverted pendulum system. It designed a PID controller for the inverted pendulum system. The simulation result showed that the inverted pendulum system based on PID had good performance. In addition, the real-time control of the pendulum has been accomplished successfully based on the real-time control model built in Simulink. The results of simulation and real-time control experiment show that the pendulum can maintain balance in vertical upward direction by the simple PID controller for the pendulum angle. Keywords: Inverted pendulum; Simulation; Real-time control.

1 Introduction The Inverted Pendulum System is one of the most difficult problems in the field of control engineering. The system is unstable, nonlinear, coupling and high-order. It is well established research benchmark that provides many challenging problems to control design. The method to control the inverted pendulum system can be applied to solve other similar control problems, such as war industry, spacelight automaton and other industry control process. For example, keep balance of automaton, the verticality control of rocket, the attitude control of secondary planet, et al. Therefore, much attention has been given to explore a better solution for the inverted pendulum system. Now, there have been many good methods to control the inverted pendulum system. Linear system theory method [1,2], fuzzy control method [3,4], cloud model control [5,6], nerve network control [7], human-simulation intelligent control [8], prediction Control [7], variable structure control, PID controller [9]. Although PID controller is not a fancy method in control field, it is still an important method for its simplicity and sufficient ability to solve many practical control problems. This paper established mathematical model of inverted pendulum system, based on the mechanical analysis for the inverted pendulum system. Then a PID controller is designed for the system. Finally a numerical simulation and a real-time experiment have been carried out. The simulation and real-time experiment have the similar M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 729–736. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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result. We can realize the stabilization control of the inverted pendulum. The pendulum can maintain balance by the simple PID controller for the pendulum angle. It can only realize the angle control of the pendulum. If the position and angle of the pendulum are both expected to control, a more complicated PID controller or state feedback controller should be designed.

2 Model of the Inverted Pendulum System The single inverted pendulum system consists of a carriage, a qualitative pendulum, hinged to the carriage at the bottom of its length and the belt which can drive the carriage move. The carriage can move backwards and forwards in the horizontal direction along a track and is pulled by a belt connected to an electric motor. The pendulum can rotate in the vertical plane and forward or backward in the same plane as the carriage. The model of the inverted pendulum system is shown as Fig. 1. A horizontal control force drives the carriage forward or backward, when belt moved with acceleration under an electrical machine’s work. To simplify the analysis of the system, we have the following assumptions. The pendulum and carriage can be looked as a rigid body; the carriage can be regarded as a particle, and there is no comparative movement between carriage and belt. The belt has no extension. The friction force acting on the carriage is proportional to its velocity. The friction torque acting on the pendulum is proportional to its rotational velocity. Two free body diagrams of the system are used to obtain the equation of motion, which were shown in Fig. 1-b and Fig. 1-c. M and m are, respectively the mass of the carriage and pendulum. b is the carriage friction factor. I is the rotational inertia of the pendulum. L is the distance between joint and mass center of pendulum, the input force F acting drives the system on the carriage along the horizontal direction. The mechanical analysis of the carriage and pendulum are respectively shown in Fig, 1-b, Fig, 1-c.

M

fs N bx

M F

P

P

F

Mg

mg

T

N

x (a) Structure of inverted pendulum

(b) Force analysis of cart

(c) Force analysis of pendulum

Fig. 1. Schematic mechanical analysis diagram of inverted pendulum

Define positive movement as carriage moving to the right. N and P are the horizontal and vertical components of the interactive force between the carriage and pendulum. f s is the force that the carriage supports the pendulum. x is the horizontal

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displacement of the carriage. x is its velocity. θ represents the angle from the vertical downward direction. θ is angular acceleration. Assume that θ = π + ϕ , where ϕ represents a small angle from the vertical upward direction. Summing the forces acting on carriage in the horizontal direction, we can obtain equation (1)

Mx = F − bx − N .

(1)

Summing the forces of the pendulum in the horizontal direction, we can get an equation (2)

N =m

d 2 ( x − l sin θ ) . dt 2

(2)

Equation (3) is obtained by simplifying equation (2) .

N = mx − mlθcosθ + mlθ 2 sin θ

(3)

Equation (4) is obtained by substituting equation (3) into equation (1).

( M + m) x + bx − mlθcosθ + mlθ 2 sin θ = F

(4)

Then summing the forces of the pendulum in the vertical direction, we got equation (5)

P − mg = m

d2 ( −l cosθ ) = mlθsin θ + mlθ 2 cosθ 2 dt .

(5)

Summing the moments around the centroid of the pendulum, we got the equation (6)

− Pl sin θ + Nl cos θ = Iθ .

(6)

Get rid of the P and N terms in the equation (3), (5), (6) and the dynamic equation (7) is derived.

(I + ml 2 )θ + mgl sin θ = mlxcosθ

(7)

The set of equations (3) and (7) completely define the dynamics of the inverted pendulum. The two equations are non-linear and need to be linearized for the operation range. Since the pendulum is being stabilized at an unstable equilibrium position, which is θ = π from the stable equilibrium position, the equations should be linearized about the θ = π . Assume that θ = π + ϕ , where ϕ represents a small angle from the vertical upward direction, which is shown in Fig.1. In general, ϕ is relatively small in the controlling process of inverted pendulum. Therefore

cos θ = −1 , sin θ = −ϕ ,

d 2θ = 0. dt 2

After linearization the set of equations completely defining the dynamics of the inverted pendulum are:

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⎧( I + ml 2 )ϕ − mglϕ = − mlx ⎨ ⎩( M + m) x + bx + mlϕ = F

(8)

To obtain the transfer function of the linearized system equations analytically, we have to take the take the Laplace transform of the system equations. The transfer function of the system is

⎧( I + ml 2 )Φ( s) s 2 − mgl Φ( s) = −mlX ( s) s 2 ⎨( M + m) X ( s) s 2 + bX ( s) s + mlΦ( s) s 2 = U ( s) . ⎩

(9)

Simplifying the above set of equations (9), the transfer function is given below.

ml s Φ(s) q = U(s) 3 b(I + Ml2 ) 2 (M + m)mgl mgbl s + s − s− q q q −

Where,

(10)

q = [(m + M )( I + ml 2 ) − (ml ) 2 ] .

3 PID Control and Simulation Result Although a lot of control algorithms are researched in the systems control design, PID controller is still the most widely used controller structure in the realization of a control system. PID control is to calculate corresponding control variable acting on inverted pendulum, according to the error between the given value and the actual output, so that the swing angle of the pendulum can remain at expectant angle (0 degree). PID control theory of inverted pendulum system is shown in Fig. 2. There, r(t) is the expectant angle of pendulum, y(t) is the actual angle of pendulum, u(t) is the resultant force acting on the pendulum in the horizontal direction.

u(t ) = kp * e(k ) + ki * ∑ e(k ) + kd * (e(k ) − e(k − 1)) The proportional term P can speed up response of the system, resulting in a big overshoot, and cause shaking, resulting in a bad stability. The integral term I can eliminate the residual steady-state error that occurs with a proportional only

r (t )

0

e (t )

Controller u (t ) D(t)

Object G(t)

Fig. 2. Structure of state feedback control

Y (t )

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Table 1. Parameters of the inverted pendulum

Angle of pendulum ĳ/(rad)

Parameters Mass of the cart M Mass of the pendulum m Friction coefficient of the cart b The distance between joint and mass center of pendulum L Rotational inertia of the pendulum I Sampling period T

Fig. 3. Pole-Zero Map

Value 2.6 Kg 0.086 Kg 0.1 N m ⋅ Sec 0.215 m 0.00033 kg ⋅ m 2 0.02s

0

-0.17

0

0.3

0.6

0.9

1.2

1.5

1.8

Time(s )

Fig. 4. The real-time control result

controller. The derivative term D has pre-action. Hence, derivative control is used to reduce the magnitude of the overshoot and dynamic error, to improve the controllerprocess stability. The physical parameters of the inverted pendulum system used in this article are tabulated as Table 1. Based on the above parameters, the pole zero map of the open loop system without PID controller was shown in Fig. 3. The poles position of the linearized model of inverted pendulum in open loop configuration shows that system is unstable, since one of the poles of the transfer function lies on the right half side of the s-plane. Thus the system is unstable. Therefore the feedback controller should be designed to stabilize the system. Putting the above parameters into equation (9), the transfer function of the system can be obtained. Assume that the initial angle ( ϕ ) of the pendulum is -0.17rad and expectant angle is 0. To stabilize the pendulum we designed the PID controller whose parameters are K P = 150 , K I = 600 , K D = 10 . The simulation result is illustrated as Fig.4. The pendulum angle can reached the expected angle quickly and the system can be stable.

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4 Real-Time Control Experiment of the Inverted Pendulum Based on the above study on simulation, we carried out the real-time control experiment with the single inverted pendulum device, which is produced by Shenzhen Ou Peng Technology Co., Ltd. The inverted pendulum experiment system consists of the computer, the motion control card, the serve motor, and its driver, inverted pendulum, photoelectric coder measurement, etc. It is shown in Fig. 5.

Fig. 5. Composition of control system

Fig. 6. Inverted pendulum control module

The Computer Simulation and Real-Time Control for the Inverted Pendulum System 3.5

0.15

3

Angle T ( rad )

Displacement (m)

735

0.10 0.05 0

2.5 2.0 1.5 1.0 0.5 0

-0.05 0

5

10

15

time (s ) (a) Displacement of the cart

20

-0.5 0

5

time (s )

10

15

(b) Angle of the pendulum

Fig. 7. Result of real-time control experiment

The inverted pendulum control problem involves a simple coupled system. If the pendulum starts off-center, it will begin to fall. The carriage forward or backward can make pendulum upright. The real-time control system introduces “Matlab/simulink” as its software platform. By angle or position, blocks in the simulink platform, we can get the realtime sampling data. By real-time control block, we can apply the external force on the carriage to control the inverted pendulum system. These blocks realize data acquisition of the control panel, transmission of the control instructions, formation of S function through C language Programming so that they realize control to the hardware in simulink environment. The main blocks of the real-time control are given in Fig. 4. XTable Pos module can read the value detected by photoelectric encoder equipped by servomotor, convert it into the displacement of the carriage. Pend Angle module can be used to read the value detected by photoelectric encoder in the pendulum and obtain the angle of the pendulum. Then the actual angle of the pendulum is compared to the expectant angle. Then the angle error is obtained. With the PID controller, the computer output the corresponding moment of the motor, to control the carriage forward or backward and keep the pendulum in balance. We did the stabilization control experiment of the inverted pendulum system. The simulink model is shown in Fig. 6. At the beginning the pendulum are not upright. In order to make the pendulum rotate to the balance position the carriage move to the negative direction at first, then move forward with positive direction. The experiment result is shown in Fig. 7. In about 1 second, the angle of the pendulum can go to the expected 0 rad. The displacement of the carriage cannot be controlled very well because the PID controller can control only one variable.

5 Conclusions In this paper we did the simulation and the real-time experiment for the inverted pendulum system with PID controller. The following conclusions can be reached.

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(1) The real-time control experiment of the inverted pendulum indicates that the conventional PID controller can successfully keep the inverted pendulum in balance. (2) Comparing the numerical simulation with the real-time control experiment, the results are similar. Hence, the computer numerical simulation is helpful to select the parameters of the real-time control experiment. (3) The conventional PID can just control pendulum angle, however, one PID controller cannot control both pendulum angle of the pendulum and displacement of the carriage simultaneously. For this purpose, PID control with two input and two output parameters or two separated PID controllers should be applied.

Acknowledgment The authors wish to thank National innovation experiment program for university students (091040313) for supporting this work.

References 1. Xu, G.L., Yang, S.Y.: Simulation study on a single inverted-pendulum system. Journal of Sichuan University 44(5), 38–40 (2007) 2. Xing, J.H., Chen, Q.G., Jiang, M.: Research on an optimal linear inverted pendulum control system based on LQR. Industrial Instrumentation & Automation (6), 3–5 (2007) 3. Yi, J., Yubazaki, N.: Stabilization fuzzy control of inverted pendulum systems. Artificial Intelligence in Engineering 14(2), 153–163 (2000) 4. Cheng, F.Y., Zhong, G.M., Li, Y.S.: Fuzzy control of a double-inverted pendulum. Fuzzy Sets and Systems 79, 315–321 (1996) 5. Zhang, F.Z., Fan, Y.Z.,, C.Z.: SHEN: Intelligent Control Inverted Pendulum with Cloud Models. Control Theory & applications 17(4), 519–523 (2000) 6. Li, D.Y.: The Cloud Control Method and Balancing Patterns of Triple Link Inverted Pendulum Systems. Engineering Science 1(2), 41–46 (1999) 7. Yong, R., Gao, Y.: Application of Fuzzy Neural Network in An Inverted Pendulum. Techniques of Automation and Applications 23(1), 20–22 (2004) 8. Yih Hsu, Y., Chan, W.C.: Optimal Variable-Structure Controller for DC Motor Speed Contro1. IEEE Proceedings 131(6), 223–233 (1984) 9. Wang, J.J.: Simulation studies of inverted pendulum based on PID controllers. Simulation Modelling Practice and Theory 19, 440–449 (2011)

Application of Fractal Dimension in Analysis of Soil Micro Pores Shu-hua Zhang* Information Communication Engineering, Changchun University of Technology, Changchun 130012 P.R. China [email protected]

Abstract. How to describe the character of micro pores has a great importance to research properties of soils. The objective of this paper is to suggest a quantitative fractal analysis method to study the micro pores distribution of loess. The microstructure photos of soils can be obtained by scanning electronic microscope (SEM), then a suitable threshold is selected to process SEM image and to get a vectorization image. The information of the micro pores such as the effective area (A) and the effective perimeter (P) can be abstracted and according to a linear relationship between A and P in double logarithm coordinate system, a fractal dimension (FD) of micro pore distribution can be obtained easily. Comparing the FD of loess micro pores to different depth and the FD before and after collapsing, it shows that (1) the larger FD is, the more complicated micro pore distribution is, and (2) the FD of micro pore distribution is slightly larger than that before loess collapsing. The FD is an effective index to describe quantitatively the distribution of micro pores and also can use to explain the mechanism of collapse deformation. Keywords: micro pore; fractal dimension; soil structure; quantitative analysis.

1 Introduction It has a great significance to establish a quantitative relationship between soils structure and their engineering properties. The soil structure means that the characteristics of particles which constitute soil mass and pores, and the space arrangement of particles. It can be separated by two types such as macrostructure and microstructure[1]. At the present, scanning electronic microscope (SEM) is a main technology to test soil microstructure. The SEM can pointwisely form the structure image according to a certain time and a certain order by two secondary imaging, and display the image on the screen. Therefore soil microstructure can be observed directly. However, the microstructure of soils is complicated. How to analyze quantitatively soil microstructure, especially how to explain the relation between microstructure and engineering properties of loess, is now still a problem to be further discussed. *

Associate professor.

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Fractal is an important branch of nonlinear science[2], which is a effective tool to describe the rule of with irregular, fractional and fragmentary objects and phenomenon. It also becomes an effective tool for quantitative analysis of soil microstructure. Fractal was defined by Mandelbrot (1986) as geometrical object with self-similarity, and fractal dimension was suggested to express the pore complexity and integrity. Its objective is to try to disclose the rule for so complicated geometrical phenomenon. Researcher such as Moore C A et al discussed the characteristics of microstructure of soil by using fractal[3-5] and got some valuable achievements. In this paper, fractal is used to analyze the characteristics of loess microstructure and a fractal dimension is used to study the micro pore distribution in different depth and mechanism of collapsibility.

2 Method of SEM Image Processing The structure photos of soils tested by SEM are showed in Fig.1. Generally, a SEM photo is a gray level image, it can be segmented and transferred to a binary image by selecting a suitable grayscale threshold. After processing, only black and white two colors are existed in the image, as illustrated in Fig.2. The threshold is a key problem in binary processing to image. If the threshold is too large, the intermediate hues will be taken as pores thus area of pore distribution will be exaggerated. Otherwise, the pore information will be lost. For escaping the disturbing of factors by man-made, it is an effective method to determine the threshold by superposition of binary image and original image.

Fig. 1. SEM photo of soil structure (magnified by 200 times)

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Fig. 2. Binary image of a SEM photo of soil.

Fig. 3. Vector image of a SEM photo of soil.

In Fig.2, information of particles and pores are likely to be separated because of only black and white colors in the image. A raster image (Fig.3) can be transferred to a vector image after vector quantization. In a vector image, it is comprised of a series of polygons of pore and particle, including information such as perimeter, area and attribute code. Therefore, the data of pores and particles can be abstracted separately according to the attribute code.

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3 Determination of Fractal Dimension The shape of pore and plane distribution can be observed in lower magnification times, but information of more petty pores under higher magnification times. The size of pore and its distribution character can be analyzed by abstracting information of perimeter and area of pores. Then the effective diameter of pores can be obtained by scale of image. Thereby the pore distribution characteristics can be known by accounting the percentage of pores with different size. In fractal theory, Equation (1) is used to describe the fractal dimension of an irregular graph:

N (r ) ~ r − D

(1)

where D is a Hausdorff dimension, r is the size of a scale and N is testing number. Solving the logarithm (base e) of two side of the equation, it can obtain: D = ln N (r ) ln N (1 r )

(2)

The value D is not always an integer for some irregular graphs but a fractal dimension. The fractal dimension D in Equation (2) can be measured by box dimension. There are many methods but usually the method of box dimension to calculate the fractal dimension of loess micro pores. According to the study from researchers Voss, Laibowitz and Allesandrini, if the microstructure has a fractal character, the effective perimeter of micro pores has a relation with the area as follows: Log( P) = D 2 Log( A) + C

(3)

where P is the effective perimeter of a pore, A is the effective area of a pore, D is the fractal dimension pore and C is a constant. 12 10

y = 0.6543x + 1.2284 R 2 = 0.9769

8 P n 6 L 4 2 0 -2

0

2

4

6

8

10

12

14

LnA

Fig. 4. Linear relation between effective area and effective perimeter of micro pores.

On the basis of vector pore information the fractal dimension of pore can measured by accounting the relation of pore area and perimeter according to Equation (3). As illustrated in Fig.4, there is a liner relation between effective area and effective

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perimeter in double logarithm coordinate system, so the fractal dimension of this image can be obtained, that is 1.31.

4 Fractal Character of Pore Distribution Two type of loess with depth 3 m and 20 m respectively are sampled and their microstructures are tested by SEM in laboratory. SEM photos are obtained as illustrated in Fig. 5. Then vector images are transferred from raster images according to foregoing method and their quantitative data are obtained correspondingly.

Fig. 5. SEM photos of loess samples (a)sample no.1-31-0014(3 m in depth); (b)sample no.1-320019 (3 m in depth); (c)sample no.1-21-0007(20 m in depth) and (d)sample no.1-2-0001(20 m in depth).

Results of relation between area and perimeter of micro pores from SEM images for different samples are listed in Table 1. It can be seen in Table 1: (1) the distribution of micro pores has a character of fractal geometry as there is an excellent linear relation between area and perimeter of micro pores in double logarithm coordinate system, and (2) result from 4 samples show FD of loess micro pores is between 1.22 and 1.41, FD in 3 m depth is slightly greater than FD in 20 m depth.

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No.

depth/m

lnP-lnA equation

FD

1-31-0014

3

lnP = 0.6962lnA + 1.3748

0.994

1.39

1-32-0019

3

lnP = 0.7071lnA + 1.3144

0.991

1.41

1-21-0007

20

lnP = 0.6125lnA + 1. 5445

0.975

1.22

1-21-0001

20

lnP = 0.7071lnA + 1.4505

0.989

1.33

coefficient

Average of FD 1.40 1.275

Fractal dimension of micro pores expresses the characteristics of microstructure to a certain degree. Therefore fractal dimension can be used to explain some engineering properties of loess such as collapsibility. For this reason, the micro pores distribution of SEM photos are compared before and after collapsing. Six photos shown in Fig.6 represent the microstructure of undisturbed sample (Fig.6. a, b, c) and samples after collapsing(Fig.6. d, e, f). The Micro pore data of SEM photos are obtained as illustrated in Fig.6 after binaryzation and vector quantization. According to Equation (3) to fit the relevance between area and perimeter of micro pores fractal dimension D is listed in Table 2. After collapsing, fractal dimension D is changed from 1.37 to 1.44, which is slightly larger than fractal dimension calculated from undisturbed samples. It shows that the characteristics of pore distribution is more complicated than before collapsing.

a

b

c

d

e

f

Fig. 6. SEM images of undisturbed samples (a: sxq1; b: sxq2; c: sxq4) and collapsed samples (d: sxh3; e: sxh4; f: sxh5).

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Table 2. Results of pore quantitative analysis for different SEM photos. FD

lnP-lnA equation

correlation coefficient

FD

lnP-lnA equation

correlation coefficient

sxq1

1.36 lnP = 0.6799lnA + 1.395

0.985

sxh3

1.52

lnP = 0.7578lnA + 1.390

0.980

sxq2

1.38 lnP = 0.6916lnA + 1.422

0.985

sxh4

1.39

lnP = 0.6927lnA + 1.388

0.978

sxq4

1.38 lnP = 0.6898lnA + 1.396

0.983

sxh5

1.42

lnP = 0.7092lnA + 1.383

0.983

average value

1.37

average 1.44 value

5 Conclusions The fractal dimension can describe quantitatively the characteristics of loess micro pores distribution. The larger fractal dimension is, the more the different type of micro pores is, and the more complicated of the distribution of micro pores. The fractal dimension can express burial condition of loess and the mechanism of collapsible deformation. The larger FD is, the shallower burial depth is. The fractal dimension of pore of loess is bigger than before collapsing. Acknowledgments. The author is grateful for the financial support from Natural Science Foundation of China under grant No. 40972171.

References 1. Wang, Y.-y., Lin, Z.-g.: Structure characteristics and physical-mechanical properties of loess in China. Science Press, Beijing (1990) 2. Zhang, J.-z.: Fractal. Press of Tsinghua University, Beijing (1995) 3. Moore, C.A., Donaldson, C.F.: Quantifying soil microstructure using fractals. Geotechnique, 1(45), 105–116 (1995) 4. Liu, S.-y., Zhang, J.-w.: Fractal Approach to Measuring Soil Porosity. Journal of Southeast University 27(3), 127–130 (1997) 5. Wang, B.-j., Shi, B., Liu, Z.-b., et al.: Fractal study on microstructure of clayey soil by GIS. Chinese Journal of Geotechnical Engineering 26(2), 244–247 (2004)

Assessment of Loess Collapsibility with GRNN Shu-hua Zhang* College of Information Communication Engineering, Changchun University of Technology, Changchun 130012 P.R. China [email protected]

Abstract. A model of generalized regression neural network (GRNN) to evaluate collapsibility of loess is suggested in this paper, in which water content, saturation degree, dry density, void ratio and plastic index are taken as input neural cells and the output neural cell is coefficient of collapsibility. Selecting a series of distribution density of the radial basis function (spd), 20 group samples are tested after the GRNN is trained by 76 group samples, and the result is compared with experiment data from laboratory and the optimized smoothing parameter spd is obtained. The result shows that GRNN has a more satisfied prediction accuracy comparing with RBF if value of spd is adapted properly. GRNN is a new effective method for collapsibility assessment of loess. Keywords: GRNN model; collapsibility assessment; RBF.

1 Introduction Collapsibility is an important mechanical property of loess. A coefficient of collapsibility measured in laboratory is usually used to assess the collapsibility. In fact, collapsibility has internal relation with physical properties. As physical properties are likely to be tested, is it possible to extrapolate coefficient of collapsibility according to relation among collapsibility and physical properties? Several methods for collapsibility assessment are introduced in reference[1], which are based on relevance of collapsibility and different physical indexes. However, this relation is complicated and usually can be not represented by a mathematical function. Artificial neural network (ANN) can process problems with certainty and uncertainty in the mean time and also can establish a complicated nonlinear mapping relation[2]. Now ANN has been widely applied in many research areas such as civil engineering[3,4]. In this paper, GRNN is discussed to assess collapsibility of loess in order to try to find out a new approach for it.

2 Theory of GRNN ANN is a nonlinear science and is a method in artificial intelligence research which developed in middle period of 80s last century[2]. GRNN is a network structure *

Associate professor.

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comprised of a radial basis function cell and a linear neural cell. In Fig.1, input of GRNN is X (R dimensional vector), the amount of training sample (X, Y) is M, output of network is S dimensional vector Y. In the first radial basis hidden layer, the amount of cells is equal to training samples M, and the weight function is Euclidean distance measure function, which is expressed by Equation (1). Radial basis hidden Function

Linear output layer

Fig. 1. Structure of Generalized Regression Neural Network

∑ (x − IW ) ， R

dist

j

=

j = 1,2, " , M .

2

i

ji

(1)

i =1

where b1 is a threshold. The multiplication of threshold and weight input by product function netprod will form net input n1 , then the result will be transmitted to transfer function radbas. The input of hidden layer is expressed by the following equation: a1j

(

(

))

= radbas netprod dist j b1 j

( )

(

⎡ dist b ⎡ n1 2 ⎤ j j 1j ⎥ = exp ⎢ − = exp ⎢− 2 ⎢ ⎢ 2σ j ⎥ 2σ 2j ⎢⎣ ⎣ ⎦

) ⎤⎥ . 2

⎥ ⎥⎦

(2)

where σ j (j=1, 2, … , M) is a smoothing factor determining the shape of radial basis function in layer j. The second layer is a linear output layer where a weight function of generalized dot product (expressed by nprod) is used. It takes dot product of previous output and the weight LW2,1 (S×M matrix) as weight input to transmit directly into a linear transfer function purlin. The output of network is expressed by follows: M

yk =

∑ LW

1 k, j a j

, k = 1,2,", S .

(3)

j =1

The structure of GRNN is similar to RBF except linear output layer. However its ability of approach and classification, and learning velocity are much better off than BP and RBF[2]. Only one threshold needs to adjust in GRNN, network training depends on samples to keep the objectivity of prediction results [5].

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3 GRNN-Based Method of Collapsibility Assessment Generally if the coefficient of collapsibility δ s is greater than 0.015 the soil has collapsibility. The greater δ s is, the stronger collapsibility is. So the soil can separated into four types such as non-collapse ( δ s <0.015), slightly collapse ( 0.015 ≤ δ s < 0.03 ), medium collapse ( 0.03 ≤ δ s < 0.07 ) and high collapse ( δ s ≥ 0.07 ) according to coefficient of collapsibility. The composition and structure of loess will greatly influence on its collapsibility. And the indexes of physical property can express the component, structure and aquiferous characteristics of soils. To a certain soil, the index of physical property is relatively stable and is likely to be measured. Except the loading, the collapsibility depends on component, structure character of soil and the sensibility to water. The indexes such as natural water content w, void ratio e, natural density ρ , dry density ρ d , degree of saturation Sr, plastic limit wL , plastic index I P , and so on, are all can express the foregoing characters of loess. Thus here five indexes, namely w, Sr, ρ d , e, and I P , are especially considered in study in collapsibility. GRNN model for the collapsibility assessment is established as illustrated in Fig.2, and in this model five factors are selected as the neural cells of input layer and coefficient of collapsibility is taken as single neural cell of output layer. w

²d ¥s

Sr …

e Ip Input layer

Hidden layer

Output layer

Fig. 2. GRNN model for collapsibility assessment

To construct the GRNN model, 96 group samples of loess are chosen from the west area of Liaoning province, in which 1 to 76 group samples are the training samples (Table1 and Table 2) and 77 to 96 group are the testing samples (Table 3). The data listed in Table 1, Table 2 and Table 3 are generalized by Equation (4) to control the data in the extent of [0,1] in order to ensure calculation convergence. And the collapsibility is not needed to be generalized as the value of this index is already between 0 and 1. xˆ =

x − xmin × 0.8 + 0.1 . xmax − xmin

(4)

where xˆ is the generalized value of samples, x , xmin and xmax are measured value, minimum value and maximum value of samples respectively.

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w/%

ρd /g.cm-3

Sr /%

E

IP /%

δs

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

26.7 24.1 25.2 26.6 25.0 24.0 25.0 21.0 23.5 23.8 22.6 26.7 19.7 19.7 16.9 27.7 25.9 32.1 26.9 27.0 27.7 26.2 27.1 23.2 28.8 29.3 27.8 23.6 14.8 27.7 27.4 27.7 26.9 26.0 23.1 21.6 25.0 26.2 26.4 23.4 27.6 22.6 27.1 24.3 27.3

1.43 1.35 1.35 1.39 1.39 1.32 1.34 1.57 1.32 1.34 1.39 1.48 1.51 1.50 1.37 1.47 1.52 1.40 1.40 1.46 1.44 1.26 1.30 1.49 1.22 1.45 1.34 1.46 1.37 1.43 1.41 1.47 1.28 1.31 1.44 1.45 1.47 1.44 1.42 1.41 1.22 1.45 1.43 1.45 1.43

81.1 64.9 67.8 76.0 71.8 62.2 66.9 79.3 60.6 63.2 64.6 88.2 68.1 66.9 47.4 89.6 90.8 92.6 77.8 86.8 84.9 62.1 68.4 77.4 63.9 91.5 74.1 74.5 41.1 84.4 81.1 89.8 65.8 66.0 71.9 68.2 80.5 80.9 79.3 69.3 61.4 70.8 82.3 76.3 82.9

0.888 1.004 1.002 0.943 0.939 1.043 1.009 0.711 1.046 1.019 0.944 0.816 0.778 0.794 0.966 0.831 0.766 0.939 0.935 0.837 0.883 1.145 1.066 0.808 1.216 0.864 1.012 0.855 0.967 0.886 0.913 0.833 1.104 1.064 0.869 0.852 0.839 0.875 0.900 0.914 1.217 0.863 0.890 0.859 0.885

12.3 11.3 11.3 12.7 11.7 11.3 11.7 10.9 11.2 11.9 12.5 8.2 9.1 10.6 12.7 9.6 8.3 15.6 11.4 9.0 14.3 14.5 10.6 12.7 13.5 12.3 11.7 11.8 10.9 12.0 11.6 13.2 13.5 13.2 11.3 10.7 11.3 11.1 11.3 11.1 14.1 11.2 11.4 11.1 8.7

0.009 0.065 0.071 0.072 0.022 0.060 0.037 0.017 0.060 0.056 0.048 0.007 0.008 0.009 0.087 0.005 0.005 0.005 0.007 0.010 0.008 0.050 0.021 0.011 0.045 0.010 0.045 0.013 0.079 0.006 0.022 0.011 0.063 0.009 0.019 0.016 0.007 0.006 0.011 0.011 0.078 0.009 0.006 0.013 0.008

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Table 2. Training sample of neural network (continue) N

w/%

ρd /g.cm-3

Sr /%

E

IP /%

δs

46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76

25.6 25.6 27.1 27.1 25.7 28.1 27.7 26.6 26.3 25.6 26.4 27.3 24.0 21.3 19.3 23.7 25.4 24.7 20.3 25.7 20.5 23.7 24.5 26.2 25.7 25.2 27.8 28.7 26.5 28.5 28.0

1.46 1.48 1.43 1.39 1.43 1.41 1.41 1.44 1.42 1.41 1.42 1.44 1.50 1.38 1.44 1.50 1.39 1.44 1.41 1.45 1.50 1.47 1.41 1.41 1.42 1.28 1.31 1.36 1.39 1.32 1.31

81.3 84.3 82.4 78.0 78.5 83.4 81.9 82.0 78.3 75.5 78.9 84.4 80.9 60.7 59.8 79.8 73.5 76.6 60.4 80.4 69.9 76.4 72.1 77.4 77.0 61.2 71.2 78.8 76.3 74.2 71.7

0.851 0.820 0.889 0.940 0.883 0.910 0.912 0.874 0.908 0.915 0.903 0.874 0.798 0.943 0.869 0.799 0.929 0.868 0.903 0.860 0.790 0.833 0.912 0.915 0.901 1.113 1.055 0.985 0.938 1.038 1.055

11.2 12.4 13.2 12.3 12.5 13.7 12.6 13.1 12.7 12.0 13.2 12.4 10.0 9.4 7.8 10.0 10.0 10.4 8.1 10.0 8.4 9.3 9.1 12.3 11.4 13.3 12.8 13.6 12.5 13.8 13.8

0.016 0.005 0.008 0.031 0.067 0.016 0.037 0.012 0.021 0.054 0.031 0.008 0.006 0.030 0.011 0.008 0.030 0.014 0.015 0.005 0.005 0.006 0.014 0.034 0.008 0.065 0.059 0.009 0.044 0.034 0.021

Under MATLAB programming, the ANN model is simulated and predicted by using 1 to 76 group training data and 77 to 97 group testing samples. As the smoothing parameter spd will influence the testing results in GRNN, an optimization method is adapted in calculating, in which the theory of minimum error of fitting is used to search for the optimal smoothing parameter spd in the range of 0.05 to 1. Comparison of calculation and test results from different spd is shown in Fig.3. It can be seen, the accuracy of fitting testing data is varied to different spd. The test results have a good agreement with experimental values when spd = 0.1. The comparison among testing value of GRNN, result of RBF and experimental data is shown in Fig.4. The prediction accuracy of GRNN is higher than prediction of RBF in collapsibility calculation. The prediction results of GRNN and RBF, when

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spd=0.1, are listed in Table 4 according to the classification of collapsibility, in which N is expressed as non-collapse, S is slightly collapse, M is medium collapse and H is high collapse. The prediction results from GRNN are in agreement with experimental data, but great errors exist in the prediction of RBF except the 86th and 87th samples. Therefore the GRNN has advantages in collapsibility assessment of loess. Table 3. Testing sample of neural network N

w/%

ρd /g.cm-3

Sr /%

E

IP /%

δs

77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

23.8 26.7 26.4 23.1 23.7 24.5 23.3 23.9 27.6 24.6 24.5 22.5 21.4 24.2 26.8 26.9 25.7 25.4 27.7 24.8

1.36 1.29 1.44 1.32 1.42 1.42 1.41 1.42 1.33 1.34 1.34 1.46 1.51 1.47 1.46 1.45 1.51 1.51 1.36 1.27

65.3 65.6 81.2 59.5 71.4 73.3 69.2 71.1 71.9 65.1 65.4 72.2 73.7 78.7 85.0 84.6 87.4 87.2 75.8 59.2

0.980 1.101 0.878 1.050 0.894 0.902 0.910 0.908 1.038 1.020 1.009 0.837 0.782 0.832 0.851 0.860 0.793 0.788 0.988 1.130

10.9 13.2 13.1 12.9 10.4 11.4 11.1 11.2 13.1 11.7 11.6 8.9 8.7 11.4 12.0 13.4 11.5 11.8 12.7 12.7

0.057 0.046 0.017 0.042 0.013 0.015 0.012 0.012 0.044 0.050 0.055 0.012 0.011 0.013 0.006 0.011 0.012 0.009 0.038 0.060

Coefficient of collapsibility

0.08 test spd=0.10 spd=0.20 spd=0.30 spd=0.40 spd=0.60 spd=1.00

0.07 0.06 0.05 0.04 0.03

spd=0.05 spd=0.15 spd=0.25 spd=0.35 spd=0.50 spd=0.80

0.02 0.01 0 77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

Sample

Fig. 3. Comparison among GRNN prediction results for different parameter spd

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Table 4. Comparison GRNN and RBF prediction (spd=0.1) with experiment data

Coefficient of collapsibility

sample number 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

test value 0.057 0.046 0.017 0.042 0.013 0.015 0.012 0.012 0.044 0.05 0.055 0.012 0.011 0.013 0.006 0.011 0.012 0.009 0.038 0.06

collapsibility GRNN collapsibility degree prediction degree M 0.0644 M M 0.0555 M S 0.0182 S M 0.0538 M N 0.0139 N S 0.021 S N 0.0115 N N 0.0127 N M 0.0457 M M 0.0539 M M 0.0561 M N 0.006 N N 0.008 N N 0.0099 N N 0.0081 N N 0.0106 N N 0.0041 N N 0.0041 N M 0.0392 M M 0.065 M

0.15

RBF prediction 0.1336 0.0736 0.0106 0.1495 0.1263 0.1001 0.0151 0.0499 0.0822 0.0428 0.0325 0.1381 0.1482 0.0678 0.1098 0.1057 0.1221 0.0888 0.1422 0.1355

collapsibility degree H H N H H H S M H M M H H M H H H H H H

test GRNN RBF

0.1 0.05 0 77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

Sample

Fig. 4. Comparison the GRNN prediction with RBF and laboratory test value

4 Conclusions A neural network model GRNN to assess the collapsibility of loess is constructed, in which five factors such as natural water content, degree of saturation, dry density, void ratio and plastic index are taken as input neural cells and the coefficient of collapsibility is regarded as output neural cell. It shows that GRNN model has a satisfied accuracy comparing with RBF model in collapsibility assessment. Artificial neural network has a stronger capacity of nonlinear mapping, which it express a well performance in discussing the relationship between loess collapsibility and its indexes of physical properties. GRNN is an effective method for collapsibility assessment with a rapid calculation velocity, a high learning efficiency and prediction accuracy.

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Acknowledgments. The author is grateful for the financial support from Natural Science Foundation of China under grant No. 40972171.

References 1. Ye, W.-m., Cui, Y.-j., Huang, Y., et al.: Collapsibility of loess and its discrimination criteria. Chinese Journal of Rock Mechanics and Engineering 25(3), 550–555 (2006) 2. Chen, X.-g., Pei, X.-d.: Technology and Application of Artificial Neural Network. China China Electric Power Press, Beijing (2003) 3. Li, X.-a., Peng, J.-b.: Estimation of loess slope stability with improved BP neural network. Journal of Geological Hazards and Environment Preservation 13(4), 56–59 (2002) 4. Chen, H.-j., Li, N.-h., Nie, D.-x., et al.: A model for prediction of rockburst by artificial neural network. Chinese Jounal of Geotechnical Engineering 24(2), 229–232 (2002) 5. Hikmet, K.C.: Application of generalized regression neural networks to intermittent flow forecasting and estimation. Journal of Hydrologic Engineering 10(4), 336–341 (2005)

Grey Comprehensive Relational Analysis of Fighting Efficiency Influencing Factors of AEW Radar Guangdong Liang1,2, Guangshan Lu1, An Zhang1, and Yanbin Shi2 1

School of Electronic and Information Northwestern Polytechnical University Xi'an, China [email protected] 2. Dept. Electronic Engineering Aviation University of Air force Changchun, China

Abstract. At present, it is usually to seek itself reasons when we analyzed the factors influenced the military equipment effectiveness, by this way, we can find some problems and good solutions. However, for some deep troubles, some cruxes usually can be found by transverse analysis and compare. In this paper, the grey comprehensive relational grade is introduced into transverse analysis and compare for several similar equipments. By analyze the grey relational principle, the relational coefficient, the selection of the distinguishing coefficient and research several relational grades, the grey comprehensive relational grade is successfully applied in analysis of radar fighting efficiency .Results of the practical show that the method based on comprehensive grey relational grade analysis has the advantages of simplified computation and feasible, consistency to the qualitative analysis result ,It can provide theoretical guidance for targeted maintenance of the equipment and improve rapidly fighting efficiency of AEW radars in war. Keywords: AEW radar; fighting efficiency; grey relational principle; grey comprehensive relational grade.

1 Introduction The main task of Airborne Warning Aircraft (AWA) is to warn the air targets and to command and control the combat of air, it plays an important role in modern war. It is known as “force multiplier”. However, the task is completed mainly by airborne early warning (AEW) radar [1]. Each function of early warning system is mostly completed by the radar information provided. Therefore, it is especially necessary that analysis of influence factors of fighting efficiency for AEW radar. Currently, there are many references about influencing factors of efficiency analysis of military equipments. Xin fei [2] studied detection efficiency factors of AEW radar by focusing on themselves, analysis given is qualitative and quantitative by speed itself of the airborne and detection radius of radar. Shi guo [3] used a common level weight method, which evaluate detection efficiency of AEW radar by multilevel analysis and expert grading. But they are all seeking failings of their own. Efficiency evaluation has certain limitation, essence and crux of the problem is hard to be found. This paper proposes a transverse M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 753–760. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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analysis method by grey comprehensive relational grade, fighting efficiency of AEW radar is analyzed by reference sequence consisted of four main indicators and comparing sequence consisted of four impact factors of AEW radar [4]. To establish grey relational analysis system and calculate grey comprehensive relational grade, according to influence degree of relational indicators for fighting efficiency and level of operating personnel, we can get the sequence of the comprehensive relational grade size, the sequence can provides theoretical guidance for targeted maintenance of equipments and increases rapidly fighting efficiency of AEW radars in war.

2 Grey Comprehensive Relational Grade Analyses 2.1 Basic Principle of Grey Comprehensive Relevancy Analysis Grey relevancy analysis compares mainly the geometry relationship of sequence curves to judge relational grade among system factors, in other words, the more approximate geometry shape of sequence curves is, the bigger its relational grade will be, and change of the situation is more close on the contrary, the smaller grey relational grade of corresponding sequence is, the more distinct is changed of the situation will be. Scheme diagram of grey relevancy analysis is shown in Fig.1.

Value

1

2 3

4

Time

Fig. 1. Scheme diagram of grey relevancy analysis

Curve 1 is corresponding to X1(reference sequence). Curve 2, 3, 4 are corresponding to X2, X3, X4( comparability sequence), the curve 2 and the curve 1 are almost parallel, their geometry shapes are very similar. So, grey relational grades of the corresponding sequence X2 and sequence X1 are very high. their change of the situation are almost same, the difference are very big between geometry shapes of curve 4 and curve1,and grey relational grades of the sequence X4 and the sequence X1 are very low, the difference of the situation change are very high. Therefore, grey relational grades of the sequence X2 and the sequence X1 are highest among sequences, change of the situation is most close, grey relational grades of the sequence X4 and the sequence X1 are most low, their difference are most clear on change of the situation[5] .Gray relevancy analysis model is sequence relationship model, which is concerned with the sequence relation, namely sequence of relevancy grade size rather than relevancy grade value size itself [6].

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2.2 Calculation Method of Grey Relational Grade

，

At present, the calculation method of grey relational grade has many kinds. Here we introduce mainly general relational grade, absolute relational grade and relative relational grade. This paper proposed creatively an analysis method by calculating comprehensive relational grade, and obtained quite good results. General Relational Grade. Assuming that γ [ x0 ( k ), xi ( k )] is the grey relational coeffi-

xi for x0 at k moment, so,

cient of

（

γ x0( k ) , xi( k )

+ ξ max max x (k ) − x (k ) ）= mi n mxi n(kx) −(kx)(−k )x +(kξ) max max x (k ) − x ( k ) i

0

k

0

Where

ξ

i

i

i

i

is distinguishing coefficient,

xi and x0 at k moment.

0

k

k

i

0

(1)

i

x0 (k ) − xi ( k ) is absolute difference of

mi n mi n x0 ( k ) − xi ( k ) is minimum differential value of two i

k

grade[4]. Supposing that γ is grey relational grade of xi for x0 , so, 1 n (2) ∑ γ ( x0 (k ), xi (k )) n k =1 Where k = 1, 2, ..., n , i = 1, 2, ..., m , and we can know from the formula (2), the calculation method of general relational grade is average method at fact. The relational coefficient γ [ x0 ( k ), xi ( k )] is monotonic increasing function of distinguishing coefficient ξ

γ ( x0 , xi ) =

and

1

ξ +1

≤ γ [ x0 ( k ), xi ( k )] ≤ 1 ,

relational coefficient

γ

for ease of intuitive analysis, the function relationship of

and distinguishing coefficient

Fig. 2. Relationship between

，

ξ

is shown in Fig.2[5].

γ and ξ

，

γ is increasing with increasing of ξ but with increasing of ξ the slope of the curve is decreasing, increasing trend of γ is gradually slow.When distinguishing coefficient assumes smaller value, relational grade γ is lesser too, and γ can not effectively reflect relational degree. When the bigger distinguishing coefficient assumes, the larger relational grade value is ,but the slope of the curve is lesser, the difference

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among each relational grade value is smaller and can not be distinguished obviously each relational sequence in order to meet definite objectivity, flexibility and intelligent for ξ in practical projects. That is to say, ξ can be dynamic changed in value with observation value and to avoid relational grade value of the whole system changing with unusual values of observation sequence. ξ ∈ [0, 1] is generally used as spe-

， ξ is derived by specific conditions [5,6].

cific value

Grey Absolute Relational Grade. Supposing the sequence length, x i = ( xi (1), xi (1), , xi (n)) x 0 = ( x0 (1), x0 (2), , x0 ( n)) so, 1 + si + s0 ε 0i ( x0 , xi ) = 1 + si + s0 + si − s0 is absolutely relational grade of

x 0 and xi is the same (3) (4) (5)

x 0 for xi . Where, s0 , si given by reference [4].

Grey Relative Relational Grade. Supposing sequence x 0 and xi is the same length, and the initial value is not equal to zero, and initial value mapping of x 0 and xi is ' ' respectively x 0 and x i , that is xi x (1) x (2) =( i , i , xi (1) xi (1) xi (1) x x (1) x (2) x '0 = 0 = ( 0 , 0 , x0 (1) x0 (1) x0 (1) x 'i =

xi ( n) ) xi (1) x ( n) , 0 ) x0 (1)

(6) (7)

Similarly, we can obtain the grey relative relational grade [4]. that is:

γ 0i ( x0 , xi ) =

1 + s0' + si'

(8)

1 + s0' + si' + si' − s0'

Grey Comprehensive Relational Grade Result obtained by above three relational grade compute has some differences, average relational grade is average value of all relational coefficient for two sequence x 0

x i , however ,the relative relational grade is considered from the relative change rate of the initial point between two sequence x 0 and x i . So as to overcome these and

one-sided relational grades and meet the requirements of real-time realistic problems, comprehensive considering (2) (5), (8), the paper proposes a new method which is grey comprehensive relational grade ρ （x0 , xi）analysis [5]:

（）（）（）

（）

ρ x0 , xi =αγ x0 , xi +βε 0i x0 , xi +( 1- α - β ) γ 0i x0 , xi

(9)

Where, α , β is variable parameters, α 0 , β 0 , α + β ≺ 1 .The grey comprehensive relational grade by (9) definition meets partial order relation.

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Proof. In reference [4], the gray average relational grad γ ( x 0 , x i ) , the grey absolute relational grade ε 0i ( x0 , xi ) and the grey relative relational grade γ 0i ( x0 , xi ) are all fit for partial order. While (9) is linear combination of the relational grade, so the grey comprehensive relational grade by (9) definition is fits for reflective characteristics, symmetry and transmission, so ρ x0 , xi is fit for the partial order relation. In model (9), values of the variable parameter α , β are based on the size of γ ( x0 , xi ) , ε 0i ( x0 , xi ) , γ 0 i ( x0 , xi ) and possible sorting situation of ρ x0 , xi in actual

（）

（）

problem, through searching method in optimization solution. According to α 0 , β 0 , α + β ≺ 1 , calculating ρ x0 , xi by choosing different α , β values in certain

（）

（）

steps h , and to choose one group values from all ρ x0 , xi which needs be coincide with α , β and as close as possible actual problem. Normally, the smaller the steps length h is, the easier sorting the results of ideal ρ x0 , xi obtain [6,7].

（）

2.3 Comprehensive Relational Grade Analysis According to above the comprehensive relational grade calculation, comprehensive relational grade matrix of efficiency indicator (JYi) as follows:

⎡ρ ⎢ρ ψ =⎢ ⎢ρ ⎢ρ ⎣

11

ρ12

ρ13

21

ρ 22

ρ 23

ρ 24 ⎥

31

ρ 32

ρ 33

ρ 34 ⎥

41

ρ 42

ρ 43

ρ 44 ⎦

ρ14 ⎤

⎥ ⎥

Then sequencing in the size of each relational grade indicator, at the end, we can obtain each relational indicator’s influence degree for system efficiency.

3

Case Analysis

The fighting efficiency and the relational indicators of AEW radars are shown in Fig.3. intelligence data rate

AEW radar fighting efficiency

resolution and localization capability

intercepted signal rate

detecting capability

recognition rate

anti-interference capability

guide capability

Fig. 3. The AEW radar fighting efficiency and the relational indicator

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The fighting efficiency is consisted by 4 indicators, including the radar detecting capability, the resolution and localization, intelligence data rate and anti-jamming capability, however, detection capability is related with intercept, recognition and guide capability etc. So influencing factors’ analysis of AEW radars fighting efficiency can be converted into 4 relational indicators. Here, in order to simplify, we consider mainly 4 relational indicators: intercepted signal rate (JY1), recognition rate (JY2), intelligence data rate (JY3) and anti-interference capability(JY4), reference sequence consists of 4 indicators (JY1-JY4), relational factors are mainly operator level (JX1) and some indicator of tactical and technical of influence system efficiency, comparison sequence consists of operator level (JX1), indicator A (JX2), indicator B (JX3) and indicator C (JX4). 3.1 Determining Original Data of the System To select 5 similar radars L1, L2, L3, L4 and L5. According to expert advice, fighting efficiency indicator (JY1-JY4) of 5 radars and the data of relational factors is in table 1[8,9]. Table 1. Each radar fighting efficiency indicator(JY1-JY4) and date of relational factors (JX1-JX4)

Index

Data

Code Radar1

Radar2

Radar3

Radar4

Radar5

Intercepted signal rate

JY1

0.26

0.68

0.56

0.82

0.88

Recognition rate

JY2

0.46

0.71

0.75

0.89

0.94

Intelligence data rate

JY3

5

7

6

8

9

Anti-Interference capability

JY4

0.62

0.67

0.87

0.71

0.92

Operator level

JX1

0.73

0.82

0.79

0.87

0.89

Indicator A

JX2

0.35

0.43

0.55

0.53

0.5

Indicator B

JX3

0.31

0.42

0.65

0.52

0.65

Indicator C

JX4

0.79

0.81

0.78

0.91

0.95

3.2 Calculating Relational Grade Firstly pre-processing the intelligence data in table 1, in order to obtain consistent polarity of all data. According to selection principle of distinguishing coefficient, where ξ = 0.18, selecting of the value ξ by reference [6].Then by formula (2) (5) (8)(9), calculating respectively average relational grades, absolute relational grade, relative relational grade and comprehensive relational grade of JYi (i = 1,2,3,4) and JXp (P = 1,2,3,4) .relational grade matrix as follow:

Grey Comprehensive Relational Analysis of Fighting Efficiency Influencing Factors

⎡⎤ ⎢⎥ ⎢ ⎥ ⎢⎥ ⎢ ⎥ ⎣⎦

⎡ ⎤ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦

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⎡ ⎤ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎡ ⎤ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦

ψ 1 , ψ 2 and ψ 3 is respectively average relational grade matrix, absolute relational grade matrix and relative relational grade matrix of radars efficiency indicator (JY1JY4). From this, we can calculating comprehensive relational grade of radars efficiency indicator (JY1-JY4), to determine α = 0.4 , β = 0.3 by searching method in the optimal solution[8,9], we obtain comprehensive relational grade matrix ρ above by the formula (9). 3.3 Comprehensive Relational Grade Analysis Because ρ13

ρ12 ρ14 ρ11 , the tactical and technical indicator B is maximum impact for interception signal rate of AEW radar, the tactical and technical indicators A and C after B, the operation level of the operator is minimal impact. Because ρ21 ρ 22 ρ 23 ρ 24 , the operation level of the operator is maximum impact for signal recognition rate of AEW radar, the tactical and technical indicators B and A after it, C is minimal impact. Because ρ33 ρ32 ρ31 ρ34 , the tactical and technical indicators B is maximum impact for interception signal rate of AEW radar, the tactical and technical indicator A and the operator level after B, C is minimal impact. Because ρ 43 ρ 41 ρ 42 ρ 44 , the tactical and technical indicator C is maximum impact for anti-jamming capability of AEW radar, the operator level and the tactical and technical indicator A after B, C is minimal impact. For the influencing factors of AEW radar efficiency , because: ⎛ 4 ⎞ ⎜ ∑ ρi 2 =3.0130 ⎟ ⎝ i =1 ⎠

⎛ 4 ⎞ ⎜ ∑ ρi1 =2.9108 ⎟ ⎝ i =1 ⎠

⎛ 4 ⎞ ⎜ ∑ ρi 3 =2.6772 ⎟ ⎝ i =1 ⎠

⎛ 4 ⎞ ⎜ ∑ ρi 4 = 2.6298 ⎟ ⎝ i =1 ⎠

Where, JX 2 JX 1 JX 3 JX 4 , it is visible that the tactical and technical indicator A is the biggest influence factor of radar fighting efficiency , the operator level and the tactical and technical indicator B after A, C is minimal impact. Therefore, the Maintenance personnel should first consider how to perfect and meet the indicator A for improving warning radar efficiency. The comprehensive relational grade is most approximate to ideal relational grade by comparing with other relational algorithm [10].

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4 Conclusion There are many factors influencing AEW radar fighting efficiency in complex electromagnetic environment, some of which contain incomplete information and uncertain problems, and the gray system theory is a powerful tool to solve this kind of problem. This paper proposes grey relational transverse analysis, through the calculation of several relational grades, we can obtain the comprehensive relational grade of influencing system. By the case analysis, to sort the influence size of the operators level and the relational factors which affect the fighting efficiency indicators, and to analysis influencing degree of every the relational factors. So, the technical personnel can maintain efficaciously and improve function of whole AEW radar, the case also shows that comprehensive relational analysis is more reasonable and accuracy than any pure relational analysis. This paper only consider the main function of the radar and a portion of relational factors, we also can increase other functions and relational factors to expand analysis by similar methods.

Acknowledgements This paper was supported by Aviation science fund of China (No:20075153017) .Thanks to School of Electronic and Information Northwestern Polytechnical University Xi'an, China.

References 1. Li, N.: Airborne Warning and Control System Introductory Theory. National Defense Industry Press, Beijing (1998) 2. Liu, X., Yan, H.: Analysis of Dynamic Efficiency of AEW Radar Detecting Area. Computer Simulation Journal 23(8) (August 2006) 3. Chen, S., Shen, Q.: An Evaluation Technique Based on Grey Theory for AEW Radar Effectivenes. Modern Radar 30(7) (July 2008) 4. Chen, Y., Ke, H.: Application Technology of Grey System Theory for Electronic Information Equipment Test. National defense Industry press, Beijing (2008) 5. Dong, L., Zhu, Y.: Grey Relevancy Analysis and Its Application to Equipment Fault Diagnosis. Joural of East China University of science and tecnology (Natural Science Edition) 34, 564–565 (2008) 6. Fen, L.: Study of Distinguishing Coefficient of Grey System Relational Grade. Systems Engineering-theory & Practice 17(6), 49–54 (1997) 7. He, M.: The Comprehensive Relational Grade of Grey and Application. Journal of Inner Mongolia Normal Universit. 28(3) (September 1999) 8. Wen, K., Chen, M.: The Weighting of the Harmonic Based on Cardinal Grey Relational Grade. In: Procceding of 4th IEEE International Conference power Electronics and Drive Systems, October 22-25, pp. 362–365 (2001) 9. Liu, S., Guo, T.: Grey System Theory and Application. University Press of Henan, Kaifen (1991) 10. Deng, J.l.: The Basic of Grey System Theory. University of Science and Technology Press, Wuhan (2002)

Diesel Misfire Fault Diagnosis Using Vibration Signal over Cylinder Head Liu Li-tian1, Liao Hong-yun1, Chen Xiang-long1, Feng Yong-min2, and Xiao Yun-kui1 1 Automobile Engineering Department, Academy of Military Transportation, Tianjin, 300161 2 Tianjin Municipal Construction and Traffic Committee No.100, Guizhou Road, Heping District, Tianjin 300051

Abstract. Vibration signals are obtained by putting three sensors on relevant position on the cylinder head. The technology of wavelet transform is applied to extract feature information under normal and misfire operation, feature in time domain of vibration signal is analyzed and single cylinder relative energy in time domain is computed to judge fault cylinder without the top dead center signal, which can realize diesel misfire diagnosis without disassemble. The experimental results of fault diagnosis in diesel engine show that it can offer a new method in the misfire fault diagnosis of diesel engine. Keywords: diesel engine; fault diagnosis; misfire; wavelet transform.

1 Introduction For the fault diagnosis of engine misfire, method of motivating average and Bi-spectrum was adopted by authors of references [1] and [2] in processing speed pulse signal respectively by measuring instantaneous speed of the engine and good experimental results were achieved. However, when the speed fluctuation signal appears more pronounced interference signal, it will be very difficult to identify the fault location and the malfunction cylinder. Noise signal of diesel engine is analyzed by authors of references [3] and [4] using wavelet analyze methods .It can give a diagnose of diesel engine misfire default, but the vibration signal of each cylinder head and the jet Oil pressure signal were needed to measure for identifying the location of failure cylinder. Operating status information is rich in the surface of diesel engine vibration signal, particularly stronger incentive to make more obvious fault features is produced in unsteady state [5]. When misfire default occurs in one cylinder , high pressure gas is not produced in the cylinder inside, and the impact on the cylinder head of the system is small, that is to say , response in cylinder head system was not produced, thus using the cylinder head vibration signals to diagnose on the combustion is effective. By collecting cylinder head vibration signals with three vibration sensors, using wavelet transform method to analyze misfire fault characteristics without measuring top dead center signals of one cylinder, monitoring ignition condition of six cylinders combustion simultaneously and computing single time-domain relative energy to identify failure location by using 3-way vibration signal can be realized. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 761–768. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 The Basic Theory of Wavelet Transform Wavelet transform is a time-frequency analysis method and is the most widely used in the field of analyzing vibration signal of diesel engine which was first proposed by the French geophysicist J. Morlet[6]. Time and frequency windows of wavelet transform change with the signal frequency. Higher frequency resolution and lower time resolution is obtained within low frequency band in which the time window is wide, frequency window is narrow. Within high frequency band, for the time window is narrow, frequency window is wide, higher time resolution and low frequency resolution is got , thus wavelet transform is known as mathematical microscope. 2.1 Definition of Wavelet Transform The basic idea of Wavelet Transform is accordance with the Fourier Transform, expressing signal with a family of functions, which is called wavelet function system. Let Ψ ∈ L ( R ) ∩ L ( R ) and Ψ (0) = 0 by stretching and translation, we can get a family of functions. 2

1

()

Ψa ,b t = a

−1

2 Ψ⎛

⎜ ⎝

t−b⎞ a

⎟, (a , b ∈ R , a ≠ 0 ) ⎠

(1)

{Ψa ,b } is called analysis wavelet or continuous wavelet, Ψ is called basic wavelet or mother wavelet and its Fourier transformation is Ψ .a is stretching factor which change the shape of continuous wavelet and b is translation factor which change displacement of continuous wavelet. A function of finite energy f ( t ) ∈ L2 (R ) , its continuous wavelet is defined as

( )

Wf a , b = 〈 f , Ψa ,b 〉 = a

Ψ (t ) represents complex conjugate of

−1 + ∞ 2 ∫ f t Ψ ⎛ t - b ⎞dt ⎜ ⎟ −∞ ⎝ a ⎠

()

(2)

( ) , symbol 〈 f , Ψa ,b 〉 represents their inner-

Ψ t

product. Both a and b are continuous variable, so it is called continuous wavelet transform. 2.2 The Discrete Wavelet Transform (DWT)

In practical applications, continuous wavelet need to discretization processing. Therefore, a and b which are factors of wavelet function Ψ need to be discrete. Let be function

( )

( )

2 1 Ψ∈L R ∩L R

{ a ,b }

， there

exist constant A

and B ,

0 < A ≤ B < ∞ , and satisfactory for the relationship in any values:

( −k ω ) 2 ≤ B

A≤ ∑ Ψ 2 k∈Z

(3)

Then Ψ is called Dyadic Wavelet and the relationship (3) is called stable condition. Further more, if A = B , then the relationship (3) is called the most stable condition.

Diesel Misfire Fault Diagnosis Using Vibration Signal over Cylinder Head

763

With regard to wavelet function Ψ , defined: 1 ⎛ x Ψ j x = Ψ j ⎜ j 2 2 ⎝2

()

⎞ ⎟ ⎠

(4) j

Then wavelet transform definition of f at a scale of 2 and location X is:

()

()

W jf x = f ∗ Ψ j x = 2 2 2

−j

⎛x−t⎞ ∫ f t Ψ ⎜ j ⎟dt R ⎝ 2 ⎠

()

(5)

Then Wf = {W f ( x )} is called Dyadic Wavelet W is called operator for Dyadic 2

j

j∈ Z

Wavelet Transform.

，processing initial sample values of discrete dyadic wavelet with iterative solution ， approximate result If sampling values of origin signals are f n ( n = 1, 2, d

N)

d

S 2 f ( n ) and wavelet decomposition result W2 f ( n ) at different scales can be obt

t

tained.Iterative formulas for discrete wavelet are as follows:

∑

⎧ S dj f ( n ) = hk S 2dj −1 f (n − 2 j −1 k ) ⎪⎪ 2 k∈Z ⎨ d g k S 2dj f (n − 2 j −1 k ) ⎪W2 j f (n) = k∈Z ⎩⎪

∑

(6)

3 Cylinder Head Vibration Signal Analysis and Processing 3.1 Vibration Signal Acquisition

Test was carried out on diesel vehicles SX2190, which engine model is 6-cylinder and 4-stroke turbocharged. Engine speed is set at 1300r / min. Three vibration sensor were put on bolts between cylinder 1 and 2, 3 and 4,5 and 6 by magnets, as shown in Figure 1. To determine the vibration waveform of each cylinder from 3-way vibration signal , the fourth way signal is injection pressure signal of the first cylinder. Set sampling frequency 12800Hz and sampling points 8192. When the engine operates at speed of 1300r/min stably, three-way vibration signals and the first cylinder oil injection pressure signals are collected. Three-way vibration signals are shown in Figure 2.

Fig. 1. Top view of vibration sensors’ position

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Fig. 2. Time domain waveform of three ways’ signal under normal condition

3.2 Characteristics Analysis of Vibration Signals in Time Domain

Using the first Cylinder’s oil injection pressure signal to obtain a working cycle of cylinder head vibration signal, the time-domain waveform is shown in Figure 3. According to engine acting order 1-5-3-6-2-4, and the vibration sensor installed location , it can be learned that pulse A is caused by the outbreak of the pressure wave of 1-cylinder, pulse B is caused by the outbreak of the pressure wave of 2-cylinder. In turn, pulse C, D, E, F, is caused by 3-cylinder, 4-cylinder, 5-cylinder, 6 cylinder combustion pressure respectively in Figure 3. It can be observed that in a engine working cycle there are two cylinders’ waveform amplitude are larger than the others from Figure 3, the main reason is vibration attenuation of two cylinders in the vicinity of vibration sensors is smaller, the other cylinders vibration attenuation is larger. According to this characteristic, vibration condition of 1,2 cylinder can be reflected by the first signal, 3,4-cylinder vibration condition can be reflected by the second signal, 5,6-cylinder can be reflected by the third signal.

Fig. 3. Time domain waveform of three ways’ signal in one working cycle

Diesel Misfire Fault Diagnosis Using Vibration Signal over Cylinder Head

765

3.3 Wavelet Transform Vibration Signal

Plenty of signals can be achieved in experiments, which including normal operating condition signal, and each cylinder misfire condition signal. Since many signals need to be analyzed, 2-way vibration signal which is collected by sensor placed between 3 and 4 cylinder is taken as example for analysis. Firstly, processing signal using wavelet decomposition for 3 layers to get four frequency subspace including low-frequency approximation signal ca and three detail signals d1 d 2 d 3 . Four groups of time-domain signals are obtained by reconstructing each frequency subspace. Figure 4 shows t each frequency band reconstruction signal when the third cylinder is misfired In order to carry out quantitative analysis and calculate the cumulative energy of signal reconstruction. Reconstruction result is expressed by succession {R (k ) | k = 1, 2, N } . i

where N is the length of the frequency signal subspace. Select sum of squares of reconstruction signals as an expression of energy, The energy of Ri ( k ) is defined as: N 2 Pi = ∑ Ri ( k ) k =1

(7)

Table 1 shows energy value of each frequency band of the normal condition and the third cylinder misfire condition. In Table 1, frequency energy of 3-cylinder has significant changes in normal operating condition and misfire fault condition in the second way signal. Vibration energy is larger in normal operating condition and reduces distinctly in misfire fault condition. The first way signal and the third way signal appears similar results. This suggests that information about energy in frequency band by using wavelet analysis can reflect normal operating condition and misfire fault condition accuratel.

Fig. 4. Each frequency band reconstruction signal when the third cylinder is misfired

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L. Li-tian et al. Table 1. Energy in each frequency band in two working situation Energy 2-Way Data 1 Data 2 Data 3 Data 4 Data 5 Data 6

Energy Of Reconstruction Signal d1 Band 287.8817

d2 Band 483.5126

d3 Band 131.6286

Ca Band 22.1507

155.7391

331.0380

96.4925

17.4446

238.3293

446.1864

162.8752

23.6201

152.0742

309.6289

86.9056

18.5651

287.8817

483.5126

131.6286

22.1507

171.7429

324.2913

142.8499

22.1442

Working Condition Normal Misfire Failure at 3-Cylinder Normal Misfire Failure at 3-Cylinder Normal Misfire Failure at 3-Cylinder

3.4 Single Cylinder Time-Domain Relative Energy Fault Diagnose via Vibration Signal

Definition 1, single cylinder absolute energy in time domain: accumulated absolute values of each point of single cylinder time domain vibration waveform. Definition 2, cylinder relative energy in time domain: ratio of the single cylinder absolute energy in time domain to the sum of all cylinders’ absolute energy . Combustion conditions can be confirmed by calculating relative energy of each cylinder in time domain . The cylinder relative energy of normal operating condition and misfire fault condition of each cylinder in time domain are listed in table 1 and table 2. The following conclusions are obtained by analyzing data in two tables and time-domain characteristics of vibration signal. (1) In normal operating conditions, 1-cylinder and 2 -cylinder time-domain relative energy calculating through the first vibration signal are higher than other four cylinders, and the sum of single cylinder time-domain relative energy of 1-cylinder and 2-cylinder is greater than 50 %.3-cylinder and 4 -cylinder time-domain relative energy calculating through the second vibration signal and 5-cylinder and 6 -cylinder time-domain relative energy calculating through the third vibration signal have the same results. (2) If one cylinder occurs misfire fault or operating condition is not good , there must be some cylinder’s time-domain relative energy less than 10% by calculating single cylinder time-domain relative energy from 1-cylinder to 6-cylinder using three-way vibration signal. By calculating single cylinder time-domain relative energy, the identification of cylinder misfire fault is realized through the following steps: (1) Collecting 3-way vibration signals at the same time, and processing them with mean removal; (2)Processing vibration signals with 3 layers wavelet transform, reconstructing wavelet frequency-domain signal at the third layer respectively, and then judging whether misfire failure occurs; (3) if there is no misfire failure , then quit, otherwise proceed to step (4); (4) According to the engine speed n and the sampling frequency fs , calculating interval points N of a working cycle of engine experienced according to formula (8),

Diesel Misfire Fault Diagnosis Using Vibration Signal over Cylinder Head

N =

767

fs * 60 * 2

(8)

n

For example, speed at 1300r/min, the sampling frequency at 12800Hz, the interval points of a working cycle of engine is 1812; (5) Looking for the maximum value in the first N points of the second way vibration signal and marking the location as t. Taking into account number of the fuel injection advance angle data interval points is q a certain speed, selecting t-q as starting point location to calculate ,and intercepting N data points to constitute signal data within a engine working cycle. Dividing these N data points into 6 section, aij (i = 1, 2,3; j = 1, 2 6) represent data sections of three way signals. It is assumed that data segment m is located in data section a22 , as shown in Figure 5. (6) According to the characteristics of vibration signals in time domain, it is sure that a 22 is the data of 3-cylinder or 4 cylinder . First, assume it as 3-cylinder data. According to engine expansion acting order 1-5-3-6-2-4, we can confirm that are belong to 5,6,2,4 and 1 cylinders’ data. Calculatai1, ai 3 , ai 4 , ai 5 , ai 6 (i = 1, 2, 3) ing a16 , a14 and a 31 , a 33 respectively to obtain time-domain relative energy of 1,2-cylinder and 5,6-cylinder, judging them accord with the conclusion (1) which is reached by Table 1 and Table 2 or not . If it does, we can confirm that

a22 is the data belong

to 3-cylinder, otherwise a22 is belong to 4-cylinder. Proceeding with steps above. Finally, waveforms that each cylinders acting once will be confirmed .It is the same with single cylinder vibration waveforms determined by 1-cylinder oil injection pressure signal. (7) Calculating 1-cylinder and 2-cylinder time-domain relative energy by the first way signal, calculating 3-cylinder and 4-cylinder time-domain relative energy by the second way signal, calculating 5-cylinder and 6-cylinder time-domain relative energy by the third way signal, if some cylinder’ relative energy is less than 10%, we can identify that that cylinder working not in good condition , or misfire failure occurs.

Fig. 5. Data segments of three ways’ signal Table 2. Time domain relative energy of each cylinder under normal condition

The 1-Way Signal The 2-Way Signal The 3-Way Signal

1-cylind er 43.11 12.83 8.15

2-cylind er 22.19 6.17 3.32

3-cylind er 14.77 45.54 7.83

4-cylind er 7.02 18.77 6.50

5-cylind er 5.43 9.23 33.15

6-cyli nder 7.48 7.46 40.95

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Table 3. Time domain relative energy of each cylinder when six cylinders are misfired espectively Working Condition 1-cylinde r Misfire 2-cylinde r Misfire 3-cylinde r Misfire 4-cylinde r Misfire 5-cylinde r Misfire 6-cylinde r Misfire

Signal s The 1-Way Signal The 1-Way Signal The 2-Way Signal The 2-Way Signal The 3-Way Signal The 3-Way Signal

1-cylind er 2.26

2-cylind er 56.93

3-cylind er 23.64

4-cylind er 3.52

5-cylind er 4.98

6-cylind er 8.66

53.62

1.65

27.18

5.17

7.27

5.10

20.29

15.11

5.25

36.98

15.68

6.69

10.47

8.60

60.12

2.05

12.55

6.21

9.77

6.15

21.45

7.98

5.12

49.53

16.87

5.13

12.59

6.49

57.15

1.76

4 Conclusion (1) Engine cylinder head vibration signal contains the combustion quality characteristics of the each cylinder. Using wavelet multi-resolution analysis techniques, characteristics of engine operating condition can be extracted from complex background noise and engine working condition can be judged on the whole. (2) Using single cylinder time-domain relative energy, working condition of each cylinder can be identified. Using 3 way vibration signals without measuring top dead center signals, time-domain relative energy of each cylinder can be calculated accurately. The experiment results show that this method is easy and convenient, improving diagnostic accuracy of cylinder combustion quality.

References 1. Qiao, G.-d., Xu, Y.-x.: Application of the Instantaneous Speed in Fuel Cut-off Fault Diagnosis of the Diesel Engine. Vehicle Engine, (5), 66–69 (2008) 2. Sun, Y.-l., Piao, J.-z., Zhang, Y.-x.: Research on Fault Diagnosis for Internal Combustion Engines Using Transient Rotate Speed and Bi-spectrum. Transaction of CSICE 22(3), 241–244 (2004) 3. Wang, H.-g., Zhang, X.-b., Li, C.-y.: Diesel Misfire Fault Diagnosis Using Acoustic Information over Cylinder Head. Journal of Vibration Engineering 15(2), 207–209 (2002) 4. Tafreshi, R.: Malfunction detection in multi-cylinder engines using wavelet packet dictionary. SAE paper:2005-01-2261 5. Xiao, Y.-k.: The Automobile Fault Diagnostics. Beijing Institute of Technology Press, Beijing (2006) 6. Grossman, J.M.: Decomposition of Hardy functions into integrable wavelets of constant shape. SIMA J. Math. Anal. 15, 723–736 (1984)

Proof of a Conjecture about k -Graceful Graph Li Wuzhuang and Yan Qiantai Dept. of computer, Anyang Nomal University, Anyang Henan 455002, P.R. China [email protected] Abstract. A conjecture given in paper [1] that crown of complete bipartite graphs is k -graceful ( (m = 1or 2 , k≥2 );(m ≥ 3,k ≥ (m − 2 )(n − 1 )) is proved in this paper. Keywords: complete bipartite graphs; crown; graphs.

k

-graceful value;

k

-graceful

1 Introduction Slater and Thuiller proposed the notion of k -graceful independently in 1982. In paper [1], some classes k -graceful graphs and a conjecture was given. In this paper we prove the conjecture. Definition 1[2]. Let G =< V,E > be a simple graph, G is called k -graceful, if there exist a injection f:V(G) → { 0 ,1,2, , E + k − 1 } such that induced mapping f :E(G) → {k,k + 1, , E + k − 1 } becomes a bijection. Where the induced mapping f * is defined as *

∀e = uv ∈ E, f * (uv) = f(u) − f(v)

, then

G

is called

k

-graceful graph,

f

is called

k

-graceful

value. Definition 2. If the vertex set V of G =< V,E > can be divided into two un empty sets X and Y , X ∪ Y = V,X ∩ Y = ∅ , but also two nodes of every edge belong to X and Y separately, the G is called bipartite graph. If ∀xi ∈ X,yi ∈ Y,(xi ,yi ) ∈ E then G is called complete bipartite graph. if X = m,Y = n , the G is marked K m,n . Definition 3. If we add a pendant edge for every nodes of is called crown of K m,n . it is marked I(K m,n ) .

K m,n

, the graph we obtained

Conjecture[1]. The crown of complete bipartite graph

is

k

In this paper, we prov the conjecture is right when

K m,n

-graceful graph.

k ≥ (m − 2 )(n − 1 ). (m ≤ n, k ≥ 2).

1.1 Main Result and Proof Theorem 1. when Proof. In graph marked

m=2

I(K 2 ,n ) ,

,

I(K 2 , n )

,

is

nodes of

X * = {x1* ,x*2 },Y * = {y1* ,y*2 ,

be adjacent. In

I(K m,n )

y*n }

k

-graceful graph.

K 2 ,n

is marked

, there

V(I(K 2 ,n )) = 2n + 4

xi

，

and

X = {x1 ,x2 },Y = {y1,y2 ,

xi (i = 1,2) *

yn } ,

be adjacent,

E(I(K 2 ,n )) = 3n + 2

pendant vertex is

y i and

yi (i = 1,2, , n)

.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 769–772. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

*

770

L. Wuzhuang and Y. Qiantai

Give labeling

f

as follow f(x 1 ) = 0

,

f(x2 ) = 2n ,

，

f(y i ) = k + E -(i + 1 ), i = 1, 2 , ...,n f(x ) = k + E -1 * 1

f(x ) = k + 2 n * 2

，，

f(y ) = 2 n- 2i + 1, i = 1, 2 , ...,n

.

* i

We proved

-graceful value. arrange from large to small, the minimum is k + 2n + 1 ; f ( y )( i = 1, 2 , ..., n ) arrange from large to small, the maximum is 2n - 1 , and difference from f ( x1 ), f ( x2 ), f ( x1* ), f ( x2* ) , hence f is a injection. The labeling of edges as follow f

is a

k

f ( y i )( i = 1, 2, ..., n )

* i

f * ( x1 x1* ) = k + E - 1

，

f * ( x1 y i* ) = k + 3 n - i + 1

when

{

}.

i ∈ {1 , 2 , ..., n } , f * ( x1 yi * ) ∈ k + E - 2, k + E - 3,..., k + E - (n + 1) f ( yi y ) = k + E - 2 n - 2 + i *

when i ∈ {1 ,

* i

,

{

}.

2 , ..., n } , f ( yi yi ) ∈ k + E - 2n − 1, k + E - 2n,..., k + E - (n + 2) *

*

f * ( x 2 yi ) = k + E - 2 n - i - 1

when

{

f * ( x 2 x 2* ) = k

then

,

}.

i ∈ {1 , 2 , ..., n } , f ( x 2 yi ) ∈ k + E - 2n − 2, k + E - 2n − 3,..., k + 1 *

f * : E ( I ( K 2, n ) )

，

→ {k , k + 1 ,..., k + E -1} is a bijection.

Hence we proved

f

Theorem 2. when

m = 1 , I (Km , n )

Proof. Give labeling

is a

f

k

-graceful value. is a

k

-graceful graph.

as follow f ( x1 ) = 0

,

f ( x1* ) = k + E - 1

，

f ( y i ) = k + E - ( i + 1) , i = 1, 2 , ..., n

f ( y i* ) = 2 n - 2i + 1, i = 1, 2, ..., n

It is similar to theorem 1, we can prove that Theorem 3. when Proof. In graph

m≥3

，

I (K m , n )

are

k

f

is a

k

,

.

-graceful value.

-graceful graph for

I ( K m , n ) , V ( I ( K m, n )) = 2m + 2n, E ( I ( K m, n )) = mn + m + n

k

.

≥(m - 2)(n - 1) .

k -Graceful Graph

Proof of a Conjecture about

Give labeling

f

as follow f ( x1 ) = 0

，

f ( xi ) = in , i = 2 , 3, ..., m

771

，，

f ( yi ) = k + E - 1 i , i = 1, 2, ..., n f ( x1* ) = k + E - 1

，

，

f (x ) = k + in + (m - i), i = 2, 3, ...,m * ii

f ( y ) = 2n - 2i + 1, i = 1, 2, ..., n . * i

We prove f is a k -graceful value. According to the labeling above f ( x )( i = 2, 3, ..., m ) arrange from small to large, the minimum is 2n , the maximum is mn ; f ( y )( i = 1, 2, ..., n ) arrange from large to small, the minimum is c; f ( y )( i = 1, 2 , ..., n ) arrange from large to small, the maximum is 2n − 1 ,the minimum is 1; f ( x )( i = 2 , 3, ..., m ) arrange from small to large, the maximum is k + m + 2n − 2 , the maximum is k + mn . According to the (a), (b), (c), (d), f ( xi ), f ( yi ), f ( yi ) ( i = 1, 2,..., n ) are difference each other, but also f ( xi ), f ( yi ), f ( yi ) are difference each other. Now we prove that f ( x ) , f ( x ) are difference each other. When K ≥ (m - 2)(n -1) , the minimum of f ( x )( i = 1,2 , m ) is k + m + 2n − 2 , but the maximum of f ( x )( i = 1,2 , m ) is mn , so k + m + 2n − 2 > mn . Hence f ( x ), f ( x )( i = 1, 2, m ) are difference. Then f is a injection. Labeling of edges as follow f ( x x ) = k + E - 1 . i

i

* i

* i

*

*

*

i

* i

* i

i

i

* i

*

* 1 1

f * ( x1 y i ) = k + E - i - 1

when

,

{

}.

i ∈ {1 , 2 , ..., n } , f ( x1 yi ) ∈ k + E - 2, k + E - 3,..., k + E - (n + 1) *

f * ( y i y i* ) = k + E - 2 n - 2 + i

when

,

{

}.

i ∈ {1 , 2 , ..., n } , f * ( yi yi * ) ∈ k + E - 2n − 1, k + E - 2n,..., k + E - n − 2 f * ( xi y j ) = k + E - j - ( i + 1) n - 1 ,

when

i ∈ {2 , 3 , ..., m }

and

j ∈ {1 , 2 , ..., n }

,

{

}.

f * ( xi y j ) ∈ k + E - 2n − 2, k + E - 2n − 3,..., k + (m − 1) f * ( x i x i* ) = k + m - i

when i ∈ {2 , 3 , ..., So

,

m } , f ( xi xi ) ∈ {k + m - 2, k + m - 3, ..., k + 1, k )} .

f * : E( I (Km , n ) )

*

*

→ {k , k + 1 ,..., k + E -1} is a bijection.

Then we proved f is a k - graceful value. For example, Give a k - graceful value of I (K 4 , 5 ) .In order to f be a injection, k + 12 > 15, k + 12 > 20, k + 16 > 20 . So, k ≠ 3,4,8 . Generally, let k + 12 > 20 , then k > 8 , that is k > 8 = (4 - 2)(5 - 1) = (m - 2)(n - 1) .

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L. Wuzhuang and Y. Qiantai

k+27 0

k+28

k+26 1

k+12 0

k+25 1

k+16 5

k+24 2

k+20 0

k+23

9 7 5 3 1

References 1. M.: Graceful Graphs. Beijing University Press, Beijing (1991) 2. Slater, P.: On -graceful graphs. In: Proc of the 13th S.E. Conference on Combinatories Graph theory and Computing, Boca Raton, pp. 52–57 (1982) 3. Maheo, M., Thuillier, H.: On -graceful graphs. Ars Combinatorics 13(1), 181–192 (1982) 4. Galliana.: A dynamic survey of graph labeling. The electronic journal of combinatorics 6 (2000) 5. Ma, K.J.: Graceful Graph. Peking University Press, Beijing (1991) (in chinese) 6. Liwuzhuang, Y.: The k-gracefulness of Trees with Diameter of Four. Journal of Guilin University of Technology 27(4), 597–599 (2007)

High Dynamic GPS Signal Analysis and Acquisition Algorithm Xiaoming Han, Guangyu Zheng, and Shusheng Peng School of Electronic and Optical Engineering, Nanjing University of Science and Technology Nanjing, China [email protected], [email protected]

Abstract. GPS satellite signal acquisition is based on the correlation characteristics of C/A codes. This paper introduces two important properties of C/A codes, and analyzes the impact of Doppler shift on the received signal in case of high dynamic environment. FFT-based parallel code phase search acquisition algorithm is introduced and a new algorithm is presented to improve the FFT and k IFFT modules by translating N points FFT and IFFT into 2 points of M groups. And simulation results are shown in MATLAB of the improved algorithm. Keywords: High Dynamic; GPS; Doppler shift; FFT-based parallel code phase search acquisition algorithm; MTLAB; averaging correlation.

1

Introduction

Global Positioning System (GPS) is a satellite-based navigation system. It is based on the computation of range from the receiver to multiple satellites by multiplying the time delay that a GPS signal needs to travel from the satellites to the receiver by velocity of light [1].When a GPS receiver is in high dynamic environment, its received signal will make high Doppler shift. Bandwidth of the frequency offset will directly lead to an increase in the amount of data processing. This would make fast signal acquisition harder. Traditional method of acquisition is difficult to meet the requirement. This paper firstly introduced the structure of GPS satellite signal and two important properties of C/A codes. Then the impact of the Doppler shift on a received signal is analyzed. In order to solve the Doppler shift in condition of high dynamic, this paper introduces the FFT-based parallel code phase search acquisition algorithm. Based on this algorithm, this paper improves the FFT and IFFT modules according to averaging correlation algorithm [4].At last, simulation results are shown in MATLAB [5] of the improved algorithm.

2

GPS Signal Structure

A GPS signal, which is composed of carrier, the navigation data and PRN codes(C/A codes and P codes), is CDMA using direct sequence to BPSK modulate the carrier frequency. The GPS C/A codes belong to the family of PRN codes known as the Gold codes. The codes are generated from the product of two 1023-bit PRN sequence G1 and M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 773–779. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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G2, both G1 and G2 are generated by a maximum-length linear shift register of 10 stages and are driven by a 1.023MHz clock[2][6]. There are two important properties of C/A codes: (1)Exactly no cross-correlation: all the C/A codes are hardly correlation with each other.

rij ( n ) =

1022

∑

m =0

ci (m )c j (n + m ) ≈ 0

(for all n);

(1)

(2)Except for zero-shift, no auto-correlation:

rii ( n ) =

1022

∑

m=0

ci ( m ) ci ( n + m ) ≈ 0

| n |≥ 1

(2)

The max-margin of auto-correlation is rii = 2 −1=1023 (n is the stages of linear shift n

register, here n=10). Figure1 shows the auto-correlation and cross-correlation of C/A codes which are the basic theoretical foundation of acquiring GPS signal.

Fig. 1. Auto-correlation and Cross-correlation of C/A Codes

The purpose of GPS signal acquisition is to determine visible satellites and coarse values of carrier frequency and code phase of the satellite signal [3], in order to provide a range for the next tracking mode. But in high dynamic the effect of Doppler shift is significant.

3 Doppler Effect of the Received Signal For the impact of Doppler, in high dynamic, carrier will make large Doppler shift. The formula of Doppler shift is: f

d

=

vfc * c o s θ (t ) c

(3)

High Dynamic GPS Signal Analysis and Acquisition Algorithm

775

For example, the receiver’s speed v=2000 m/s, referenced frequency is fc=1575.42MHz, c is the velocity of light, so the max Doppler shift is 10.5 kHz. GPS satellite signal acquisition is based on the correlation properties of C/A codes, this shift will make a large impact on the correlation margin. If we demodulate the received signal directly excluding the solution of Doppler shift, the demodulated signal will be very different with the local C/A codes. Figure 2 demonstrates this. So we must analyses the Doppler shift in acquisition mode.

Fig. 2. Doppler Effect of the Received Signal

4

GPS Signal Acquisition Algorithms

Parallel code phase search acquisition algorithm would be adopted to acquire the GPS signal in high dynamic condition [3]. The algorithm is able to solve the problem of a large Doppler shift and large amount of data processing. This paper reduces the point number of FFT and IFFT modes according to the averaging correlation algorithm, so the improved algorithm has significant advantage in computation and time-consuming. 4.1

Theory of Parallel Code Phase Search Acquisition Algorithm

DFT’s of the finite length sequences x(n) and y(n) both with length N are:

（）=∑ x(n)e N −1

X k

Y (k ) =

− j 2π kn / N

n=0

and

N −1

∑

y (n )e

− j 2 π kn / N

n=0

(4)

The circular cross-correlation sequence between two finite length sequence x(n) and y(n) both with length N and with periodic repetition[2] is computed as

Z (n) =

1 N −1 1 N −1 x(m) y(m + n) = ∑ x(−m) y(m − n) ∑ N m=0 N m=0

(5)

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X. Han, G. Zheng, and S. Peng

Omitted the scaling factor 1/N, the discrete N-point Fourier transform of z(n) is N −1 N −1

N −1

N −1

n=0 m=0

m=0

n=0

Z (k ) = ∑∑ x(−m) y(m − n)e− j 2π kn/ N = ∑ x(m)e j 2π km/ N ∑ y(m + n)e− j 2π k (m+n)/ N = X * (k )Y (k ) (6)

So we can multiply the received signal in frequency domain then translate it into time domain by IDFT. By this way, we can get the cross correlation. Figure 3 is the block diagram of parallel code phase search acquisition algorithm.

Fig. 3. Block Diagram of Parallel Code Phase Search Acquisition Algorithm

The navigation message is transmitted with a rate of 50 bps, This results in data bit transitions every 20ms[3],To avoid this situation, we use a data length of 1ms for the acquisition algorithms, which is the length of one complete C/A code exactly. 4.2

Theory of a Improved Algorithm

The principle of a improved algorithm is as follows: Supposed there are N Points FFT, split it into 2 k points of M groups, and the number of group is the first number of each group and each have 2 k points .By this way we translated N points FFT into 2 k points FFT of M groups. If this algorithm is adopted in the FFT and IFFT modes of the parallel code phase search acquisition algorithm, it will reduce the amount of computation absolutely in this two modes. 4.3

Simulation of the Improved Algorithm

Parameters of this simulation are: sampling frequency, 16.3676MHz, intermediate frequency, 4.13043676MHz and signed char sampling format, 8bit. So the number of sampling points is 16368,in a data length of 1 ms. Figure 4 shows the spectrum of received signal after A/D. In this simulation 16368 points are splitted into 8 groups of 2046 points. The interval of each point is 8. So the simulation needs 8 groups of 2046 points FFT and IFFT. Figure 5 is the flow diagram of the simulation. As is shown in Figure 6, acquired satellite number are:03,15,19,22. And the signal of NO.22 satellite is the strongest. Figure 7 shows the three-dimension acquired map of NO.22 satellite.

High Dynamic GPS Signal Analysis and Acquisition Algorithm

Fig. 4. The Output of A/D

Fig. 5. Flow Diagram

777

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X. Han, G. Zheng, and S. Peng

Fig. 6. Acquired Result

Fig. 7. Three-Dimension Acquisition Map of NO.22 satellite

5 Conclusion As shown, under the condition of high dynamic, Doppler frequency shift would destroy the correlation properties of C/A codes of GPS signal. In order to remove the Doppler shift, this paper improve the FFT and IFFT modes in the FFT-based parallel code phase search acquisition algorithm, according to the averaging correlation algorithm. From simulation results, the improved algorithm has good performance and will be a guide in practical.

Acknowledgment This work was supported in part by the NUST Research Funding, No. 2010ZYTS030.

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779

References 1. Kaplan, E.: Understanding GPS: Principles and Applications. Artech House, Norwood (1990) 2. Tsui, J.B.-Y.: Fundamentals of Global Positioning System Receivers. John Wiley and Sons, Chichester (2000) 3. Borre, K., Akos, D.M.: A Software-Defined GPS and Galileo Reciver 4. Starzyk, J.A., Zhu, Z.: Averaging Correlation for C/A Code Acquisition and Tracking in Frequency Domain. IEEE, USA (2001) 5. Schilling, R.J., Harris, S.A.L.: Fundamentals of Digital Signal Processing Using, USA 6. Liu, H.: Signal Processing Applications in CDMA Communications. Artech House Publishers, New York (2000)

On the Mutual Information and Capacity of Coded MIMO with Interference Alignment Haitao Li and Haiying Yuan College of Electronic Information and Control Engineering Beijing University of Technology Beijing, 100124, P.R. China

Abstract. In this paper, we discuss the problem of mutual information and capacity for coded MIMO transmission system over interference channel. Firstly, we have formulated the binary and non-binary LDPC coded MIMO system model, which has joint precoder and decoder design to implement interference alignment. Then, the closed-form expression of the mutual information for binary LDPC coded MIMO system is derived. And the system capacity of nonbinary LDPC coded MIMO is analyzed. These theoretical results can provide guideline for the design of practical interference alignment. Keywords: Interference alignment, mutual information, MIMO, LDPC.

1 Introduction Recent information theoretic breakthrough established that each user in K-user interference channels can enjoy half the capacity of the interference free case. Such a surprising result is possible when Interference alignment (IA) is employed. IA was shown in [1] to approach the maximum number of degrees of freedom (DoF, i.e., the multiplexing gain achieved for a transmitter-receiver pair), it is defined as confining the interference to a subspace of the received signal space at each receiver while ensuring that the desired signal does not completely lie in that subspace. IA has been attracted considerable attention as a candidate for reducing interference in multiuser wireless environments. Interference alignment can be classified into two categories: signal level alignment and signal space alignment. While the alignment in signal scale lends tractability to DoF characterization, interference alignment in signal space provides an attractive way to realize interference alignment in practice. The signal space can be generated in several ways, such as by concatenating time symbols, frequency bins, or space domain. Of these two approaches, MIMO techniques seem to be more robust and attractive for realistic fading wireless channels. Up to now, much work has been done on the feasibility analysis and beamforming (or precoder) algorithms of IA. Several algorithms have been proposed to design interference-aligning beamformer at transmitters and receivers for the MIMO interference channels. Although the beamforming algorithm of MIMO channel have been studies, little is know the up bound of mutual information (MI) of interference alignment technique. And the mutual information represents the highest spectral efficiency that can be M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 781–788. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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H. Li and H. Yuan

attained using standard Gaussian codebooks. In this paper, we focus on the achievable mutual information using IA over binary coded MIMO interference channel. Especially, note that all modern wireless systems include channel coding and multiple antennas, often combined with multi-carrier modulation and adaptive coding and modulation. It is therefore of special interest to combine these approaches in an efficient way. And it has been shown that the combination of non-binary LDPC (NBLDPC) codes with spatial multiplexing and higher-order modulation can offer higher capacity over their binary counterparts. Thus, the capacity of interference MIMO with non-binary LDPC coded modulation (CM) is also analyzed. The rest of this paper is organized as follows: in section 2, the binary LDPC or NB-LDPC coded MIMO transmission model over interference channel is presented. The derivation of mutual information for binary LDPC coded MIMO with IA is given in section 3. Also, in this section, the capacity of NB-LDPC coded MIMO system is shown. The conclusions are stated in section 4. The following notation are used throughout the paper, the superscripts H , † denote the conjugate transpose and pseudo-inverse respectively, of a matrix (or a vector), Tr

(⋅) indicates the trace. E [⋅] denotes the expectation operator over x . x

2 System Model Firstly, we consider the binary or non-binary LDPC coded MIMO single link, which is shown in Fig. 1. NB-LDPC is the generalization of binary LDPC codes and the parity-check equations are written using symbols in Galois field of order , i.e. .The input message is first encoded by the LDPC encoder into a codeword symbol in have to be mapped to MQAM symbols. At the receiver, the soft demapper is utilized to compute APP (a posteriori probability) log-likelihood values, which constitute a sufficient statistic of the receive signal and form the input of the decoder.

NB-LD PC encoder

Signal Mapper

Detector

D ecoder

Fig. 1. LDPC coded modulation link.

The K user MIMO interference channel is shown in Fig.2, where transmitter

j = 1" K is equipped with M j antennas and receiver i with N i antennas. The matrix Hij denotes MIMO channel between j transmitter and receiver i , is assumed to be frequency-flat and block Rayleigh fading. The elements of Hij are complex Gaussian i.i.d. random variables, with zero mean and unit variance and they remain constant for the L symbols within that block.

On the Mutual Information and Capacity of Coded MIMO with Interference Alignment

783

Fig. 2. K user interference channel model.

For the ter j

(

)

i th 1 ≤ i ≤ K receiver, which receive interference from other transmit-

≠ i in addition to its intended signal, the received signal is given by, y i = H ii Pi si +

where si is the di

K

∑

j =1, j ≠ i

H ij P j s j + n i

(1)

(

)

× Li data symbol for the i th transmitter, di ≤ M i is the number of

data streams for receiver i .Here, we assume that all symbols of si are independently generated with unit variance, i.e., E ⎡si si

⎣

H

⎤ = Id . The power constraint for transmit⎦ i

(

)

2 E ⎡ Pi si ⎤ ≤ Pi , i.e. Tr PiH Pi ≤ Pi , Pi defining ⎢⎣ ⎥⎦ the M i × di precoding unitary matrix. Also, n i is the additive Gaussian noise with

ter i can be expressed as

each element having a variance equal to

ρi =

Pi

d iσ 2

σ 2I N

i

,and the SNR is defined as

.

For the K user interference channel, it can obtain the lower bound on the achievable DoF using IA scheme. If there exist precoding matrix Pi and received BF unitary matrix Wi such that,

WiH Hij Pj = 0, ∀j ≠ i

784

H. Li and H. Yuan

(

)

rank WiH Hii Pi = di , ∀i Then, the DoF

(2)

(d "d ) can be achieved, where d each corresponds to the multii

K

1

plexing gain. If a solution to the system of equation (2) is found, the received signal can be projected onto the interference-free subspace, therefore

y i = WiH y i = WiH H ii Pis i + WiH ni = H iisi + n i where H

ii

(3)

= WiH H ii Pi , note that the interference term in equation (1) is perfectly

suppressed, and the power of the signal that lies in the interference subspace is lost.

3 Mutual Information and Capacity Firstly, the closed form formulas for the mutual information achieved by interference alignment can be derived. The MI of user k

(1 ≤ k ≤ K ) is expressed as

I ( sk ; y k ) = h ( y k ) − h ( y k sk )

(4)

where h ( ⋅) denotes the differential entropy operator. In the following, each term in equation (4) can be analyzed in detail. 3.1 Derivation of h ( y k ) Let

p ( y k ) be the unconditional output distribution, then, h ( y k ) can be written by h ( y k ) = − E ⎡⎣log 2 p ( y k ) ⎤⎦ = -∫ p ( y k ) log 2 ( y k )dy k

Thus,

(5)

h ( y k ) can be computed if p ( y k ) was approached.

(

)

p ( y k ) = E ⎡ ∫ p y k H k , s k p ( s k )ds k ⎤ ⎣ ⎦ =

1

(πρ k )

M k Lk

⋅

1

πN L

k k

⎡ Lk E ⎢∏ ∫ exp − yl − H k sl ⎢ l =1 ⎣

(

where yl ,s l is the l th column of the diagonal element of ΛΛ is †

2

)

⎛ sl 2 ⎞⎤ dsl ⎟ ⎥ (6) × exp ⎜ − ⎜ ρk ⎟⎥ ⎝ ⎠⎦

y k , s k respectively. Let H k = UΛV† , sl =V†sl ,

(

)

λ = diag λ1 " λN k ,therefore,

On the Mutual Information and Capacity of Coded MIMO with Interference Alignment

p (yk ) =

{

exp − y k

(πρk )

M k Lk

2

}

πN L

×

(7)

k k

⎧⎪ Lk ⎛ N k ⎡ 1 2⎛ E ⎨ ∫ ∏ ⎜ ∏ × exp ⎢ 2 Re ( yl† U k Λ k sl ) − sl ⎜ λk + ⎜ ρk ⎝ ⎪⎩ l =1 ⎝ k =1 ⎣

⎛ s 2 ⎞⎤ ⎫ ⎞ ⎤ ⎞ ⎡⎢ M k l ,n ⎜ ⎟ ⎥ ⎪⎬ exp − ds ⎟ ⎥ ⎟⎟ ∏ l ,n ⎢ ⎜ ⎟⎥⎪ ρk ⎠ ⎦ ⎠ ⎣ n = Nk +1 ⎝ ⎠⎦ ⎭

⎛ B2 ⎞ A 2 exp − x A + xB dx =exp ( ) ⎜ ⎟ ∫−∞ ⎝ 4A ⎠ π

，Eq. (7)

∞

Utilizing the following results

785

can be expressed by

{

exp − y k

p ( yk ) =

Let

2

πN L

k k

} E ⎧⎪

⎡ λk ρ k U k † y k y †k U k ⎤ ⎛ ⎞ 1 exp ⎨∏ ⎟ ⎢ ⎥ ×⎜ λk ρ k + 1 ⎪⎩ i =1 ⎣ ⎦ ⎝ λk ρ k + 1 ⎠ Nk

A ( λ ) be a diagonal matrix with the diagonal element ak =

Lk

⎫⎪ ⎬ (8) ⎪⎭

λk ρ k .Note λk ρ k + 1

that U k ,Vk are random matrices and are isotropically distributed, therefore,

p (yk ) =

Let

{

exp − y k

πN

k Lk

2

}

∫

Nk ⎛ ⎞ 1 p(Λ)∏ ⎜ ⎟ i =1 ⎝ λk ρ k + 1 ⎠

eig ( y k y †k ) contain

det V ( x ) =

the

Lk

{∫ p ( U ) exp ⎡⎣Tr ( A ( λ ) U k

diagonal

element

† k

}

y k y k† U k ) ⎤⎦ dU k dλ

of

y k y †k

,

(9)

define

∏ ( x − x ) for a Vandermonde matrix V , and utilize the integral

1≤i , j ≤ M

i

j

in [3], then, Eq. (9) can be rewritten by

∏ (i − 1)!exp {− y Nk

p ( yk ) =

i =1

(

2 k

)

}

det V eig ( y k y †k ) π Nk Lk

∫ p (Λ)

(

det exp A ( λ ) , eig ( y k y k† ) det V ( A ( λ ) )

)

Lk

⎛ ⎞ 1 (10) ⎜ ⎟ dλ ∏ i =1 ⎝ λk ρ k + 1 ⎠ Nk

The density distribution of the eigenvalues in λ equals

(

p ( λ ) = det V eig ( y k y 2

† k

Nk

λkM

i =1

k

)) ∏ ( N

k

exp {−λk } − i ) !( M k − i ) !

− Nk

(11)

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And,

1

(

det V eig ( y k y k† )

)

=

1

(N ρk

2 k − Nk

∏ (λ ρ × Nk

)

k =1

k

(

k

+ 1)

N k −1

det V eig ( y k y k† )

)

(12)

Substituting (11) and (12) into (10) yields

p ( yk ) =

{

exp − y k

(

)

(

det V eig ( y k y †k ) ρ k

(

2

}

N k2 − N k

)

)

Nk

∏ ( N k − i )!

× (13)

i =1

(

)

k † † ∫ det V eig ( y k y k ) / ρk det exp A ( λ ) , eig ( y k y k ) ×∏ k =1

N

exp( −λk )λkM k − N k

( λk ρk + 1)

N k −1

Lk +1− N k

dλ

Using the multiple integral in [4], Eq.(13) can be given by

p (yk ) =

{

exp − y k

π

N k Lk

2

}×

det Z

(

det exp A ( λ ) , eig ( y k y

)) ∏ ( N Nk

† k

i =1

k

− i )!

(14)

i −1

∞

⎛ x ⎞ ⎛ xρ k ⎞ x M k − Nk − x⎟ dx . Plugging (14) where Z ij = ∫ ⎜ ⎟ exp ⎜ Lk +1− N k ρ ⎝ x ρ k + 1 ⎠ ( x ρ k + 1) 0⎝ k ⎠ into (5),

h( y k ) = −∫

{

exp − y k

π

N k Lk

2

}

det Z

(

det exp A ( λ ) , eig ( y k y k† )

(

)∏( N Nk

i =1

k

− i )!

× log 2 ( y k ) dy k (15)

)

3.2 Derivation Derivation of h y k s k and Mutual Information Since Wi , Pi are truncated unitary matrices, H ii = Wi H ii Pi has Gaussian i.i.d eleH

ment of the same variance as H ii . Therefore,

⎡ ⎤ ⎛ P H⎞ h ( y k sk ) = E Hkk ⎢log 2 det ⎜ I + k 2 H ikH k ikk⎟ ⎥ ⎝ d kσ ⎠⎦ ⎣

(16)

On the Mutual Information and Capacity of Coded MIMO with Interference Alignment

787

Then, we have directly from [5] that

h ( y k s k ) = N k exp(

⎧⎛ 2 p − 2q ⎞ ) log 2 ( e ) × ∑ ∑∑ ⎨⎜ ⎟ ρk p = 0 q = 0 l = 0 ⎩⎝ p − q ⎠ d k −1 p

1

2q

⎛ 2q + 2Lk − 2 N k ⎞ ( −1) ( 2q ) !( Lk − N k + l ) ! ×⎜ ⎟ × 2p −l 2q − l ⎝ ⎠ 2 q！l！( Lk − N k + l ) ! l

×

Lk − M k +l

∑ n=0

⎛ 1 E n +1 ⎜ ⎝ ρk

(17)

⎞ ⎪⎫ ⎟ ⎬ + N k Lk log 2 (π e) ⎠ ⎭⎪

where E n ( ⋅) denotes the exponential integral funcation order n, i.e., E n (τ ) =

∫

∞

1

t − n e−τ t dt .Therefore, the MI can be achieved by substituting (15)

and (17) into (4),

I ( sk ; y k ) = −∫

{

exp − y k

2

πN L

k k

}×

det Z

(

det exp A ( λ ) , eig ( y k y − exp(

)) ∏ ( N Nk

† k

i =1

k

− i )!

× log 2 ( y k ) dy k

⎧⎛ 2 p − 2q ⎞ ) log 2 ( e ) ∑ ∑∑ ⎨⎜ ⎟ ρk p = 0 q = 0 l = 0 ⎩⎝ p − q ⎠ 1

d k −1 p

2q

⎛ 1 ⎛ 2q + 2Lk − 2d k ⎞ ( −1) ( 2q ) !( Lk − d k + l ) ! Lk − M k +l ×⎜ × ∑ E n +1 ⎜ ⎟ 2p − l 2q − l n =0 ⎝ ⎠ 2 q！l！( Lk − d k + l ) ! ⎝ ρk l

⎞ ⎪⎫ ⎟⎬ ⎠ ⎭⎪

(18)

3.3 Non-binary System Capacity The expression (18) holds for Gaussian distributed transmit signal, i.e., the binary coded system. However, for NB-LDPC coded MIMO system, the transmit symbols are taken out of a QAM constellation X . Then, the coded modulation system capacity is given by

⎡ ⎢ C = R0 − E ⎢ log 2 ⎢⎣

∑ p(y

)

sk , Hk ⎤ ⎥ z∈X p y k s k , H k ⎥⎥ ⎦

(

k

)

(19)

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H. Li and H. Yuan

Where, R0 = MRq is the number of transmitted bits per channel use, Rq = log 2 Q is the number of bites per Q-ary QAM symbol, and defining the set X , of cardinalR ity X = 2 0 , for all possible transmit vectors.

4

Conclusions

In conclusion, we have analyzed binary and non-binary LDPC coded MIMO interference system model. Then, the closed-form expression of the mutual information for binary LDPC coded MIMO system over interference channel is derived. And the system capacity of non-binary LDPC coded MIMO is presented. These theoretical results can provide guideline for the design of practical interference alignment scheme.

Acknowledgments The authors would like to thank the support of the BJNSF under Grant 4112012 and the NSFC under Grant 61001049.

References 1. Cadambe, V.R., Jafar, S.A.: Interference alignment and degrees of freedom of the K-user interference channel. IEEE Transactions on Information Theory 54(8), 3425–3441 (2008) 2. Pfletschinger, S., Declercq, D.: Getting Closer to MIMO Capacity with Non-Binary Codes and Spatial Multiplexing. In: IEEE Globecom 2010 (2010) 3. Itzykson, C., Zuber, J.-B.: The planar approximation II. J. Math. Phys. 21, 411–421 (1980) 4. Chiani, M., Win, M.Z., Zanella, A.: On the capacity of spatially correlated MIMO rayleighfading channels. IEEE Trans.Inform. Theory 49, 2363–2371 (2003) 5. Rusek, F., Lozano, A., Jindal, N.: Mutual Information of IID Complex Gaussian Signals on Block Rayleigh-faded Channels, http://arxiv.org/abs/1001.2283 6. Tresch, R., Guillaud, M.: Cellular interference alignment with imperfect channel knowledge. In: IEEE ICC (June 2009) 7. Suh, C., Ho, M., Tse, D.: Downlink Interference Alignment, arXiv:1003.3707v1 (March 2010)

Interdisciplinary Education Model of Fashion Marketing and E-Commerce Zhaoyan1 and Renli2 1 Zhejiang Sci-Tech University, No.5,Street 2, Xiasha Higher Education Zone, Hangzhou, China 2 Zhejiang Sci-Tech University, No.5,Street 2, Xiasha Higher Education Zone, Hangzhou, China

Abstract. This paper analyses the current impact of the rapid development of E-Commerce on the shortage of human resource within the China textile and apparel industry. It is based on an analysis of the characteristics of the textile and apparel industry, as well as a summary of teaching practices, this article proposes the additional of extra courses in e-commerce within the fashion marketing curriculum for undergraduates according to the actual needs of the teaching practice. The combination of fashion with marketing and E-commerce will make the curriculum for this major more targeted and more meet the needs of the student. Keywords: Electronic-Commerce Fashion Marketing Education.

1 E-Commerce Has Changed the Traditional Fashion Marketing Model In recent years the rapid development of electronic information technology and the wider use of e-business marketing models has had a tremendous impact on the traditional marketing of the textile and apparel industry. This huge impact makes businesses, both large or small, famous or not, unable to survive by ignoring the trend in e-commerce. The setting up of sales channels on company websites, opening of online stores and co-operation with major network platforms are all new marketing models that the apparel industry is constantly working to remain competitive and increase their presence in the market. E-commerce has turned the traditional sales model upside down by forcing companies to adapt or die. In some cases brands such as “VANCL”, a famous brand for garments have moved away from the traditional store front to using only the internet to sell their products. Agreements with network platforms such as TaoBao, Amazon, Google, Facebook and Twitter are helping even small companies to meet customers that a traditional storefront would have no chance to sell to. For example, in the summer of 2010 the sports brand Adidas opened a shop on Taobao. Other companies such as American casual clothing brand Gap and the Japanese clothing retailer Uniqlo have recently opened an online store and provide other online services at the same time. The statistics from Taobao in 2010 show that the NO. 1 sales is in women’s apparel. The sales in this one area are higher than the combined sales of the next nine. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 789–793. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Internet research firm Analysis data show that online sales in China in 2010 doubled from 253 billion yuan ($USD 38 million) to 500 billion yuan ($USD compared to 2009. [1].

2 Huge Demand for E-Commerce Talent within the Fashion Industry Opening several well-known domestic textile and apparel job search web sites, it is not hard to see that there are a lot of fashion companies looking for web site designers and E-commerce sales and marketing personnel. But according to research, most fashion companies have found many difficulties with finding the right person to fit their requirements. The main problems are:1, Many e-commerce professionals have little or no background of the fashion industry, and even a basic understanding of background art are rare. This is a great pity for clothing companies who have developed their reputation through the creation of unique designs and brand names 2, A lot of individuals and agencies provide web design services, but most of them are over-formatting. Agencies who can provide services combining the specialties of fashion design philosophies with modern internet techniques are very rare. E-commerce education in China over the last ten years has improved and developed significantly due to the support of the National Ministry of Education. In addition, under China's current higher education system, China is not lacking in personnel skilled in marketing, customer services, production management, administrative, financial and other fields of e-business talents. However, in terms of the requirements of the apparel industry, personnel who are familiar with advanced management concepts, the operation of modern business processes, the characteristics of apparel products and the application of composite cross-border high-skilled talent is limited.

3 Special Knowledge Structure of E-Commerce Talents According to the requirements of the apparel industry and the analysis of the enterprises within the industry, it was concluded that E-business talent in fashion industry needs additional knowledge in the following three areas: I, Fashion design, II, Fashion marketing III, E-commerce. First of all, the understanding of fashion design is the base of all other knowledge as it provides the concept of art and color as well as the principles of fashion design and manufacturing technology, etc. this basic knowledge is the foundation for E-commerce talents within the fashion industry. Secondly, an understanding of fashion marketing is very important too. This is a new system especially for the fashion industry, which has come out of the general theory of marketing after analysis and summary combined with the features of fashion products. The Fashion marketing system includes the basic theory of marketing, market research, data analysis, etc. Fashion marketing knowledge is the support for the whole structure of E-business talents. One can use the E-commerce knowledge properly only after thoroughly understanding requirements of the industry.

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Last but not least, Knowledge of E-commerce is the focal point for the whole knowledge system. It is the key point of this knowledge system. Knowledge in the process of E-commerce, website design and internet basics provide new methods for making the marketing concept come true. We could say one masters the subject of fashion E- commerce, after learning the basic knowledge of fashion design and marketing and knowing how to use the skill of E-commerce to accomplish a marketing plan. But an international perspective, quick reaction to new trends, and the proper judgment of fashion events are also necessary for E-business talents. To summarize the three knowledge areas, a knowledge in fashion design plays a key role as the foundation, with the knowledge of fashion marketing acting as the structure, building upon the initial foundation and the application of E-Commerce with the foundations and structure allows the right elements to be combined to meet the fashion industries requirements

4 The Possibility of Interdisciplinary Education Model of Fashion Marketing and E-Commerce Facing the rapid development of society, the education curriculum system’s update is crucial at this moment. Otherwise, it is difficult to meet the urgent needs of talent as required from the fashion industry. Based on the above analysis I believe that providing E-commerce related courses under the direction of marketing professionals is a pressing matter. Due to the existing mature higher education courses in apparel design and fashion marketing throughout the whole country, it is not difficult to combine these two and provide the foundation for a fashion E-commerce education system as we are discussing here. It is comparatively easy to train the students with knowledge in fashion design and marketing. How to bring e-commerce into the curriculum at an appropriate level to meet the industries requirements requires more detailed study. We can bring in some of these courses and integrate them with courses within the fashion industry and form a relatively complete series of courses of professional clothing e-commerce. According to the different requirements of technology and business, education circles normally set two categories of E-commerce, which are Technology-based E-commerce jobs, for example, website designer, database maintenance personnel; and business-oriented jobs, for example, internet marketing, sales, logistics and back stage service management. During the planning of the education system for the major of accessory design and marketing in Zhejiang Sci-tech University, we have already tried to put in some courses related to E-commerce. First, add into the curriculum how the internet affects the fashion industry in the freshman course. Introduce successful commercial models like Taobao, Vancle and Twitter ,etc. Through a variety of interesting events to give the students the initial curiosity to identify the relationship between the Internet and the clothing industry. Guide students in their daily life and concerns related information and be prepared for future studies.

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In the second and third years of study, more in depth courses in both theory and practical direction are introduced. Based on the fashion companies’ requirements for E-technology talents, courses in website design are the first thing to teach. Students will be asked to do market research for one fashion brand and simulate the process of website design, and complete it. Back stage service management courses are the next step. Students would need to design an independent back stage service system based on a existing clothing brand. Based on the fashion companies’ requirements for E-commerce talents, Courses like Fashion internet merchandising will introduce the feature of E-commerce and the classification of websites. Students will have to do a general market research with regards to consumers, analyze the difference between different website users and complete a marketing plan course will be required. In the teaching process, Students can practice on the school network teaching platform which provides website design software, a Taobao simulation system, internet shopping mall simulation system, and back stage simulation system. These practice can not only improve their operational capability but also broaden their vision. A website design competition sponsored by companies, simulating some foreign online store are all good ways to enhance the student's interest and practical ability. In addition, the learning of professional English will help students to experience more about the advanced e-commerce platform. In this four-year full-time undergraduate teaching program planning, fashion design and marketing courses would still take up the majority of the curriculum, with the E-commerce related courses being an important but not dominant part of the course. By doing so, students will gain knowledge about fashion design, fashion marketing and E-commerce. More importantly this new teaching system will bring more job opportunities for students who have a new level of experience and knowledge. The research of fashion marketing and E-commerce cross-border teaching model will definitively provide a successful interaction between theory and practice, in class and out of class, traditional and modern. And it will fully represent the principle of higher education which is to serve the society based on future requirements.

Year One Year Two Year Three Year Four

Fashion Design Courese

Fashion Marketing Courese

E-commerce Courese

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5 Conclusion The new merchandising model, E-commerce, not only have changed the marketing model of contemporary businesses but has also changed the consuming model of

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customer. One cannot predict the future of E-commerce but we can all tell that the needs for experienced personnel with knowledge in E-commerce and the fashion industry are absolutely essential. This is especially applicable to meet the needs of people within the fashion industry. So this article discussed how to set up an interdisciplinary education model of fashion marketing and E-commerce which is suitable for the current domestic business environment and the requirement of the fashion industry in the future. We hope that this paper will facilitate new ideas to develop graduates with the right skills to meet the needs of the fashion industry.

Reference 1. Information on China National Garment Association Network, http://www.cnga.org.cn

Research on the Image Registration Algorithm Based on Regional Feature Xiaoyan Cao1, Xiangxin Shao2, Xinying Li1, Chunying Wang1, and Yan Zhou1 1

，

ChangChun University Of Technology Software Institute ChangChun JiLin 130012 2 JiLin University INSTITUTE of Electronic Science and Engineering ChangChun JiLin 130012

，

Abstract. By analyzing image registration algorithm based on region and feature, it presents a new image registration algorithm based on regional characteristics. In this paper, it presents iterative threshold segmentation algorithm to segment image and extracts the enclosed area, then determine the correct registration point using identification principle of fuzzy sets to selection closeness of the two regions .This algorithm stitches on the two images with overlapping regions on image registration system. Experiments show that algorithm can stitch the image accurately and quickly, it has a good effect. Keywords: image registration, iterative threshold segmentation algorithm, identification of fuzzy sets, registration point.

1 Introduction Image registration, namely, transforms and processes two or more images on different time, different perspectives or different sensors, it makes the image correspond in geometry. The goal of image registration is to find the best transformation that maps the image point to the reference image point, to remove the inconsistencies in geometry, such as translation, rotation and deformation between the pending registration image and reference image. It makes the same goal in different images have the same coordinates In modern information processing it is a very important technology, various data sources all need to merge the information to get the final image analysis tasks necessarily. It has a wide range of applications. Currently, the common criteria for the classification of image registration methods is divided into two categories: methods based on image region and methods based on image feature. The first method relies on statistical information of gray, more suitable for a image of a linear relationship on gray published; the last method relies on the feature of an image such as edge, region and texture, more suitable for a image of distinctive feature. It causes the difference by a camera and imaging time and different point of view between the images, so that makes very difficult to find the same name point controlling which relies on the image to gray scale features. Absolutely, the accuracy is not high. in registration method of the image feature , one can use a lot of features, such M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 795–801. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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as contour, corner, edge, high curvature points, regional features. The more the common features of the image registration has, the higher registration accuracy. The image registration method has the inherent defects , it presents a new image registration algorithm based on regional characteristics. In this paper, it presents iterative threshold segmentation algorithm to segment image and extracts the enclosed area, then determine the correct registration point using identification principle of fuzzy sets to selection closeness of the two regions .This algorithm stitches on the two images with overlapping regions on image registration system. Experiments show that algorithm can stitch the image accurately and quickly, it has a good effect.

2 Image Registration of the Regional Features In this paper, shape features of two images overlap in the region as the regional features for image registration, the algorithm's basic idea is: from the two images overlap region, segmentation algorithm is used iterative threshold segmentation, extracting enclosed area of splited, with its perimeter, area, flat level, aspect ratio features as a regional shape feature. Recognition method is used fuzzy sets to measure the closeness of the two regions to find the features of the same area, so that it can find the correct registration point. 2.1 Description of Regional Feature In order to achieve the correct registration, selecting the shape characteristics of the region as a registration collection, using iterative threshold segmentation of the image area describes the two images region .The features described the region are:

（1）Moment

Given two-dimensional continuous function f (x, y), it is defined moment of pqorder

M pq = ∫

+∞ +∞

∫

−∞ − ∞

x p y q f ( x, y )dxdy

p、q=0,1,2…

(1)

For a binary image {f (i, j); i, j = 0, l, 2, ... ..., N-1}, it defines the monent of pq- order:

M pq = ∑ ∑ f (i, j )i p j q

(2)

Starting from the moment, a considerable number of digital features can be defined:

①centroid

− −

(i , j ) = (

②central moment

M 10 M 01 , ) M 00 M 00

m pq = ∑∑ f (i, j )(i − i) − p ( j − j )

(3)

−q

(4)

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③Hu Moment Group

Hu moments group is composed of the first 8 {Mpq} function moments. which satisfy the translation, rotation invariance, it applies to shape recognition in graphics area. (2) area Regional area is defined as the number of pixels in the region:

S=

M 00 max

(5)

Where, max is the maximum gray level of the binary image. (3) perimeter and roundness Region adjacent perimeter L with area and distance between points to represent. Roundness, is used to indicate the extent of almost circular shape of the object, the calculation formula is: Region perimeter L is represented with distance between adjacent points . Roundness, is used to indicate the close to the circular extent, its formula is:

R=

4πS L2

(6)

Where, S is the regional area; L is the perimeter; the range of R is 0 < R ≤ 1 . The larger R is, the closer region to travel. (4) flatten degrees The flat is defined as the ratio of the length shaft in the region:

e=

m20 + m02 + (m20 + m02 ) 2 − 4m20 m02 + 4m112 m20 + m02 − ( m20 + m02 ) 2 − 4m20 m02 + 4m112

(7)

Usually, one can take the first a few orders moment, of course, this brings approximation and error. (5) The aspect ratio The aspect ratio is defined as the ratio of x and y coordinates of the direction:

AspectRatio =

y max − y min xmax − x min

(8)

It can be seen that the calculation of the aspect ratio is relatively simple, reflecting the geometry of the picture area, but it is just a similar the expression of the region. In order to achieve high-precision image registration, it need further parts of the image registration for the key features. Fuzzy recognition method based feature extracting

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feature points increase the precision and accuracy of registration. In this image registration, image features are used in contour, corner, edge, high curvature points. 2.2 Identification of Fuzzy Sets Fuzzy theory is based on the mathematical theory of fuzzy sets ,which is founded by electrical engineering professor L.A.zadeh in the University of California at Berkeley in 1965.It includes fuzzy set theory, fuzzy logic, fuzzy reasoning and fuzzy control and other aspects. Fuzzy set is defined as follows: A fuzzy set

A on U is for any ~

the membership of fuzzy sets

u ∈U

A for u, and ~

, has identified a number

μ A (u ) . It called ~

μ A (u ) ∈ [0,1] . ~

μ A : U → [0,1] is called the membership function of A . For ~ convenience, the μ A (u ) is denoted A(u ) .Fuzzy sets are characterized entirely by Mapping:

~

~

~

its membership function. When the range of subset,

μA ~

is {0,1}, it degenerates into features function of an common

A degenerates into a common subset. Common subset is a special form of ~

fuzzy sets. By the same name region registered in the region, it uses the principle of close recognition method in the fuzzy sets, excluding false registration, find the registration point quickly. The proximity principle: for the actual pattern recognition problems, sometimes the object is not to be identified a certain element on the domain U , its a subset of U,. At this time ,the object under discussion is not the collection membership for an element, it is the close to level between two fuzzy sets. Located A , B is the two fuzzy sets in the domain U, if U = {u1, u2, ..., un} is a finite ~

~

set, defined: 1

1 n M ( A, B) = [ ∑ | A(u i ) − B (ui ) | p ] p ~ ~ ~ n i =1 ~

(9)

p is a positive real number.

M ( A, B) is the distance that fuzzy set A , B is ~ ~ ~ ~

relative Minkowski. When p = 1,

M ( A, B) is the distance relative Hamming, denoted ~ ~

by

M H ( A, B) . ~

~

MH

( )

1 n A, B = ∑ A (u i ) − B (u i ) ~ ~ ~ n i =1 ~

(10)

Research on the Image Registration Algorithm Based on Regional Feature

When p = 2,

M ( A, B) is the distance relative Euclid, denoted by M E ( A, B) . ~ ~ ~ ~

M E ( A, B) = ~

Accordingly, sets

799

~

2 1 n A ( u ) − B ( u ) ∑ i ~ i n i=1 ~

(11)

N H ( A, B) = 1 − M E ( A, B) is the Hamming proximity of fuzzy ~ ~ ~ ~

A and B . N E ( A, B) = 1 − M E ( A, B) is Euclid proximity of fuzzy sets A and ~ ~ ~ ~ ~ ~ ~

B . Proximity is used to measure the "similarity" of two fuzzy sets A1 ~

~

， A ，…， 2 ~

An are the fuzzy sets in the domain U. N ( B, Ai ) is the proximity of the fuzzy sets ~ ~

~

B and A . If there exists i0, such that: ~

~

⎧ ⎫ N ( B, Ai0 ) = max ⎨ N ( B, Ak )⎬ ~ ~ 1≤ k ≤ n ⎩ ~ ⎭ ~ The fuzzy set

(12)

B are identified as Ai0 class. This principle is called proximity ~ ~

principle. Since there are many similar regional features in the overlapping regions for two images, so as overlap region is determined the domain U. Perimeter, area, flat level, aspect ratio shape feature is composed of fuzzy sets

A , searching fuzzy set B which ~ ~

has similar feature in the registrating area. Then it is used the principle of proximity, so that it can determine the correct registration of the two regions as long as the formula (12).

3 Image Stitching Based on the Regional Features Using VC + + 6.0 software to design a stitching image system based on region feature, and it can implement image stitching algorithm in this paper. It achieves satisfactory results using the actual image processing. The main functions of the image stitching system: enter two or more than two images to be spliced, use the algorithm to stitching image; remove seams and grayscale differences existing image stitching. Below is a random image of four overlapping regions using a digital camera. Based on regional features in the registration algorithm, it can achieve accurately, rapidly splicing in the regional features of the stitching system. Experimental results are shown that stitching has a good image mosaic effect.

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Original image 1

Original image 2

Original image 3

Original image 4

Image after stitching

4 Conclusion It is proposed image registration algorithm in-depth study on the basis of image registration techniques. It improved accuracy and reliability of the image mosaic algorithm combining region features with the fuzzy recognition algorithm .Through a combination of image preprocessing, image registration and image fusion, it design a stitching system to verify advantages ot the image registration algorithm based on regional feature. The registration algorithm can improve the robustness of image mosaic, it is effective to implementation of the image mosaic, it has been done some preparatory work for the future continuing to study in image stitching and their application areas and other image processing technologies.

References 1. Zheng, Z.: Study of image registration techniques. University of Science and Technology. Master of Signal and Information Processing Papers (June 2006) 2. Zitova, B., Flusser, J.: Image registration methods: a survey. Image and vision Computing 21(11), 977–1000 (2003) 3. Brown, L.G.: A survey of image registration techniques. ACM Computing Surveys 24(4), 325–376 (1992) 4. Song, Z.: Search on Image Registration and Its Application. PhD thesis, Fudan University (April 2010)

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5. Guo, Z., Xiao, G.: Image Fusion-Theory and Applications. Higher Education Press, Beijing (2007) 6. Yang, C.: Search on Image fusion and registration method. PhD thesis, Xi’an University of Electronic Science and Technology(April 2008) 7. Russo, E.: Evolutionary neural fuzzy systems for data filtering. In: IEEE Instrumentation and Measurement Technology Conference, pp. 826–834 (1998) 8. Russo, F.: A user-friendly research tool for image processing with fuzzy rules. In: IEEE International Conference on Fuzzy Systems, pp. 561–568 (1992a)

Accurate Curvature Approximation of 3-Dimension Discrete Points Shilin Zhou1, Jianping Yin2, Xiaolin Yang3, and Junyun Wu1 1

Information Engineering School of Nanchang University, Nanchang, jiangxi 330031, P.R. China 2 Department of Electrical and Information Engineering, Nanchang Institute of Technology,330044, P.R.China 3 School of Mechanics engineering Nanchang University, Nanchang, jiangxi 330031, P.R.China zhousl999@ 163.com

Abstract. 3-dimensional scanner is a data acquisition tool in recent years. The scanned datum are 3-dimension discrete points. Veracity of approximation curvature of 3-dimension discrete points is difficulty. In this paper, according to geometrical properties of 3-dimension curve, a method of curvature calculation of 3-dimension discrete points on edge curve is introduced. Concretely, Firstly, feasibility in theory of method in this paper is proved; secondly, virtual arc is used to approximate discrete edge curve, so that curvature and correction-curvature will be calculated; thirdly, method in this paper is contrasted with method dealing with similar question in experiment, and the result of experiment manifests that accuracy of method in this paper is better than method contrasted; finally, an application experiment is made.

、

、

Keywords: discrete points curvature 3-dimension.

1 Introduction Curvature is important geometric properties of curve. Curvature reflects bend degree of curve. Especially for discrete point cloud by three-dimensional scanner, curvature calculation of discrete point cloud has important effect on simplification model of point cloud and extraction character from point cloud and point cloud denoising. Curvature is a necessary and sufficient condition about curve matching in two dimensional direction. Curvature is a necessary condition about curve matching in three dimensional direction. So that curvature calculation perform an important role in dealing with scanning point cloud. Curvature approximation method of planar discrete points is mature. There are many methods to deal with planar discrete points curvature approximation. These methods includes three kinds: curvature estimation based on tangent direction[1][2], curvature estimation based on second derivative [3], curvature estimation based on osculating circle[4][5]. In these three kinds of methods , in curvature estimation based on second derivative, position of discrete points is excursion from right position because of being deconvolved by second derivative function, but offset of every point is different, so M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 803–810. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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curve is distorted, so that curvature estimation is bad; character of curvature estimation based on tangent direction is that position of corner is accurate ,but curvature estimation is not accurate; character of curvature estimation based on osculating circle is that curvature is accurate. Curvature approximation method of three dimension discrete points is not mature. Because of computer hardware development, time complexity of algorithm isn’t important; technology difficulty is accurate degree between curvature estimation and true curvature. At present, Curvature approximation method of three dimension discrete points includes three dimension image curvature estimation [6]and three dimension scanning points curvature computation[7][8]. Curvature estimation in literature[6] mainly dealing with three dimension image, and three dimension image transform from two planar image, not true three dimension points, so that position of discrete point by this method is not accurate and curvature estimation from these discrete points isn’t accurate too. Curvature estimation in literature[7] use least square fitting method and bicubic B-spline to obtain bicubic B-spline surface replacing point-sample surface, then curvature is estimated from bicubic B-spline surface, this method is fit for cursory curvature computation. There is more accurate method in literature[8] than method in literature[7], in this method, continuum curve is nearest to discrete points, and curvature is computed by curvature formulary of curve, because there is difference between nearest curve and true curve, curvature estimation by this method will not be very accurate. Aim of this paper is to attain an accurate curvature computation algorithm of edge discrete points which is fit for three dimension fragments reassembly. In this paper, aim object is scanned by three dimension scanner, so that position of discrete points is accurate. In literature[4][5], curvature computation by osculating circle is very accurate, enlightened by upper methods, planar curvature computation method is used to compute curvature of three dimension discrete points. Firstly, curvature computation prove to be feasible in theory. Secondly, curvature computation formulary is obtained. Thirdly, experiment is made. Concretely, from local geometric property of discrete points, dummy arc is used to approximate discrete segment curve. Then curvature is obtained from the relationship between radius of arc and curvature of three dimension curve. Result of curvature computation by method in this paper is compared with result of curvature computation by method in literature[8].

2 Curvature Calculation After three dimension fragment is scanned, three dimension discrete points are produced. Then edge discrete points are detected from discrete points. Curvature of edge discrete points is computed in this paper. 2.1 Theoretical Basis Theorem 1 (Meusnier)[9] given point p of surface curve(C),normal section(C0) has the same tangent with curve(C) and the same point p. curvature center c of point p of surface curve(C) is projection of curvature center c0 of the same point p of normal section(C0) on osculation plane.

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Theorem 2 three points p1,p2,p3 not in same line determine a circle R0. The centre O1 of circle R0 is curvature center of circle R and centre O1 is curvature center of point p2.Curvature k of point p2 is 1/r where r is radius of circle R0. Prove: three points p1,p2,p3 not in same line determine a plane. On the same plane, three points p1,p2,p3 not in same line determine a circle R0. Centre of circle R0 is O1. Suppose point p4 is not on the same plane with three points p1,p2,p3. Then these four points determine a sphere S. Suppose centre of sphere is point O. Connect O with p2. Then all tangent of sphere through point p2 is on a plane which plumbs OP2. Big circle through O p2 is normal section through point p2 and curvature centre of normal section is the centre O of the sphere S. Projection of point O on the plane of three points p1,p2,p3 is O1. According to Meusnier, O1 is curvature center of point p2.

Fig. 1. Curvature centre sketch map

2.2 Computation Formulary According to upper theory when three points is in same line, curvature of meddle p2 is 0; when three points p1,p2,p3 not in same line, they are regarded as three bordering on points of one circle. Radius of circle is computed, curvature of point p2 is reciprocal of radius. Suppose three points p1(x1,y1,z1),p2(x2,y2,z2),p3(x3,y3,z3) coordinates is well known. According to three point determining one circle and belong to one plane. We have

(x1 − x0 )2 + ( y1 − y 0 )2 + (z1 − z 0 )2 = (x 2 − x0 )2 + ( y 2 − y 0 )2 + (z 2 − z 0 )2

(1)

(x1 − x0 )2 + ( y1 − y0 )2 + ( z1 − z 0 )2 = ( x3 − x0 )2 + ( y3 − y0 )2 + (z 3 − z 0 )2

(2)

x 0 − x1

y 0 − y1

z 0 − z1

x 2 − x1 x3 − x1

y 2 − y1 y3 − y1

z 2 − z1 = 0 z 3 − z1

(3)

Equation group includes in formulary (1),formulary (2),formulary(3). X0,y0,z0 is unknown. Convenient for computation and expression, we have

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a 2 = x2 − x1 , a3 = x3 − x1 , b2 = y 2 − y1 , b3 = y 3 − y1 , c 2 = z 2 − z1 , c3 = z 3 − z1 , x = x0 − x1 , y = y 0 − y1 , z = z 0 − z1

。

Use these formulary to replace equation group some things, we have 2 2 2 2 2 2 2 2 2 2 x = 2(a42a+2bb22+−c82a)(aa2bb3b−a−38ba2ba3 −ca3cc2+c34+aa2c2c23+)4−a22(ba32 ++4b3b+2cc23−)(8ab2bb2bc3 −ca+3b42a−2ac32c+24+ba22cc22c3 ) 2 3

2 3 2 3

2 3 2 3

2 3

3 2

2 3

2 3 2 3

3 2

3 2

2 2 2 2 2 2 2 2 2 2 y = −2 (4a2a+2bb22−+8ca2 )(aab2ab3b3−−8aa3 ba2 c−bc2c+3 4+ab32cc22c+34)+a22b(a23++4bb32+cc23−)(8ba2bb3c−ac2+a34ba2 2−cb22+c24cb32+cb23c2 ) 2 3

2 3 2 3

2 3 2 3

2 3

3 2

2 3

2 3 2 3

3 2

3 2

2 2 2 2 2 2 2 2 2 2 z = 2 (a42a+2bb22+−c82a)(aa3bc2b−a−28aa3ca3 −cb2cb3+c34+ab23c c2 2+)4−a22(ba32 ++4b3b+2cc23−)(8ab2ab3cc2 −c a+2 c43a−2bc22 c+34+bb22cb23c2 ) 2 3

2 3 2 3

2 3 2 3

2 3

3 2

2 3

2 3 2 3

3 2

3 2

(4)

(5)

(6)

And radius of circle is

(

r = x2 + y2 + z2

)

1 2

(7)

Curvature of point p2 is

k=

1 r

(8)

Curvature of discrete points can be computed by formulary(4),(5),(6),(7),(8). Curvature of every point is related to three points, so that if one point is wrong, three points curvature will change. Computation result correlate with the least scanning unit, the smaller the least scanning unit is, the better computation result will be. This thing will be incarnated in experiment. Upper curvature formulary are fit for discrete points of accurate position.

3 Experiment In this part two questions will be validated: whether discrete curvature is nearest to continuous curvature. Experiment 1 right-handed screw line curvature estimation. The aim of this experiment is that whether discrete curvature is nearest to continuous curvature. Firstly, get one well known line which we can get accurate curvature of every point of this line. Secondly, get a series of discrete points from right-handed screw line. Thirdly, compute accurate curvature use curvature formulary of continuous curve, and respectively compute estimation curvature by method in this paper and method in literature[8]. Quintic B-spline was used to approximate curve to compute curvature of discrete points of curve in literature[8]. Continuous curve is right-handed screw line: x=acost, y=asint, z=bt

-∞
(9)

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Accurate curvature of right-handed screw line is constant. In table 1 t step-length is 0. 1 and in table 2 t step-length is 0.5, content of table 1 and in table 2 include in: accurate curvature, estimation curvature computed by method in this paper and estimation curvature computed by method in literature[8]. Table 1. When t step-length is 0.1 curvature compare

Accurate Method in this paper Method in literature

1st Point 0.0551724 0.0551467

2nd point 0.0551724 0.055147

3rd point 0.0551724 0.0551464

4th point 0.0551724 0.0551467

5th point 0.0551724 0.0551469

0.00890948

0.0230731

0.0289741

0.0369364

0.0436521

Table 2. When t step-length is 0.5 curvature compare

Accurate Method in this paper Method in literature

1st Point 0.0551724 0.0545298

2nd point 0.0551724 0.0545298

3rd point 0.0551724 0.0545298

4th point 0.0551724 0.0545298

5th point 0.0551724 0.0545298

0.172027

0.0242362

0.0718709

0.0668149

0.0521045

a. t step-length is 0. 1 curvature compare Fig. 2. Curvature compare

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b. t step-length is 0.5 curvature compare Fig. 2. (continued)

Experiment manifest that curvature curve by method in this paper well tally accurate curvature curve; but curvature curve by method in literate[8] is different with accurate curvature curve. Especially, when the smaller t is, the better curvature curve by method in this paper tally accurate curvature curve. Data by method in this paper manifest that when t step-length is 2, relative error is 0.187; when t step-length is 0.5, relative error is 0.011647; when t step-length is 0.2, relative error is 0.00186; when t step-length is 0.1, relative error is 0.000464. In a word, experiment result manifest that method in this paper has the advantage over method in literature[8]; when step-length is small, curvature by method in this paper is close to accurate curvature; the larger step-length is, the bigger difference between curvature by method in this paper and accurate curvature will be. Computation work by method in this paper compare with computation work by method in literature[8]: computation work in this paper is O(n), computation work in literature[8] is O(n3). Underside is a practice application of method in this paper. Aim object is a piece of chinaware fragment, sampling equipment is three dimension scanner LSH800. The least sampling unit is 0.5 millimeter. Edge detection method will present in another paper. Here, detect 380 edge points, the result is in figure 5.

Accurate Curvature Approximation of 3-Dimension Discrete Points

Fig. 3. Scanning points show by CATIA

Fig. 4. Inner edge curve

Fig. 5. Curvature space of inner edge curve

From upper figure.5, method in this paper is little sensitive to noise.

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4 Conclusion Firstly, method in this paper prove to be feasible in theory. When discrete point position is accurate, a three dimension discrete point curvature approximation method based on geometric property is given. Veracity experiment and application experiment are done. Experiment result and analysis manifest that method in this paper either in work or in curvature veracity has advantages over method in literature [8]. Method in this paper is fit for all discrete points curvature approximation which belong to some curve.

References 1. An, G.L.: Analysis of Feature Detectability from Curvature Estimation. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 235–240. Michigan, Ann Arbor (1988) 2. Rosenfeld, A., Weszka, J.S.: An Improved Method of Angle Detection on Digital Curves. IEEE Transactions on Computers 24(9), 940–941 (1975) 3. Václav, H., Tomás, P., Milos, S.: Improvement of the curvature computation. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition Computer Vision and Image Processing, Jerusalem, pp. 536–538 (1994) 4. Coeurjolly, D., Miguet, S., Tougne, L.: Discrete curvature based on osculating circle estimation. In: Arcelli, C., Cordella, L.P., Sanniti di Baja, G. (eds.) IWVF 2001. LNCS, vol. 2059, pp. 303–331. Springer, Heidelberg (2001) 5. Li, X.: On the Irregular planar Fragments Matching (2006) 6. Nguyen, T.P., et al.: Curvature and torsion estimators for 3D curves. In: 4th International Symposium On Advance in Visual Computing, pp. 688–699 (2008) 7. Wu, J., et al.: Estimating curvatures for point-sampled surfaces. Chinese Journal of Scientific Instrument 27(12), 1557–1562 (2006) 8. Zhu, Y., et al.: Algorithm for Three-Dimensional Fragments Reassembly. Journal of Image and Graphics 12(1), 164–170 (2007) 9. Mei, X., et al.:Differential geometry. People’s education publishing house (1981)

The Research of a Symmetrical Component Method and Dynamic Reactive Power Compensation of Electric Arc Furnace Xuezhe Che, Fenglong Shen, and Jianhui Wang College of Information Science and Engineering, Northeastern University, Shenyang 110004, Liaoning Province, China [email protected], [email protected], [email protected]

Abstract. Traditional reactive power compensation for electric arc furnace (EAF) to be measured phase angle, the problem can not achieve real-time compensation, finds a real-time symmetrical component method for calculating the value of reactive power of the system and its application in the EAF for the control system. Simulation results show that: the method of reactive power compensation can be achieved with no time delay, thus ensuring the stability of reactive power to solve the reactive power fluctuation of voltage fluctuations. Keywords: Electric arc furnace, Dynamic reactive power compensation, Instantaneous reactive power, Symmetrical component method.

1 Introduction In recent years, with the capacity of EAF increased, the disturbances on the grid are increasingly serious. However, it often appears the short-circuit phenomenon when the charge is melting, so the reactive power consumption and reactive current amplitude are larger, power consumption is greatly increased and the voltage fluctuation is appeared. In addition, the high harmonic currents generated by arc can cause supply voltage waveform distortion, three-phase asymmetrical load changes and flicker etc. Also, since changes in the length of each phase arc are inconsistent, it will cause harmonics current [1]. To curb the influence of the voltage distribution networks cause by EAF operation [2], the domestic and foreign scholars have proposed a number of reactive power compensation methods. At present, the reactive power compensation method based on instantaneous reactive power theory is widely used, the real-time compensation effect in three-phase balance system is better, but it will cause the compensation delay and control error in unbalanced three-phase system. The key of this paper lies on using instantaneous symmetric component method to realize the real-time reactive power compensation for the Static Var Generator (SVG). M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 811–818. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 The Theoretical Basis of Reactive Power Compensation 2.1 The Instantaneous Reactive Power Theory In order to make the operation of the SVG device reliable, the detection of reactive current speed should meet the requirements of fast and real-time. Traditional power calculations need to measure the phase angle, and it can be calculated based on the vector, so the digital measurement system can not calculate the system real-time power. In early time, analog filters have been used to detect the reactive and harmonic current, and later with the development of electronic technology, Fourier analysis method is used to detect the reactive current and harmonic current. But for these methods, some are very sensitive to the parameter variation on power system components, the error is relatively large; Some have longer time delay for the reason of large amount of calculation , also, the real time of detection is bad[3]. Since instantaneous reactive theory was proposed in 1980s, it had a successful application in many ways, especially in reactive current detection aspects it has obvious advantages. To break through the traditional definition of power which is based on average value, the theory defines the instantaneous reactive power and instantaneous active power systematically. Through continuous improvement, it has appeared p, q operation mode and ip, iq operation mode, both of two operation modes are used to detect the reactive current[4]. Using p, q operation mode or ip, iq operation mode in three phases balance system the rms of active and reactive power that under the traditional definition of power system can be obtained in real time. When the instantaneous reactive power theory is applied to three-phase unbalanced systems, p, q are not direct current component, but to the sine wave with a direct current component, it does not match with the traditional power definition. In order to get the amount of reactive power compensation, it should use a low pass filter, but it is not conducive to real-time analysis. 2.2 Instantaneous Symmetrical Component Method How to apply the theory of instantaneous reactive power in the three-phase imbalance system and obtain the same result with the traditional definition of power? Instantaneous symmetrical component method has provided a way to solve this problem. The principle of symmetrical component method is decomposed the three-phase unbalanced system into three balanced system that are positive sequence, negative sequence and zero sequence, At the same time gave the analysis respectively [5] [6] [7]. The traditional symmetric component method is still based on the phasor, it needs to measure the phase angle of the voltage and current signal, the transformation of positive and negative sequence can not be done in real time using its instantaneous value, only the zero sequence components can be instantaneous obtained. Thus the real-time advantage of instantaneous reactive theory will be offset. Instantaneous symmetric component method makes full use of the real-time of instantaneous value to complete the transformation of positive, negative, zero sequence, and obtain the reactive power of the system. Let u = ua + ju β represents the vector on the coordinate ( u is vector), Vectorgraph of three-phase voltage on the α , β coordinate is shown as following.

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Fig. 1. Vectorgraph of three-phase voltage on the α , β coordinate

If the three-phase voltage is positive sequence fundamental voltage, then

u = ua + juβ is the vector that rotating anti-clockwise with angular velocity ω on α , β coordinate, and it can be denoted by u+ ; if the three-phase voltage is negative sequence fundamental voltage, u = ua + juβ is the vector that rotating clockwise with angular velocity ω on α , β coordinate, and it can be denoted by u− ; if the threephase voltage sequence components have both positive and negative sequence components, then u = ua + juβ is the elliptical orbitat the oval track on the αβ coordinate, which is synthesized by the vector that rotating anti-clockwise with angular velocity ω and the vector that rotating clockwise with angular velocity ω . In the time of t and t + Δt ,

⎧u (t ) = u+ (t ) + u− (t ) ⎨ ⎩u (t + Δt ) = u+ (t + Δt ) + u− (t + Δt )

(1)

Without loss of generality, in the time of t , let

u+ (t ) = U + e jϕ + , u− (t ) = U − e jϕ −

(2)

u (t ) = u+ (t ) + u− (t ) = U + e jϕ + + U − e jϕ −

(3)

then,

In the time of t + Δt , the amplitude of the positive sequence vector was unchanged but the angle of the positive sequence vector was anti-clockwise rotated by ωΔt , while the amplitude of the negative sequence vector was unchanged but the angle of the positive sequence vector was clockwise rotated by ωΔt .

u+ (t + Δt ) = U + e j (ϕ+ +ωΔt ) , u− (t + Δt ) = U − e j (ϕ− −ωΔt )

(4)

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then,

u (t + Δt ) = U + e j (ϕ+ +ωΔt ) + U − e j (ϕ− −ωΔt ) =e

jωΔt

u+ (t ) + e

− jωΔt

(5)

u− (t )

According to the equation (1), (2) and (5), we have

⎧ −e − jωΔt u ( t ) + u ( t + Δt ) ⎪u+ (t ) = 2 j sin ωΔt ⎪ ⎨ jωΔt e u ( t ) − u ( t + Δt ) ⎪ = u t ( ) − ⎪ 2 j sin ωΔt ⎩

(6)

Let Δt tends to infinitesimal, then

u+ (t ) = lim

−e − jωΔt u ( t ) + u ( t + Δt ) 2 j sin ωΔt

Δt →0

u− (t ) = lim

e jωΔt u ( t ) − u ( t + Δt )

Δt → 0

2 j sin ωΔt

=

=

1 1 du (t ) u (t ) + × 2 2 jω dt

1 1 du (t ) u (t ) − × 2 2 jω dt

(7)

(8)

As u = ua + juβ , so

⎡1 1 duβ (t ) ⎤ u+ (t ) = ⎢ uα (t ) + × ⎥+ 2ω dt ⎦ ⎣2 = uα + (t ) + ju β + (t )

1 duα (t ) ⎤ ⎡1 j ⎢ u β (t ) − × 2ω dt ⎥⎦ ⎣2

(9)

⎡1 1 duβ (t ) ⎤ × u− (t ) = ⎢ uα (t ) − ⎥+ dt ⎦ 2ω ⎣2 = uα − (t ) + juβ − (t )

1 duα (t ) ⎤ ⎡1 × j ⎢ uβ (t ) + dt ⎥⎦ 2ω ⎣2

(10)

Thus, the positive sequence fundamental component and the negative sequence fundamental component of three-phase voltage can be calculated.

1⎡ 1 1 3 d [ub (t ) − uc (t )] ⎤ ua1 = ⎢ua (t ) − ub (t ) − uc (t ) ⎥ + × 3⎣ 2 2 dt ⎦ 6ω 1⎡ 1 1 3 d [ub (t ) − uc (t )] ⎤ ua 2 = ⎢ua (t ) − ub (t ) − uc (t ) ⎥ − × 3⎣ 2 2 dt ⎦ 6ω

(11)

(12)

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The fundamental positive sequence component and the fundamental negative sequence component of b and c phase can be obtained similar to a phase, and the results are shown as following.

1⎡ 1 1 3 d [ uc (t ) − ua (t )] ⎤ ub1 = ⎢ub (t ) − uc (t ) − ua (t ) ⎥ + × 3⎣ 2 2 dt ⎦ 6ω uc1 =

1⎡ 1 1 3 d [ua (t ) − ub (t ) ] ⎤ uc (t ) − ua (t ) − ub (t ) ⎥ − × ⎢ 3⎣ 2 2 dt ⎦ 6ω

(13)

(14)

3 Simulation of Instantaneous Symmetrical Components Using the instantaneous reactive power theory and the instantaneous symmetric component method can real-time obtain the reactive power of positive, negative, zero sequence. To select for three-phase system as the research object, using MATLAB / SIMULINK simulation to verify the correctness of the theory. The positive and negative sequence reactive power can be obtained by positive and negative sequence voltage and current waveforms that are received from SIMMULINK/PLL model through α , β coordinate transformation and p, q operation mode. After computing the difference between reactive power and the reference value of positive and negative sequence reactive power, PI controller can determined the dynamic reactive power compensation by the difference. Using α , β anti-coordinate transformation and p, q anti-operation mode, the positive and negative sequence reactive current can be obtained. At last, through the addition operation between positive sequence current and negative sequence current, we can have three-phase current value of reactive power compensation. The simulation diagram is shown as figure 2, and sub7 and sub8 are to remove the impact pulse. In three-phase system, for the three-phase electrical quantities (voltage or current) sum to zero, i.e.

f a + fb + f c = 0

(15)

So zero sequence component is zero, it can be ignored. Assuming that the instantaneous electrical quantities of the system is

⎧ f a = sin(ω t + 1.3) ⎪ ⎨ f b = sin(ω t − 2π 3 + 1.3) ⎪f = −f − f a b ⎩ c

(16)

Where ω represents the power system angular velocity and ω = 100π . The current and voltage amplitude of a phase is change into 0.8 at the moment of 0.04 second. The power positive sequence component waveforms of active power and reactive power are real-time obtained which is shown in Figure 3.

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Fig. 2. The Simulation diagram of three-phase reactive power compensation.

Fig. 3. The power waveform of positive sequence component

p1, q1 respectively represent active power and reactive power of positive sequence component. From figure 3, it can be seen that when the voltage or current state of power system changes, using the above method can obtain the corresponding power changes in real time. The shock pulse in figure 3 is caused by the differential link of instantaneous symmetric component.

4 Design of SVG Control System Making the reactive power of EAF as the control object and controlling stability of the reactive power as the goal reduce reactive power fluctuation in EAF system and

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influence of power system voltage, using PI control mode to produce the reactive power compensation current. Control block diagram is shown in Figure 4.

Fig. 4. Control block diagram

In figure 4, Q1ref is a reference reactive compensation value of positive sequence , Q2ref is a reference reactive compensation value of negative sequence. The aim of reactive compensation is to make reactive power of system side stable in this value as far as possible, or within permitted range. Still using the above example, to facilitate the simulation, it is necessary to assume that the voltage and current amplitude of a phase is changed at the moment of 0.04 second. The waveform that synthesized by the positive sequence compensating current and negative sequence compensating current is shown as figure 5.

Fig. 5. Compensation current waveforms

From the simulation waveform, it can be seen that the response to mutations in power system state of the control system is less than half a cycle, which is the great advantage of control strategy, and the delay is mainly caused by PI control section. Accompanying with changes of the system state, there has been existed the impact of

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pulse. However, the SVG control system, which is composed by digital signal processing chip (DSP), can reduce operation errors of possibilities by limiting amplitude.

5 Conclusion Reactive power fluctuation of EAF will cause voltage fluctuation and flicker, so it is necessary to maintain the stability of reactive power of the EAF control system. Compared with the traditional reactive power compensation method, there is no time delay when calculate the reactive power by using the real-time compensation method of this paper, and the computing speed is faster. It has great significance to improve the stability of EAF system and reduce the voltage fluctuation.

References 1. Shukai, X., Qiang, S., Wenhua, L., Luyuan, T.: Research on the Power Quality Problems and Compensation Scheme for Electric arc Furnace in Distribution Supply System. Proceedings of CSEE 27(7), 93–98 (2007) 2. Mao, T.: Study on the Static Var Generator in the Low Voltage Based on DSP, pp. 9–18. Guangxi University (2005) 3. Zhaoan, W., Qun, Y., et al.: Compensation of Harmonic Suppression and Reactive Power. China Machine Press, Beijing (2006) 4. Hirofumi, A., Yoshihira, K., Akira, N.: Instantaneous reactive power compensators comprising switching devices without Energy Storage Components. IEEE Transactions on Industry Application 20(3), 625–630 (1984) 5. Hui, W., Xianrong, C.: A method based on improved instantaneous symmetrical components and three-point algorithm for synchronized phasor measurement. In: 2010 5th International Conference on Critical Infrastructure (CRIS), pp. 1–5 (2010) 6. Lingyu, Z., Limin, L.: Application of the symmetrical components method in the polyphase asymmetrical AC systems. Journal of Changsha Telecommunication and Technology Vocational College 8(1), 62–64 (2009) 7. Xufeng, Y., Shijie, C., Jinyu, W.: An Improved Method of Instantaneous Symmetrical Components and Its Detection for Positive and Negative Sequence Current. Proceedings of the CSEE 28(1), 52–57 (2008) 8. Feifeng, J., Khan, M.M., Chen, C.: Static Var Compensator Based on Rolling synchronous symmetrical component method for Unbalance Three-phase System. In: IEEE International Conference on Industrial Technology, ICIT 2005, pp. 621–626 (2005)

Numerical Simulation of Electrokinetic Flow in a Nanotube with Variable Physical Properties Davood D. Ganji, Mofid Gorji-Bandpy, and Mehdi Mostofi Faculty of Mechanical Engineering, Babol Noshiravani University of Technology, Babol, Iran, P.O. Box 484 [email protected], [email protected], [email protected]

Abstract. In recent decades, after introducing micro- and nano-fabrication technologies, several possibilities in the case of micro- and nano-fluidic devices have been invented. This idea has been followed by some modern technologies such as Lab-on-a-Chip. In this paper, an electrokinetic flow of a water electrolyte in a nano-channel will be studied. This study will be with existence of the Electric Double Layer (EDL). Governing equation for the EDL is PoissonBoltzmann. In addition, Navier-Stokes equations for electrolyte flow, Species and mass conservation equations are in use. Induced electric potential force the electrolyte ions and decrease the mass flow rate. The electrical potential at the solid–liquid surface is experimentally difficult to measure. As a result, the electrical potential at the shear plane, the edge of the inner layer, is called the zeta potential. In the case of large zeta potential, numerical solution is needed. In this paper, after numerically solve the Poisson-Boltzmann equation, effect of temperature rise on the physical properties and consequently, on the velocity and potential distribution is investigated. According to the achievements of this paper, if analytical solution will be employed for large zeta potentials, significant errors will be occurred near tube walls and consequently, numerical solution is strongly recommended. In addition, as temperature varies, both velocity and potential distributions especially the latter will be affected. This important phenomenon should be considered in nano-tube design and fabrication, so that, it may help in some engineering cases. Keywords: Electrokinetics, Large Zeta Potential, Variable Temperature, Poisson-Boltzmann Equation.

1 Introduction In recent decades, after introducing micro- and nano-fabrication technologies, several possibilities in the case of micro- and nano-fluidic devices have been invented. This idea has been followed by some modern technologies such as Lab-on-a-Chip. One of the most important subsystems of the micro- and nano-fluidic devices is their passage or “Micro- and nano-channel”. Nano-channel term is referred to channels with hydraulic diameter below 100 nm [1]. By decrease in size and hydraulic diameter some of the physical parameters such as surface tension will be more significant while they are negligible in normal sizes. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 819–825. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Concentrating surface loads in liquid – solid interface makes the EDL to be existed. Generally, the potential at the edge of the inner layer (Stern layer) is called zeta potential. If the loads are concentrated in the end of nano-channels, a potential difference will be generated that forces the ions in the nano-channel. Rice and Whitehead [2], Lu and Chan [3] and Ke and Liu [4] studied the flow in capillary tube. None of them solved the problem based on the curvilinear coordinates system. Also, all of them studied the problem with existence of the pressure gradient while in the modern applications, the pressure gradient can be eliminated and consequently, solving the problem considering this fact is necessary. In addition, there are a few available articles that show the effect of using large zeta potentials. In this case, Chang et. al. [5] studied this nonlinear problem for a case study, polystyrene particles. Also, Kang and Suh [6] and Wang and Kang [7], investigated the roughness effects on the problem with large zeta potential and consequently, strong nonlinear problem. Furthermore, very rare scientists research on the temperature variation effects on the achievements of electrokinetically driven flows. Tang et. al. [8] and Chein et. al. [9] studied the electrokinetic flows with variable temperature that was based on the Joule heating effects. In this paper, velocity profiles for large zeta potentials without pressure gradient will be studied based on the curvilinear coordinates in a capillary tube. In advance, results will be compared with the data collected by analytical solution for small zeta potential in [10]. At last, physical properties will be assumed to be variable in order to investigate the effect of temperature variation on potential and velocity distribution.

2 Mathematical Modeling Governing equations in electroosmotic phenomena are mass conservation (Eq. 1), species conservation (Eq. 2), Navier-Stokes (Eqs. 3-5) and Poisson-Boltzmann (Eq. 6) equations [11] as follow: ε1

∂u ∂v ∂w + +ε2 =0 ∂x ∂y ∂z

⎛ 2 ∂2 X ∂2 X ∂2 X ⎞ ⎜⎜ ε1 + 2 + ε 2 2 2 ⎟⎟ = 2 ∂x ∂y ∂z ⎠ ⎝ ∂(X E y ) ⎡ ⎛ ∂( X E x ) ∂X ∂X ∂ ( X E z )⎤ ∂X +α y +v +ε2 αz +ε2 w ⎥ + Re Sc ⎜⎜ ε1 u ⎢ε1 α x ∂x ∂x ∂y ∂y ∂z ⎦ ∂z ⎣ ⎝

⎛ ∂u ∂u ∂u Re ⎜⎜ ε 1 u + v + ε 2 w ∂z ∂y ∂x ⎝

⎞ ⎟⎟ ⎠

(2)

(3)

⎞ ∂p F c ϕ 0 ∂ϕ ⎟⎟ = − + (X p − X n ) + ∇ 2 v ∂y μ U 0 ∂y ⎠

(4)

F c ϕ 0 ∂ϕ ⎞ ∂p ⎟⎟ = − ε 2 (X p − X n ) + ∇ 2 w +ε2 μ U 0 ∂z ∂z ⎠

(5)

⎛ ∂v ∂v ∂v Re ⎜⎜ ε 1 u + v + ε 2 w ∂x ∂y ∂z ⎝ ⎛ ∂w ∂w ∂w Re ⎜⎜ ε 1 u +ε2 w +v ∂z ∂y ∂x ⎝

F c E x E0 Y 2 ⎞ ⎟⎟ = − (X p − X n ) + ∇ 2 u μU0 ⎠

(1)

Numerical Simulation of Electrokinetic Flow in a Nanotube

ε 2 ∇ 2ϕ = − β (X p − X n )

In the above equations, x, y and z represent non-dimensional coordinates,

ε2

821

(6)

ε 1 and

are relations of the reference normal coordinates to reference of nanometric coordinate, E is electrical strength, Re and Sc are Reynolds and Schmidt numbers respectively, α ’s and β are flow parameters, X is species ratio (either positive ion or negative ion), u, v and w are non-dimensional velocities, ε is Debye-Huckel parameter,

εe

is dielectric constant of the electrolyte, R is universal gas constant, T is tem-

perature, F is Faraday constant, U0 is free stream velocity, ϕ is non-dimensional potential distribution and μ is electrolyte viscosity. In addition, several simplifications will be applied on all of the above equations based on order of magnitude analysis, existence of only one nanometric coordinate and uniform electrical current in only one direction. After translations of the achieved set of equations into curvilinear ones, the following set of equations will be achieved: 1 ∂ ⎛ ∂ϕ ⎞ − β (X p − X m ) ⎜r ⎟= r ∂r ⎝ ∂r ⎠ ε 2

(7)

1 ∂ ⎛ ∂u ⎞ − β ε e E0 R T (X p − X m ) ⎜r ⎟= r ∂r ⎝ ∂r ⎠ ε 2 F μ U 0

(8)

⎡ ⎛ ∂X p ∂ϕ ⎞ ⎤ ⎟⎥ = 0 +α X p ⎢r ⎜⎜ ∂r ⎟⎠⎦ ⎣ ⎝ ∂r

(9)

∂ϕ ⎞⎤ 1 ∂ ⎡ ⎛ ∂X m r⎜ −α X m ⎟ =0 ∂r ⎠⎥⎦ r ∂r ⎢⎣ ⎝ ∂r

(10)

1 ∂ r ∂r

It should be noted that, Eqs. 9,10 can be decoupled by bulk assumption. In this case, according to the literature [12], equations extracted from the Eq. 2 will be solved analytically. Fig. 1, shows a typical solution of (Eqs. 9,10) for Sodium Chloride ions.

Fig. 1. Distribution of the ion species based on bulk assumption.

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As a result, two major equations should be solved: 1 ∂ ⎛ ∂ϕ ⎞ sinh ϕ ⎟= 2 ⎜r r ∂r ⎝ ∂r ⎠ ε

(11)

1 ∂ ⎛ ∂u ⎞ sinh ϕ ε e E0 R T ⎜r ⎟= r ∂r ⎝ ∂r ⎠ ε 2 F μU0

(12)

In small amount of zeta potential, we can assume sinh ϕ = ϕ and consequently, problem will be solved analytically [10], but in this paper, this assumption is no longer valid. As a result, numerical simulation has been employed. By using finite difference method [13] powered by Newton-Raphson algorithm [14], flow and velocity fields have been obtained through nano-tube.

3 Results and Discussion According to the approach mentioned in previous section, (Eqs. 11,12) will be solved by the following boundary conditions: du (13) @ r = 0 ⇒ u =U 0 , = 0 , @ r = 1⇒ u = 0 dr @ r =0⇒

dϕ = 0 , @ r =1⇒ ϕ = ϕ 0 dr

(14)

which ϕ 0 represents potential value in the solid-liquid interface. Most of the researchers call this value as zeta potential [2-6,11]. This assumption has very good consistency in most of the applied cases. Convergence criterion in this solution was 1E-7 difference between the iterated values of potential and velocity. This amount of error is too smaller than the needed one, because in the nano scale, many simplifications will be made and consequently, we naturally cannot improve as any value as we are interested in. Figs. 2 and 3 show potential and velocity distributions in the case studied Nanotube. As both of this figures mention, plug flow is the pattern for both of them as expected.

Fig. 2. Normalized distribution of potential as a function of normalized radius.

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Fig. 3. Normalized velocity profile as a function of normalized radius.

In continue, the main part of the paper contribution starts. Temperature assumed to be variable in the range of liquid water (0 to 99.6oC). In this case, some of the electrolyte (water) properties will be variable such as viscosity μ and dielectric constant ε e . Variations of these parameters are investigated based on [15, 16]. About dielectric constant variation over temperature, it is noted that, it is assumed all phenomena are in standard pressure (p = 100 kPa constant). By these considerations, simulations are done. In Fig. 4, potential distribution in four specific temperatures is investigated. As it can be considered, no very significant effect is made due to temperature variation in potential distribution (around 20% from 20oC to 80oC). it is clear that, except in the near wall area, most of the tube has no significant sensibility to temperature variation. As it can be inferred from Fig. 5, considerable variations will be made according to temperature and consequently, physical properties variation (more than three times from 20oC to 80oC). In this case, it is clear that, by variation of the temperature, quality of the mass transfer will be significantly affected and consequently, between these two investigated parameters, velocity plays an important role as the controlling parameter of electrokinetic flows. As it can be seen in this figure, rate of the increase in Reynolds number is not absolutely smooth. This is because of the temperature variations on both fluid velocity and viscosity. In order to have a better view about the flow regime in these cases, Fig. 6 shows the effects of temperature variations on Reynolds number.

Fig. 4. Potential distribution over nano-tube in different temperatures ( ϕ 0 = 0.025 V).

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Fig. 5. Velocity distribution over nano-tube in different temperatures.

Fig. 6. Temperature variations effect on Reynolds number in nano-tube.

4 Conclusion In this paper, electrokinetic phenomena in nanotube with large zeta potential have been investigated. Poisson-Boltzmann and simplified Navier-Stokes equations have been solved numerically. Ion species distributions were obtained based on bulk assumptions (Fig. 1). In addition, velocity and potential distributions were solved numerically through FDM method powered by Newton-Raphson algorithm (Figs. 2 and 3). Another achievement of this investigation is that, temperature variation has some effects on velocity and potential distributions in a nanotube especially in the case of velocity distribution (Figs. 4 and 5). In addition, if water electrolyte can safely works in temperatures near boiling point, Reynolds number significantly increases. This fact can be employed in nano-electromechanical devices (Fig. 6).

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References 1. Kandlikar, S., Garimella, S., Li, D., Colin, S., King, M.R.: Heat Transfer and Fluid Flow in Minichannels and Microchannels. Elsevier Limited, Oxford (2006) 2. Rice, C.L., Whitehead, R.: Electrokinetic Flow in a Narrow Cylindrical Capillary. J. Phys. Chem. 69, 4017–4023 (1965) 3. Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999) 4. Keh, H., Liu, Y.C.: Electrokinetic Flow in a Circular Capillary with a Surface Charge Layer. J. Colloids and Interface Surf. 172, 222–229 (1995) 5. Chang, M.H., Dosev, D., Kennedy, I.M.: Zeta-Potential Analyses using Micro Electrical Field Flow Fractionation with Fluorescent Nanoparticles. Sens Actuators B Chem. 124, 172–178 (2007) 6. Kang, S., Suh, Y.K.: Electroosmotic Flows in an Electric Double Layer Overlapped Channel with Rectangle-Waved Surface Roughness. Microfluidics and Nanofluidics 7, 337–352 (2008) 7. Wang, M., Kang, Q.: Electrokinetic Transport in Microchannel with Random Roughness. Anal. Chem. 81, 2953–2961 (2009) 8. Tang, G.Y., Yang, C., Chai, C.K., Gong, H.Q.: Numerical Analysis of the Thermal Effect on Electroosmotic Flow and Electrokinetic Mass Transport in Microchannels. Analytica Chemica Acta 507, 27–37 (2004) 9. Chein, R., Yang, Y.C., Lin, Y.: Estimation of Joule Heating Effect on Temperature and Pressure Distribution in Electrokinetic-Driven Microchannel Flows. Electrophoresis 27, 640–649 (2006) 10. Ganji, D.D., Gorji-Bandpy, M., Mostofi, M.: Analytical Solution of an Electroosmotic Flow in a Nano-Channel using Curvilinear Coordinates. Int. Rev. Mod. Sim. 3, 202–205 (2010) 11. Karniadakis, G., Beskok, A.N.: Microflows and Nanoflows. Springer, New York (2005) 12. Zheng, Z.: Electrokinetic Flow in Micro- and Nano- Fluidic Components. D. Thesis, Ohio State University, Ohio, USA (2003) 13. Lomax, H., Pulliam, T.H., Zing, D.W.: Fundamentals of Computational Fluid Dynamic. Springer, Heidelberg (2010) 14. Stoer, J., Bulirsch, R.: Introduction to Numerical Analysis. Springer, Berlin (1993) 15. Wylie, V.L., Streeter, E.B.: Fluid Mechanics: First SI Metric Edition. Mc-Graw Hill, New York (1983) 16. Uematsu, M., Franck, E.U.: Static Dielectric Constant of Water and Steam. J. Phys. Chem. Ref. Data. 9, 1291–1306 (1980)

Physical Properties of Camellia Oleifera Heated by Micro-wave Jian Zhou1, Lijun Li1, Yihua Hu2, Ke Cheng2, Zhiming Yang1, and Ye Xue1 1

Machinery and Electrical Engineering College of Centre South University of Forestry and Technology, Changsha, 410004, P.R. China 2 Lushan college of Guangxi University, Liuzhou,545616, P.R. China zhoujian1134}@163.com

Abstract. For obtaining the target which separates the shell and seed of camellia oleifera rapidly and easily, the article studies physical properties of camellia oleifera while heated by micro-wave. In the practice, water content of shell of camellia oleifera sample A is about 76%, water content of sample A camellia oleifera seeds is about 55%. One hundred camellia oleifera fruits are selected, the quality distribution is obtained. From the test, camellia oleifera shell of specific heat capacity is between 2.81~3.41 J/g. , camellia oleifera seed of specific heat capacity is between 2.11~2.61J/g. .

℃

℃

Keywords: camellia oleifera; water content; quality distribution; specific heat capacity.

1 Introduction Camellia oleifera is one speciality of china,its unsaturated fatty acid can be over 92%, after long eating it can prevent heart blood vessel of brain sickness, such as high blood pressure, angiosclerosis, etc[1]. It is the important raw material of medical treatment and chemical industry production[2]. Sediment of camellia oleosa seed is high quality aquafeed protein source, and the cover has plenty pentosan,cellulose, lignin, they are the ideal material for manufacturing xylose and furfural. Camellia oleifera also can be the raw material for obtaining active carbon, deodorant, so it has very high economic value[3].Micro-wave has high penetrability, it can increase the temperature evenly of camellia oleifera fruit from surface to the inner, the wave energy can change to heat energy in the deep layer of the fruit, and make the deep layer water evaporate to form the higher inner vapor pressure, then separating the camellia oleifera cover from the seed,thus keep the good dried quality,it is better thean the usual heat drying way in this point[4].At the same time,micro-wave can put biological effect on the material,thus killing the germs in short time, for example, ova, escherichia coli and spore, etc[5].

2 Raw Material and Main Equipments Fruit of camellia oleifera, produced by Hunan province,china;the micro-wave stove,the output power is 700W,the wave working frequence 2450MHz,the furnace chamber volume is 21L;PT platinum series micro temperature sensor of hot resistance,the M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 827–832. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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measuring temperature range -30-300 ; infrared index temperature-indicating instrument, the type OPTEX PT-303 electronic balance; digital camera; vernier caliper; measuring glass; crucible. Water content of shell of camellia oleifera is measured, material water content formula as below:

；

x%=

massbegining − massdried massbegining

(1)

Specific heat capacity of camellia oleifera fruit is analysised.Firstly,measuring the water mass mwater,then weighting the fruit mass mfruit,testing water’s beginning temperature twater,then measuring the fruit inner temperature tfruit by the sensor while the micro-wave stove stops;Secondly,putting the heated camellia fruit in the water in crucible immediatly,then covering the crucible, the water has enough time to exchange the heat with water, then measuring the average temperature so called taverage, using the heat balance rule[6],then the following formula can be obtained.

C fruit =

mwater C water (t average − t water ) m fruit (t fruit − t average )

(2)

While measuring the crack length of camellia oleifera fruit, using the blank paper, drawing two points of the crack, then measuring the paper’s points length, thus the crack length can be obtained.

3 Analysis of the Process of the Experiment 3.1 Water Content of Camellia Oleifera In the experiment, the water contents of camellia oleifera shells and seeds are both measured, the selected samples are in random, the quality of camellia relation to the baking time is as Fig1.

Fig. 1. Sample A camellia oleifera out-shell quality relation to heating time graph

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According to formula(1),water content of shell of camellia oleifera sample A is about 76%.For further studying the microwave heating influence on the whole seeds of camellia oleifera, the seeds quality relation to the baking time also is drawn as Fig2.

Fig. 2. Sample A camellia oleifera seed quality relation to heating time graph

According to formula, water content of sample A camellia oleifera seeds is about 55%.In the process of heating camellia oleifera seeds by microwave, when the heating time is 30S,there are a few cracks of shells and nuts of camellia oleifera seeds, this is meaning that a few shells and nuts of camellia oleifera seeds can be separated quickly while heated by microwave, but method should be adopted for getting high whole core rate. 3.2 Quality Distribution of Camellia Oleifera One hundred camellia oleifera fruits are selected, the quality distribution is as Fig3.

33%

23% JEHORZ J J J

36%

8%

Fig. 3. Camellia oleifera fruit quality distribution graph at random

3.3 Crack Types of Camellia Oleifera Whether it is heated by sunshine or heated by microwave, there are 900 seperating (cross) type or near 1200seperating type mainly. Whether shell crack type of

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camellia oleifera is “cross”, “near 1200seperating type” or other types, the cracks locate the most raised place of camellia oleifera seed,Fig4 is one example of the whole camellia oleifera.

Seed

Fruit shell

Natural bottom

Fig. 4. Camellia oleifera cracks location graph

3.4 Specific Heat Capacities of Shell and Seed of Camellia Oleifera Fruit

，

Specific heat capacity of water is 4.2J/g.℃ according to formula (2), specific heat capacities of shells of camellia oleifera fruits can be obtained, shown as Tab1. From the repeated tests, specific heat capacities of shells of camellia oleifera fruits are between 2.81~3.41 J/g.℃. Specific heat capacities of seeds of camellia oleifera fruits shown as Tab2,the values between 2.11~2.61 J/g.℃. Table 1. Different specific heat capacities relation to different shells of camellia oleifera fruits

Twater Mwater ˄ć˅ (g)

T average ˄ć˅

Tfruit ˄ć˅

Mfruit ˄g˅

1

9.0

97.72

19.8

93.0

21.55

2.81

2

8.0

101.38

22.5

101.1

26.36

2.98

3

8.1

316.89

15.1

100.1

35.36

3.10

4

8.0

356.1

12.2

90.5

25.55

3.14

5

9.8

421.82

14.8

101.1

31.58

3.25

6

10.0

340.0

13.0

60.0

26.73

3.41

No

shell specific heat capacity˄J/g.ć˅

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Table 2. Different specific heat capacities relation to different seeds of camellia oleifera fruits

Twater Mwater ˄ć˅ (g)

T average ˄ć˅

Tfruit ˄ć˅

Mfruit ˄g˅

1

9.0

97.72

19.8

93.0

28.16

2.15

2

8.0

101.38

22.5

101.1

33.00

2.38

3

8.1

316.89

15.1

100.1

41.99

2.61

4

8.0

356.1

12.2

90.5

38.02

2.11

5

9.8

421.82

14.8

101.1

45.62

2.25

6

10.0

340.0

13.0

60.0

41.06

2.22

No

shell specific heat capacity˄J/g.ć˅

4 Conclusion 1)Water content of shell of camellia oleifera fruit is about 76%, water content of seed of camellia oleifera fruit is about 55%; 2)After heated by microwave, crack types of shells of camellia oleiferas are regular,the main are 900seperating or cross type, near 1200 seperating type, etc; 3)Specific heat capacities of shells of camellia oleifera fruits are between 2.81~3.41 J/g.℃. Specific heat capacitiies of seeds of camellia oleifera fruits values between 2.11~2.61 J/g.℃; 4) Shell of camellia oleifera will be soften soon by microwave, the seed and shell will separate easily after rubbing, so it is a good way for separating camellia oleifera shell and seed heated by microwave. Acknowledgement. "Eleventh Five-Year" plan supported by key project of Chinese National Science and Technology (2009BADB1B0304-5)；The national natural science foundation of china(30972399); Scientific research financial project aided by education department of Hunan Province(09C1017).

References 1. Vander Wall, S.B.: The evolutionary ecology of nut dispersal. Bot. Rev. 67, 74–117 (2001) 2. Zhang, H., Smith, P., Adler-Nissen, J.: Effect of Degree of Enzymatic Interesterification on the Physical Properties of Margarine Fats: Solid Fat Content, Crystallization Behavior, Crystal Morphology and Crystal Network. J. Agric. Food Chem. 52, 4423–4431 (2004) 3. Xiao, Z.S., Jansen, P.A., Zhang, Z.B.: Using seed-tagging methods for assessing post-dispersal seed fate in rodentdispersed trees. Forest Ecol Manage 223, 18–23 (2006)

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4. Vander Wall, S.B.: Effects of seed size of wind-dispersed pines(Pinus) on secondary seed dispersal and the caching behavior of rodents. Oikos 100, 25–34 (2003) 5. Miyawaki, A.: Restoration of urban green environments based on the theories of vegetation ecology. Ecological Engineering 11, 157–165 (1998) 6. Zhou, J., Hirata, Y., Shiotani, H., Ito, T.: Studies on the chimeras in Citrus I. Molecular analysis of the chimeras between ‘Kawano natsudaidai’ and ‘Fukuhara orange’. Breed Res. 3, 831 (2001)

Research on Trust-Based Dynamic Role Access Control Model Dongping Hu1,2,*, Guohua Cui1, Aihua Yin2, and Liang Chen1 1

Laboratory of Information Security, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China 2 School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, Jiangxi, China [email protected]

Abstract. In the grid environment, the peer’s behavior is uncertainty and dynamic which make the traditional Role-Based Access Control Model(RBAC) cannot effectively identify various malicious behaviors of peer. We introduce trust mechanism into RBAC Model, and dynamically grant access authority through value of peer’s general trust. Experiments show the feasibility and benefit of our approach. Keywords: Grid environment, Trust, Dynamically access control.

1 Introduction Access control is an important mean for system security and can be used to prevent unauthorized access. Role-based access control is a hot topic in information security area. The basis thoughts of RBAC are as follow: the permission is correlated with role. Peer obtains the role’s permission when he is regarded as a member of corresponding role. Actually, role is a permission set related to special job. Peer’s behavior has the traits of uncertainty and dynamic in grid environment, that is, when and where the peer accesses to resources are uncertainty. It brings system a lot of security issues. Once role is defined, the corresponding permission is certainty in traditional RBAC model. If the RBAC is applied directly in grid environment, it will make access control mechanism of system inflexible. We introduce trust mechanism into RBAC model, and dynamically grant access authority through value of peer’s general trust. Two basic trust parameters in computing general trust of a peer are introduced, namely, recommended trust and transaction trust. The formula of general trust is intensively described in this paper.

*

Corrresponding author.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 833–838. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Trust Evaluate We identify three important parameters for such evaluation: z Recommended trust rt ( a, b) : denotes how peer a is more trustworthy in terms of the collective evaluation by the peers who have had transactions with peer a . Peer b can derive trust relationship with peer a to determine whether to perform the next transaction with peer a , z Transaction trust tt ( a, b) : denotes the feedbacks in terms of amount of satisfaction peer a receives regarding its service comes from the transactions peer b have had with peer a and reflects how well peer a has fulfilled its part of the service agreement and z General trust r ( a ) : weighting accumulated generated by rt ( a , b ) and tt ( a , b ) .

Peer a ’s permission is defined by r ( a ) . 2.1 Recommended Trust Metric

Let common ( a , b ) denote the peers who had performed with both peer a and peer b in the past period, common( a , b ) denote the total number of such peers, I ( a , x ) denote the total number of transactions performed between peer a and peer x , I (b , x ) denote the total number of transactions performed between peer b and peer x , S ( a , x , i ) denote the satisfaction value peer a receive from peer x in its i th transaction, S (b, x , i ) denote the satisfaction value peer b receive from peer x in its i th transaction, The satisfaction value a peer receives from another peer during a transaction is simply a statement from the latter regarding how satisfied he feels about the quality of the information or service provided by the former. The recommended trust value peer a has over the peer b , denoted by rt ( a, b) , is defined as:

rt (a, b) = 1 −

⎡ ∑ iI=(1a, x ) S (a, x,i) ∑ iI=(1b, x) S (b, x,i) ⎤ − ∑ x∈common(a,b) ⎢ ⎥ I ( a , x ) I (b, x) ⎣ ⎦ common(a, b)

2

(1)

.

Peers can rate satisfaction value each other after each transaction at 4 levels: 1 means very satisfaction, peer is trustworthy; 0.5 means quite satisfaction, peer can trust; 0 means general satisfaction, peer can trust or not trust; -1 means dissatisfaction, peer is far less credible. Table 1 summarizes the satisfaction value. The metric consists two parts. The right side of subtraction sign is the difference between the average amount of credible satisfaction peer a receives from peer x and the average amount of credible satisfaction peer b receives from peer x . If a peer is peer a ’s accomplice and send high satisfaction value to peer a for complicity, value of the right side of subtraction sign would be high and the final result of

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rt ( a, b) would be low. Instead of increasing the trust value of peer a , its credibility is decreased. In this way, the complicity threat can be restrained. If the peers who had performed with both peer a and peer b are the members of collusive circle peer a and peer b inside in, the recommended trust value is sure to high between these members and then value of the right side of subtraction sign would be low and the final result of rt ( a, b) would be high. This situation is fit for the fact. That is, the members of the same collusive circle have higher credibility each other. Table 1. Satisfaction Rank

Satisfaction VerySatisfaction

Value

1

QuiteSatisfaction

GeneralSatisfaction

0.5

0

Dissatisfaction

-1

2.2 Transaction Trust Metric

Let α i denote the time attenuation factor. The transaction trust value peer a has over the peer b , denoted by tt (a, b) , is defined as: I ( a ,b )

∑ S ( a , b, i ) ∗ α

tt (a, b) =

i =1

I ( a, b)

i

.

(2)

The reference value of satisfaction value decays with time, that is, the earlier satisfaction value is submit, the less affects the value of transaction trust. In a grid community, one may wish to use the recent transaction history of a peer and at the same time consider the historical ratings a peer receives in the past but with a lower weight than the recent history in order to evaluate the peer based on its consistent behavior. But most systems lack temporal adaptability by either counting all the transaction history of a peer without decaying his importance of old transactions in the far past or only count the recent transactions. This historical behavior of a peer is one type of community context that is important to be incorporated into the trust model to give the trust system a temporal adaptability. Let +t denote the interval between the current time and the i th transaction happened time. +t is computed by the day. The time attenuation factor, denoted by α i , is defined as: i i

α= 1 i

+t +1 i

.

(3)

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2.3 General Trust Metric

The general trust, denoted by r ( a ) , is defined as:

r (a ) = λ ∗

∑

x∈u ( a )

rt (a, x )

u (a)

+ (1 − λ ) ∗

∑

x∈u ( a )

rr (a, x )

u (a)

.

(4)

Where u ( a ) is the peers who had performed with peer a , u ( a ) denotes the total number of such peers. λ is a weight factor( 0 ≤ λ ≤ 1 ). 2.4 Trust Initialization

If a peer is a new comer of grid community without any transaction history, we set his recommended trust value to 0 and give him a period of testing time. During this period, other peers of the same community have to manage the risk involved with the transactions without prior experience and knowledge about new comer’s reputation. They can choose less important transactions and perform with new peer.

3 Dynamic Role Access Control Process Different from traditional access control model, the trust-based dynamic role access control model we proposed has trust management database and trust management server. Trust management database is used to store satisfaction value all peers receive and three kinds of trust of each peer. Trust management server is a control part for computing trust according to approach we proposed above. The dynamic role access control process is as follow: 1) 2) 3) 4) 5) 6) 7) 8)

Before access to resource or service, peer interacts with certificate authority by submitting certificate and request permission firstly; Certificate authority queries the database, confirms peer’s role and its correlated permission; When the role has permission to resource, certificate authority signs authorization and sends it to peer; Peer with corresponding role access to resource and performs transaction, rate each other after finish transaction; All satisfaction values will be stored in trust management database; Trust management server computes the latest general trust value of each peer with the data stored in trust management database; The latest general trust value will be transferred to trust management database and modify the previous one; Certificate authority redefines the role’s permission according to the latest general trust value.

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4 Experimental Evaluation We set the total number of peers to 128. The total number of untrustworthy peers is set to 16. These untrustworthy peers make false statements about peer a ’s service many times due to jealousy or other types of malicious motives. After 240 malicious behaviors in the community, we then compute the trust evaluation accuracy of peer a . For comparison purpose, we use the complaint-based trust method described in [4] as an example of the conventional method. The method uses the number of complaint sonly as the trust measure. It only supports a binary trust output, i.e. whether the peer is trustworthy or not. We refer to this method as the complaint-only approach.

Fig. 1. Trust Evaluation Accuracy with Malicious Behavior

Figure 1 represents the trust evaluation accuracy of the two models with respect to the malicious behavior number in the community. We can make a number of interesting observations. First, out approach and the complaint-only approach perform almost equally well when the malicious behavior number is low. This is because the complaint-only approach relies on there being a large number of trustworthy peers who offer honest statements to override the effect of the false statement provided by the untrustworthy peers and thus achieves a high accuracy. Second, as the malicious behavior factor increases, our approach stays effective while the performance of the complaint-only approach deteriorates. This can be explained as follows. When the malicious behavior number in the community increases, the chances for trustworthy peers to interact with untrustworthy peers and receive fake complaints increase. Since the complaint-only approach only uses the number of complaints for computing the trustworthiness of peers and does not take into account the credibility of the complaints, the trustworthy peers with fake complaints will likely be evaluated as untrustworthy incorrectly. On the contrary, our approach uses the credibility factor to offset the risk of fake complaints and thus is less sensitive to the misbehaviors of untrustworthy peers.

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5 Conclusion We presented an adaptive trust-based trust model for grid communities. We identified the three trust metrics for quantifying and comparing the trustworthiness of peers. We also reported experimental results, demonstrating the feasibility and effectiveness our trust model.

Acknowledgments This paper is supported by the Natural Science Foundation of Jiangxi Province (2010GZS0065).

References 1. Ferrailolo, D.F., Sandhu, R., Gavrila, S., et al.: Proposed NIST Standard for Role-Based Access Control. J. ACM Transactions on Information and System Security 4, 224–274 (2001) 2. Hildmann, T., Barholdt, J.: Managing Trust between Collaborating Companies Using Outsourced Role Based Access Control. In: 4th ACM Workshop on Role-based Access Control, pp. 105–111. IEEE Press, New York (2000) 3. Li, X., Ling, L.: Peer Trust: Supporting Reputation Based Trust for Peer to Peer Electronic Communities. In: IEEE Transactions On Knowledge And Data Engineering 2004, pp. 843–857. IEEE Press, New York (2004) 4. Aberer, K., Despotovic, Z.: C: Managing Trust in A peer-to-peer Information System. In: 2001 ACM CIKM International Conference on Information and Knowledge Management, pp. 181–184. ACM Press, New York (2001)

Design of Special Welding Machine Based on Open CNC System Haibo Lin Department of Mechanical and Electrical Engineering, Taizhou Vocational & Technical College, Taizhou, China 318000 [email protected]

Abstract. The Motion control card is used to design the rotational parts of the welding machine. The motion control card or the entire CNC unit is inserted into the expansion slot of general PC. PC is used for file management, user interface and communications functions of the non-real-time part. The real-time control is carried out by the units of expansion slot or the motion control cards.2D laser displacement sensor is used to measure the welding data, transmitted via the serial port control system to realize the real-time measurement and the real-time welding. The results of the test show that this mechanism boasts of the advantages of stability, smoothness etc. Positioning accuracy can be achieved as well, Therefore it complies with the requirement of welding. Keywords: Open system; welding Design; Control System; real-time servo control.

1 Introduction With the development of national economy, NC system is the core technology for NC. Technology of automated processing has become an important indicator of national science and technology. And it is also the key factor in modern weapons and equipment development of the defense industry. The rotational welding parts is widely used in industrial areas. However there exists low degree of automation, high labor intensity, low productivity and other shortcomings in traditional welding process. Welding becomes an important process in rotating parts production. The Welding quality and speed has become a major concern of production. The open CNC system is based on personal computer (PC) and software. It is run by Modular, hierarchical architecture. And also it can provide a unified application program interface for various forms. Thus the equipment can meet the need of implementing complex control functions for the users, shorten the processing time and improve processing quality and flexibility.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 839–844. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Specific Components of the Welder 2.1 Overall Structure The arc welding automatic machine based on open CNC system has achieved the Welding of rotational parts, including the following sections: Special welding machinery parts, Measurement system, Control (servo) system, Welder and other auxiliary equipment. The motion control card considered as the core of the open CNC system was designed in the system, which controls the cooperation between the motion torch and the work piece to achieve synchronous operation. The automatic welding system based on measurement-components is composed of the mechanical parts, control systems, laser profile measuring instrument, the software part and welding etc. Overall structure is shown in Figure 1.

Fig. 1. The overall structure of welding

2.2 Mechanic Parts The mechanical parts consists of the body, screw, rail, slider, seam location , mounting brackets for tracking system and other components, completing the functions of welding joint position, torch and laser scanning sensors installation. Welding fixtures completed the manual welding aligning with the fixed clamping parts. In order to avoid deviation when the parts is lifted, deformation of radial axis for the die, Stretching deformation and vibration while the horizontal rotary of die. All of them affect the welding speed and the accuracy of track. Meanwhile the machine turns into horizontal rotary table body, composed of the rotating tray and AC servo drives and transfer mechanism, completing the level of rotation and automatic welding process.

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Fig. 2. The diagram of Mechanic system

2.3 Measurement System Data Acquisition for welding seam is carried out by laser scanning products. It passes through the serial port and leads to IPC. After calculation, it passes to the Motion Control card, controlling the welding torch along with the radial and axial movement of the work piece in the end. 2.4 Design of Open CNC Module The welding process is a multi-parameter mutual coupling time-variable nonlinear system. Many factors have affected welded seal quality and those factors have remarkable randomness, it is very difficult to describe by actuary mathematical model, so former controls method have bad adaptability and good dependence for experience in the different degrees. The welding parameters refers to welding torch path, the parameters in particular, like welding torch speed, stop time, displacement, rotable angle and so on. So we need to introduce intelligent control method to solve those problems. At present, most welding torches adopt control technology which regards MCU as core and adopt “PC+ movement control card structure” that inserts the movement control card or the entire CNC unit in the general PC expansion slot (included imbedded PLC). The PC manages non-real-time function like document, user interface and communication. CNC unit that inserts PC expansion slot or movement control card manages real-time function. At the same time, this control system adops 2D laser displacement sensor measures welded seal data. Those data are sent to control system through the serial port, so it can realize real-time measurement and welding. The control system is composed of IPC (embedded movement control card, data acquisition card, digital IO card and so on), AC servo driver, three-phase AC transformer and other electric appliances (like air switch, AC contactor, limit switch, magnetic switch). It can completes

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many functions like welding height control, welding position control, welding current parameter settings, start , stop , speed control of welding positioner and so on; it can realize Welding process plan ,welding path plan, welding anti-collision and anti-interference control, arc welding real-time fault processing and so on. The main control components of control system for special welder are as follows: (1)there are three Panasonic AC servo motors, one is for the rotary table for the circular motion, the other two servo motors is for the platform of micro-displacement of the gun for X-Z table, achieving the movement of displacement of torch.(2)A stepper motor is used to adjust the overall height of frame for welding operations.(3) The limit of X, Z, H-axis and the zero detection signal are set up in this system. Serial port communication is adopted by this system between motion control card and PC because of its few lines and low cost. The system may construct serial port connection between upper machine and motion control card, send commands, exchange data, monitor and response all kinds of errors and events which possibly occur in the course of communication through a serial of standard communication commands provided by VC++ communication controls, and then make use of it to build full-duplex, event driven, efficient and practical communication program.

Fig. 3. Structure of Control system

2.5 Design of Software Module This subject researches and develops special-purpose welding machine open numerical control system software. It is divided into the real-time control module and the non-real-time control module. Real-time display is that current operating position and speed of the motor is monitor and display in the movement. Because of transmission of information in this system by serial port communication, data exchange between control card and PC is carried on with the aid of function comms register, all those functions can be achieved with help of the VB language. The software module, includes input and output module, initialization module, data processing analysis module, the error processing module, movement control module and so on. Those modules control input and output, system initialization, parameter

Design of Special Welding Machine Based on Open CNC System

、

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、

initialization, welding seal data de-noising smoothness interpolation, error processing and so on. Communication and driver software program of numerical control system based on PMAC movement control card uses VC language with the help of its Pcomm32 dynamic link library, so as to realize communication between machine and PMAC card.Pcomm32 includes more than 200 functions. It covers all communication methods between PC and PMAC card. Part of program is as follows: Private Sub Command1_Click() Load Initial Initial.Show End Sub Sub stopp() If MSComm1.PortOpen = True Then MSComm1.PortOpen = False End If timeKillEvent uID Timer2.Enabled = False MintController1.DoStop (2) MintController1.DoStop (0) MintController1.DoStop (1) End Sub After welding, welded seal position will change in X, Z coordinate directions all the time. In order to track welded seal contour position accurately, we need to limit X, Z directions of initial welded seal and set initial measuring region in the contour oscillogram. After settings of initial welded seal position, follow-up undetermined welded seal position will be tracked by algorithm automatically. We may define welded seal central and characteristic points, obtain the accurate welded seal center and edge positions by analyzing enlarged image of welded seal and different image characteristics so as to produce welding torch aimed at the welded seal center. Because of preprocessing welded seal coordinates data from primitive measuring welded seam data have error and mutagenicity of initial value, we need secondary treatment for preprocessing welded seal coordinates data so as to obtain stable, continual, effective welded seal coordinates. In the model, it makes use of secondary treatment N spots data predict N+1 actual welded seal coordinate position.After that, it will N+1spot send to movement control card and act; after welding torch completes, it makes use of N+2 spots predict fellow-up welded seal coordinates position again, the rest may be deduced by analogy. This method can guarantee welding torch act continuously and stably. At the same time, its speed does not change suddenly, and thus guarantees welding quality.

3 Conclusions The motion control card expansion slot or the entire CNC unit is inserted into CNC machine in the common. PC is used for file management, user interface and communications functions of the non-real-time part. The real-time control is carried out by the insertion of PC expansion slot of the CNC unit or motion control cards. The characteristic of the system is much stronger than the traditional advantages of numerical

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control system. It has advantages of processing speed, high control precision and stable running. It has also achieved the real-time servo control for special welding equipment and semi-closed loop control, reducing the vibration for accuracy tracking. Results show that the equipment is easy to adjust and stability of this mechanism is good, improving the welding quality and productivity.

References 1. Ye, C.L., Lian, L.X., Zhao, Y.H.: Simulation Of Welding Intersecting Cylinders Line For Welding Robot. Journal of Shenyang University of Technology 25(5), 426–429 (2003) 2. Ma, X.F.: Robot mechanism. China Machine Press, Beijing (1990) 3. Liu, C.L., Zheng, W.G.: Study on kinematics and computer simulation of RV121 6R welding robot workspace. Robot (9), 333–341 (1998) 4. Li, G., Yang, J.D.: Development of the PC -based Open Architecture Numerical Control System Machine Tool & Hydraulics (4), 82–83 (2004) 5. Zheng, L.L., Huang, W.Q., Zhao, C.S.: Design and implement of visual object tracking system driven by ultrasonic motors. Transducer and Microsystem Technologies 26(12), 77–79 (2007) 6. Ma, Y.Z., Hang, W.D., Zhang, P.X., et al.: Developm ent of subm erged arc welder with digital control. Electric Welding Machine 32(8), 11–15 (2002)

Optimization Algorithm on the Intelligence Schedule of Pubic Traffic Vehicles Cui Yonghong1 and Wang Qingrong2 1 School of Traffic and Transportation Engineering, Lanzhou Jiaotong University Lanzhou, Gansu Province, China [email protected] 2 School of Electronic and Information Engineering, Lanzhou Jiaotong University Lanzhou, Gansu Province, China [email protected]

Abstract. In this paper A model and the Genetic Algorithm for intelligent schedule of public traffic vehicles is researched according to the characteristics of the public transportation vehicles’ scheduling, It adopts the true value of the coding method using the start time as the variable and uses the penalty function method to add a variety of constraints to the objective function when constructing the fitness function, which simplified the calculation. Finally, the simulation results are obtained by using the improved Genetic Algorithm for solving the non-uniform grid schedule, and a case study is carried out to verify the validity, objectivity and applicability of this model through calculated and analyzed practical data. Results show that the improved Genetic Algorithm can find the approximate best result in the huge search space of optimization. Keywords: intelligent schedule; optimization algorithm; genetic algorithm; fitness function.

1 Introduction Public traffic vehicles scheduling is the core of the pubic traffic vehicles which is the basic foundation for scheduling staff, drivers and bus operation. Timetable provides departure frequency and the arrangements of the first and last bus departure time on the basis of passage flow volume. To the public transportation enterprises, timetable represents the service level. The less departure time interval is, the higher service level is, which leads to higher cost. So the arrangement of timetable is a multiobjective problem to reach the maximum cost with satisfying the passage flow volume. At present, genetic algorithm (GA) is an efficient method to solve the public traffic vehicles problem. But there exist some problems in adopting GA: (1) simplifying GA may lead to deviate the results and low computational efficiency [1][2]. (2) If model is too complicated, then it is hard to solve[3]. (3) Based on one statistical period (such as one hour), we can obtain the uniform departure time interval[4][5]. The interval ignores the variation in other periods. Based on above consideration, intelligence public traffic scheduling vehicles model adopting improved GA is proposed. The benefits of public traffic enterprise and passage, maximum and minimum M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 845–851. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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departure time interval, the difference of neighbour departure time interval and other constraints are all taken into account. At last, we can get the non-uniform departure timetable which improves computational efficiency.

2 Scheduling and the Establishment of Model The purpose of public scheduling is to determine the scheduling operation of optimal or approximate optimal. According to this scheduling table, public busteam can achieve supply-demand balance. This project select lanzhou NO.139 bus as scheduling object representatively, only considerate downlink lines and take autumn, winter for example, and optimizing its driving schedule. This line has 16 stations, first-run bus start at am 6:40,last-run bus end at pm 8:30.All operation bus depart in the whole minutes, regarding the intervals time distanced the first as time of departure. The time of departure is selected as decision variables, the unit is minutes, the total number is m within a day,830 minutes for the total time, i.e. x1, x2,…,xm. Where x1 is defined as first-run moments corresponding to am 6:40,and xm is defined as last-run moments(the 890th minites) corresponding to 20:30,i.e. decision variable meet the condition:x1+x2 +…+xm=830. The passenger flow volume in the whole day is given. the goal is to arrange transport vehicles reasonable to run operations, in order to achieve supply-demand balance, meet the performance of system. 2.1 Establish the Objective Function The objective of the model is to reach maximum operation benefits and minimum waiting time. The total waiting time: m

n

WTij′ = ∑∑ rj × i =1 j =1

(ti − ti −1 ) 2 2

(1)

Total waiting time cost: the average annual income of the Xiamen is 32343 yuan/per person. Besides the weekends and official holidays which are about 115 days, there are 250 days to work. And the average working hours in one day are about 8 hours. So the total waiting time cost: m

n

i =1

j =1

w ′ = 0 . 27 × ∑ ∑ r j ×

(t i − t i −1 ) 2 2

(2)

Where: M denotes the total bus frequency in the one scheduling period. N denotes the total stations along the bus route. t i denotes the departure time of the i-th

）. r denotes the passage arrival rate varying with time in the scheduling period （ j=1,2,…,n ） . WT denotes the total waiting time for all pasrun（i=1,2,…,m

j

'

ij

sage(min). w' denotes the total waiting time cost. And the public traffic scheduling model that is a double objective function transformed into a single objective function and the function shows:

Minz = α × w′ − β × R ′

(4)

Optimization Algorithm on the Intelligence Schedule of Pubic Traffic Vehicles

（

m

n

Minz= α × 0.27×∑∑rj × i =1 j =1

847

ti m n (ti − ti −1)2 − β × (∑∑ ∑rj × t × P − C × L × m) (5) 2 i =1 j =1 t =ti −1

Where: R ' is operation revenue; c is variable cost vehicles(yuan/vehicle.kilometer). L is the total length of route (kilometer); p denotes uniform fare (yuan/perspm.time); α denotes the passage waiting time cost coefficient. β denotes revenue coefficient. 2.2 Model Constraint Conditions Constraint with average loading rate m

avergeload ingrate =

n

ti

∑∑ ∑r i =1

j =1 t = t i −1

Q capacity

j

×t

×m

× 100 % > θ

(6)

Where: Qcapacity denotes maximum seating capacity(person/per bus); θ denotes the average expectation loading rate per bus(0< θ <100%).

3 The Design of the Public Traffic Scheduling Genetic Algorithm With the consideration of characteristic of public traffic vehicles scheduling, we adopt real encoding . In this situation, the value of each loca in individual chromosome is true value of decision variable. Chromosome X corresponding to X = [x1 , x 2 ,..., x m ]T denotes string [x 1 , x 2 ,..., x m ] . With real encoding , code position’s value (loca) denotes time measuring from this moment to the first departure time. The first departure time is 0 with minute as unit. For example: the string’s length is m(0, 3, 6,…, 965). Here, 0 represents first departure time and 3 represents that the second bus leaves after 3 minutes. 6 means the third bus leaves after 6 minutes. 956 means that the interval is 956 minutes between the first and the m-th bus. There are below advantages in adapting real encoding as chromosome genes with the public traffic vehicles scheduling[6][7]: (1) It performances better in genetic search in larger space and also do well in representing larger value. (2) It can reduce complicity and decrease the time of encoding and decoding. In the meantime, computational efficiency can be improved. (3) It can process complicated decision variables. Adopting real encoding, the next generation can inherit from the genetic fragments in individuals with good characters. So the genetic fragments in the new one are still good. So this encoding method makes certain high efficiency of GA’s generation evolution. The next generation may not inherit good genetic fragments with other methods. To deal with optimization model with constraints, one approach is to transform the constraints problems into serialization unconstrained problem. Penalty function is an effective approach among the possible solution. As a nonlinear optimization method with constraints, the basic idea is to establish a new function which transforms the constraints problems into a series of non-constraint problems. The function can classify into two types based on penalty method which are interior method and outer point

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method. The interior point method’s characteristic is that an initial solution in feasible region can be obtained at first. Then, the trial point must be searched in feasible region. In the paper, interior method is adopting in establishing penalty items. Finally, the objective function is:

（ ∑∑ ∑ r × t）/ m⎫⎬⎫⎪⎬⎪

m ⎧ ⎧ ⎪ Min f(x) = z + λ1 ∑ ⎨max ⎨0,θ × Qcapacity − i =2 ⎪ ⎩ ⎩

+λ

m

2

∑ {max{0, T i=2

min

m

n

ti

j

i =1 j =1 t =ti −1

m

⎭⎭

− (t i − t i −1 )}} + λ 3 ∑ {max {0, (t i − t i −1 ) − Tmax }}

(8)

i=2

Where, z is the original objective function, f(x) is the objective function value in the use of penalty function. λ1 λ 2 λ3 λ 4 > 0, is the coefficient impacted by penalty

、、、

function( penalty coefficient) respectively. The set of fitness function plays an important role in the entire process. Based on the established objective function, we set fitness function. Public traffic bus scheduling model is to reach the problems’ minimum scale. Fitness function is proposed to ensure that fitness values of chromosome are nonnegative. F ( X ) = C max − f ( X )

(9)

Where: f(X) is individual’s objective function. C max is the maximum objective value in the same generation. On account of the characteristic of public traffic vehicles scheduling, the initial group is generated with experience. Because passage flow volume determines the optimization process and the passage flow volume varying with time doesn’t follow uniform distribution, so there are some high-peak hours in one day when the number of arrival passages will arrive the peak. Therefore, at these hours the departure time should be shorten and other periods departure time intervals should be extended. As a result, initial group generated relying on random number generator can not meet with practical needs. There are some adjustments in generating initial group.

xi′ = x k ⋅ a Where:

i −k

+ xi ⋅ (1 − a

i−k

)

(10)

xi′ is the adjustment value based on xi . xk is the contracting center. a is the

number between (0, 1)which is called contracting coefficient. Adjusted compare method and optimization reserved strategy are adopting. The method is: First, we compare individuals in the group P(t). All the different will consist of a intermediate group. A new generation can be generated after the same selection procedure repeated M times. This method mainly put effect on late stage in genetic search. The individual in the group are many the same in this situation. With advanced method, excellent individuals can be copied to the next generation in larger possibility. At the same time, optimization strategy can protect excellent individuals from crossover and mutation. So the advanced method performs better in local search.

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Single point crossover is chosen. The basic process is: a chromosome is chosen from group following possibility PC. Then a chromosome breakpoint is chosen randomly. A new generation can be generated exchanging with parents’ breakpoints on the right. Applying with uniform mutation operation, the basic process is blow: first, we assign the loci as mutation points in sequence. Then, for mutation point, uniform distribution random numbers from corresponding genetic range can replace original genes with small mutation possibility. For the reasons that departure time follows uniform distribution and every chromosome gene is time, the range of x k is ( x k −1 x k +1 ).

，

The mutated genes are:

x′k = x k −1 + r × ( xk +1 − xk −1 )

(11)

Where; r is a random number in (0,1) following uniform distribution.

4 Public Traffic Scheduling Simulation Experiment with GA Basic parameters: a large number of experiments are carried out to determine crossover possibility, mutation possibility, evolutionary generation etc. finally, efficient parameters can be obtained: mutation possibility is 0.0005, crossover possibility is 0.6, group scale is 200,evolutionary generation is 600. Reference value of routes: L=20.5,n=26,m=95,P=1,C=3.2, Q capacity =65, Tmax =14, T min =6, α =200, β =1,

、、、

ε =4, θ =70. λ1 λ2 λ3 λ4

is 1,1.4,2.2,2.1 respectively. Departure time of the first

bus is 6:00 in the morning and the last bus is 22:05 at night. Driving speed is 20 kilometers/ per hour. The simulation experiments are in the use of MATLAB. At last, algorithm reaches the steady situation. Experimental process shows that there is obvious convergence tendency among individuals’ average objective value and optimization individual’s objective value. However, optimization individual’s convergence speed is faster than individuals’ average objective value. Figure 3 can describe the experimental results. Accordingly, departure time corresponding to optimization individual value in the last generation is the final result. It can be seen that GA can get approximated solution in huge search scope that is efficient in public traffic scheduling. In the high-peak hours(large passage flow volume) and low-peak hours( small passage flow volume), what can be seen is that average interval in high-peak hours is smaller that low-peak hours. And that can meet with the real needs. Combining GA with TS can learn from each other and give play to their respective advantages, which can make up for some deficiencies of the single optimization method . TS is introduced into GA, so that TS is adopted to do the optimization process independently for each individual of the population, during the "nurturing process", And then using mutation operations to get the better population characteristics, thus achieving the optimal evolution. To verify the effectiveness of the GA-TS algorithm to optimize transit vehicle scheduling problem, GA-TS algorithm is used here and compared with a simple GA. Directing at the scheduling problem of Lanzhou NO.139 bus for of separate operation optimization problem solving is done respectively. (see figere 1).

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Fig. 1. Comparison of convergence rate based on traditional GA and the improved GA-TS

5 Conclusion In conclusion, advanced genetic in the public intelligence scheduling can be effective. And it can find the approximated optimization solution in the huge search space. Of course, the problems has been simplified and added some assumptions. But in reality, passage flow volume perhaps don’t follow uniform distribution and the driving speed may not be constant. So there can be deeper research in this part.

References 1. Yu, F.: Application of the Remnant Error Model in Forecasting the Number of Passengers for Civil Aviation Transportation. Journal of xihua university 25(6), 20–26 (2006) 2. Fang, L.J., Wu, Z.: Application of GM (1,3) in Highway Passenger Capacity Forecast of Transportation System. Journal of Highway and Transportation Research and Development 26(3), 163–166 (2006) 3. Zhang, X.Y., Yang, L.: Study on Combina tion Forecasting Model for Traffic Energy Demand. Journal of Nanjing Institute of Technology 6(2), 62–66 (2008) 4. Fan, M., Shi, W.R.: Application Of Acombination Forecasting Mode. In: Local Financial Revenue Forecasting. Journal of Chongqing University 31(5), 536–541 (2008) 5. Zhang, F.L., Shi, F.: Stochastic Grey System Model for Forecasting Passenger and Freight Railway Volume. J. Cent. South Univ. 33(4), 529–535 (2007) 6. Liu, D.B., Chen, Y.J.: Inventory Optimization of the Manufacturing / Remanufacturing Hybrid System with Random Grey Lead times. Journal of East China University of Science and Technology 32(4), 29–35 (2007)

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7. Wang, F.X., Zhang, L.S.: Combination Gray Forecast Model Based on the Ant Colony algorithm. Mathematics in Practice and Theory 39(10), 102–106 (2009) 8. Zhang, Y., Wang, F.Z.: Combination Gray Forecast Model Based on the Ant Colony algorithm. Mathematics in Practice and Theory 39(1), 49–53 (2006) 9. Hong, F.X., Zhang, L.S.: Gray Forecast Model Based on the Ant Colony algorithm. Mathematics in Practice and Theory 35(7), 823–825 (2005)

The Criterion of Intrinsic Safe Circuit Characteristic Based on Electric Arc Power Ligong Wang

＆

College of Electical engineering Information, GuangDong Polytechnic Normal University, GuangZhou 510665, China [email protected]

Abstract. Intrinsic safety electric equipments are commonly used in coal wells . Because intrinsic safety electric equipments have not the flameproof shell, the determinacy whether the electric arcs can ignite gas becomes the important investigated subject . The energy criterion takes the minimum burning energy as standard. However, it can be known that discharge power must be considered except discharge energy when we talking about igniting flammable gas by electric spark. Discharge needs appropriate energy and power so as to ignite flammable gas mixture. Because electric arc discharges concentratively and energy has little loss in this condition. It’s hard to ignite flammable gas mixture if discharge power is quite small though discharge time is long enough. This criterion of intrinsic safe circuits’ characteristic is based on electric arc power. Dynamic V-A characteristics model is adopted to analyze. The relationship between maximum arc power and arc energy is gained. The electric power criterion of intrinsic safe circuits is deduced by electric energy criterion. The electric power criterion is a supplement to that of electric energy criterion. Keywords: Criterion of intrinsic safety; Electric Arc Power, V-A characteristics.

1 Introduction Gas explosion happens in coal mine in China once a year at least. It causes a lot of people to die or hurt and the losing of millions of dollars. Gas explosion affects the safety of coal production badly. Electric arc that ignites gas is one of the important factors that bring gas explosion. Intrinsic safety electric equipments are commonly used in coal wells which contain flammable gases. Because intrinsic safety electric equipments have not the flameproof shell, the determinacy whether the electric arcs can ignite gas becomes the important investigated subject of safety application and design. But until now energy criterion of intrinsic safe circuits characteristic is established only by domestic and overseas scholars. The energy criterion takes the minimum burning energy as standard. However, it’s not enough to explain the problem when only considering discharge energy through analysis. That is to say the discharge energy and power of electric arc must be both considered when talking about igniting flammable

，

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 853–858. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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gas by electric spark. It’s hard to ignite if discharge energy is more than igniting energy and discharge power is quite small though discharge time is long enough. Therefore, electric spark will ignite flammable gas mixture when electric arc discharge appropriate energy and discharge power is big enough. Because electric arc discharges concentratively and energy has little loss in this condition[1]. At present, the scholars has established four kinds of lower energy electric arc discharge mathematical models of intrinsically safe circuits, such as current linear attenuation model[2], current parabola model, static V-A characteristics model and dynamic V-A characteristics model[3]. The dynamic V-A characteristics model is more accurate among these models. This article will analyze differentiation method of circuit intrinsic safety performance based on dynamic V-A characteristics.

2 Dynamic V-A Characteristics Model Dynamic V-A characteristics model gains the relationship between the slope of V-A characteristics line and the value of inductance. Hereby the mathematical expressions of electric arc current, voltage, resistance, power and energy can be deduced. The maximal merit of this model is that electric arc voltage and current have accurate mathematical expressions. The theory is in accord with the fact. The model can be considered as the factual condition with enough excuse [4]. 2.1 Electric Arc Current Direct current inductance circuit will give birth to electrical arc. The formula of electrical arc current of this model is [5]

V ⎡ ⎧ R − (39 +13L) ⎫⎤ i = I − arc min ⎢1− exp ⎨− t ⎬⎥ g R − (39 +13L) ⎣ L ⎩ ⎭⎦

(1)

Varcmin is minimum arcing voltage[6] in the formula above. The statistic of Varcmin is 16V for intrinsically safe circuit. 2.2 Electrical Arc Voltage

The formula of electrical arc voltage can be gained from the formula (1) and loop voltage equation. Varc min ⎡ ⎧ R − (39 +13L) ⎫ ⎤ V = R − (39 +13L) exp ⎨− t ⎬⎥ g R − (39 +13L) ⎢⎣ L ⎩ ⎭⎦

(2)

If t amounts to zero, Vg will equal to Varcmin. Videlicet electrical arc voltage reaches minimum arcing voltage immediately when the arc occurs. Minimum arcing voltage is a very important parameter which reflects arcing characteristic.

The Criterion of Intrinsic Safe Circuit Characteristic Based on Electric Arc Power

855

2.3 Discharge Time

From the formula (1) we can work out T=

V +[ (39 +13L ) − R ] I L ln arc min (39 +13L) − R Varc min

(3)

The intercept “39” in the formula above can omit when L>10mH. Then the formula can turn into

T=

L

V +13LI − E R ln arc min L V (13× ) −1 arc min R

(4)

This shows that discharge time has relationship with circuit time constant L/R and the product between inductance and stable current LI. 2.4 Electrical Arc Energy

Discharge energy can be deduced from the formula (1), the formula (2) and the formula (3) through integral. V ELI 2 (Varc min − A) Varc min − A ELI 2 1 + − A) ln (2V W = LI 2 + arc min arc min 2 Varc min A3 2 A2

(5)

In the formula: A=E-(39+13L)I. 2.5 Electrical Arc Power A 2A ⎫ ⎧ − t − t⎪ IVarc min ⎪ LI +Varc min ( A−E)e LI ⎬ P = ⎨E( A−Varc min )+[ A( A−E)+Varc min (2E−A)]e g 2 A ⎪ ⎪ ⎩ ⎭

(6)

If dPg/dt=0, the time which corresponds to instantaneous power maximum can be gained. t

pmax

=−

LI A( E − A) +Varc min ( A − 2 E ) ln A 2Varc min ( A− E )

(7)

Therefore, maximal instantaneous power is 2 V I P = (2V + E − A + arc min ) max 4 arc min E−A

(8)

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3 The Deduction and Establishment of Power Criterion 3.1 The Relationship between Maximal Discharge Power and Electrical Arc Energy

2 V I P = (2V + E − A + arc min ) max 4 arc min E−A

2 V A3 (2Varc min + E − A+ arc min ) E− A = W = αW Varc min − A 3 2LI[ A +2Varc min E(Varc min − A)ln + AE(2Varc min − A)] Varc min

(9)

In the formula, 2 V A3(2Varc min +E− A+ arc min ) E− A α= is proportion V −A 3 arc min 2LI[ A +2Varc min E(Varc min − A)ln + AE(2Varc min − A)] Varc min modulus. We can draw conclusion that maximal discharge power increases as the discharge energy increases from the formula (9) and α has relationship with E and L. The voltage E must satisfy the following inequation because α must be a positive number. (39 + 13L) I < E < V + (39 + 13L) I arc min

(10)

The relationship of α and E is shown in Fig. 1 and the relationship of α and L is shown in Fig.2.

Fig. 1. The relationship of proportion quotiety and voltage

Fig. 2. The relationship of proportion quotiety and inductance

It can be seen that α decreases as the voltage increases and also decreases as the inductance increases from the Fig.1 and Fig.2. It also shows that discharge power decreases when voltage or inductance increases in the same discharge energy.

The Criterion of Intrinsic Safe Circuit Characteristic Based on Electric Arc Power

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3.2 The Power Criterion of Circuit Intrinsic Safe Characteristic

According to the minimal ignition energy, the critical qualification of gas explosion caused by the make or brake of circuits is W =α W g 1 min

(11)

In the formula: Wmin is minimal ignition energy. The minimal ignition energy of methane is 0.28mJ. α1 is a coefficient which must be considered when measuring minimum ignition energy[7]. α1=2.65~3. The energy criterion of circuit intrinsic safe characteristic is W <α W g 1 min

(12)

3.3 The Power Criterion of Circuit Intrinsic Safe Characteristic

The power criterion of circuit intrinsic safe characteristic can be established according to the formula (9) and (12). P < αα W max 1 min

(13)

The formula (13) shows that the electric arc power maximum Pmax has a minimum. The circuit is intrinsic safety circuit when Pmax is less than the minimum. The difference between power criterion and energy criterion can be seen comparing the formula (12) and (13). The energy criterion takes energy minimum as critical qualification. While the power criterion takes the minimum of maximal power as critical qualification.

4 Conclusion The relationship of maximal discharge power and circuit parameters can be gained through theory analysis and formula deduction based dynamic V-A characteristic model of inductive intrinsically safe circuit. The power criterion can be established more. The power criterion is an important supplement to that of energy criterion. It can play a role in the further design and check of intrinsically safe circuits.

References 1. Meng, Q.: Mathematical Models of Arc Discharge of Intrinsically Safe Circuits and Non-explosion Assessing Intrinsic Safety Method. Ph D Thesis. Xuzhou: China University of Mining and technology 35 (1999) (in Chinese) 2. Zhang, B.: Safety Spark circuit, p. 105. China Coal Industry Press, Beijing (1981) (in Chinese)

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3. Meng, Q., Mou, L., Wang, C.: Characteristics of low energy arc discharging instant. Journal of Xi’An University of Science & Technology 22(1), 56–58 (2002)(in Chinese) 4. Meng, Q., Xu, Y., Hu, T.: Analysis of Arc Discharge V-A Characteristics in Inductive Intrinsically Safe Circuits. Journal of China University of Mining & Technology 28(4), 379–381 (1999) (in Chinese) 5. Meng, Q., Mou, L., Lin, B.: Comparisons of Mathematical Model of Low Energy Arc Discharge. Journal of China University of Mining & Technology 31(2), 201–203 (2002) (in Chinese) 6. Zhang, G.: Electrical Appliances, p. 37. China Machine Press, Beijing (1982) (in Chinese) 7. Yang, H.: Electric Discharge and Explosion Protection Characteristic of Grating Spark. China Coal Industry Press, Beijing (1990) (in Chinese) 8. Jing, L., Wang, L.: Analysis of low energy arc discharge characteristics based on dynamic V-A characteristics model. Journal of Coal Science & Engineering 12(2), 91–93 (2006) (in English)

Multiple View Locality Preserving Projections with Pairwise Constraints Xuesong Yin1, Qi Huang2, and Xiaodong Chen1 1 School of Information and Engineering, Zhejiang Radio & TV University, Hangzhou, 310030, China [email protected], [email protected] 2 School of Biological Engineering, Zhejiang University of Science & Technology, 310023, China [email protected]

Abstract. In this paper, we consider a general problem of multiple view learning from pairwise constraints in the form of must-link and cannot-link. As one kind of side information, the must-link constraints imply that a pair of instances belongs to the same class, while the cannot-link constraints compel them to be different classes. Given must-link and cannot-link constraints, we propose a new locality preserving projections approach for multiple view semisupervised dimensionality reduction. The goal of the proposed approach uses pairwise constraints to derive embedding in each view together with the unlabeled instances. Hence, the consensus pattern can be learned from multiple embeddings of multiple representations. Experimental results on real-world data sets show the effectiveness of the proposed algorithm. Keywords: Locality preserving projections; Dimensionality reduction; Side information.

Multiple

view

learning;

1 Introduction Dimensionality reduction (DR), aiming to obtain a low-dimensional faithful representation of high-dimensional data, has been applied to computer vision, statistical learning and pattern recognition [1],[2],[3],[4]. Traditional DR methods fall into two general approaches we call supervised, and unsupervised. In supervised approaches, the optimal projection is obtained by the labeled samples, such as linear discriminant analysis (LDA) [1],[2] and maximum margin criterion (MMC) [3]. In unsupervised approaches, the unlabeled samples are used to find the optimal projection, such as principal component analysis (PCA) [5], locality preserving projections (LPP) [6], Locally Linear Embedding (LLE) [7] and Graph-optimized Locality Preserving Projections (GoLPP) [8]. In fact, the above methods assume that data are represented in a single vector or graph space. In many important data mining applications, however, the same instances may be represented in several different vector spaces, or by several different graphs, or even as a mixture of vector spaces and graphs. For example, in bioinformatics, genes can be represented in the expression vector space (corresponding to the genetic activity under the biological M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 859–866. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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conditions) and also in the term vector space (corresponding to the text information related to the genes) [9] and [10]. We often call this kind of learning problem as multiple view learning [11]. Under multiple view case, traditional well-known DR works are relatively inappropriate to reduce the dimensionality of the data, since the instance has different formulations in each view. There are two major approaches to multiple view learning called the supervised and the unsupervised. The former is to design learn a consensus pattern from multiple representations with the labeled instances [11] and [12]. The latter pays more attention to multiple representations derived by the unlabeled instances simultaneously in order to produce an accurate and robust partitioning of the data than single-view clustering [10],[13],[14]. Clearly, the unsupervised method adapts some applications better than the supervised one, since it only needs a large number of the unlabeled instances. More currently, Eaton et al. [15] propose a semi-supervised clustering method for multiple view learning with an incomplete mapping in the context of constrained clustering with a set of pairwise constraints in each view. In this paper, we propose a novel multiple view semi-supervised DR approach (MSDR). We focus on side information in the form of pairwise constraints which consist of must-link constraints (instance pairs belonging to the same class) and cannot-link constraints (instance pairs belonging to different classes). Through computing the embedding in each view, MSDR can construct representations of each view. These results on different real data sets confirm the effectiveness of our method.

2 Related Work 2.1 Semi-supervised Discriminant Analysis Given a few labeled instances and a large number of unlabeled instances, Cai et al. [4] proposed Semi-supervised Discriminant Analysis (SDA) using a LDA-based discriminant. SDA utilizes the unlabeled data in an extra local scatter term in the objective function of traditional LDA, that is

a SDA = arg max a

aT Sb a . aT ( St + α Slocal )a

(1)

where the local scatter matrix is defined as

Slocal

1 = 2 2n

n

n

∑∑W ( x − x )( x − x )

T

i =1 j =1

ij

i

j

i

j

.

(2)

The projective vector a that maximizes the objective function is given by the maximum eigenvalue solution to the generalized eigenvalue problem:

Sb a = λ ( St + α Slocal )a .

(3)

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2.2 Semi-supervised Dimensionality Reduction Tang et al. [16] propose a semi-supervised feature projection method to handle weaker semi-supervised settings where only information in the form of pairwise constraints is available. The must-link constraints imply that a pair of instances belong to the same class, while the cannot-link constraints compel them to be from different classes. This method maximizes the following objective function:

J (a) =

∑

( xi , x j )∈C

( aT xi − aT x j ) 2 −

∑

( xi , x j )∈M

( aT xi − aT x j ) 2 ,

(4)

where C and M denote the must-link and cannot-link sets, respectively. Similarly, Yin et al. [17] apply pairwise constraints to learn a Mahalanobis distance metric. In fact, the unlabeled instances play a vital role in learning the projection [4]. Zhang et al. [18] proposed a semi-supervised dimensionality reduction (SSDR) method. Different from the above methods, SSDR use both unlabeled instances and pairwise constraints to seek a projection. The objection function of this method is maximized as follows:

J SSDR (a ) =

1 2n 2

n

∑ (a

i , j =1

T

xi − aT x j )2 + −

α 2nC

β

2 nM

∑

(aT xi − aT x j ) 2

∑

(a T xi − a T x j ) 2

( xi , x j )∈C

( xi , x j )∈M

,

(5)

where α, β >0 are trade-off parameters, nC and nM are the instances number of C and M sets, respectively. Although SSDR can also apply to partially labeled cases, it fails to preserve local neighborhood structures of instances in the reduced low-dimensional space [5],[19].

3 The Algorithm In this section, we will describe our MSDR algorithm in detail, which aims to discover the consensus low dimensional representations under multiple view situations. Given a data matrix X = [ x1, x2,…, xn] (xi ∈ ℜ ) together with must-link constraints (M) and cannot-link constraints (C), for each column vector xi, we have l D

views, i.e., xi = [xi(1), xi(2),…, xi(l)] with the column vector xi(k)

∈ ℜ Di ,

∑D l i

i

=D.

For each view v, must-link constraints and cannot-link constraints are denoted as Mv and Cv. For convenience, let us denote X(i) = [ x1(i), x2(i),…, xn(i)].

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3.1 Semi-supervised Local Dimensionality Reduction (SLDR) We can use a k-nearest neighbor graph to model the relationship between nearby data points. Specifically, we put an edge between nodes i and j if xi and xj are close, i.e. xi and xj are among k nearest neighbors of each other. Let the corresponding weight matrix be P, defined by

⎧ - xi − x j 2 ⎪ Pij = ⎨e 2σ if xi ∈ N k ( x j ) or x j ∈ N k ( xi ) , ⎪0 otherwise ⎩ 2

(6)

where Nk (xi) denotes the set of k nearest neighbors of xi. In general, if a weighted value Pij is large, xi and xj are close to each other in the original space. Thus, in order to preserve the locality structure, the embedding space obtained should be as smooth as possible. To learn an appropriate representation with the locality structure, it is common to minimize the following objective function [6],[20]:

J L ( a ) = ∑ ( aT xi − aT x j ) 2 Pij .

(7)

i, j

The intuition behind Eq. (7) is to make near instances in the original space as close as possible in the transformed low-dimensional space. However, it considers only the unlabeled instances. Researches have shown that the most discriminative subspace can be obtained by both the constraints are and the unlabeled data [4],[17],[18]. Hence, the extended objective function of Semi-supervised local dimensionality reduction (SLDR) is defined as minimizing J(a) w.r.t. aT a = 1 where

J (a) =

1 2n 2 −

∑ (a

T

xi − aT x j ) 2 Pij +

i, j

β 2nC

∑

( xi , x j )∈C

(a xi − a x j ) T

T

α 2nM

∑

( xi , x j )∈M

(aT xi − aT x j ) 2 .

(8)

2

The first term of Eq. (8) describes the near relationship between instances in the transformed low-dimensional space, which is equivalent to locality preserving projections essentially. The latter two terms are to let the average distance in the transformed low-dimensional space between instances involved by the cannot-link set C as large as possible, while distances between instances involved by the must-link set M as small as possible. There exists a concise form for Eq. (8):

J (a) =

1 ( aT xi − aT x j ) 2 Sij , ∑ 2 i, j

(9)

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where

⎧ Pij α ⎪ n2 + n M ⎪ ⎪⎪ Pij β Sij = ⎨ 2 − ⎪ n nC ⎪ Pij ⎪ 2 ⎪⎩ n

if ( xi , x j ) ∈ M if ( xi , x j ) ∈ C .

(10)

otherwise

Through further reducing Eq. (9), we have

J (a ) =

1 (aT xi − aT x j ) 2 Sij = aT X ( D − S ) X T a ∑ 2 i, j

= aT XLX T a where D is a diagonal matrix whose diagonal entries are column (or row, since S is symmetric) sum of S, that is

Dii = ∑ j =1 Sij . L = D − S is the Laplacian matrix. n

Thus, Eq. (8) or Eq. (9) can be simplified as minimizing J(a) w.r.t. aT a = 1, where

min J (a ) = aT XLX T a subject to aT a = 1

.

(11)

Clearly, the projective vector a that minimizes Eq. (11) is given by the minimum eigenvalue solution to the generalized eigenvalue problem:

XLX T a = λ a .

(12)

3.2 Multiple View Data Representation We assume that X(v) is the data matrix of the v th view together with some must-link constraints Mv and cannot-link constraints Cv. In this subsection, we apply the same strategy as SLDR to describe the instances and constraints for each view. More concretely, we compute the Laplacian matrix L(v) for the v th view, v = 1, 2, ... , l.

L( v ) = D ( v ) − S ( v ) .

(13)

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where

Sij( v )

⎧ Pij( v ) α ( v ) ⎪ 2 + ( v ) if ( xi ( v ) , x j ( v ) ) ∈ M v nM ⎪ n v ( ) ⎪P β (v ) ⎪ = ⎨ ij 2 − ( v ) if ( xi ( v ) , x j ( v ) ) ∈ Cv . nC ⎪ n ⎪ P (v ) ⎪ ij 2 otherwise ⎪⎩ n

(14)

and D(v) is a diagonal matrix whose elements are the column sums of S(v), i.e.,

Dij( v ) = ∑ j Sij( v ) . nM( v ) and nC( v ) are the cardinality of Mv and Cv, respectively. α(v) and β(v) are two balance parameters. The goal of MSDR is to find a consensus pattern based low dimensional representations where instances involved by M should be close while instances involved by C should be far under multiple view points. Thus, MSDR aim at optimizing the embedding for each view, i.e.:

X ( v ) L( v ) X ( v )T a ( v ) = λ a ( v ) .

(15) (v)

(v)

(v )

The optimal projection matrix is denoted by Av = [ a1 , a2 ,…, adv ] for the v th view, and the low dimensional embedding of the v th view is

AvT X ( v ) . MSDR expect

that multiple view points can share the consensus pattern as much as possible. Thus, after getting the embedding for each view Av (v = 1, 2, ... , l), we use serial combination that is similar to serial combination of canonical correlation analysis to reduce the dimensionality of the data.

4 Experimental Results In this section, we evaluate the performance of the MSDR algorithms on a broad range of data sets. We compare MSDR with the recently published methods LPP [6], SSDR [18] and PCA [5]. An appealing semi-supervised DR approach which could make a better use of unlabeled data for performance gain is of great practical significance. Thus, this section explores the potential of unlabeled data when incorporating them into dimensionality reduction together with pairwise constraints. All the experiments perform on three text data sets including News-Different-300, News-Similar-300 and News-Same-300, derived from 20-Newsgroup, and Sonar data set. In each data set, we randomly choose 10 percent of all the instance pairs for each view. If two instances belong to the same class, we connect them by a must-link, otherwise, a dissimilarity pair is created. After reducing the dimensionality, we employ K-means for clustering. Moreover, in the following experiments, the

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parameters are determined by five fold cross validation and all numerical results are averaged by 30 independent trials. We employ normalized mutual information (NMI) to evaluate the clustering performance [16],[17],[20]. Table 1 shows the performance comparison of different algorithms. We observe that MSDR nearly always achieves the highest accuracy on all data sets. It can also be shown from Table 1 that in most cases the performance of PCA is the worst. We believe that this is because PCA does not use the constraints. On the other hand, the poor performance of SLDR implies that only using a view is difficult to find the consensus pattern. Intuitively, since our method could integrate the side information and the discriminant information in low dimensional subspaces, MSDR should also perform the best. Table 1. Clustering accuracy (NMI) on four data sets (mean±std-dev%) Data sets News-Different-300 News-Same-300 News-Similar-300 Sonar

PCA 57.09±3.97 43.47±4.38 44.40±4.22 47.40±5.31

LPP 61.21±3.12 43.05±4.75 45.60±3.85 49.43±5.12

SLDR 71.17±2.52 57.73±3.27 58.61±2.77 59.42±4.37

MSDR 75.17±2.11 60.73±2.24 62.61±2.32 63.21±3.76

5 Conclusions In this paper, we present a novel semi-supervised dimensionality reduction approach MSDR. For dimensionality reduction and clustering, MSDR integrates the embedding of multiple view points to obtain an optimal subspace with must-link and cannot-link constraints. Moreover, SLDR can be regarded as a special case of our method. Compared with existing typical dimensionality reduction algorithms on a collection of benchmark data sets in terms of clustering accuracy, MSDR can always achieve a clear performance gain. Acknowledgments. The research reported in this paper has been partially supported by Natural Science Foundation of Zhejiang Province under Grant No. Y1100349 and Foundation for the Open University of China under Grant No. GFQ1601.

References 1. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley Interscience, Hoboken (2000) 2. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997) 3. Li, H., Jiang, T., Zhang, K.: Efficient and robust feature extraction by maximum margin criterion. IEEE Trans. Neural Networks 17, 157–165 (2006) 4. Cai, D., He, X., Han, J.: Semi-supervised discriminant analysis. In: Eleventh IEEE International Conference on Computer Vision, pp. 1–7 (2007) 5. Jolliffe, I.: Principal Component Analysis, 2nd edn. Springer, Heidelberg (2002)

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6. He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems, vol. 16 (2003) 7. Saul, L.K., Roweis, S.T.: Think globally, fit locally: Unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research 4, 119–155 (2003) 8. Zhang, L., Qiao, L., Chen, S.: Graph-optimized locality preserving projections. Pattern Recognition 43, 1993–2002 (2010) 9. Glenisson, P., Mathys, J., Moor, B.D.: Metaclustering of gene expression data and literature-based information. SIGKDD Explorations Newsletter 5, 101–112 (2003) 10. Zhou, D., Burges, C.: Spectral clustering and transductive learning with multiple views. In: 24th International Conference on Machine Learning, pp. 1159–1166 (2007) 11. Ruping, S., Scheffer, T.: Learning with multiple views. In: 22th International Conference on Machine Learning Workshop on Learning with Multiple Views (2005) 12. Brefeld, U., Scheffer, T.: Co-EM support vector learning. In: 21th International Conference on Machine Learning, Banff, AB, Canada, pp. 16–23 (2004) 13. Tzortzis, G.F., Likas, A.C.: Multiple view clustering using a weighted combination of exemplar-based mixture models. IEEE Transactions on Neural Networks 12, 1925–1938 (2010) 14. Long, B., Yu, P.S., Zhang, Z.: A General Model for Multiple View Unsupervised Learning. In: 8th SIAM International Conference on Data Mining, pp. 822–833 (2008) 15. Eaton, E.: desJardins, M., Jacob, S.: Multi-view clustering with constraint propagation for learning with an incomplete mapping between views. In: 19th ACM International Conference on Information and Knowledge Management, pp. 389–398 (2010) 16. Tang, W., Xiong, H., Zhong, S., Wu, J.: Enhancing semi-supervised clustering: a feature projection perspective. In: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 707–716 (2007) 17. Yin, X.S., Chen, S., Hu, E., Zhang, D.: Semi-Supervised Clustering with Metric Learning: An Adaptive Kernel Method. Pattern Recognition 43, 1320–1333 (2010) 18. Zhang, D., Zhou, Z., Chen, S.: Semi-supervised dimensionality reduction. In: 7th SIAM Conference on Data Mining, pp. 629–634 (2007) 19. Li, Y., Yin, X.S.: Semi-supervised Transductive Discriminant Analysis. In: IEEE International Conference on Intelligent Systems and Knowledge Engineering, pp. 225–230 (2010) 20. Yin, X.S., Huang, Q.: Integrating Global and Local Structures in Semi-supervised Discriminant Analysis. In: 3th International Symposium on Intelligent Information Technology Application, pp. 757–761 (2009)

Analysis of E-SCM Huang Hua and Peng Cong Nanchang Technology Institute, China 330099 [email protected]

Abstract. After the study of the connotation, the architecture, the core ideology and the characteristics of E-SCM, the paper discusses how to achieve E-SCM, referring to the principles, the holistic scheme and steps of the implementation of E-SCM. Finally, the paper draws lessons from the investigation of the current situation and the content of the implementation of the E-SCM of the Wal-Mart.

；Supply Chain Management.

Keywords: E-SCM; Integration

1 Introduction With the development of computer network, communication technology and the popularization of Internet networks since 90's, tremendous changes have taken place in the way deal with the business. Internet-based e-commerce, as an advanced way in business trade transactions, it makes enterprises, users, suppliers and other business and trade links that needed link up closely so that the information on the enterprise supply chain between the nodes is integrated and shared to a high degree. In turn, it has become a tool to manage the supply chain, and gives new meaning to supply chain management. With the development of information network technology, electronic commerce is playing its effectiveness, it makes a lot of integration in the supply chain can be completed more efficiently, thereby increasing operational efficiency and reduce operating costs. Only the implementation of E-SCM (supply chain management e-commerce) can be better adapt to the changes in market demand and develop in the fierce competition.

2 Overview of E-SCM 2.1 Content of E-SCM E-SCM (Electronic Commerce Supply Chain Management, e-commerce supply chain management) refers to the management in all the processes in the entire supply chain, such as planning and forecasting, procurement, inventory, production, logistics, sales and information and other resources and customer satisfaction , which is achieved by the means of e-commerce of information technology. It is the product that integrated by e-commerce and supply chain management.Moreover, it is a new hotspot in the modern enterprise management. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 867–874. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2.2 Architecture of E-SCM The integration of E-commerce and supply chain management, making the operation of supply chain management has changed, which can be seen in Figure 1. From this figure,we can see that E-SCM optimizes and integrates the logistics, capital flow, business flow and information flow on the supply chain, by the use of Internet / Intranet which promotes communication among enterprises, helps to improve the efficiency of business processes and maintains a low inventory of zero returns.

— Two-way flow of information flow / business stream ---- One-way flow of logistics (counterclockwise) / cash flow (clockwise) Fig. 1. E-SCM architecture

2.3 Core Idea of E-SCM The core idea of E-SCM can be summarized as "CEO". C refers to the collaborative commerce (Collaborative Commerce); E refers to the E of Enterprise (E-business) and O refers to the outsourcing (Outsourcing). [1] (1) collaborative commerce Collaborative Commerce refers to an enterprise cooperates, shares resources with the participating entities in supply chain by using new technologies (eg Internet technology) , to jointly develop products and provide services etc.and improve the

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competitiveness of enterprises. Collaborative Commerce makes the enterprise achieve the transition from the closed traditional vertical thinking to the open lateral thinking. The implementation of collaborative commerce can be achieved from the following four aspects: a. sharing of information and other resources. Share information and other resources within the enterprise , among enterprises or between the enterprise and customers , using Internet / Intranet and other technologies; b integration business. Making all the business processes in the supply chain links toward a common goal to good convergence; c create opportunities for cooperation. The participating entities need assistance outside the field , which provide a professional knowledge and skills to improve efficiency; d to enhance their own credibility. Only a good reputation can maintain good relations of cooperation. (2) E Enterprise E enterprise is a new business that achieves internal and external electronic transactions . It is a new method to promote efficiency throughout the organization and enterprise value. The biggest feature of E enterprise is the change from forecasting production in the past into the production based on actual demand information, making the style of customer "pull" supply chain operations come true. The realization of E enterprise requires three E system. The first E is the EIP (Enterprise Information Portal), for centralized management of information, so that employees, customers and partner can communicate and share information freely to achieve cooperation; the second E is the ERP (Enterprise Resource Planning), which is built on the base of information technology, adopts a systematic management thinking pattern and provides a management platform of run and decision for Corporate decision-making and employees ; the third E system is EAI (Enterprise Application Integration). EAI is based on a variety of different platforms and programs created with different integration of heterogeneous application systems to achieve seamless integration among two or more enterprise systems. (3) Outsourcing Outsourcing refers to that the enterprises should focus on their key business, take advantage of their strengths and specialize in a particular area, in order to form their own core competencies in one field. Meanwhile, they should establish strategic cooperative relations with the right companies worldwide. Besides, the non-core business in the corporate should be outsourced to other companies to complete. The substance of the outsourcing is to use resources of others, including the following three areas: talents, time and money. a talent. With the development of high-tech, the core competitiveness of enterprises has reversed from the capital to knowledge and operational capacity. Many companies are improving their business talents by use of the outside help. b time. As the cycles of product life are getting shorter, time is playing a more and more important part in the market competition. Outsourcing makes other companies spend less time to complete the business. c fund. The traditional business model often bases on the whole process of self-invested and self-construction , leading to a high debt ratio, liquidity shortages and less guaranteed equipment maintenance and technology upgrade, which results in the late operation of the enterprise can not be carried out successfully. The operating usually faces a difficult time when the project is completed.

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2.4 Features of E-SCM (1) strategy of cooperation between members E-SCM takes advantage of the advanced information and network technology and manufacturing technology to pursuit the overall optimality and puts emphasise on the sharing resources and integration of information systems between the participating entities in the supply chain supply chain. It also requires the participating entities strengthen connection with each other to improve the overall competitive advantage and earn the profit. (2) Intelligent of Information Management By the use of the information technology to collect and analyse various information in the supply chain , the business can manage full range of. information on the purchase of raw materials, production, distribution, marketing, customer relationship and so on. E-SCM makes a lot of calculations , logistics and decisions, such as production planning, the choice of transport routes, etc. The related information technology support can be fixed quickly under the intelligent help, without consuming a large amount of manpower. (3) The Agility And Flexibility of Business Facing the fast pace of the global market and the personalization of the users’ demand, a single enterprise, individual functions can not only consider using their own resources to meet customers’ demand, but also need to integrate the resources of other enterprises or functions to change the fate of the competition between entities into a "win-win" relationship, using the fully autonomous, distributed coordination work instead of the management structure of multi-storey pyramid and carry out the "multi-variety, small batch, multi-batch, short-cycle" of agile flexible production operation, thereby implement the concept of E-SCM ----- take the customer demand as central . (4) integration of the network organization The integration of the network organization has two meanings: a network between enterprises in the supply chain. Necessary data and analysis results can be obtained quickly and accurately by means of each node company on the Internet b network of organization. Information can be transferred and exchanged in-house by the use of Intranet.

3 E-SCM Implementation 3.1 The Principle of E-SCM Implementation (1) strategic principles E-SCM is built on the base of e-business supply chain’s survival and long-term development, whose system architecture development should be consistent with strategic planning of corporate.

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(2) the principle of flexible As the ever-changing and uncertainty of consumer’s demand, and companies must meet the unique needs of consumers, so the management of supply chain must be very flexible to adapt to the uncertainty of e-business environment needs. (3) the principle of coordination In the environment of e-commerce, whether the performance of supply chain management is good or not depends on the harmonious of partnership in the supply chain to some extent, so establishing a good strategic partnership with each node company in the supply chain is an effective protection to achieve the best performance of E-SCM 3.2 The Overall Program of E-SCM Implementation (1) change of conception We should change the concept that do not fit the e-commerce times, such as the narrow short-term concept, the concept of limits, so that people understand the importance of the implementation of E-SCM, which enable people to look for the overall long-term production and operations consciously and achieving value-added for each node on the supply chain. (2) remodeling of tissue Organizational structure not only reflects the rights of the supply chain relationships and contacts, but also determines the way of information transmission in supply chain, whether it is good or not impact on the most active factor of the supply chain─the maxmum of people's enthusiasm and ability. The remodeling of tissue needs two things: a functional organization must develop toward the flat, network. A two-way interaction exits between all levels of the relationship and network connections exit between the functions, information transfer divergence from the source at the same time to the other departments to achieve network; b decentralization. In e-commerce environment, as the changing of market conditions and customer needs, only decentralization can respond quickly to stay competitive and maximize the motivation and enthusiasm of employee. (3) Process Reengineering Simply put, the process of restructuring is to change those not suited to the needs of E-SCM to improve efficiency of supply chain and customer satisfaction, and reduce the operating costs of supply chain. There are three main strategies for restructuring process: a to eliminate non-value added activities. Improve the quality consciousness of all aspects, to reduce the inventory in processes, repeated transfer in transport, rework and other activities; b to achieve the integration of work. Reduce the transfer procedures and improve efficiency levels; c change the line of the program flow to synchronous flow. Thus not only can shorten the cycle, but also can discover problems in order to improve efficiency and reduce waste through the process of exchange and mutual adjustment,.

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(4) reorganization of resources The traditional enterprise resource services for a single need. With changes into process-oriented in business practices and improvement in requirements of the business flexibility and leanness, companies must re-construct the resource allocation model to make it consistent with the e-commerce environment to share the resources. The reorganization of resources can be achieved from the following three aspects. a. restructuring of suppliers and customers resources. Make full use of Internet to make comprehensive evaluation of the capital, manpower, technology and other areas of suppliers, from which select a provider suitable to the e-commerce supply chain strategy to form part of the supply chain. Customer needs and the market situation are analysed at the same time to define the target and customer base of the company, forming the downstream part of the supply chain; b the capital, technology, human and information resources of enterprises in supply chain. Advantages of the resources of each node enterprise are tapped to be fully used, forming the core competitiveness and creating a benefit, mutual complementarity of the situation; c time. As the life cycles of product are getting shorter, time is playing a more and more important part in the market competition. By outsourcing to make other companies spend less time to complete the business and response to market demand timely. 3.3 Implementation Steps of E-SCM (1) internal realization of electronic The members of the supply chain , especially the core business has improved and sophisticated information systems, which means the smooth flow of information and the internal business processes. Through the enterprise information sharing system, the core business on the chain will integrate planning, procurement, production, inventory, sales, etc. in their systems , on which supply chain members can determine viable system docking programs to ensure automated data exchange and smooth implementation of the workflow after the docking of upstream and downstream systems. So bringing into internal ERP / MIS system to make enterprises have sophisticated information systems is the premise of the successful implementation of E-SCM. (2) eliminating barriers of heterogeneous systems among enterprises The parties involved must set up a joint project team when a project is under way , so as to jointly determine the technical standards and platforms of the project and the format of the data to be exchanged. The parties can develop their own side of the web server in accordance with agreed standards . Various members just follow the need of supply chain to open the desired function and the members of the chain can be called at any time, which can effectively reduce integration time and costs. What’s more, as more enterprises participating in the supply chain, the original system does not need any change or just need slightly modified, then it can meet other members of the supply chain or other supply chain requirements, which help to improve the initiative of enterprises to participate in projects. (3) E of business processes In order to achieve real E of supply chain management, firstly, we must analyze business processes to eliminate and integrate inefficiencies aspects and re-engineer the

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business process to meet the collaboration needs of supply chain. Import or update the internal ERP, BRP and the EFT is the core to optimize business processes model, they can better reflect the efficiency and flexibility in supply chain to meet the needs of the external supply chain to reduce the delay of each part of the business and eliminate transmission of information distortions, so as to avoid blind production, inventory, trade waste and different accounts. (4) set up a performance evaluation system Evaluation the existing E-SCM. The new assessment system must be able to make a reasonable assessment for the role of each trading partner and each functions in the supply chain optimization. On this basis, the benefits from supply chain optimization must be allocated reasonablly. (5) continuous improvement and optimization of E-SCM Supply chain is a network , with highly integrated, continuous operation and dynamic development. We need to improve and optimize E-SCM according to the changes of the market conditions and the form of competition continually to adapt to the changes of market competition environment .

4 Conclusion In the information age, innovation has become a major factor in promoting social development. Through the innovation which is accomplished by the integration of E-commerce and supply chain management to build a new management model ---E-SCM, enterprises and supply chain will go through a revolutionary change. E-SCM will change the decision-making, production and operation methods of the corporate fundamentally and constantly enhance the level of information, responsiveness, operational efficiency and customer satisfaction. E-SCM change the static narrow supply chain relationship into the dynamic range supply gateway system, which make the supply chain more flexible to link the island of each node in the supply chain effectively and make information and resources to be shared and integrated, thus improving operational efficiency of the supply chain and reducing operating costs. With the emergence and rapid development of Internet, the new business model of e-commerce is changing the whole process of the supply chain, from raw material procurement, product manufacturing, distribution, to delivery to the end user, with the speed, depth and breadth that are hard to imagine, changing the information flow, logistics, capital flow, stream flow and operation of business in supply chain. How to use Internet and e-commerce to manage supply chain to maintain and expand the competitive advantage as soon as possible is an urgent task that lies ahead for large and medium enterprises, especially some of the finest leader. Cisco-based global e-commerce supply chain management provides them with a good reference. Internet-based E-supply chain management system (ESCM) has made the whole world become a huge value chain essentially. Companies must invest in settlement solutions of electronic supply chain that cover product development, supply chain management, product marketing, sales and payments to achieve the efficient management objectives of integration of logistics and information flow.

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References 1. Liang, J.: Content and core ideology of electronic supply chain management. J. Dongfang Electrical Machinery 31(4), 371–376 2. Hong, L.: Analysis of supply chain management based on e-commerce. J. Economic & Trade Update (September 2007) 3. Lier, B., Powell: E-supply chain management. Machinery Industry Press, M. Xie Dongmei (2002) 4. Jiang, S.: Electronic Commerce Supply Chain Management and Innovation. J. Theory of observation (April 2005) 5. Tang, X., Wang, W.: Electronic commerce-based supply chain management mechanism. J. Science and Technology Management Research (March 2006) 6. Zhou, L.: Supply chain management of e-commerce. J. Business and Economy (August 2006)

Information Sharing of Partnership in Supply Chain and Enhancement of Core Competitiveness of Enterprises Huang Hua and Peng Cong Nanchang Technology Institute, China 330099 [email protected]

Abstract. With the intensifying of the economic globalization and market competition , the competition among firms ,which used to be traditional and existed purely among individual enterprise, is replaced by competition in supply chain. In order to improve the quality of their products and services enhance the core competitiveness of enterprises, they have to develop full co-operation and co-ordination and share information with enterprises in supply chain ,only to create a seamless integration in supply chain, which facilitate the optimization of the overall performance of the supply chain system. This paper focuses on how to share the information in supply chain to achieve the aim of enhancing the core competitiveness of enterprises. Firstly, the paper supplies the concept of supply chain and management of supply chain , information sharing and core competitiveness of enterprises , and then explains the relationship between information sharing and the enhancement of core competitiveness of enterprises by combining with the role of information sharing in supply chain. Finally , The information sharing strategies are put up according to different partnership of supply chain .

，

Keywords: supply chain management; information sharing; enterprise; core competitiveness; partners.

1 Introduction Since 80s in 20th century, with the continuous progress of science and technology, market demand volatiles increasingly. In the fierce market competition, corporate business model has developed from the traditional "vertical integration" to the "horizontal integration." Horizontal Integration highlights the ability of core competitiveness of enterprises, tends to put emphasis on the division of labor and cooperation of society, focuses on the use of external resources of business. Competition among enterprises are switched into competition among supply chains. Accordingly, the operation of the supply chain has changed from the "push" model---production to boost the demand to the "pull" mode -- demand-pull production. Therefore, to enhance the core competitiveness of enterprises, supply chain must be under rational and effective management. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 875–883. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Information, inventory, transportation, facilities are the four main drivers in the supply chain. Among these four factors, information is the basis of the entire supply chain management, which provides factual basis on which the supply chain manager can make decisions , it can be seen in the fact that timely, accurate and appropriate information is needed in the supply chain to develop strategies on inventory ,transport, facilities. Information sharing is the core of supply chain management, and only by improving the information integration in supply chain can reduce the variability caused by information asymmetry and information risks, enhance the coordination among node enterprises in supply chain, and ultimately achieve the aim of improving the core competitiveness of enterprises.

2 Information Sharing and Core Competitiveness of Enterprises in Supply Chain 2.1 Information Sharing in Supply Chain 2.1.1 The Concept of Information Sharing in Supply Chain Information sharing is a key factor to the coordination of the supply chain network, whether in the JIT production process, or in the continuing replenishment and rapid response process in the retail part, the information on scheduling, delivery or manufacture needs to be distributed to relevant agencies. Information sharing can effectively reduce the “Bullwhip Effect” which is generated in the process of end-consumer demand information transferring up along the supply chain. Information sharing can also enable the members of the supply chain nodes to arrange under production operations and inventory distribution plans according to the shared information, the quantity and flow of logistics can be controlled by the dissemination of information shared by the supply chain nodes, which can effectively reduce the costs caused by the various changes in supply and demand. In short, information sharing in supply chain can be defined as follows: It is an activity in which information on shipping, demand, inventory and cash flow is shared by the partners in the supply chain, in order to optimize inventory and reduce the bullwhip effect, tend to achieve the aim of lowering the total cost, enhancing the core competitiveness by their rapid response to the market. 2.1.2 The Type of Information Sharing in Supply Chain The theory of supply chain management has been evolved into a variety of practical forms, center around Japan and the United States since the 80s in 20th century. Information is the glue to connect business processes, whether within the organization or among organizations. So from the start, information management is an important aspect of supply chain management, supply chain information sharing in the main types of Table 1.

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Table 1. The main types of information sharing in supply chain the shared type

joint type

prediction type

developmet type

hand-over type

query type

describe of the concept Upstream and downstream sectors or enterprises delivered information with clear content and regularity with each other , such as shipping, the library, receiving instruction and so on. there is no directly relationship between Share information and business transactions among enterprises , they are indirectly related, for example, retailers provide sales information to manufacturers, to forecast the future market movements, offerde as the basis for the decision-making. a member of the supply chain provides information on product development to another member. Generally, it appears in the form of new product development of the joint project team. A business passes the information which is often needed to be delivered directly to its supply chain partners to deal with , they only keep the right to know the information. the third-party logistics company provides cargo condition information to customers.

the fit enviroment Common existed in the daily business operations. Commonly existed between retailers and manufacturers.

Commonly existed in the manufacturer, to develop new products marketable. exist generally between the supplier and retailer other nodes except the end-users

information type post-trade information

post-informa tion

beforeinformation

Concurrentinformation

Concurrentinformation

2.2 Core Competitiveness of Enterprises Since the 90s in 20th century, the core competitiveness of enterprises has become one of the hottest topics in business and academic circles. The standard definition of the core competitiveness of enterprises have not been given at home and abroad. Prahalad and Hammer’s original point of view is followed in the theoretical circles in China. Throughout scholars’ study on the content and elements of the core competitiveness these years, it is mainly defined from the following three types of perspectives :"narrow", “fairly”, "width". (just as Table 2 shows) we can see from the table that the core competitiveness is a kind of resource with unique internal capacity. It is formed in the course of business and can not easily be emulated by competitors, so that enterprises can have a long-term advantage in a competitive market. This advantage, with its unique technical, cultural or and mechanisms, is formed in the process of production and operation, development of new products, service and various decision-making in enterprises. The enormous capital power and operating strength can be generated relying on this advantage. Core competence is a basis on which enterprises can get a long-term stable advantage for competitive and its essence is enabling customers to get

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irreplaceable value, products, services and culture which are really better than and higher than that of their competitors. Table 2. Definition of core competence defined by scholars at home Field view

of

“narrow” “fairly”

“width”

definition the combination of series of complementary of skills and knowledge in companies is mainly composed of the ability to insight and foresee and the ability of execution in the front-line the key skills, tacit knowledge and intellectual capital owned by enterprises . a serious of key processes, capabilities, resources and mechanisms ,such as the market prediction,research development, marketing, manufacturing, management and decision-making, human resource development, brand strategy, corporate culture, strategic management and industrial enterprises, innovation,which are owned by the companies and better than their competitors, are the core elements of competitiveness

3 Enhance the Core Competitiveness of Enterprises by the Information Sharing in Supply Chain 3.1 The Role of Information Sharing in Supply Chain Management Any knowledge, message which are useful to the decisions and operation of the business can be called information. Information sharing among members in supply chain helps to promote the positive results such as effective forecasting, coordination of the supply chain, accomplishment of aggregate effect, the rapid response to support supply chain and decreasing of the operations risk of supply chain and so on. It can not be substituted in the role of improving the overall performance of the entire supply chain.All kinds of information sharing have an effect on the SCM , which are showed in Table 3. Table 3. The effect of all kinds of information sharing on the SCM The content of the information sharing Order information sharing Demand information sharing

The effect on the supply chain management Orders between enterprises are often in the primitive form of information sharing , the initial supply chain uses the upstream of the order information as the collaboration link The demand information from the end consumer is shared in all aspects of the whole supply chain and the transferring of the demand information develops from the linear structure into a network structure, which effectively avoids the distortion of information and reduces the supply chain inventory by the weakening of the bullwhip effect.

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Table 3. (continued)

Inventory information sharing

Forecast Information Sharing

Plannd Information Sharing

Inventory information is the key to maintain the information of the whole system smoothly and completely. Access to accurate and timely inventory information will help the enterprises improve the supply orders, reduced inventory costs, rationalize production and make market forecasts. To the members of the supply chain, the more close to the market, the more likely to understand the market, the more accurately predict to market demand can be made. If the downstream members in the supply chain are willing to share the forecast information with their upstream partners, it can help members of its upstream design more accurate production plans and improve the level of customer service. in the two-stage supply chain of suppliers and manufacturers, manufacturers can improve the standard of their plans by taking advantage of the production and distribution plan made by suppliers, and providers can provide a reliable supply according to the plan of the production of the manufacturer . Similarly,in the two-stage supply chain level of manufacturers and distributors , manufacturers and vendors can share their production plans and marketing plans.

3.2 The Main Way to Achieve Information Sharing in the Supply Chain and Enhance Core Competitiveness of Enterprises Core competitiveness of enterprises is the driving force to maintain long-term development of a company, it is also the basis to enable the company to stand out from its competitors. Through the studies at home and abroad, we know that there are mainly two ways to enhance the core competitiveness of enterprises -- the "inside" way and the "outer" way. The external means can not be achieved without the whole external environment of enterprises. In the environment of "horizontal integration" and supply chain management, the immediate external environment is the supply chain which the company's in. Enterprises can enhance the core competitiveness by the management of supply chain. Information sharing is the basis of supply chain management, the means to enhance core competitiveness of enterprises by information sharing in the supply chain are: (1) Strengthen the infrastructure of the information sharing in supply chain, ensure companies respond quickly to market. Information technology platforms and communications systems support are needed to achieve high quality of transferring and sharing of information of each node. Sales starting point Data Systems (POS), electronic data interchange (EDI), information sharing technology based on the Internet, vendor managed inventory (VMI), efficient customer response (ECR), a continuous supplement program (CRP), Collaborative Planning, Forecasting and supplements (CPFR) and other supporting technologies can enable the information to be shared effectively. Between It enables the enterprise to collect and transmit data and information relating to business quickly and accurately, and response to market rapidly, then gradually reduce the information distortions in the

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process of transferring, reduce the extra costs brought by the traditional trade, and finally achieve the aim of enhancing the core competitiveness. (2) Establish organizational structure to adapt the supply chain management and achieve the dual optimization both in internal and external business processes. Supply chain management is a new management ideas and methods and its operation needs the guarantee of organizational structure. Implementation of organizational reform, promote the construction of a flat organizational structure, cut down the size of the staff, financial, and material resources that hinder business operations and affect achievement of strategic goals, then the path of organizational information transmission can be shorten, operating costs of the business can be reduced and the responsiveness to market demand will be enhanced, the efficiency and effectiveness of the production and management of the enterprises will be improved . Strengthen the double optimization in internal business processes and external business processes .Through the control and coordination of the material flow in production and management , information flow in the management process and decision-making flow in the decision-making process, organically integrate the management of the enterprise's internal and external supply chain, in order to achieve the optimization outside the enterprise value chain. (3) Information System Integration Based on Supply Chain Management The coordinated operation of the supply chain is established on the basis of high-quality of information transfer and sharing among all nodes of enterprises, therefore, an effective supply chain management can not do without the support of information technology systems, which can improve the accuracy of information exchange in enterprises and reduce the human error caused in the complex, repetitive work. Thereby reducing the waste of time and economic losses resulted from errors and improving the operational efficiency of supply chain management. The various information management systems which has been widely used in in many medium and large enterprises, such as: inventory management, supplier (VMI), customer relationship system (CRM), enterprise resource planning (ERP), Collaborative Planning, Forecasting and Availability (CPFR ), and rapid response (QR) and so on.Considering their essence, the core of these concepts are nothing more than information sharing, the only difference lies in the content, approach and degree of the information sharing. As the enterprise information system in the supply chain has not been planned comprehensively at the beginning of establishment and the company alliance is dynamic, the Information System Integration based on SCM is a systematic project and should be executed under a scientific, normative principle. In the face of the compatibility between information systems, enterprises are required to take future expansion into account when they implement their own information systems and use information systems which are extensible, platform-independent, with standardized data format. Of course, this is only applicable to information systems to be implemented before the implementation of the enterprises. Most companies have not taken the needs of future integration into account when they build their own information systems. For those who need to integrate information systems of other enterprises but do not leave business interfaces, the information systems which are constructed based on the needs of the integrated information system, will meet the need of inter-enterprise integration to a greater degree, but also companies need to invest heavily in the update of the

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information system and staff training, so as to alleviate the heavy financial burden caused by the system integration.

4 Partnerships between Enterprises in Supply Chain and Their Corresponding Information-Sharing Strategy 4.1 Supply Chain Partnership between Enterprises Supply chain partnership (supply chain partnership, SCP) is a coordination relationship formed by two or more independent members within the supply chain to ensure the realization of a particular goal or benefit. The aim of establishing the relationship between supply chain partners is to reduce the total inventory product in the supply chain, reduce costs and improve operational performance throughout the supply chain.All these are achieved by improving the level of information sharing . In reality, the supply chain systems which have achieved success tend to focus on co-operation between chains. The ability to maintain a flexible and efficient cooperation between chains are critical to each node. Accroding to the different cooperated purposes of nodes in the chain and reference the orbit model thought, cooperative relations between the supply chains are divided into the following three types:common, mutual coordination and strategy-based model, taking the degree of information sharing and transaction costs as the two rectangular coordinate system. (just as Figure 1 shows).

higher

M

The degree information sharing

X1 X2

Z1 Y1 Z2 Y2 Zn Yn

of Strategy type mutual coordinatio

commonn

Transaction larger costs

Fig. 1. Orbit model of partners (Note: X,Y,Z is partners of the note M)

(1) Ordinary relationship type. It is the most common collaboration in supply chain, with many partners but has a relatively sparse relationship with multi-node enterprise and the minimum mutual dependence with each other, so the transaction costs is highest. (2) co-ordination partnership type. Partners has a more intimate relationship with the node enterprises and the mutual dependence is stronger, and transaction costs are lower .Generally, establish a long-term, stable relations between cooperations will bring higher benefit to node M business.

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(3) strategic partnership type. It is the most important type of relationship in the supply chain. They lead neither a simple sale and purchase transactions relationship nor counterparties relationship, but is rather a close working relationship. This type has smaller number of partners, but their relationship between the node M is most closely and the dependence between each other is also strong, thus the transaction costs is minimum. 4.2 Information Sharing Strategy Corresponding to Different Partnership Different relationship must correspond to different information sharing strategy. In order to guarantee the reasonable operation and optimization of the whole supply chain system, to achieve the aim of reducing the total costs of the supply chain and enhancing the core competitiveness. (1) information sharing strategy of the ordinary cooperation relationship type Bilateral cooperation has less restrictive conditions and the threshold is low, the main purpose of the node enterprises is to reduce business transaction costs. Therefore, the two sides adopt order information, product information sharing to help reduce the cost of order processing and achieve the purpose of reducing transaction costs. (2) information sharing strategy of co-ordination partnership type Partners focus on the long-term cooperation, information sharing aims not only to lower transaction costs, but also look forward to reducing the negative effects brought by "bullwhip effect" and improving operational efficiency by the cooperation through information sharing. But under the volatiled demand situation, order delays also can occur under the situation of demand information sharing alone, thereby reducing the rate of supply orders. So it also needs to share sales, inventory, production, transportation and other information to achieve this goal. (3) information sharing strategy strategic partnership type Information sharing is intended to respond quickly to customer needs, thereby enhancing customers’ satisfaction and support long-term development of enterprises. As core competetiveness is emphasized, companies would hardly be able to complete all of the product development by their own strength. Share the information technological advances and research and development information between them, jointly develop products has become a way to meet market demand. Therefore, share information on R & D, development strategies with a strategic partner will help to improve customer satisfaction and long-term development of enterprises.

5 Conclusion With the economic globalization and the advent of the information economy in 21st century, the personalization and diversification of products are required increasingly by the end-customer, product cycles are shortening and market uncertainty is increasing. Companies must pay more attention to coordinate with collaboration partners in supply chain by information sharing, so as to enhance their own core competitive power and seek for the way of survival and development in the highly competitive market environment.

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References 1. Liu, X., Xia, X.: Supply Chain Management and improvement of core competitiveness of enterprises. J. Human social sciences (April 2005) 2. Wang, Y.: Supply Chain Game Information Sharing and Incentives. J. China Management Science 13(5) (October 2005) 3. Zhang, X.: Supply chain competitiveness, pp. 69–156. M. China Renmin University Press (2005) 4. Cohen, S., Roussel, J.: strategic supply chain management the five disciplines for top performance, pp. 149–172. Post&Telecom Press (2006) 5. Lee, H.L., So, K.C., Tang, C.S.: The value of information sharing in a two-level supply chain. J. Management Science 46(5), 626–643 (2000)

Novel Spatial Diversity Equalizer Suitable for 16-QAM Signal Rao Wei Nanchang Institute of Technology Nanchang, 330099, China [email protected]

Abstract. Blind Spatial Diversity Equalizer (SDE) is a famous adaptive equalization technique to overcome signal fading and inter-symbol interference (ISI) simultaneously without the aid of training sequences. The key blind algorithm in SDE is constant modulus algorithm (CMA). And the weaknesses of these algorithms such as slow convergence rate and large residual error can also occur in SDE and degrade the performance of SDE. By studying the geometrical relationship of the 16-QAM signal coordinates, a novel SDE suitable for 16-QAM signal is proposed. Compared with SDE based on CMA or MCMA, the proposed SDE has faster convergence rate and lower residual error in computer simulation. Keywords: Blind equalization; spatial diversity equalizer; constant modulus algorithm.

1 Introduction In wireless transmission system, the radio signal is often degraded by the effects of multipath propagation. Multipath fading may cause severe time dispersion of the signal, called frequency-selective fading, leading to ISI which is a major source of impairment in high-capacity digital radio system. Since in high-fading activity area, frequency diversity alone cannot provide the desired transmission availability for digital radio system, spatial diversity technique is increasingly used to combat the effects of multipath fading. Spatial diversity results from vertically spacing the two receiver antennas sufficiently far apart. It has been known that fading effects can be ameliorated based on the fact that deep fades of signals received on two vertically spaced receiving antennas rarely occur simultaneously. But diversity by itself cannot resolve the problem completely. Accordingly, spatial diversity is typically used in conjunction with adaptive equalization, which is called Spatial Diversity Equalization or Spatial Diversity Equalizer (SDE), to achieve satisfactory performance in the presence of frequency-selective fading. An optimum SDE using linear or decision feedback equalization has been presented by Balaban and Salz [1] [2]. The numerical results show that the dual diversity combining and equalization can provide about two orders-of-magnitude improvement in the average error rate or in outage probability. Venkataraman [3] has analyzed three classes SDE structures, and proposed a new SDE structure which had lower M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 885–891. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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computational complexity or/and better performance. In [4] and [5], SDEs have been applied to underwater communication and DTV, respectively. Unfortunately, in all of the aforementioned papers, the performance of adaptive equalization algorithms applied to SDE has not been analyzed. The conventional SDE usually adopts the well-known Constant Modulus Algorithm (CMA) [6], because CMA is one kind of blind equalization which operating without any explicit training sequence, unlike the conventional adaptive equalization, can save the bandwidth effectively, and CMA has strong robustness and can be easily implemented [7]. The problems of CMA, however, such as moderate convergence rate and residual error may not be sufficient for SDE to obtain adequate performance. In [8] we make full use of the character which is that the 16-QAM signals locate on four circles with the same radius. By adjusting the coordinates of the signals simply, all signals can be equalized with the same unit circle. This method can overcome the disadvantage which is that expect for some special data constellations, the error function of CMA can not become exactly zero even when the channel is perfectly equalized. Therefore, compared with the conventional CMA the simple constant modulus algorithm based on the new error function has faster convergence rate and lower residual error.

2 Spatial Diversity Equalizer For simplicity, we consider a SDE with dual equal gain combining and its schematic diagram is shown in figure 1.

x (1) ( k )

x( 2) (k )

w F(1) ( k ) w F( 2) ( k )

y1 ( k ) y2 ( k )

y (k )

sˆ( k ) w B (k )

Fig. 1. Structure of SDE

The received signal, x (i ) (k ) , at the ith sensor is x (i ) ( k ) =

∑c

(i ) j (k ) s(k

− j ) + n (i ) (k ) , i = 1,2 ,

(1)

where s (k ) is the data symbol, c (ji ) (k ) is an element of impulse response vector of the ith channel, and n (i ) (k ) is the additive noise uncorrelated with signals. From the figure 1, the SDE consists of two feedforward filters w F(i ) (k ) ( i = 1,2 ) and one feedback filter w B (k ) . Furthermore, each of feedforward filters has feedback filter has LB taps. The output of SDE is given as

LF taps and

Novel Spatial Diversity Equalizer Suitable for 16-QAM Signal

y (k ) = y1 (k ) − y2 (k ) =

∑ [w

(i ) T F ( k )]

x ( i ) (k ) − [ w B (k )]T sˆ(k − 1) ,

887

(2)

where x (i ) (k ) is a LF -length input signal vector of the ith feedforward filter and sˆ( k − 1) is LB -length input signal vector of the feedback filter. The cost function of CMA is defined by J (k ) = E[(| y (k ) |2 − R 2 ) 2 ] ,

(3)

where E[⋅] indicates statistical expectation. R 2 is the constant depending only on the input data symbol s(k ) and it is defined as R 2 = E[| s( k ) |4 ] / E[| s(k ) |2 ] .

(4)

Using a stochastic gradient algorithm as updating rule, the error signal e(k ) used to update the weight vectors of both feedforward and feedback filters is defined by e(k ) = y (k )[| y (k ) |2 − R 2 ] .

(5)

Each of weight vectors is update by w F(i ) (k + 1) = w F(i ) ( k ) − μ F e( k )[ x (i ) (k )]* ,

(6)

w B (k + 1) = w B (k ) + μ B e( k )[ sˆ(k − 1)]* ,

(7)

where μ F and μ B are the step-sizes for each feedforward filter and feedback filter, respectively. The SDE based on equal gain combining and CMA is described as (5)-(7), and we call it EG-SDE or SDE simply in this paper. The problems of CMA are slower convergence rate and larger residual error which can occur in SDE, so such SDE based on CMA has the same defects as CMA.

3 A New Spatial Diversity Equalizer In this section, we introduce the method used in [8] firstly. And then we employ it to the SDE structure. 3.1 New Style of Error Function of CMA In [8] we consider the 16-QAM signal constellation as seen in figure 2. It is clear that the 16-QAM signal with symbol values {± 1 ± i,±1 ± 3i,±3 ± i,±3 ± 3i} locate on four grayer circles with the same radius. Now we adjust the coordinates of the signals simply according to the arrows in the figure, the new coordinates of the 16-QAM signals are shown in table 1.

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R. Wei Im 3

1 -3

-1

1

3

Re

-1

-3

Fig. 2. The changes of 16-QAM signals’s coordinates Table 1. The changes of 16-QAM signals’s coordinates

Original coordinates 1+i 1+3i 3+i 3+3i -1+i -1+3i -3+i -3+3i -1-i -1-3i -3-i -3-3i 1-i 1-3i 3-i 3-3i

New coordinates -1-i -1+i 1-i 1+i 1-i 1+i -1-i -1+i 1+i 1-i -1+i -1-i -1+i -1-i 1+i 1-i

Assuming the original coordinates is equal to X and the new coordinates is equal to Y , then above changes can be expressed as Y = [ X r − 2 sign( X r )] + i[ X i − 2 sign( X i )] ,

(8)

where X r and X i are the real and imaginary parts of X respectively and sign[⋅] denotes sign function. J (k ) = E[(| y′( k ) |2 − R′2 )2 ] ,

(9)

y′(k ) = { yr (k ) − 2sign[ yr (k )]} + i{ yi ( k ) − 2 sign[ yi (k )]} ,

(10)

where

R ′2 =

E[| [ ar (k ) − 2 sign(ar (k ))] + i[ai ( k ) − 2sign(ai ( k ))] |4 ] E[| [ ar (k ) − 2 sign(ar (k ))] + i[ai ( k ) − 2sign(ai ( k ))] | ] 2

.

(11)

Novel Spatial Diversity Equalizer Suitable for 16-QAM Signal

889

As seen form figure 2 the error function (7) attempts to drive the output data symbols to reside on a circle whose radius is equal to R ′ . It should be noted that for such 16-QAM signal constellations, this new style error function will become exactly zero when the channel is perfectly equalized. Such method has been proved very efficient used in CMA [9, 10] and implemented in [11]. 3.2 Proposed SDE

In order to make the SDE has faster convergence rate, lower residual error, we extend the above method to the SDE structure, which is described as follows: y (k ) = y1 ( k ) − y 2 (k ) =

∑[w

(i ) T F ( k )]

x (i ) (k ) − [ w B (k )]T sˆ(k − 1) ,

J ( k ) = E[(| y ′(k ) |2 − R ′ 2 ) 2 ] .

(12) (13)

Using a stochastic gradient algorithm as updating rule, the error signal e′(k ) used to update the weight vectors of both feedforward and feedback filters is defined by e′(k ) = y ′(k )[| y ′(k ) |2 − R ′2 ] .

(14)

Each of weight vectors is update by w F(i ) ( k + 1) = w F(i ) ( k ) − μ F e′(k )[ x (i ) (k )]* ,

(15)

w B ( k + 1) = w B (k ) + μ B e′( k )[ sˆ(k − 1)]* .

(16)

The proposed SDE is described as (12) ~ (16).

4 Simulation Results In this section, for illustrating the performance of the proposed SDE, we carry out a numerical simulation that compares the proposed SDE with SDE. A specific pair of discrete channels has been investigated. Their respective impulse responses are: c1 = [0.005,0.009,0.024,0.854,0.218,0.049,0.016]T ,

(17)

c 2 = [0.004,0.03,0.104,0.52,0.273,0.074,0.02]T .

(18)

The source signals are uniformly distributed random sequences (16-QAM modulated). The SNR is 30 dB at each input. Each feedforward filter of EG-SDE and MEG-SDE has 11 taps and each feedback filter of them has 7 taps. In SDE, μ F and

μ B are set to 0.0006 and 0.0006, respectively. In proposed SDE, μ F and μ B are set to 0.025 and 0.0008, respectively. At two channels, the original received signals are all “eye-closed” (see the constellations shown in figure 4 and figure 5). Compared with SDE, proposed SDE has faster convergence and lower residual error as show in figure 3. From figure 6 and figure 7, it is clear that the proposed SDE’s output constellation is clearer than SDE’s

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Fig. 3. The learning curves of algorithms

Fig. 5. Received signals of channel 2

Fig. 4. Received signals of channel 1

Fig. 6. Equalized signals of SDE

Fig. 7. Equalized signals of proposed SDE

5 Conclusion In this paper, in order to enhance the performance of spatial diversity equalizer to overcome signal fading and ISI simultaneously much better, we proposed a new spatial diversity equalizer which makes full use of the character which is that the 16QAM signals locate on four circles with the same radius. Accordingly, the proposed SDE can not only speed up the convergence rate but also reduce the residual error.

References 1. Balaban, P., Salz, J.: Optimum diversity combining and equalization in digital data transmission with applications to cellular mobile radio – Part I: Theoretical considerations. IEEE Transactions on Communications 40(5), 885–894 (1992) 2. Balaban, P., Salz, J.: Optimum diversity combining and equalization in digital data transmission with applications to cellular mobile radio - Part II: Numerical results. IEEE Transactions on Communications 40(5), 895–907 (1992)

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3. Venkataraman, S., Mahmoud, S.A., Goubran, R.A.: Filter structures for equalization and diversity combining in wireless communications. In: The 3th annual international conference on universal personal communications, pp. 204–209 (1994) 4. Hou, B., Woodward, B.: Performance of spatial diversity for underwater communications. In: The 7th international conference on electronic engineering in oceanography, pp. 183–185 (1997) 5. Moon, S.-H., Kim, J.-Y., Han, D.-S.: Spatial diversity technique for improvement of DTV reception performance. IEEE Transactions on consumer electronics 49(4), 958–964 (2003) 6. Godard, D.N.: Self-recovering equalization and carrier tracking in two dimensional data communication system. IEEE Transactions on Communications 28(11), 1867–1875 (1980) 7. Johnson, C.R., et al.: Blind equalization using the constant modulus criterion: a review. Proceedings of the IEEE 86(10), 1927–1950 (1998) 8. Rao, W., Yuan, K., et al.: A simple constant modulus algorithm for blind equalization suitable for 16-QAM signal. In: Proceedings of 9th International Conference on Signal Processing, pp. 1963–1966 (2008) 9. Zhao, X., Guo, Y., Rao, W.: Blind Equalization Algorithms Based on Orthogonal Wavelet and Coordinate Transformation. In: International Conference on Intelligent HumanMachine Systems and Cybernetics, vol. 1, pp. 284–287 (2009) 10. Guo, Y., Zhao, X., Liu, Z., Gao, M.: A Modified T/2 Fractionally Spaced Coordinate Transformation Blind Equalization Algorithm. Int’l J. of Communications, Network and System Sciences 3(2), 183–189 (2011) 11. Zhao, X., Guo, Y.: DSP implementation and blind equalization algorithms based on coordinate transformation. In: 2nd International Asia Conference on Informatics in Control, Automation and Robotics, vol. 2, pp. 79–82 (2010)

Fuzzy Logic Controller for a Pneumatic Rotary Actuator Logah Perumal Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia [email protected]

Abstract. This paper presents advantages of fuzzy logic in representing unmodeled parameters of a plant. It is a challenging task to represent a plant accurately using mathematical models, due to the random parameters such as coefficient of friction, environmental disturbances, sensor noises, and etc. These are known as unmodeled dynamics or parameter perturbations and these usually present in a plant modeling. Classical control design techniques use simplified mathematical model to represent a plant and the true plant remains unknown. Therefore, plant behavior may change in unpredictable ways. These problems can be prevented by using intelligent control technique such as fuzzy logic. In this paper, practical application of fuzzy logic to represent unmodeled parameters in direct control is demonstrated by controlling angular position of a pneumatic rotary actuator. The pneumatic rotary actuator is controlled in real time using Matlab-simulink xPC target environment, without aid of any mathematical models format. Keywords: Fuzzy Logic Control, Direct Control, Matlab Simulink xPC Target, Real time control, Pneumatic rotary actuator.

1 Introduction For a simple problem where the system behavior is well understood, mathematical equations are used to model the system precisely. Model-free methods are used to model systems with moderate level of complexity by the aid of experimental input and output data to train neural networks. The two methods described above cannot be used to model very high complex systems, in which only imprecise and ambiguous data are available. This problem can be solved by using fuzzy system. Fuzzy logic system can be used to model highly non-linear system since it allows representation of uncertainties of a plant, the treatment of nonlinearities and the generation of smooth control actions [1]. Fuzzy reasoning enables us to understand a system's behavior by interpolating between observed input and output data. Fuzzy logic controller (FLC) has been widely used in many industrial applications since it does not require any mathematical model to control the system. There have been vast researches on potential implementation of FLSs [1, 2, 3, 4], to name a few. This paper consists of six sections. Practical application chosen is described in following section 2. Hardware and experiment set-up are explained in section 3 while M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 893–901. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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section 4 is used to describe the controller program developed for the application. Section 5 is used to discuss the results obtained and finally the paper is concluded in section 6.

2 Practical Application of Direct FLC Objective of FLC is to reset attitude of a single axis attitude system as shown in Fig. 1. Reset angle is set to zero degree. Thus, controller will detect initial angle (θ0) and resets it to zero degree (θ = 0o) by activating thrusters 1 and 2.

Fig. 1. Single axis attitude system

Equation of motion describing the motion of the single axis system18 is given by:

M a = θI + θC

(1)

Where Ma is applied moment due to thruster, I is moment of inertia of the system, C is coefficient of friction,

θ

is angular rate and

θ

is angular acceleration.

3 The Hardware The pneumatic rotary actuator is controlled in real-time by using Matlab-simulink xPC target environment which consists of a host computer, target computer, and demo kit (demo kit houses pneumatic rotary actuator), as shown in Fig. 2. Nomenclature of pneumatic rotary actuator is as shown in Fig. 3. Angle of pneumatic rotary actuator is determined based on pulses generated by inductive proximity encoder, as shown in Fig. 4. The gear has 18 teeth/rev, giving physical resolution of 20o/teeth. Block diagram for entire experiment setup is as shown in Fig. 5 (Block diagram for the experiment set up).

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Fig. 2. Real time control using xPC Target. i) Host computer, ii) Target computer, iii) Demo kit

Fig. 3. Pneumatic rotary actuator. 1) Nozzle1, 2) Nozzle2, 3) Solenoid valves1, 4) Solenoid valve2 and 5) Beam

Fig. 4. Close-up view of inductive proximity sensor and gear assembly. 1) Inductive proximity sensor and 2) 18 teeth gear

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Fig. 5. Block diagram for experimental setup

Resolution of inductive proximity sensor is coarse, but it provides latency for fuzzy controller so that outputs Thruster 1 and Thruster 2 are available to solenoids within their response time [5]. The latency time is also necessary to build necessary pressure at nozzles. Solver step size is kept at 0.01sec, determines sampling rate of 100Hz, which is necessary for interrupt driven scheduler of xPC Target kernel [6]. Inlet pressure is maintained at 3 bars by using an air regulator. Airflow rate at nozzle outlet is determined based on characteristic graph provided by FESTO [5]. Air is treated as incompressible since air density changes only slightly at velocities much less than speed of sound. Force produced by nozzle is calculated to be 2.2 Newton based on general thrust equation [7]. Moment, Ma produced by nozzle force at half beam length is calculated to be 0.314 Nm. Coefficient of friction, C due to bearing contact, misalignment and unbalance is considered to be 0.4, based on [14]. Moment of inertia of beam in Fig. 3 is evaluated using parallel axis theorem and found to be 0.00244 kgm2. Parameters of the pneumatic rotary actuator are summarized in Table 1: Table 1. Specification of pneumatic rotary actuator

Parameters Ma I C

Description Thruster moment Moment of inertia Coefficient of friction

Value 0.314 Nm 0.00244 kgm2 0.4 kgm2/s

4 Real-Time xPC Controller Program Simulink real time control program developed to control angular position of pneumatic rotary actuator is shown in Fig. 6. State flow chart is used to calculate angle of the pneumatic rotary actuator. Angle rate is calculated in separate block; “Speed”.

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These calculations are done based on pulse inputs from inductive proximity sensor. Calculated results are then supplied to FLC, which in turn determines which valve to be activated.

Fig. 6. Simulink real time control program

Relay is used to filter out noises in pulse inputs from inductive proximity sensor (supplied by NI data acquisition card; PCI-6024E). Output from fuzzy logic controller is either 1 or -1 where output 1 represents activation of thruster 1 while output -1 represents activation of thruster 2. Relays are used to separate single controller output into two outputs; one for thruster 1 (T1) and the other for thruster 2 (T2). Either one of the thrusters is activated at a time. Output values from FLC (1 or -1) are amplified to 5 or -5 to suit threshold value of circuit breaker. Fuzzy bang-bang controller is used since it is proven to yield minimum time response and they are inexpensive to implement compared to analog actuators [8, 9, 10 and 11]. For both controllers, universe of discourse used for inputs is -30 ≤ X ≤ 30 and for output is -1 ≤ Z ≤ 1. Fuzzy bang-bang controller consists of two inputs and one output. Linguistic variables are: x1 = “Angle”, x2 = “Angle Rate” and y1=“Thruster”. Five linguistic values are used for each x1 and x2 and two linguistic values are used for y1; ~ ~ ~ ~ ~ ⎧~ ~ ~ ~ ~ ⎫ ⎧ ~ ⎫ x1 = A1j = ⎨ A11 , A12 , A13 , A14 A15 ⎬ = ⎨ LN , SN , Z , SP , LP ⎬ ⎭ ⎩ ⎭ ⎩ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ⎧ ⎫ ⎧ ⎫ x2 = A2k = ⎨ A21 , A22 , A23 , A24 , A25 ⎬ = ⎨ LN , SN , Z , SP, LP ⎬ ⎩ ⎭ ⎩ ⎭ ~ ⎧~ ~ ⎫ ⎧~ ~ ⎫ y1 = B1p = ⎨ B11 , B12 ⎬ = ⎨T 1, T 2⎬ ⎭ ⎩ ⎭ ⎩

(2)

Membership functions are shown in Figs. 7-9, where ~

~

~

~

LN =" La rge Negative" , SN =" Small Negative" , Z =" Zero" , SP =" Small Positive" , ~

~

~

LP =" L arg e Positive" , T 1 ="Thruster 1" and T 2 ="Thruster 2"

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Output is either 1 or -1 similar to bang-bang controller output, which activates or deactivates system actuators based on fuzzy inputs. Each linguistic value/ fuzzy set is ~ parameterized by variables. For instance, linguistic value SN for linguistic variable x1 is represented by: ⎧1 + ( x1 + 1.5) / 28.5 ⎪

μ ( x1 ) = ⎨1 − ( x1 + 1.5) / 1.5 ~ A12

⎪ ⎩

if

−28.5 ≤ ( x1 − 1.5) ≤ 0

if

0 ≤ ( x1 − 1.5) ≤ 1.5

0

otherwise

(3)

where modal point is -1.5, lower ~ half width is 28.5 and upper half width is 1.5. Domain for linguistic value is 0 < SN < −30 . When linguistic variable falls onto domain of this linguistic value, input would be fuzzified:

μ

~

A12

(4)

= fuzzify ( x1 )

Similar process is done for linguistic variable x2. Once inputs are fuzzified, mamdani max-min fuzzy inference is applied:

μ ( z ) = max[min[ μ ( x1 ), μ ( x 2 )]] ~

~

~

Zk

A1k

A2k

(5)

Finally, LOM defuzzification is applied to yield crisp output which represents switching signals.

Fig. 7. Membership functions of input angle

Fig. 8. Membership functions of input angle rate

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Fig. 9. Membership functions of output

Based on rules of thumb for membership functions reported in [12], density of the fuzzy sets are made highest around the optimal control point of the system and thin out as the distance from that point increases. Relationship between the inputs and output is described in terms of fuzzy rules. Table 2 shows relationship between inputs and output in tabular linguistic format. Using angle as only input to fuzzy controller would cause the system to become unstable and diverge from reset angle. Thus, angular velocity information is used as additional input to fuzzy controller to stabilize the system. These observations are reported in [13]. Table 2. Relationship between the inputs and output for bang-bang switching

Angle

LN

SN

Z

SP

LP

T1 T1 T1 T1 -

T1 T1 T1 T2

T1 T1 T2 T2

T1 T2 T2 T2

T2 T2 T2 T2

Angle rate LN SN Z SP LP

5 Simulation Results and Discussion Pneumatic rotary actuator is controlled in real-time using xPC target environment. Rotary actuator is tested for four different initial angles at both positive and negative sides. Initial angles used are: ±60, ±50, ±40, and ±30. Simulation result for initial angle of 30 degrees is shown in Figs. 10 and 11. A curve fitting is added to discrete output pulses of the encoder in Fig. 10 to approximate continuous angle convergence to 00 . The encoder output has resolution of 200/teeth, which is obvious in Fig. 10 and is not a limitation for continuous time simulation. The thruster firing cycle to reset the beam angle is shown in Fig. 11 and it can be seen that only half cycle is required. Time required to reach 00 is 0.6 seconds. Bang-bang switching then causes oscillation about reset angle 00, at which controller would be switched off.

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Fig. 10. Encoder output of rotary actuator system; resetting the angle from 30o to 00 with 200/teeth resolution

Fig. 11. Thruster switching voltage level for solenoid valves 3 and 4.

Thrusters will be switched off once system reaches attitude of ±10degrees at speed of less than or equals to 0.7644 deg/sec. Time delays are necessary to prevent system from stopping due to friction at angles between 0 and ±10degrees. Speed limit is imposed to prevent overshoot once thrusters are switched off. From simulation results, it can be clearly seen that FLC is a suitable method to represent unmodeled parameters or parameter perturbations in a plant modeling. Angle of pneumatic actuator is controlled without any aid of mathematical models, thus shortens engineering development. Drawback of using level switching control systems is that control systems become nonlinear [11] and this inherent property of switching control systems leads to oscillation of controlled parameters. Oscillation at steady state can be avoided by switching off controller once system reaches steady state.

6 Conclusion From simulation results presented in the paper, it can be seen that fuzzy logic can be used to represent unmodeled parameters of a plant. Practical application of fuzzy logic in controlling angular position of a pneumatic rotary actuator, without use of any mathematical model is demonstrated to validate suitability of fuzzy logic to

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represent unmodeled parameters of a plant. Fuzzy logic as direct control is suggested for simple system (system‟s parameter do not change significantly with time, plant is not subjected to different operating conditions). For complex systems where system is subjected to different operating conditions, direct FLCs are not sufficient to cover the plant s full operating envelope. For these cases, supervisory FLCs can be used in which control is done based on combination of FLC and conventional controller.

References 1. Ortega, G., Giron-Sierra, J.M.: Fuzzy Logic Techniques for Intelligent Spacecraft Control Systems. IEEE International Conference on Systems, Man and Cybernatics 3, 2460–2465 (1995); Intelligent Systems for the 21st Century 2. Chun, L.L., Huai, W.S.: Intelligent Control Theory in Guidance and Control System Design: an Overview. Proceedings of the National Science Council 24(1), 15–30 (2000); Republic of China 3. Ervin, J.C., Alptekin, S.E.: Fuzzy Logic Control of a Modern Airplane. IEEE International Conference on Systems, Man, and cybernetics 3, 2320–2325 (1998) 4. Wu, S.F., Engelen, C.J.H., Chu, Q.P., Babuska, R., Mulder, J.A., Ortega, G.: Fuzzy Logic Based Attitude Control of the Spacecraft X-38 Along a Nominal Re-entry Trajectory. In: 4th ESA International Conference on Spacecraft Guidance, Navigation and Control Systems, Noordwijk, The Netherlands, October 18-21 (1999) 5. FESTO online catalogue. Products (2007), http://catalog.festo.com/ 6. xPC Target for Use with Real-Time Workshop – User Guide, version 2, The MathWorks, Inc. Natick, MA, USA (November 2003) 7. Tom, B. (ed.): General Thrust Equation., Glenn Research Center, http://www.grc.nasa.gov/ (Last update on June 15, 2007) 8. Perumal, L., Nagi, F.H.: Largest of maximum (LOM) method for switching fuzzy control system. Australian Journal of Electrical and Electronics Engineering 4(2), 167–178 (2008) 9. Nagi, F., Perumal, L.: Optimization of fuzzy controller for minimum time response. Mechatronics 19(3), 325–333 (2009) 10. Nagi, F., Perumal, L., Nagi, J.: A New Integrated Fuzzy Bang-Bang Relay Control System. Mechatronics 19(5), 748–760 (2009) 11. Jensen, T.H.: Modeling A Resistive Wall Mode Control System Of The Bang Bang Type, General Atomics Project Report (2003), http://www.osti.gov/energycitations/servlets/purl/ 821784-grGtEC/native/821784.pdf 12. Taylor, C.W., Venkatasubramanian, V.: Wide-Area Stability and Voltage Control. In: Proc. VII Symposium on Specialists in Electric Operational and Expansion Planning, VII SEPOPE, Curitiba, Brazil (2000) 13. Gulley, N.: Simulink Application Note. Aerodynamic Devision, NASA Ames Research Center (1991) 14. SKF bearing online catalogue, http://www.skf.com/skf/productcatalogue/jsp/calculation/ calculationIndex.jsp?&maincatalogue=1&lang=en

Evolutionary Neural Network Based on Immune Continuous Ant Colony Algorithm Gao Wei Wuhan Polytechnic University Hubei, Wuhan 430023, P.R. China [email protected]

Abstract. Combination of the evolutionary optimization algorithm and traditional neural network, one new kind of neural network which is called evolutionary neural network can be generated. To overcome the demerits of previously proposed evolutionary neural networks, combining the immune continuous ant colony algorithm proposed by author and BP neural network, a new evolutionary neural network is proposed. In this new evolutionary neural network, the architecture and connection weights of BP neural network are all optimized simultaneously by immune continuous ant colony algorithm. At last, to verify this new evolutionary neural network, the typical XOR problem is used. And also, the new evolutionary neural network is compared and analyzed with BP neural network, traditional evolutionary neural network based on genetic algorithm and evolutionary neural network based on evolutionary programming. The computing results show that the precision and efficiency of the new evolutionary neural network are all the best. Keywords: evolutionary neural network; neural network; ant colony algorithm; immune continuous ant colony algorithm.

1 Introduction By combining of evolutionary algorithm and traditional neural network, a new neural network-Evolutionary Neural Network is generated [1-2]. In evolutionary neural network, the auto-adaptability of evolutionary algorithm and learning capability of traditional neural network can be combined effectively. And the evolutionary neural network can mimic the evolution of the natural neural network system. So, from the bionics principles, the evolutionary neural network should be the next generation of neural network [2]. Based on above ideas, a lot of researchers have studied the evolutionary neural network and already proposed many evolutionary neural networks [1-9]. Through carefully analyzing the existing evolutionary neural networks, we can see that, the evolutionary algorithm used in evolutionary neural network is mainly genetic algorithm and evolutionary programming. And the evolutionary neural network based on genetic algorithm is the main one that has been studied for a long time and has obtained a lot of achievements. But this traditional evolutionary neural network has a lot of shortcomings [9]. To overcome these shortcomings, some researchers proposed M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 903–910. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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some evolutionary neural networks based on evolutionary programming [6,7,9], those evolutionary neural networks can overcome these shortcomings effectively. But in those evolutionary neural networks, users must use their experiences to estimate some parameters that have strong relationship with the performance of the evolutionary neural network, which makes the robustness of the evolutionary neural network very poor. To get good evolutionary neural network, here the immune continuous ant colony algorithm [10] proposed by author is introduced and one new evolutionary neural network whose architecture and connection weights evolve simultaneously is proposed. The theories of neural network have proved that, three-layer feed forward neural network can approximate any mapping from input space to output space with any precision [11]. Also, the previous studies have proved that [11], in order to improve the generalization of neural network, its architecture should be as simple as possible. So, in this new evolutionary neural network, the three-layer feed forward neural network is applied. Also, to make the model as simple as possible, the fulllinking network is applied. At last, this new evolutionary neural network is verified by the typical XOR problem.

2 New Evolutionary Neural Network 2.1 Immune Continuous Ant Colony Algorithm Ant colony algorithm is a new evolutionary optimization algorithm from mimic the behavior of ant colony, and proposed by Italy scholar M. Dorigo in 1990’s [12-14]. The original intention of ant colony algorithm is to solve the complicated combination optimization problems, such as TSP, so the traditional ant colony algorithm is a very good combination optimization method. From imitation information exchange among ant colony, the ant colony algorithm can solve the complicated combination optimization problem, and its effect is better than that of traditional methods. So according to the information cooperation of ant colony, the continuous optimization method based on principles of ant colony should be feasible. Based on above thought, some continuous ant colony algorithms have been proposed [14-17]. To study simply and effectively, here a new continuous ant colony algorithm is proposed. In this new continuous ant colony algorithm, there exists only one operation in the internal cycle, which is the probability move of ant individual. So, the ant individual cannot explore effectively in internal cycle, the efficiency and precision of whole algorithm will be damaged largely. To improve continuous ant colony algorithm, the mature methods used in evolutionary algorithm and artificial immune system are introduced into continuous ant colony algorithm, and one immune continuous ant colony algorithm is proposed for the first time. The detailed modification is that, in internal cycle, after the probability move is finished, the self-adaptive mutation operation and immunized selection operation are used. After this kind of modification, the ant individual can move more purposively, so the search effect can be improved very high.

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The flow chart of this new algorithm is as follow Fig. 1.

Initialization Extrinsic cycle start, k=1 Random distribution ant colony Fitness computing, record the best fitness Internal cycle start, s=1 Probability move of ant individul Self-adaptive mutation operation Immunized selection operation s=s+1 s<sm

Yes

Hormone update to each ant k=k+1 k
Yes

Output result

Fig. 1. Flow chart of immune continuous ant colony algorithm.

2.2 New Evolutionary Neural Network The details of the new evolutionary neural network are given as follows. Individual Expression. According to requirement of new evolutionary neural network, the individual should include number of hidden neuron and linking weights and thresholds of whole network. In order to express network simply, the threshold of neuron is put into the matrix of linking weight. To express easily, the individual expression is taken structural data type. So, the pseudocode of individual is as follows. Type individual Integer N Real W[i][j] End type individual where, W[i][j] is the matrix of linking weight, that include two matrixes which are matrix of linking weight between input layer and hidden layer and matrix of linking weight between hidden layer and output layer. Parameter of N is the number of hidden neuron.

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Creation of Ant Initial Population. Randomly generating certain number of neural networks as initial population, whose hidden neuron number and linking weights are generated in their initial scope. That is to say, the ant colony is randomly distributed in the solution space. The site of one ant individual is corresponding to one point of solution space. Expression of Fitness Function. In order to improve the generalization of evolutionary neural network, the network individual is trained by “sample counterchanging method”, and then its fitness is calculated. The sample counterchanging method is that, as to training network individual of each generation, not the whole training sample set is used, that is to say, a part of training sample set ( the 80% of whole sample set) is randomly drawn out to train the individual of each generation. So, the used training sample set for neural network of each generation is changed, and then the fitness of individual whose generalization capacity is poor is small while the fitness of individual whose generalization capacity is strong is large. Consequently, the performance of the whole evolutionary neural network is improved through selection. The error function of neural network is expressed as follows.

E=

1 N M [ y k (wk ; x a ) − t ka ] 2 ∑∑ 2 a =1 k =1 .

(1)

The individual fitness of neural network is expressed by follow transformation of error function of neural network.

f =

1 1+ E .

(2)

Probability Move of Ant Individual. The probability move of ant individual is the key operation of algorithm. The probability to move can be expressed as follows.

Pij =

[τ j ]α [η ij ] β

∑

[τ k ]α [η ik ] β

.

(3)

k

where, τ j is hormone intensity of ant individual. At initial stage, it is a constant, which is τ 0 = c . In this study, we take c = 0.001 . τ j is related with f(i) through Δτ .

η ij = f i − f j , which express the modification quantity of objective function after the ant individual moves. When f i = f j is occurred, the movement should be repeated, until they are not equal. The α and β are two variables, which ranges are as follows, 1 ≤ α ≤ 5 , 1 ≤ β ≤ 5 . Here, we take, α = 1 and β = 5 .

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Mutation Operation. Here, the mutation operation is a kind of adaptive Cauchi mutation, which form is as follows. Suppose one individual of the population is X = ( a1 ,..., a4 ) , the new mutated

individual will be X ′ = ( a1′,..., a4′ ) , then the component of the new individual can be described as follows.

ai′ = ai +σi ⋅ T ⋅ Ci (0,1) .

(4)

σi = 1.0

(5)

where,

βi F( X ) + γ i

.

where, σ i is the standard deviation of the parameters; Ci(0,1) is the Cauchi random number; F(X) is the fitness of individual; β i , γ i are two parameters, there β i = 1 , γ i = 0 ; T is a adaptive parameter, which description is as follows. T=

T0 Fmax − Favr

(6) .

where, T0 is the initial parameter, which value is 2.5; Fmax, Fmin are the maximum and minimum fitness of the current population. Selection Operation. According to the stimulative reaction and restraining reaction in immune system, one kind of thickness factor is introduced into selection operation to adjust the score of individual. If the thickness of individual is high, the premature is easy to appear, so the individual whose thickness is high should be restrained and the selection probability of individual whose fitness is large must be high. The detailed method is as follows, the adjustive modification based on thickness and fitness of individual is added to the score of individual, which is as follows. p′(i).scores = p(i).scores+ α ⋅ C ⋅ (1 −

F (i) ) ⋅ p(i).scores Fmax .

(7)

F (i) +β ⋅ ⋅ p(i).scores Fmax

where α , β are parameters which are in the range of [0~1], and here α = β = 0.5 ; C expresses the thickness of individual, whose definition is the ratio of number of individuals whose fitness is highest or near highest to total number of population, and here it can be expressed as follows, C=

t ⋅ (0.8 ⋅ Fmax → Fmax ) . N

(8)

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where, numerator is the summation of individuals whose fitness is between Fmax to 0.8*Fmax; F(i) is the fitness value of individual whose sequence number is i; Fmax if the maximum fitness of the population. Hormone Update Operation. After the above operations, the hormone information of new ant colony must be updated. The method of hormone update can be expressed as follows.

τ new = ρ ⋅ τ old j j +

∑ Δτ

k j

(9)

.

k

where, ρ is volatile rate of hormone, which can be taken as about 0.3. Δτ j is residual quantity of hormone, that can be expressed as follow forms. ⎧Q ant moved along pathway ij . Δτ = ⎨  ⎩ 0 otherwise

(10)

where, Q is a constant.

3 Simulation Experiment In order to verify the new evolutionary neural network proposed here, the typical problem of XOR is applied to test the new evolutionary neural network. The training sample set of XOR problem is as follow Table 1. Table 1. Training Set of XOR Problem. X1 X2 Y

0 0 0

1 0 1

1 1 1

0 1 1

1 -1 0

0 -1 1

-1 -1 1

-1 0 1

-1 1 0

In new evolutionary neural network, the controlling parameters are as follows. Number of input neuron is 2. Number of output neuron is 1. The number scope of hidden neuron is [1, 10]. The initial value scope of linking weight is [–1.0, 1.0]. The population size of ant colony algorithm is 100. The generation threshold is 200. At the same time, the maximum error of best individual is given as 10-5. In order to compare the new evolutionary neural network with BP network and evolutionary neural networks in [8] and [9], the three networks are applied to solve the same XOR problem. In BP network, the controlling parameters are as follows, learning-rate ν = 0.6 , momentum λ = 0.2 . The architecture of network is 2-2-1. In evolutionary neural network in [8], the controlling parameters are as follows, Pm = 0.05, Pc = 0.8, N = 30 . The evolutionary generation threshold is 200. The controlling parameters of evolutionary neural network in [9] are as the same with those of new evolutionary neural network proposed here.

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Training the four networks with training set in Table I, we can get the follow results. The network architectures of the three evolutionary neural networks are all 2-3-1. The comparison results are as in follow Table 2. Table 2. Training Set of XOR Problem.

Input (0, 0) (1, 0) (1, 1) (0, 1) (1, -1) (0, -1) (-1, -1) (-1, 0) (-1, 1) Training error Iterative numbers

BP network 0.003864 0.998074 1.000000 0.996921 0.000773 0.999867 0.997784 0.999777 0.001867 0.000014 700000

ENN in [9] 0.000041 0.999937 0.999745 0.999688 0.000013 0.999945 0.999991 0.999798 0.000137 0.000007 41

ENN in [10] 0.000000 1.000000 0.999997 1.000000 0.000002 1.000000 1.000000 1.000000 0.000005 0.000000 14

New ENN 0.000000 1.000003 0.999997 1.000001 0.000002 0.999996 1.000000 1.000001 0.000001 0.000005 5

Output 0 1 1 1 0 1 1 1 0

From above experiment results we can see that, the computation effect of new evolutionary neural network is obviously better than that of BP network and other evolutionary neural networks. Its computation precision is higher and iterative time is fewer. So, the new evolutionary neural network proposed in this paper is a very good evolutionary neural network and can be applied in many complicated engineering problems.

5 Conclusion Evolutionary neural network is a new kind of neural network combining evolutionary computation and neural network theory. Because in this neural network, the autoadaptability of evolutionary computation and learning capability of neural network can be combined effectively, the evolutionary neural network has become the inevitable tendency of neural network. As to the importance of this study, combining the immune continuous ant colony algorithm proposed by author and BP neural network, a new evolutionary neural network whose architecture and connection weights evolve simultaneously is proposed in this paper. At last, this new evolutionary neural network is verified by typical XOR problem, and is compared with BP network and other evolutionary neural networks. The results show that, the new evolutionary neural network can obviously improve the computation precision and computation efficiency and is a very good neural network. Acknowledgments. The financial supports from The National Natural Science Foundation of China under Grant No. 41072233 and 40872187 are gratefully acknowledged.

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References 1. Yao, X.: A Review of Artificial Neural Networks. Int. J. of Intelligent Systems 8, 539–567 (1993) 2. Muhlenbein, H.: Limitations of Multi-Layer Preceptron Networks-Steps Towards Genetic Neural Networks. Parallel Computing 14, 249–260 (1990) 3. Baluja, S.: Evolution of an Artificial Neural Network Based Autonomous Land Vehicle Controller. IEEE Trans. on SMC 12, 450–463 (1996) 4. Skourikhine, A.N.: An Evolutionary Algorithm for Designing Feed-forward Neural Networks. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 629–635. Springer, Heidelberg (1998) 5. Takahashi, H., Agui, T., Nagahshi, H.: Designing Adaptive Neural Network Architectures and Their Learning Parameters Using Genetic Algorithms. In: Proc. Science of ANNII, Orlando, Florido, pp. 208–215 (1993) 6. Yao, X., Liu, Y.: A New Evolutionary System for Evolving Artificial Neural Networks. IEEE Trans. on NN 8, 694–713 (1997) 7. Fang, J., Xi, Y.G.: Neural Network Design Based on Evolutionary Programming. Artificial Intelligence in Engineering 11, 155–161 (1997) 8. Maniezzo, V.: Genetic Evolution of the Topology and Weight Distribution of Neural Networks. IEEE Trans. on NN 5, 39–53 (1994) 9. Gao, W.: Study on new evolutionary neural network. In: Proc. of ICLMC 2003, pp. 1287–1293. IEEE Press, New York (2003) 10. Gao, W.: Study on immunized Ant Colony Optimization. In: Proceedings Of Third International Conference On Natural Computation, pp. 792–796. IEEE Press, New York (2007) 11. Jin, F.: Foundation of Neural Computation Intelligence-Principles and Methods. Southwest Jiaotong University Press, Chengdu (2000) 12. Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a colony of cooperaing agents. IEEE Trans. on SMC 26, 29–41 (1996) 13. Dorigo, M., Di Caro, G.: The Ant Colony Optimization: A New Meta-Heuristic. In: New Ideas in Optimization, pp. 11–32. McGraw-Hill, New York (1999) 14. Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004) 15. Monmarché, N., Venturimi, G., Slimane, M.: On how Pachycondla apicalis ants suggest a new search algorithm. Future Generation Computer System 16, 937–946 (2000) 16. Mohit, M., Sachin, B.K., Sidhartha, P., et al.: Ant Colony Approach to Continuous Function Optimization. Ind. Eng. Chem. Res. 39, 3814–3822 (2000) 17. Dréo, J., Siarry, P.: Continuous interacting ant colony algorithm based on dense heterarchy. Future Generation Computer Systems 20, 841–856 (2004)

Reliable and Delay-Efficient Routing Algorithm in Wireless Sensor Networks Mohammad Ali Jabraeil Jamali, Adel Fathi, and Javad Pashaei Islamic Azad University, Shabestar Branch, Shabestar, Iran [email protected], [email protected], [email protected]

Abstract. In the last years with according to the increasing application of wireless sensor networks in military, space, medicine, safety, fields and etc, many methods and algorithms have been represented to improve the quality of routing. But according to existence limits in this network, the representative methods aren’t properly responsible and the goals should be improved. Our goal is that we should prepare a soft QoS for different packets that the data on route will be not available easily in wireless networks. In wireless network, it has been worked a lot about reliability, delay and energy but in this network the energy problem is the main goal and also must be work it. In this paper determining the route unlike many of papers is based on the node's local information and links. Keywords: QoS, Wireless sensor network, Routing, Reliability.

1 Introduction We know that increasing in use of wireless sensor networks in different dimension and various environments with special and different topology it makes obvious the requirement of designing new algorithms or improve previous ones sensor networks used in unpredictable alarms, safety works and military and medicine and space. In all of sensor networks nodes distribute in the special places and in their life that their energy are so restricted, they receive information from neighbor nodes and send it to base station or sink and we can use various path for sending information. In these networks, the sensor nodes send the sensed data in a packet form to the stand or motion station in conformity with our needs and requests. Some of the QoS parameters for instance packets delay, reliability, energy consumption, packet's safety, packet's miss, network's constraint,… and economical should be satisfied. For example, in multimedia packet's like video or audio packets if some of the parts is lost it is acceptable, but more delay than it's rule, it will decrease the quality of photos and sounds. While in safety networks one packet should deliver quickly and without any lost, according to the above text, finding the route of QoS is a main subject in wireless sensor networks. In this paper, we will consider both of delay limits and reliability in the route of QoS, we will try to use a suitable way to improve reliability, reduce delay and control energy consumption and balance them. We should regard that in wiry networks, QoS M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 911–919. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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routing with multiple constraints has been studied perfectly and reliability is not the main factor on them. The paper is organized as follows. Section 2 explains previous works on sensor network. Section 3 explains the E2E-QoS and its problems. Section 4 explains the MCMP (Multi Constraint Multi Path) paper for delay-reliability constraint and present equals for update nodes that we use of them. Section 5 presents our algorithm for delay-reliability constraint and management consuming energy. Section 6 includes simulation and section 7 concludes the paper.

2 Related Works In wired networks, many papers have proposed exact or heuristic algorithms targeted at MCP or MCOP (Multi-Constrained Optimal Path) problems [2,3,5,8,9,10]. Every data packet is assigned a priority level based on its Information content and criticality. When a node receives a packet, it looks at its priority level and acts accordingly to provide the desired reliability. The concept of such a differentiated services and data prioritizing framework for sensor networks was introduced in [6], a multipath-multi packet forwarding protocol was proposed for delivering data packets at required reliability based on data priority. By using redundancy in packets the protocol controls the reliability of packet delivery. The protocol uses a probabilistic flooding scheme to create multiple paths form source to sink. It adapts to any channel error and topological changes. However the main drawback of the algorithm is that it requires periodic update of forwarding parameters to adapt to the changes. Reinform does not require any periodic reinforcements. In [7], multiple paths from source to sink are used in diffusion routing framework [4], to quickly recover from path failure. The multiple paths provided by such protocols could be used for sending the multiple copies of each packet. However it incurs extra overhead of multiple path formation and maintenance of path state in each node and is not adaptive to channel errors. On generating a packet the source node determines the importance of the information it contains and decides the desired reliability (rs) for it. It also knows the local channel error (es) and its hop distance from sink (hs). Using these values, the source computes the number of paths (or equivalently, the number of copies of the packet to be sent) P required for delivering the packet at desired reliability to the sink as [11]:

p( rs , es , hs ) =

log(1 − rs ) log(1 − (1 − es ) hs )

(1)

The source broadcasts the packet and puts certain DPS (dynamic packet state) fields in its header. The key aspect of our algorithm is the DPS mechanism which allows the receiving nodes to take local decisions about forwarding the packets.

3 E2E QoS Reliability define as route delivered packets that has successfully received by sink or BS to the number of packets generated by source nodes, reliability in E2E calculate like below[1]:

Reliable and Delay-Efficient Routing Algorithm in Wireless Sensor Networks

∏

rij

913

(2)

( i , j )∈ p

Where rij is the reliability of link (i, j) on path p, we see that reliability increase multiply, a variation in each link p will change reliability sizably. Even a degradation of 5% on each link will cause to decrease about 19% on the path p will 4 hops. Then with increase in number of hops the E2E reliability will reduce. S 0.95

0.95

0.95

0.95 d

r1= r2 = r3 = r4 = 0.95 r =

∏

(1 − ri )

r ≈ 0.81

According to above problem and also unique route between source and destination and probability of disconnection and destroy one of nodes using one route will not be able to do the QoS requirements. If we select r1 as the least reliability for delivered packets only various routing will be able improve confidence ability. If we consider d1 as the delay constraint to packets any route between source and destination has delay more than that it is not acceptable.If we accurate to the above constraint we will find out that choosing a unique route can't gain our goals. Using multi path and also multiple routes will increase the energy cost and will increase the conjection that in this way, the performance network will reduce then we should be aware of all routes. 3.1 Problem of E2E Wireless links are susceptible to fading, interference and traffic variation. It is impossible to get correct temporary data from connection state. Update periodic data of nodes will have overhead more on system and it will cause fading data and increase delay. The number of nodes may be use old data due to last so much nodes information update. This situation is most clear in sensor networks with large scale. Storage of voluminous E2E route information needs a big memory but sensor nodes were equipped with very limited memory. The above reason justify us to use routing method base on link, because in this way the suitable neighbor data for taking decision is enough and this will reduce overhead and also reduce the costs.

4 MCMP (Multi Constraint Multi Path) Algorithm In many papers that multi path routing is subject of them it has been used from hop count as a distance between the source node and destination node. For instant we can indicate to MCMP paper [1], which it was used hop count for metric route measuring. In this paper, two neighbour nodes are related with a link, the space between them is just one hop. Routing in MCMP is defined as follows: At first it is selected neighbours of source node in case that the number of its hops up to destination is less than source node. Now among those neighbours a node is selected for routing that verity in this inequality: 2 ⎞ ⎛ α xj⎜ ( Δdij ) 2 + 2 Ldi d ij − d ij2 ⎟ ≤ Ldi ⎠ ⎝1− α

Where xj is neighbour of node i.

(3)

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For calculating and up to dating reliability and delay, we can use below equations [1]:

Δdij ( t ) = (1 − ρ ) Δdij ( t − 1) + ρ d ij ( t ) − d ij ( t − 1) Δ ( t ) = (1 − γ ) Δ ( t − 1) + γ rij ( t ) − rij ( t − 1) r ij

r ij

(4) (5)

Where parameter ρ and γ smooths the variations of dij and rij in time for realistic wireless sensor networks, this is reasonable because current link state depends on historical link state. Table 1. Notation

(i, j) N (i) hi rij α £ Li Li Di Ri dij rij xj dij rij ej T d ij r ij

link from node i to node j the neighbour set of node i hop count from current node i to the sink reliability of link lij soft-QoS probability for delay soft-QoS probability for reliability hop requirement for delay at node i hop requirement for reliability at node i actual delay of the packet arriving at node i reliability requirement assigned to the path through node i delay of link lij , described as a random variable reliability of link lij, described as a random variable decision variable of whether link (i, j) is used mean of dij mean of rij energy of node j threshold of energy for life of any node Standard deviation of dij Standard deviation of rij

5 MCMPM (Multi Constraint Multi Path Modified) Algorithm Fault of MCMP method is that despite selecting route with less delay and more reliability, almost has deterministic state and consume the most energy of any node in route and also make traffic in route. In this paper we try to present a new algorithm to resolve this problem, reduce the delay and increase the reliability this algorithm is based on multi pass routing. For calculating the distance between two points, we can use this equal:

d ( x, y ) = ( x2 − x1 ) 2 + ( y2 − y1 ) 2

(6)

Reliable and Delay-Efficient Routing Algorithm in Wireless Sensor Networks

915

In figure 1 it has shown how to select nodes among neighbour nodes, we choose N1, N2 and N3 to forwarding. The distance between N1, N2 and N3 are less than distance between source node and BS. Now we don’t choose the nearest, we define energy threshold for any node. The node is selected that has less distance and its energy is more than threshold, this action continue up to arriving to BS, next step with assistance of equal (3) existence nodes become up to date and optimize, paths is gained and these routes are dynamic because the variant of energy nodes is essential to select the route, it makes to use all the possible routes so that it causes management in energy consumption and prevent from heavy traffic.

5 d1

1

8

2 6 d2

3

d3

9

4 7 Fig. 1. Reliability distribution between s-d pair Table 2. MCMPM routing algorithm

0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Delay-reliability Constrained Multipath Routing AL candidate = {d(j, BS) < d(i, BS) and Tj is max; forwarding = Ø L di

D

D

j N (i)}

i

if ( Ldi 0) discard the packet and return; else Lri

Ri

d

r (t ) Update ij (t ) ijand while (candidate = Ø){ if (inequality (3) holds for dij and add link lij to forwarding; candidate = candidate lij }};

using equations (4) and (5); d ij

(t )

){

In figure 1 we show that how to select neighbour nodes for routing, in this figure d1, d2 and d3 are less than distance between source node and destination node, so node1, node2, and node3 are candidate for forwarding packets, node4 don’t choose because its energy is less than threshold, node6 and node7 are neighbours of node3

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Li ≤ 0

that d(6,d)
6 Simulation The simulation is implemented in matlab. The simulations are performed on a uniform topology consisting of 100 nodes spread in a square area of 100m × 100m. Base station is selected random. The transmission range of all nodes is 25m. Success probability of each transmission is randomly picked from [0.8, 1], which implies that the link reliability ranges from 0.8 to 1. Link delay is also randomly distributed in the range of [1, 50] ms. Link delay is the elapsed time for successfully transmitting a packet after receiving it. So it includes queuing time, contention time, transmission time, retransmission time and propagation time. Link states randomly vary at all transmission instants. So it is a worst case comparing to real networks. The delay requirement is uniformly distributed between 120 to 260 ms with an interval of 10 ms, which produces 15 delay requirement levels. Likewise, the reliability requirement uniformly ranges from 0.7 to 1 with an interval of 0.05. This gives 7 distinct reliability requirement levels. Each simulation run randomly selects 15 nodes to generate packets at the speed of 1 packet/second. Data packet has a fixed size of 150 bytes. We change the random seed to generate different traffic across the network at each of the 12 runs. For the same traffic setting, three algorithms are executed for comparison [1]. 6.1 Simulation Results Figures separately show delay-reliability constraint difference MCMPM with other routing and indicate that it has the least delay and also indicate that its reliability constraint is as same as MCMP routing but it is justifiable and it will be described in forward. In figure 2, 100 node spread randomly in zone with a 50 * 50 dimension at first 15 nodes are selected randomly for sending packets that after run program the sending path of packets appears as is obvious figure. It shows BS neighbor nodes are used almost equally in routing so it means multipath and energy optimal consumption. Figure 3 shows that if we for instant consider delay constraint between 140 to 160 micro seconds (ms), in MCMP routing packets in average have less delay than 25 ms, while braided has delay between 25 and 27 and MCMP has between 45 and 50 ms, god routing has more than 50 so that with increasing in delay constraint packets delay average increase continuous, while for delay constraint 120ms is at least and 260ms is most, for MCMP is less than 25ms and almost is without change, this result achieve with parameter α=0.85.

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6.2 Energy Model We assume l is length of packet (on bit), d is destination between nodes and T is threshold, Efs is free space energy, EMP multipath energy and EDA is data aggregation energy that Eelec=50nj/bit, Efs=10pj/bit/m2, EMP=0/0013pj/bit/M4 EDa=5nj/bit/signal and Ecpu=7nj/bit ithms.

Fig. 2. Multi path multi constraint indicate base station

Fig. 4. Packet delivery ratio vs. reliability requirement

Fig. 3. Average end-to-end packet delay

Fig. 5. Multipath multi constraint (α=0.9)

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Fig. 6. End-to-end packet delay (α=0.9)

Fig. 7. Packet delivery ratio vs. reliability (α=0.9)

7 Conclusion In this paper we propose a new method for routing using MCMP paper for sensor wireless networks base of this model is using multipath that the node has most energy and least distance is selected and information of nodes delay and reliability of and path become up to date by existing equations. Unlike MCMP the existing routing algorithms in this network just consider one parameter while our routing algorithm increase delay and it causes to correct consumption of energy and prevent overhead and needs less memory for nodes. While these nodes have limited resource within energy and memory, simulation results for different state nodes pitch, agree and says and indicated that performance of our routing is similar to ideal state. This ideal state is achieved by the multipath routing with perfect link knowledge. In according to this routing is based on local nodes information so variety of network topology will not cause destroy routing because routing performance achieve without regard to global information, this routing perform with calculating direct distance between source node and BS. If we have a big guard between the source nodes and distance nodes we should try to resolve this fault, there is an idea that we can use an interface nodes for calculating nearest distance.

References 1. Huang, X., Fang, Y.: Multi constrained QoS multipath outing in wireless sensor networks. Springer Science + Business Media, LLC (2007) (Published online: January 4, 2007) 2. Mieghem, P.V., Kuipers, F.A.: Concepts of exact QoS routing algorithms. IEEE/ACM Trans. on Networking 12(5), 851–864 (2004) 3. Yuan, X.: Heuristic algorithms for multi constrained quality of service routing. IEEE/ACM Trans. on Networking 10(2), 244–256 (2002) 4. Intanagonwiwat, C., Govindan, R., Estrin, D.: Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks. In: Proc. of ACM MOBICOM (2000) 5. Mieghem, P.V., Kuipers, F.A.: On the Complexity of QoS Routing. Computer Communications 26(4), 376–387 (2003)

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6. Bhatnagar, S., Deb, B., Nath, B.: Service Differentiation in Sensor Networks. In: Proc. of Wireless Personal Multimedia Communications (2001) 7. Ganesan, R., Govindan, S.: Shenker and Estrin, D.: Highly Resilient, Energy Efficient Multipath Routing in Wireless Sensor Networks Mobile Computing and Communications Review (MC2R) 1 (2002) 8. Neve, H.D., Mieghem, P.V.: A multiple quality of service routing algorithm for PNNI. In: Proc. of the ATM workshop IEEE, May 1998, pp. 324–328 (1998) 9. Korkmaz, T., Krunz, M.: Bandwidth-delay constrained path selection under inaccurate state information. IEEE/ACM Trans. on Networking 11(3), 384–398 (2003) 10. Znati, T.F., Melhem, R.: Node delay assignment strategies to support end-to-end requirement in heterogeneous networks. IEEE/ACM Trans. on Networking 12(5), 879–892 (2004) 11. Deb, B., Bhatnagar, S., Dataman, B.N.: ReInForM: Reliable Information Forwarding using Multiple Paths in Sensor Networks. In: Proc. of LCN 2003, pp. 406–415 (2003)

Interfacial Impedance Sensor Employing Bio-activated Microbeads and NiHCF-Coated Interdigitated Microelectrodes: A Model Analysis Nuno M.M. Pires, Tao Dong*, Zhaochu Yang, and Lei Zhang Norwegian Center of Expertise on Micro and Nanotechnology, Institutt for Mikro- og Nanosystemteknologi, Vestfold University College, Horten, Norway [email protected]

Abstract. An impedance sensor based on NiHCF-coated interdigitated microelectrodes is presented as a promising solution for ultra-low level pathogen detection. The micro sensor employs micro-sized beads, which are coated with anti-pathogen specific antibodies, to achieve the high-sensitive detection through the amplified variations of the interfacial impedance. The effect of beads on the microelectrodes solution interface and subsequent impedance change were analyzed and demonstrated by fitting the experimental data to an equivalent electrical circuit model. Keywords: interfacial impedance sensor, NiHCF film, interdigitated microelectrodes, bio-activated microbeads, equivalent electrical circuit model, highsensitivity pathogen detection.

1 Introduction Advances in electrical detection technologies provide significant solutions: impedance spectroscopy (IS) based assays have been suggested as appealing alternatives to conventional readouts owing to their low cost, low power consumption, and ease of miniaturization [1]. The basis of IS detection systems stems from binding-induced changes in the resistive and capacitive properties of the electrode–solution interface. The electrode geometry has been shown to be extremely important, with arrays of interdigitated microelectrodes (IDMEs) providing greater device sensitivity. Due to proximity of cathodic and anodic electrodes, minute amounts of ionic species can be efficiently cycled on the electrodes, giving the IDME a promising advantage in detecting biological particles at pico mole level concentrations [2]. As an important inorganic polymeric compound of metal hexacyanoferrate (MHCF) class, nickel hexacyanoferrate (NiHCF) have attracted considerable interests in electronic biosensing applications, recently [3]. When coating the electrodes, thin films of NiHCF exhibit unique characteristics such as good electroactivity, ion exchange and, especially, electrode-confined oxidation-reduction (redox) reaction. As for these properties, *

Corresponding author.

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NiHCF films eliminate the use of additional chemical mediators (e.g. [Fe(CN)6]3-/4-), thus greatly simplifying the immunoassay system and decreasing analytical time and sensor costs. Micro- and nano-sized beads are effective to capture and trap bioparticles on-chip through their antigens [4]. In the present study, bio-activated microbeads were employed to magnify the variation in impedance of the NiHCF film coated IDMEs solution interface. The interfacial impedance change was thoroughly analyzed using an equivalent circuit model and its simulation. To this end, the ISAV antigen, a devastating aquaculture pathogen, was targeted in the microfabricated impedance sensor chip (MISC).

2 Device Design Fig. 1a shows the MISC device, which integrates sets of NiHCF film coated gold IDMEs into PDMS microfluidic channels. The microelectrodes comprise 25 digital pairs with 200 μm overlap, 5 μm finger width and 5 μm inter-finger gaps. Such design is consistent with the optimal IDME dimensions for electrochemical impedance measurement bio-applications [2]. As NiHCF particles have the highly affinity to Au surfaces, the film immobilization on the electrodes is very stable. Electrical tests were performed on 200 μm height fluid channel [5] where the inlet and outlet were set with 210 μm and 230 μm wide, respectively.

Fig. 1. (a) The MISC device incorporates NiHCF-coated IDMEs with a microfluidic channel in one glass chip, where (b) bio-activated beads will bind on top of the microelectrodes as long as their captured pathogen matches to the antibodies that coated the NiHCF electrode surface.

To achieve an effective detection, the surface of 10 μm polystyrene beads and MISC electrodes are activated with anti-pathogen specific antibodies (see Fig. 1b) and 2a). Therefore the target pathogen can easily be recognized and captured by their corresponding bio-activated microbeads, and then being immobilized on the electrodes surface, as illustrated in Fig. 2b. The IS test is performed to determine the presence of the immobilized pathogenic particles, through the measurement of subsequent variations in the interfacial impedance, after un-bonded beads being flushed out from the MISC channel.

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2.1 NiHCF Film Coated IDME Solution Interface As a unique property, NiHCF film provides the reversible [Fe(CN)6]3-/4- redox reaction confined to the IDME surface, when a small sinusoidal voltage ( ~ 10 mV) is applied. The reaction is represented in (2.1) where A is an alkali metal element and A+ (e.g. Na+ or K+) is the corresponded cation. Thus, ionic and electron transfers occur at the interfaces electrolyte-film and film-electrodes, as shown in Fig. 2a. Precisely, cations migrate from the electrolyte solution to the film on the cathode area; while electrons are transported to the film from the Au electrode. On the anode area NiHCF releases cations and electrons to the solution and electrode, respectively. ANi[FeIII(CN)6] + A+ + e- ⇌ A2Ni[FeII(CN)6] .

(2.1)

Such electrochemical process will not be hindered by high ionic strengths; oppositely, it benefits from the high concentration of alkali cations [3]. It would be an advance to the MEMS electrochemical sensor for probing complex samples.

Fig. 2. (a) Ionic and electron transfers are generated from the [Fe(CN)6]3-/4- redox process, which is confined to the NIHCF film coated gold IDMEs. (b) When the bio-activated beads attach on the top of the film, they cause dielectric and insulating effects at the interface between electrodes and electrolyte solution.

When the microbeads attach to the NiHCF electrode surface, the local electric field can be influenced due to their insulating effect to the cation from migration to the film (see Fig. 2b), thereby affecting the charge transfer. Simultaneously, the dielectric characteristics of the electrochemical double layer can also be affected. In addition, influence on electrical behaviour at the film-electrolyte interface would be expected, since the dielectric and conductivity properties of antibody-coated beads are very different than from that of the electrolyte solution. These effects cause the variation in impedance at the interface from the baseline values. The magnitude of the interfacial impedance variation depends on the number of bonded beads, which is proportional to the target pathogen concentration. 2.2 Equivalent Circuit Model The interfacial impedance can be described using an electrical equivalent circuit, which is represented in Fig. 3. Each branch corresponds to one set of microelectrodes, and is the parallel circuit between CPE1 and the series circuit formed by Rct and CPE2.

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The constant-phase elements CPE1 and CPE2 express the effect of the double layer capacitance and the Warburg impedance resulting from diffusion of cations to the NiHCF film, respectively. The Rct element is the charge transfer resistance. The two sets of electrodes are connected in series through the electrolyte solution Rs. The complete circuit consists of the Ccell element connected in parallel with that series, which corresponds to the capacitance on the electrodes surface.

Fig. 3. The NiHCF-coated IDMEs is represented by the series circuit connection between two sets of microelectrodes. Each set consists of the resistance Rct and the constant-phase elements CPE1 and CPE2. The resistance Rs connects the two sets of electrodes. The capacitor Ccell completes the circuit.

In the present work, an analytical model was derived to express the real (Ztotal,Re) and imaginary (Ztotal,Im) parts of the complex interfacial impedance (Ztotal) for the NiHCF-modified IDME sensor. As a result, the complex impedance relative to the CPE2 parameter is expressed by the effective Warburg coefficient (σeff), the distribution parameter for the Warburg coefficient (ψ) and the resistance Rct, as a function of frequency [6]: WCPE

2

= 2ψ σ eff 2ψ Rct 1 − 2ψ ω −ψ [ cos (ψπ 2 ) − j sin (ψπ 2 )]

(2.2)

Such impedance considers the electrode inhomogeneity introduced by NiHCF particles. The above expression can be separated into two equations for the series resistance Rss and the series capacitance Css: R ss = 2 ψ σ eff 2 ψ R ct 1 − 2 ψ ω − ψ cos (ψπ 2 )

C ss = 2

−ψ

σ eff − ψ Rct − (1 − 2ψ )ωψ − 1[sin (ψπ 2)]

(2.3) (2.4)

The CPE1 element can be represented by their equivalent parallel resistive (Rp) and parallel capacitive (Cp) components [7], which corresponding expressions include the effect of the electrolyte resistance: ⎡ R p = ⎢ ⎛⎜ ω Cdeff ⎝ ⎣⎢

1−α − ⎤ ⎞⎟ Rs α sin (α π 2 )⎥ ⎠ ⎥⎦

−1

(2.5)

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( )

C p = C deff 1 − α ωRs −α cos (απ 2 )

925

(2.6)

The variables Cdeff and α are the effective double-layer capacitance and the corresponded distribution parameter, respectively. Using (2.2) to (2.6), successive transformations series-parallel were performed to obtain the resistive (Rset) and the capacitive (Cset) elements of the complex impedance Zset corresponded to the two electrodes sets. The component Rset results of the series connection between the resistances Rse1 and Rse1, while Cset is the equivalent series combination between the capacitances Cse1 and Cse2 of each set of electrodes. Rset = Rse1 + Rse 2

(

(2.7)

C set = 1 C se1 + 1 C se 2 − 1

)

(2.8)

Z set = Rset − 1 ω Cset

(2.9)

The total interfacial impedance is the equivalent parallel between Zset and the impedance Zcell due to the capacitor Ccell, as represented in (3.9). From that, generalized (2.12) and (2.13) can be derived to express their real and imaginary parts, where Xset and Xcell is the electrical reactance relative to Zset and Zcell, respectively. 1 1 1 = + Z total Z set Z cell

(2.10)

Ztotal = Ztotal, Re + Ztotal, Im

(2.11)

Z total , Re =

(X cell Rset )(X cell + X set )− Rset X set X cell Rset 2 + (X cell + X set )2

(2.12)

Z total , Im=

(X cell Rset )Rset + X cell X set ( X cell + X set ) Rset 2 + ( X cell + X set )2

(2.13)

3 Simulation Results and Discussion 3.1 Bead Effects on the Interfacial Impedance The effects of bonded beads at the interface can be explained through the model derived in (2.2) to (2.13). By fitting the experimental data measured in 0.1M NaCl to the model using the complex non-linear regression least-squares method, the baseline values of Rs, Rct and Cdeff was obtained for the NiHCF IDME system, which were 8.24kΩ, 8.48kΩ and 3.40nF, respectively (see Table 1). The percentage mean deviations inherent to the fitting process were 2.4% which indicates the adequate quality of the model used. The data was recorded at the frequency band ranging from 0.1Hz to 1MHz, thus excluding the contribution from Ccell to the total impedance. As shown in table 1, the baseline values of Rs, Rct and Cdeff changed when the experimental data

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measured at different ISAV antigen concentrations were fitted to the model. Higher variations were obtained as more antigen concentrated solutions were used which means more beads bonded on top of the film. Increases of 3.8 and 9.2% in Rs were observed with 1ng/ml and 1μg/ml ISAV antigen, respectively. The beads “membrane” coated with antibody added a parasitic resistance to the interface, when they attached on top of the NiHCF film, thus enhancing the electrolyte resistance. Table 1. Fitting values and percentage changes of Rs, Rct and Cdeff for the NiHCF-coated IDMEs (baseline) and after ISAV antigen binding at different concentrations Rs(kΩ) NiHCF IDMEs 8.24±0.22 1ng/ml antigen 8.55±0.20 binding 1µg/ml antigen 9.21±0.19 binding

ΔRs (%) Rct (kΩ) 8.48±0.12 3.8 9.10±0.12

ΔRct (%) 7.3

Cdeff (nF) 3.400±0.272 3.395±0.237

ΔCdeff (%) 0.15

9.2

12.3

3.386±0.251

0.4

9.52±0.11

The resistance Rct was significantly increased (around 12.3%) with 1µg/ml antigen. Such result showed that beads had insulation effects on the electrodes, thus hindering the redox reaction to some level. Indeed the bead surface provided a local barrier to the cation migration to the film. The Cdeff capacitance was decreased, even though the variations occurred in a smaller scale. The gap between bonded beads and film was filled with the electrolyte which highly minimized the effect of the beads membrane capacitance on the electrochemical double layer. With the values of Rs, Rct and Cdeff input into the model, standard Nyquist plots were generated to demonstrate the influence of their variations on the total interfacial impedance. As the magnitude of the impedance values increased with the increasing percentage changes of Rs and Rct, it indicates that the more antigens bonded, the higher the impedance values. The simulated Nyquist spectrum for Rs and Rct variation is presented in Fig.4. There were no changes in spectrum with the Cdeff variation.

Fig. 4. The Nyquist plot shows that bio-activated beads can affect the interfacial impedance through the variation in (a) the solution resistance Rs and (b) charge transfer resistance Rct. The baseline condition concerns to the NiHCF IDME system with no antigen bonded.

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3.2 Optimal Frequency Band To estimate which frequency band the magnitude of the impedance variation was maximum, plots impedance change with tested frequencies ranging from 0.1Hz to 1MHz were generated for the percentage values of Rs, Rct and Cdeff. The expression to determine the impedance variation (ΔZ/Z) was defined in (3.1), where Zbaseline is the baseline impedance (used as the reference value), and the variable ZAg is the impedance magnitude due to the variation in Rs, Rct and Cdeff. Δ Z Z baseline − Z Ag = Z baseline Z

(3.1)

As observed in Fig. 5, the higher magnitude value of the impedance variation due to Rs was obtained at frequencies above 10kHz. Actually, the interfacial impedance was dominated by the solution resistance at the frequency range 10kHz to 1MHz, which was set as the optimal frequency range in this work to test the sensitivity of the MISC device.

Fig. 5. Changes in Rs and Rct caused significant variations in the interfacial impedance at the frequency range of 10kHz-1MHz and 0.1Hz-1kHz, respectively. In practice, such frequency band was selected to realize the experimental impedance measurements.

The influence of the Rct changes on impedance predominated at frequencies below 1kHz. The values of impedance variation showed high order of magnitude, which was similar to the Rs case. Hence, the test at frequencies ranging from 0.1Hz to 1kHz could be addressed in a future work. For the plot corresponding to the Cdeff changes, an optimal frequency was clearly observed at 10kHz around. However, the order of magnitude was ~ 10-3, which meant over 10 times lower than those values in Rs and Rct cases.

4 Experiments To demonstrate the capability of the MISC device to detect pathogens, the ISAV antigen was targeted over a range of concentrations. Results from experiments in 0.1M NaCl solution showed a significant increase of the impedance variation as the ISAV concentration increased from 1pg/ml to 1μg/ml, as can be observed in Fig. 6, and it was consistent with the magnitude levels of ΔΖ/Ζ obtained in the previous

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simulation (see Fig. 5). The increase in target antigen concentration reflected in more beads being attached on the NiHCF-coated IDME surface, thus resulting in larger changes in the interfacial impedance. Samples containing beads with no target antigen were tested as the control experiment, and therefore the detection limit was 10pg/ml.

Fig. 6. Significant variations in the interfacial impedance magnitude (ΔZ/Z) were obtained by targeting 1pg/ml to 1μg/ml ISAV antigen. The detection limit of 10pg/ml demonstrates that the MISC device is able to detect pathogens at very low concentrations.

5 Summary The capability of the NiHCF-coated IDMEs based sensor for detecting pathogens was thoroughly analysed using an equivalent electrical circuit model. Variations in the interfacial impedance with a high order of magnitude employing bio-activated microbeads were demonstrated, thus making the sensor very promising to realize the highly sensitive detection.

References 1. Katz, E., Willner, I.: Probing biomolecular interactions at conductive and semiconductive surfaces by impedance spectroscopy: routes to impedimetric immunosensors, DNA-sensors, and enzyme biosensors. Electroanal 15, 913–947 (2003) 2. Varshney, M., Li, Y.: Interdigitated array microelectrodes based impedance biosensors for detection of bacterial cells. Biosens. Bioelectron. 24, 2951–2960 (2009) 3. Chen, W., Tang, J., Cheng, H.-J., Xia, X.-H.: A simple method for fabrication of sole composition nickel hexacyanoferrate. Talanta 80, 539–543 (2009) 4. Dong, T., et al.: A smart fully integrated micromachined separator with soft magnetic micro-pillar arrays for cell isolation. J. Micromech. Microeng. 20, 115021 (2010) 5. Dong, T., Yang, Z., Su, Q., Tran, N., Egeland, E., Karlsen, F., Zhang, Y., Kapiris, M., Jakobsen, H.: Integratable non-clogging microconcentrator based on counter-flow principle for continuous enrichment of caski cells sample. Microfluid. Nanofluid., 1–11 (2010) 6. Retter, U., Widmann, A., Siegler, K., Kahlert, H.: On the impedance of potassium nickel(II) hexacyanoferrate(II) composite electrodes-the generalization of the randles model referring to inhomogeneous electrode materials. J. Electroanal. Chem. 546, 87–96 (2003) 7. Brug, G.J., van den Eeden, A., Sluyters-Rehbach, M., Sluyters, J.: The analysis of electrode impedances complicated by a phase element. J. Electroanal. Chem. 176, 275–295 (1984)

An Improved Fp-Tree Algorithm Haijun Zhang1, Changchang Zhang2, and Bo Zhang1 1

School of Information, Beijing WuZi University, Beijing, China [email protected], [email protected] 2 Institute of Information and Engineer, North China University of Technology, China [email protected]

Abstract. Fp-tree algorithm plays an important role in mining maximal frequent patterns and searching association rules. However, because of recursive function call and more pointer fields of each node, traditional Fp-tree algorithm brings about large memory usage and low efficiency. This paper proposed an improved Fp-tree algorithm which used a non-recursive mechanism to reduce the use of the stack memory, and reduced a pointer field of each node to save memory usage. Experimental results showed that the improved algorithm is more efficient in the case of large volumes of data. Keywords: Fp-tree, frequent item sets, association rules, pointers flip.

1 Introduction With the development of information society, data mining research heads to a deeper and more practical technology direction. How to extract useful information from massive amounts of data efficiently and accurately has become a key issue in data mining. Mining frequent item sets is the basis for mining association rules. Without generating and testing candidate item sets, Fp-tree algorithm has a high efficiency in mining frequent item sets [1]. However, the traditional Fp-tree algorithm scans the transaction database twice to build Fp-tree recursively which will take a lot of time, especially in the case of larger transactions, at the same time, the memory cost increased greatly because of lot pointer fields, which makes it impossible to build a Fp-tree in memory. How to improve the algorithm and advance mining efficiency is very important. This paper improved the classical Fp-tree from the aspects of efficiency and memory usage.

2 Traditional Fp-Tree Algorithm 2.1 The Basic Steps of Traditional Fp-Tree Algorithm The basic steps of traditional Fp-tree algorithm is as follows[2]: (1) Sort frequent item sets in descending order. Firstly, scan the transaction database D to obtain frequent 1-itemset and the support count of each item in frequent 1-itemset, and then sort the frequent 1-itemset in descending order to get a frequent item list L. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 929–936. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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(2) Construct FP-tree. The process of building Fp-tree is as follows: Create a root node T labeled with root, and then do some operation as follows on each transaction denoted by trans in D: Remove the items from trans which are not contained in L. Sort the remained items in trans according the order in list L and let [i|P] denote the sorted item list where i is the first item and P is the remaining items. Call insert-tree(T, [i|P]) method to insert the frequent items of this transaction into the Fp-tree. If T has a child node N and meet the condition N.item_name = i.item_name, that is, the two nodes have the same item name, insert-tree method will increase the support count of node N by 1. Otherwise, a new child node N will be created and its support count is assigned to 1, and then, insert-tree method will link this newly created node N to its parent node T and insert it into the node chain with the same item name. If P is not empty, insert-tree method will call insert-tree(N, P) recursively. After all the transactions are scanned, a full Fptree is built completely. (3) Mining FP-tree to obtain frequent item set. Construct conditional basic pattern of each frequent 1-item in L. On the base of conditional basic pattern all the frequent item sets can be composed. This step needs traversing Fp-tree from the lower node up to the root node. From the above algorithm, we can see that each node explicitly established two pointer fields, one pointer links to its parent node and the other links to its brother node which has the same item name. In addition, in order to build this tree from top to bottom, each node must keep some pointers pointing to its child nodes, so each node has three pointers fields. 2.2 Problems of Traditional Fp-Tree Algorithm First, traditional Fp-tree algorithm uses the recursion mechanism to build Fp-tree. When scanning each transaction trans, as long as the remaining items list P in trans is non-empty, method insert-tree (N, P) will be called recursively. Recursive function call is a function calls itself. A second call is done before the first call returns a result. This process is always executed until the termination condition is met. According to the principle of variable scope distribution, within the recursive functions, the memory space occupied by variables and sub-functions must be retained until the recursion call ends. This means that every function call must save corresponding space allocated. In order to manage the memory space better, the system will set a common stack to store all required data during calling and returning function. Especially when a transaction has a lot items, it will require a large number of recursion, and the repeated pushing stack, popping stack and other operations are extremely timeconsuming. In addition, the stack provided by system is used for a variety of recursive functions rather than for one or several functions, so the stack stores a lot of information irrelevant to building the Fp-tree, which to some extent also has a negative impact to the efficiency of the algorithm. Secondly, in the traditional Fp-tree, each node has three pointer fields: parent, child, and next which points to the parent node, child nodes and the brother node with the same item name respectively. The parent pointer is mainly used for mining

An Improved Fp-Tree Algorithm

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conditional basic patterns from bottom to up, and the child pointer is mainly used for constructing this tree from top to bottom, and the next pointer is used for mining the other conditional basic pattern of this frequent item. However, so many pointer fields spend a lot of memory space; especially in the case of a large number of transactions it is impossible to build a Fp-tree because of lack of memory [3]. Therefore, how to improve the efficiency of traditional Fp-tree algorithm and reduce the memory usage is very important.

3 Improved Fp-Tree Algorithm 3.1 Construction of Fp-Tree In improved algorithm, Fp-tree is built by auxiliary pointers and loop operation instead of the traditional recursive mechanisms, and each node in the algorithm has only two pointer fields, one is child pointer pointing to its children nodes, the other is next pointer pointing to the next item with same name. Improved Fp-tree algorithm is as follows: Algorithm 1. NewFp-tree_built(T, minsup) Input: the transaction set T in transaction database, minimum support count denoted as minsup. Output: an improved Fp-tree. 1. L = Scan(T); 2. Node * root = createNode (); 3. Node * pointer=null; 4. While (T ! = ∅ ){ 5. t=GetAndRemoveTransaction (T); 6. St=dropNotFrequentItem (t); 7. pointer =root; 8. While (St != ∅ ) { 9. i=FetchAndRemoveItem (St); 10. if (pointer.ischild (p) && p.item=i.item) 11. p.count++; 12. else { 13. p=creatNewChild (pointer); 14. p.item=i.item; 15. p.count=1; 16. pointer.Child = p; 17. L.getItem (p.item).lastItem().next = p; 18. } 19. pointer = p; 20. } 21.}

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Line 1 scans transaction set T and obtains frequent 1-itemset and support count of each item in set, and then sorts the frequent 1-itemset based on support count in descending order and lets L denote this list. Line 2 creates the root of Fp-tree. Line 3 define auxiliary pointer, initialized to null. Line 4 begins the outer loop to scan transaction set T again and begin to create Fptree. Line 5 extracts a transaction t from transaction set T and deletes t from T. Line 6 deletes non-frequent items in transaction t, and sorts it by support count in descending order and assigns it to the new transaction St. Line 7 lets a pointer point to the root node before dealing with each transaction Line 8 begins the inner loop to scan each item in a transaction St and add the item to the tree. Line 9 extracts an item i from transaction St and deletes i from St. Line 10 to line 20 insert an item into Fp-tree. Line 14 assigns item name to the newly created child node p. Line 17 links p to the node chain with same item name in frequent item list L. In this algorithm, there is no recursive function calls, thus reducing the memory usage, and improve the efficiency. Let’s use the data shown in table 1 to describe the tree-building process of the algorithm: Table 1. Transaction Data Set

TID t1 t2 t3 t4 t5 t6 t7

Items c,g,b d,c,b,h a,f,e,j a,c,d,f,m,n a,d,c,e,f,g e,a,c,b,k c,b,n,l

St c,b,g c,b,d a,e,f c,a,d,f, n c,a,d,e,f,g c,a,b,e c,b,n

Firstly, we scan transaction data set T, and obtain a frequent 1-itemset and its support count, and then sorts the frequent 1-itemset based on support count in descending order and lets L denote this list [4]. If the minimum support count is 2, then, L = {c: 6, a: 4, b: 4, d: 3, e: 3, f: 3, g: 2, n: 2}, and then, delete the non-frequent item from each transaction and sort each transaction according the item order in L, and then, we get new transaction set St. Secondly, we scan transaction data set T again to insert each transaction in St into Fp-tree. For example, let we use transaction t1 and t2 to build Fp-tree. The key point is before adding a transaction every time, the auxiliary pointer should point to the root node and after adding an item in the transaction into the tree, let the pointer point to the newly added node.

An Improved Fp-Tree Algorithm

pointer

root

root

root

root

pointer

c,1

c,1

pointer

b,1 g,1

g,1 ( 1) cr eat e r oot

( 2) add c of

pointer

t1

( 3) add b of

root c,2

b,2

( 9) end of

b,2 d,1 scanni ng t 2

( 4) add g of

root

c,2

g,1

t1

g,1 ( 8) add

( 7) add b of

scanni ng t 1

root

pointer

c,2

c,2 b,1

g,1 t2

( 5) end of

b,2

pointer

d of

t1

root pointer

d,1

c,1

b,1

pointer

b,1

root

pointer

c, 1

933

g,1 t2

( 6) add c of

t2

Fig. 1. Scan Transaction t1 and t2 to Build Fp-tree

3.2 Pointer Flip In traditional Fp-tree each node contains three pointers: a parent pointer points to parent node, a child pointer points to child node and the next pointer points to the node with same item name [5], as shown in the first figure in figure 2. In order to save memory usage, the new algorithm proposed in this paper makes each node in the tree retain only child and next pointer. However, the process of mining frequent item sets is from bottom to up of the tree [2]. In order to traverse Fp-tree from bottom to up, the child pointer in the new Fp-tree needs to be flipped, that is child pointer of each node doesn’t point to its child nodes but point to its parent node, and the next pointer still points to the next node with the same item name. Specific operation is shown in the second and third figure in figure 2. We traverse each node of the newly generated Fptree by the root-left-right sequence and flip its child pointer to point to the parent node and keep next pointer unalterable. The specific operation steps are as follows. (1) (2) (3)

(4)

Traverse node c from the beginning of root, and save the address of root temporarily, and then assign null to the child pointers of root. Save the address of node c and its child node b temporarily, and modify the child pointer of c to point to root. Find node b by the address saved in step 2, and store the address of node b and its child node g and d, and then assign the address of node c to the child pointer of node b, which is to let the child pointer of node b point to node c. Traverse and flip the child pointer of node g and d in the same way, and after the whole process the modified tree structure is shown in the third figure in figure 2. By this modified Fp-tree, we can mine frequent item sets from bottom to up of the tree.

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root c,2

next b node

b,2 d,1 g,1

root next c node

next g node

next c node

c,2

I mpr oved Fp- t r ee

next b node

b,2

next d node

next g node

ⅰ

Modi f y chi l d poi nt er

next d node

d,1 g,1

root

ⅱ

next c node

c,2

next b node

b,2

next d node

d,1 g,1

next par ent chi l d

next g node ⅲ

Fig. 2. Tree Structure Comparison between Traditional Fp-tree and Improved Fp-tree

3.3 Mining Frequent Item Sets The biggest difference demonstrated clearly in figure 2 is that each node of the improved tree has only two pointer fields. Compared with traditional tree the new tree reduced one pointer filed. Frequent item sets can still be mined by the tree by using the pointer pointing to its parent node. Operation of mining is not different from the traditional algorithm and the specific operation is as follows [5]. (1)

(2)

(3) (4)

Traverse the frequent 1-item list L to find the node in Fp-tree by its reference pointer, and then find the path from this node to root in this tree. List the nodes in this path by the sequence from up to bottom, and get a basic conditional pattern. Remove the node in the basic conditional pattern whose count does not meet the given minimum support, and the list of the rest nodes in the basic conditional pattern is a conditional pattern corresponding to this frequent element. Construct all frequent multi-item sets containing this frequent element according to conditional pattern of this frequent element. Frequent item set of the original transaction data set is constituted by combining each frequent multi-item sets generated from the step 3 with each element of frequent 1-item list L.

4 Experimental Verification 4.1 The Performance Comparison between Fp-Tree and Improved Fp-Tree Algorithm The data was divided into three categories: small scale, moderate scale and large scale samples. The time spending on mining the frequent item set by traditional Fp-tree algorithm and improved Fp-tree algorithm was recorded respectively and was shown in Figure 3.

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Fig. 3. Mining Efficiency Comparison between Fp-tree and Improved Fp-tree

4.2 Result Analysis The improved algorithm does not change the mining process of original algorithm. So the different performance is mainly brought out by the different tree-building process. In the case of small scale of data, the two algorithms’ time consumed is almost equal, and the efficiency is not improved much. With the increase of the data scale, that is, the number of tree nodes increases, the time consumed rises; at this circumstance the improved method is superior to traditional methods modestly. When the data scale further increases, the time spent on the improved algorithm is less than the traditional method obviously, and the efficiency has a substantial increase.

5 Conclusion This paper proposed an improved Fp-tree algorithm which reduced the pointer fields of each tree node and aborted the recursive function call during building Fp-tree, and by these improved measures the efficiency and memory usage has been greatly improved. By a specific instance this paper described detailedly the process how to build a new Fp-tree and how to flip the pointer to link to its parent node. Finally a quantitative analysis on the improved algorithm was done. Experimental results showed that the improved algorithm was more efficient than traditional algorithm in case of large data scale.

Acknowledgment This paper is based on work supported by Funding Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality under Grant No. PHR200906210, Funding Project for Base Construction of Scientific Research of Beijing Municipal Commission of Education under Grant No. WYJD200902, Funding Project for Science and Technology Program of Beijing Municipal Commission of Education Under grant number

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KM201010037002 and National Science and Technology Supported Project between Eleventh Five-Year: Regional International Logistics Service System and Application Model under Grant No. 2009BAH46B06. This paper is also funded by Logistics Technology Engineering Research Center of Beijing Universities and Characteristic Specialty Construction Project of Beijing.

References 1. Han, J., Pei, J., Yin, Y., Mal, R.: Mining Frequent Patterns without Candidate Generation: a Frequent-Pattern Tree Approach. Data Mining and Knowledge Discovery 8(1), 53–87 (2004) 2. Han, J., Kamber, M.: Data Mining Concepts and Techniques, Second Edition, translated by Ming Fan and Xiaofeng Meng. Machinery Industry Press (2007) 3. Wang, H., Chun-an, H.: Mining Maximal Patterns Based on Improved FP-tree and Array Technique. In: Third International Symposium on Intelligent Information Technology and Security Informatics, pp. 567–571 (2010) 4. Trabelsi, C., Latirit, C., Ghedira, K.: Generating Closed Frequent Itemsets under Constraints FP-Tree Structure. In: IMACS Multiconference on ”Computational Engineering in Systems Applications” (CESA), October 4-6, pp. 1528–1530 (2006) 5. Hui, L., Qian, X.: Non Check Mining Algorithm of Maximum Frequent Patterns in Association Rules based on FP-tree. Journal of Computer Applications 7(30), 1923–1924 (2010)

A Decoupling Method for Evaluating Lightning-Induced Overvoltages on One Certain Line of an Overhead Transmission Lines System Dongming Li, Cheng Wang, and Xiaohu Liu Engineering Computation and Simulation Institute, Huazhong University of Science and Technology, Wuhan 430074, China

Abstract. There is a major disadvantage in the widely used modified Agrawal model to evaluate the overvoltages on the overhead transmission lines, i.e., one has to carry out a complete calculation for all the overvoltages on all the lines in the system even if one is only interested in the overvoltages on a certain line of the multi-lines system. In the present paper, we propose a decoupling method to overcome this demerit of the modified Agrawal model. The proposed method removes the intercoupling effects of lines by introducing an equivalent impedance in the field-to-transmission line coupling equations. Thus the overvoltages of one certain line can be evaluated independently in the multi-lines system. It is shown by examples that the proposed method is valid and efficient when comparing with the modified Agrawal method. Keywords: overvoltages, equivalent impedance, lightning, decoupling, overhead transmission lines.

1 Introduction In study of lightning-induced overvoltages on the overhead transmission lines system, the field-to-transmission line coupling model proposed by Agrawal et al. [1] is frequently employed [2~7]. The Agrawal model has been verified by triggered lightning experiments [8, 9] and reduced-scale simulations [10, 11]. However, in the Agrawal model the ground is regarded as an ideal conductor. In order to consider the effects of ground conductivity on the overvoltages, a modified Agrawal model has been proposed [12~14] (refer as modified Agrawal model). When using the modified Agrawal model to evaluate lightning-induced overvoltages on the overhead power lines system, the total computational time increases rapidly with the number of the transmission lines in the system. The model is quite inefficient when one is only interested in the overvoltages on one certain line of the multi-lines system, because one has to carry out a complete calculation for all the overvoltages on all the lines in the system. In the present paper, an efficient decoupling technique is proposed to overcome this demerit of the modified Agrawal model, thus the overvoltages of one certain line can be evaluated independently in an overhead transmission lines system. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 937–944. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2 Formulations of the Modified Agrawal Model In the modified Agrawal model [12~14], the field-to-transmission line coupling equations of the lines above an imperfectly conducting ground are presented as

∂ ⎡⎣Vi s ( x, t )⎤⎦ ∂x ∂ ⎡⎣ii ( x, t ) ⎤⎦ ∂x

∂ ⎡ii ( x, t ) ⎤⎦ ∂ ⎡ii ( x, t ) ⎤⎦ ⎤ t i + ⎡⎣ Lij ⎤⎦ ⎣ + ∫ ⎡⎣ξij ( t − τ ) ⎣ ⎥ dτ = ⎡⎣ Ex ( x, hi , t ) ⎤⎦ 0 ∂t ∂τ ⎥⎦

+ ⎡⎣Cij ⎤⎦

∂ ⎡⎣Vi s ( x, t ) ⎦⎤ ∂t

(1)

=0

where, Lij and Cij are the inductance and the capacitance of per-unit-length, respectively. ξij ( t ) is the transient ground resistance. Vi s ( x, t ) and ii ( x, t ) are respectively

the scattered voltage and the current on the i th line. Exi ( x, hi , t ) is the horizontal component of the incident electric field along the line, which can be obtained by the Cooray-Rabinstein formula [15]. The conductance and the internal impedance of the lines are neglected. The boundary conditions for the scattered voltage are

⎡⎣Vi s ( x0 , t )⎤⎦ = − [ Z i 0 ] ⎡⎣ii ( x0 , t ) ⎤⎦ − ⎡⎣Vi e ( x0 , t ) ⎤⎦ ⎡⎣Vi s ( xl , t ) ⎤⎦ = [ Z il ] ⎡⎣ii ( xl , t ) ⎤⎦ − ⎡⎣Vi e ( xl , t ) ⎤⎦

(2)

where Zi 0 and Zil are the loading impedance and taken as the characteristic impedance

Z ic of the i th line given by

Z i 0 = Z il = Z ic =

Lii . Cii

(3)

And Vi e ( x, t ) is the incident voltage defined as ⎡ hi ⎤ ⎡⎣Vi e ( x, t ) ⎤⎦ = − ⎢ ∫ Eze ( x, z, t ) dz ⎥ ≈ − ⎡⎣ hi E ze ( x, 0, t ) ⎤⎦ ⎣⎢ 0 ⎦⎥

(4)

where E ze ( x, z , t ) is the incident vertical electric field.

The total induced overvoltages Vi ( x, t ) on the overhead transmission line are given

by ⎡⎣Vi ( x, t )⎤⎦ = ⎡⎣Vi s ( x, t ) ⎤⎦ + ⎡⎣Vi e ( x, t )⎤⎦ .

(5)

A low-frequency time domain approximation expressions for the transient ground resistance ξij ( t ) without singularity at the early times has been developed by Rachidi et al. [16]:

A Decoupling Method for Evaluating Lightning-Induced Overvoltages

⎛ τ ⎞ 1 ⎤ ⎫⎪ μ0 μ0 ⎡ τ ii 1 + exp (τ ii / t ) erfc ⎜ ii ⎟ − ⎥ ⎬ , ⎢ ⎜ ⎟ ε πτ ii ⎣⎢ 4π t 4 ⎝ t ⎠ 4 ⎥⎦ ⎭⎪

⎧⎪ 1 ⎩⎪ 2π hi

ξii ( t ) = min ⎨

⎧⎪ 1 μ0 μ 0 , ˆ ⎪⎩ 2π h ε π Tij

ξij ( t ) = min ⎨

)

⎛ Tij ⎞ − an ⎜ ⎟ ∑ 2 π n=0 ⎝ t ⎠ 1

where, Tij e an =

jθij

∞

2 n +1 2

(6)

⎡ Tij cos (θ ij / 2 ) ⎢ ⎢⎣ 4π t

⎛ Tij ⎞ 1 + exp Tij cos (θ ij ) / t cos ⎜ sin (θ ij ) − θ ij ⎟ 4 t ⎝ ⎠

(

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(7)

⎫ ⎛ 2n − 1 ⎞ cos (θ ij ) ⎤ ⎪ ⎥⎬ θ ij ⎟ − cos ⎜ 4 ⎥⎪ ⎝ 2 ⎠ ⎦⎭

hi + h j rij2 rij ⎞ ⎛ hi + h j = μ0σ g ⎜ + j ⎟ ; τ ii = hi2 μ0σ g ; hˆ = ; + 2 2⎠ 2 ( hi + h j ) ⎝ 2

2n ; hi and h j are the height of the i th and the j th lines respectively; 1⋅ 3 ⋅⋅⋅ ( 2n + 1)

rij is the horizontal distance between the i th and the j th lines. Using the modified Agrawal field-to-transmission line coupling equations (1-5) and the expressions of the transient ground resistance (6-7), the overvoltages on all the overhead transmission lines above a finite conducting ground can be evaluated.

3 The Decoupling Method Sometimes, one may only concern the overvoltages on one certain line of an overhead transmission lines system. However, according to the modified Agrawal model, one has to calculate the overvoltages on all the lines of the system. In this section, we describe a decoupling method to evaluate the overvoltages on the concerned line of an overhead transmission lines system. We define an “equivalent” loading impedance of the line:

Lii − Zie =

Cii −

n

∑

Lij

∑

Cij

j =1, j ≠ i n j =1, j ≠ i

(8)

where, Lii and Lij are the self and the mutual inductance of per-unit-length, Cii and

Cij are the self and the mutual capacitance of per-unit-length. Noticeably, the summations in (8) do not contain the overhead ground line(s).

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According to the transmission line theory [17], a voltage or current wave will be reflected when it meets discontinuity in its propagation process. For the overhead transmission lines systems, the reflection coefficient of the wave at the line termination is

Γ=

Zl − Zc Zl + Zc

(9)

where Z l is the loading impedance; Z c is the characteristic impedance of the line. In the modified Agrawal model, the loading impedance is choosen as the characteristic impedance (see equation 3). Thus we have Z l = Z c = Z ic , and Γ = 0 in (9), which means that in the modified Agrawal model, the reflected wave at the terminations disappears. In the proposed decoupling method, we use the equivalent loading impedance defined in (8), instead of the characteristic impedance given by (3). Thus, it causes Z l = Z ie ≠ Z c and the reflection coefficient Γ ≠ 0 . This means a reflected wave would be produced when the lightning-induced overvoltages propagate to the line terminations. The process of the proposed mehtod can be described as the following: (1) the overhead transmission lines system is simplified into a reduced system which contains the concerned line and the overhead ground line(s), all the other lines in the original system are ignored; Z Z (2) For the boundary conditions in (2), the impedance i 0 and il of the concerned * * Z Z line are replaced by the equivalent impedances i 0 and il given in (8). Thus, the overvoltages on one certain line of an overhead transmission lines system can be evaluated independently, and much computation time can be saved.

4 Examples Two different overhead transmission lines configurations are chosen for analysis: the horizontal configuration (Fig.1 (a)) and the vertical configuration (Fig.1 (b)). The radius of the lines and the overhead ground line are 10mm and 5mm respectively. The length of the line is 1km. The relative permittivity and the ground conductivity are taken as ε r = 10 and σ g = 0.001S/m respectively. The stroke point is located at a distance d = 50m away from the lines center. A channel-base current typical of subsequent return strokes with 12 kA peak amplitude and 40 kA/μ s maximum time-derivative [18], the Modified Transmission-Line model with Linear current decay with height (MTLL) model [19], and a return stroke velocity 130 m/μs are used. The overvoltages on the 1st and the 2nd line of the horizontal and vertical configuration line systems with and without ground line are calculated by the modified Agrawal model and proposed method. Fig.2 shows the results at the middle point, termination, and the point 250 meters away from the termination of the 1st and the 2nd lines in the line system without ground line, while Fig.3 shows the results of line system with a ground line. In

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the figures, “Modified Agrawal(Horizontal)” and “Modified Agrawal(Vertical)” represent the results of the modified Agrawal model in the horizontal and vertical configurations, “Decoupling Method(Horizontal)” and “Decoupling Method(Vertical)” represent the results of the proposed method in the horizontal and vertical configurations.

(a)

(b)

Fig. 1. Two different configurations of the overhead distribution lines systems for analysis. (a) The horizontal configuration. (b) The vertical configuration.

It can be seen from Fig.2 that the time history curves of overvoltages obtained by the two methods are consistent. The relative errors of the maximal peak overvoltages at the three points are listed in Table 1. The computation time of the modified Agrawal model for the two considered transmission lines configurations is the same and is about 12 hours. But the computation time of the proposed method is only 4 hours, which is one third of that of the modified Agrawal model. Table 1. The relative errors of the maximal peak overvoltages at the three points (line system without ground line)

The relative error

At the midpoint

At 250m away from the extremity

At the endpoint

Horizontal Vertical

0.7% 1.4%

1.3% 3.7%

3.0% 6.5%

Fig.3 shows the time history curves of overvoltages obtained by the tow methods. The relative errors of the maximal peak overvoltages of the proposed method are shown in Table 2. Again, we can find that the results of the modified Agrawal model and the proposed method are consistent. The computation time of the proposed method and the modified Agrawal model is 8 hours and 16 hours, respetively. Table 2. The relative errors of the maximal peak overvoltages at the three points (line system with a ground line) The relative error Horizontal Vertical

At the midpoint 0.6% 0.8%

At 250m away from the extremity 1.2% 1.9%

At the endpoint 5.8% 6.1%

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It can be seen from the results that the relative errors of the maximal peak overvoltages of the proposed method increase when the point approaches the termination. This is caused by the approximation of the boundary conditions in the proposed method. In the proposed method, the decoupling “equivalent” impedance defined in (8) is employed on the boundary conditions for the scattered voltage.

(a)

(b)

Fig. 2. The overvoltages at different points obtained by the two ways without overhead ground line case. (a) At the midpoint. (b) At the point of 250m away from the extremity. (c) At the endpoint.

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(b)

(c)

Fig. 3. The overvoltages at different points obtained by the two ways with overhead ground line case. (a) At the midpoint. (b) At the point of 250m away from the extremity. (c) At the endpoint.

5 Conclusion In this paper, we propose a method to decouple the widely used modified Agrawal field-to-transmission line coupling equations by introducing an equivalent impedance. Thus, utilizing the proposed method, lightning-induced overvoltages on one certain line of the multi-lines system can be evaluated independently. By numerical examples, it’s shown that the maximal peak lightning-induced overvoltages, which are most important in protection design of the power distribution lines system, obtained by the proposed method are well consistent with those obtained by the modified Agrawal model. The relative error of peak value is less than 6.5% in the examples. The proposed method is very efficient, and the computation time of examples is 1/3~1/2 of that of the modified Agrawal model.

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References 1. Agrawal, A.K., Price, H.J., Gurbaxani, S.: Transient Response of a Multiconductor Transmission Line Excited by a Nonuniform Electromagnetic Field. IEEE Trans. Electromagn. Compat. 22(2), 119–129 (1980) 2. Rachidi, F., Nucci, C.A., Ianoz, M., Mazzetti, C.: Response of Multiconductor Power lines to nearby Lightning Return stroke Electromagnetic fields. IEEE Trans. Power Del. 12(3), 1404–1411 (1997) 3. Kannu, P.D., Thomas, M.J.: Lightning Induced Voltages on Multiconductor Power Distribution Line. IEE Proc.-Gener. Transm. Distrib. 152(6), 855–863 (2005) 4. Rakov, V.A., Rachidi, F.: Overview of Recent Progress in Lightning Research and Lightning Protection. IEEE Trans. Electromagn. Compat. 51(3), 428–442 (2009) 5. Piantini, A., Janiszewski, J.M.: The Effectiveness of Surge Arresters on the Mitigation of Lightning Induced Voltages on Distribution Lines. J. Lightning Res. 2, 34–52 (2007) 6. Paolone, M.: Modeling of Lightning-Induced Voltages on Distribution Networks for the Solution of Power Quality Problems and Relevant Implementation in a Transient Program. PHD. Dissertation, Dept. Elect. Eng., Univ., Bologna (2001) 7. Nuccii, C.A.: Lightning-Induced Voltages on Distribution Systems Influence of Ground Resistivity and System Topology. J. Lightning Res. 1, 148–157 (2007) 8. Georgiadis, N., Rubinstein, M., Uman, M.A., Medelius, P.J., Thomson, E.M.: Lightning-Induced Voltages at both Ends of a 448-m Power-Distribution Line. IEEE Trans. Electromagn. Compat. 34(4), 451–460 (1992) 9. Barker, P.P., Short, T.A., Eybert-Berard, A., Berlandis, J.B.: Induced Voltage Measurements on an Experimental Distribution Line during Nearby Rocket Triggered Lightning Flashes. IEEE Trans. Power Del. 11(2), 980–995 (1996) 10. Ishii, M., Michishita, K., Hongo, Y., Ogume, S.: Lightning-Induced Voltage on an Overhead Wire Dependent on Ground Conductivity. IEEE Trans. Power Del. 9(1), 109–118 (1994) 11. Nucci, C.A., Borghetti, A., Piantini, A., Janischewskyj, W.: Lightning Induced Voltages on Distribution Overhead Lines: Comparison between Experimental Results from a Reduced-Scale Model and most Recent Approaches. In: Proc. 24th Int. Conf. Lightning Protection (ICLP), Birmingham, U.K.,, pp. 314–320 (1998) 12. Tesche, F.M.: Comparison of the Transmission Line and Scattering Models for Computing the HEMP Response of Overhead Cables. IEEE Trans. Electromag. Compat. 34, 93–99 (1992) 13. Rachidi, F., Nucci, C.A., Ianoz, M., Mazzetti, C.: Influence of a Lossy Ground on Lightning-Induced Voltages on Overhead Lines. IEEE Trans. Electromagn. Compat. 38(3), 250–264 (1996) 14. Kannu, P.D., Thomas, M.J.: Lightning Induced Voltages on Multiconductor Power Distribution Line. IEE Proc.-Gener. 152(6), 855–863 (2005) 15. Cooray, V.: Some Considerations on the ‘Cooray–Rubinstein’ Formulation Used in Deriving the Horizontal Electric Field of Lightning Return Strokes over Finitely Conducting Ground. IEEE Trans. Electromgn. 44(4), 560–566 (2002) 16. Rachidi, F., Loyka, S.L., Nucci, C.A., Ianoz, M.: “A New Expression for the Ground Transient Resistance Matrix Elements of Multiconductor Overhead Transmission Lines. Electric Power Systems Res. 65, 41–46 (2003) 17. Jin, K.A.: Electromagnetic Wave Theory. John Wiley & Sons, Wiley-Interscience (1990) 18. Nucci, C.A., Diendorfer, G., Uman, M.A., et al.: Lightning Return Stroke Current Models with Specified Channel-Base Current: A Review and Comparison. J. Geophys. Res. 95, 20395–20408 (1990) 19. Rakov, V.A., Dulzon, A.A.: Calculated Electromagnetic Fields of Lightning Return Stroke. Tekh. Elektrodinam. 1, 87–89 (1987)

Power Flow Calculation in the Voltage Output Stage Sun Qiuye, Li Zhongxu, Ma Dazhong, and Zhou Jianguo School of Information Science & Engineering, Northeastern University, 110004 Shenyang, Liaoning Province, China [email protected]

Abstract. The distribution system represents the final stage in the transfer of power to the individual customers. In the distribution system analysis, the power flow calculation is the most basic and important calculation and is the foundation of the operation, plan, security, reliability analysis and optimization of the power system and is the foundation and springboard of the various electromagnetic transient analysis. A lot of uncertain factors exist in the distribution system. Uncertainty in the distribution system must be addressed in any analysis. In this situation, traditional certain power flow methods have limitation. The flow calculation in the fault condition is studied. The forward-backward sweep method is adopted to calculate fault power flow. The simulation research shows that the calculation results with the polynomial load model are proper in the practical application when the fault occurs. Keywords: distribution system, forward-backward sweep method, uncertainty, fault power flow1.

1 Introduction Electric power distribution feeders are susceptible to faults caused by a variety of situations such as adverse weather condition equipment failure, traffic accidents, etc. When a fault occurs or a distribution line, it is very important for the utility to identify the fault location as quickly as possible for improving the service reliability [1]. There has been much research in the fault location problem for transmission systems. This includes a traveling wave-based scheme and harmonics-based scheme. The apparent impedance calculated using a fundamental component is the most widely used one [2]. A fault location in the distribution system is not an easy job due to its high complexity and difficulty caused by nonhomogeneity of line, fault resistance, load uncertainty, and phase unbalance. However, the basic approach to calculate the fault location using voltage and current measurement is still the same as the transmission system case, which is to calculate the impedance using the fundamental component or harmonics [3]. An additional calculation burden is needed for the compensation of the characteristics unique to the distribution system [4]. A Fuzzy approach to determine the most possible 1

Projects(60904101, 60972164, 50977008) supported by the National Natural Science Foundation of China; Project(N090404009) supported by the Fundamental Research Funds for the Central Universities; and Project(20090461187) supported by China Postdoctoral Science Foundation.

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 945–952. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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fault location is suggested [5]. An effective fault location algorithm for the distribution feeder systems is proposed [6]. Higher accuracy has been obtained by updating the voltage and current at the load branching point of the feeder and by introducing the load estimation equation that reflects the load current change due to the voltage drop. The load uncertainty has been investigated considering various load models. A single location is identified comparing the current pattern with the expected one due to the protective device operation during the fault. Further, the load current change, due to the circuit interruption, is utilized. Effectiveness of the proposed scheme has been proved by a number of EMTP simulations on many realistic distribution systems [7]. The forward-backward sweep method is adopted in this paper.

2 Proposed Fault Model Description 2.1 Proposed Fault Model Description In this section, forward-backward sweep method is adopted to calculate fault power flow. The load has a constant impedance and its impedance is known. The load constant is determined according to the load characteristic [8]. Here, a simplified polynomial load model can be described as follows: ⎧⎪ P = a1U 2 + b1U + c1 ⎨ 2 ⎪⎩Q = a2U + b2U + c2

(1)

where a1 , b1 , c1 coefficient of active polynomial load model, a2 , b2 , c2 coefficient of reactive polynomial load model. Two situations are considered when the fault occurs. One situation is that the load power is constant in the fault process. This is called constant power load model. The other situation is that the load is changing with the nodal voltage in the fault process. This is called polynomial load model [9]. The changing relationship may be depicted in Equation (1). 2.2 Fault Power Flow Calculation Method Based on Polynomial Load Model Here, we consider polynomial load model. The basic procedure of fault power flow calculation based on forward-backward sweep method is used: We can set the iteration time k = 0 , the initial values of root node and other nodes voltage, the calculating polynomial load data. Setting out from each load node, we can calculate fault current distribution from son branches to father branches. The nodal injection current *

J

( k +1) i

⎛ S ⎞ = ⎜ ( ik ) ⎟ − Yi Vi ( k ) ⎝ Vi ⎠

(2)

where J i( k +1) is the injection fault current of node i in the (k+1) th iteration, Vi ( k ) is the fault voltage of node i in the k th iteration, Si is the injection of node i , which mainly refers to load power in distribution network, Yi is the parallel admittance of node i

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(1)

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Backward process

I i( k +1) = − J i( k +1) + ∑ I (j k +1)

(3)

j∈Ci

where I i( k +1) is the fault current of branch i in the (k+1) th iteration, I (j k +1) is the fault current of branch j in the (k+1) th iteration. Setting out from the root node, we can calculate fault voltage with Equation (2) Forward process

（4）.

V j( k +1) = Vi ( k +1) − I (j k +1) Z j

(4)

where Z j is the impedance of branch j , Vi ( k +1) is the fault voltage of branch i in the (k+1) th iteration, V j( k +1) is the fault voltage of branch j in the (k+1) th iteration.

（）

If the convergence condition is satisfied with Equation 5 , calculation process stops; otherwise, k = k + 1 , the new turn forward-backward sweep begins until the convergence condition is satisfied. Convergence condition

V j( k +1) − V j( k ) < ε

(5)

where ε is the convergence precision. 2.3 The Analysis of Convergence of Algorithm

In the radial distribution subnetwork, to branch bi , j , there is

U j = U i − Ii , j ( Ri , j + jX i , j )

(6)

If the last node of branch bi , j is tip node, then current Ii , j of this branch is equal to current through tip node, which is also equal to load current I Lj of this tip node:

I i , j = I j = I Lj

(7)

The load current I Lj of node v j may be expressed as

I Lj =

Pj − jQ j U *j

(8)

where Pj − jQ j is conjugative load power of node v j , U *j is conjugative voltage of node v j . If the last node of branch bi , j is not tip node, then current Ii , j of this branch is equal to current through tip node and current of all sub branches:

Ii , j = I j +

∑

k ≠i,k ≠ j

d (i, k )I i , k

(9)

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When iteration convergence condition is satisfied: sup

i , j∈a , i ≠ j

{max(ΔU )} < ε i, j

(10)

where a is set of all load nodes through branch bi , j , ε is iterative precision. Iteration stops. Actually, the above iterative method is applied. According to Equations (7) and (8), the current through branch bi , j may be expressed as P − jQ Ii, j = ∑ k * k (11) Uk k ∈d We have assumed that the voltage of nodes of set d is approximately equal to the voltage of tip nodes of branch bi , j . Then, Equation (11) can simplify to Ii, j =

∑P

k

k ∈d

− jQk (12)

U *j

To random branch, we give voltage U i of first node and voltage U j of last node. Pi , j + jQi , j is load value through branch bi , j . Ri , j + jX i , j is equivalent impedance of this branch. I i , j is current of this branch.. According to Equation(6)and (9), we have U j = Ui −

Pi , j − jQi , j U *j

( Ri , j + jX i , j )

(13)

A polynomial load model can be described as Equation (1).We set ei is expressed as the real part of voltage of node. f i is expressed as the imaginary part of voltage of node. ⎧⎪U i = ei + jf i ⎨ ⎪⎩U j = e j + jf j

(14)

Substitution of Equation (14) in Equation (13) yields

e j 2 + f j 2 = (ei e j + fi f j ) − ( Ri , j Pi , j + X i , j Qi , j ) fj =

fi e j ei

+

( Ri , j Qi , j − X i , j Pi , j ) ej

(15) (16)

Substitution of Equation (14) in Equation (1) yields ⎧ P = a (e 2 + f 2 ) + b (e 2 + f 2 ) + c 1 1 1 j j j j ⎪ i, j ⎨ 2 2 2 2 ⎪⎩Qi , j = a2 (e j + f j ) + b2 (e j + f j ) + c2

(17)

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The angle of voltage of node differs very small compared with power supply angle, so we can abandon quadratic part of the imaginary part. Equations (15) and (16) are approximately e j = ei −

( Ri , j Pi , j + X i , j Qi , j )

(18)

ej

⎧⎪ Pi , j = a1e j 2 + b1e j + c1 ⎨ 2 ⎪⎩Qi , j = a2 e j + b2 e j + c2

(19)

Substitution of Equation (19) in Equation (18) yields

ej = If X I =

(b1 R j + b2 X j ) c1 R j + c2 X j ei − − (1 + a1 R j + a2 X j ) (1 + a1 R j + a2 X j ) e j (1 + a1 R j + a2 X j )

1 (1 + a1 R j + a2 X j )

，X

P

=

，X (1 + a R + a X ) (b1 R j + b2 X j ) 1

j

2

j

Z

=

(c1 R j + c2 X j ) (1 + a1 R j + a2 X j )

(20)

.

Equation (20) can be written as e j = X I ei − X P − Here, ϕ (e j ) = X I ei − X P −

XZ ej

(21)

XZ , generally, the voltage of power supply is known. So we ej

may handle each branch from the node of power supply to the tip node, we can see the voltage of beginning node of each branch known. According to Equation (21), we can calculate the tip voltage e j of branch bi , j with iteration method. Substitution of e j in Equation (16) yields f j .So, if e j has convergence, f j may have convergence. According to Equation (21), e j = ϕ (e j ) , e j ,0 is the initial value of e j . ζ j is the final solution of e j . e j , n is the result of the nth iteration. The procedure of solving e j may be depicted using iteration method: e j ,1 = ϕ (e j ,0 ), e j ,2 = ϕ (e j ,1 )

e j , n +1 = ϕ (e j , n )

(22)

The iteration value of the ( n + 1)th subtracts the final solution ζ j e j , n +1 − ζ j = ϕ (e j , n ) − ϕ (ζ j )

(23)

According to differential median theorem, we have e j , n +1 − ζ j = ϕ ′(ξ )(e j , n − ζ j )

(24)

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where ξ is in the interval of e j , n and ζ j .We have e j , n +1 − ζ j e j ,n − ζ j

= ϕ ′(ξ ) = ϕ ′(ξ )

The differential coefficient of expression ϕ (e j ) is ϕ ′(e j ) = have ϕ ′(ξ ) =

XZ

ξ2

(25) XZ . When e j = ξ , we ej2

. Then we have e j , n +1 − ζ j e j,n − ζ j

=

XZ

(26)

ξ2

Where ξ is in the interval of the nth iterative value e j , n and the final value ζ j . With the increase of iterative time, ξ is approximately equal to ζ j . Substitution of ξ in Equation (26)yields e j , n +1 − ζ j e j ,n − ζ j

=

XZ

(27)

ζ j2

From Equation (27), in order to ensure that the ( n + 1)th iterative error absolute value e j , n +1 − ζ j is smaller than the nth iterative error absolute value e j , n − ζ j , the condition ϕ ′(ξ ) =

XZ

ζ j2

< 1 must be satisfied. So, iterative error absolute value is be-

coming smaller in the iterative procedure. The error goes to zero, that is to say, the iterative procedure has convergence. In the practical application of distribution network, load active, reactive power and voltage of node may be expressed using per-unit value. The iterative initial value of voltage of each node is 1.0. Therefore, the first iterative error (c1 R j + c2 X j ) (c1 R j + c2 X j ) n , …… , the nth iterative error is . So, the converis (1 + a1 R j + a2 X j ) (1 + a1 R j + a2 X j )n gence condition of fault power flow calculation method based on the polynomial load model is (c1 R j + c2 X j ) (1 + a1 R j + a2 X j )

<1

(28)

3 Simulation Research Fig.2 is the block diagram of main program. In order to validate the correctness of algorithm, we may adopt forward-backward sweep method based on branch current to calculate power flow with American PG&E company 69-bus system shown in Fig.3 [10].

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Table 1. The fault power flow of constant power load in the interval of 20 and 21 node Node 1 3 10 13 15 19 20 21 22

voltage

10000 9994.17970531489 8917.53797648588 7766.71086891561 6513.8221190944 5975.37699898739 5849.84838675472 5751.12300064299 5751.12299980732

active power 5494315.59673885 5490835.37138075 4932585.31150751 4314471.83248 3641201.57317932 3351712.82173761 3284257.554089 61.3000001927099 56.00000000193

reactive power 821986.758300085 821023.817763136 576789.83525716 370175.433911157 147692.31432962 51919.940260173 29626.7618459758 43.5000000006621 40.0000000006473

Fault power flow can be caused by a variety of situations. Here, we assume that the fault is caused by short circuit. In order to calculate fault power flow, the fault location may be identified among multiple candidate locations. In order to simplify the problem, we assume that the fault location occurs in certain branch. But, in the branch, the fault point is random. Here, we choose two branches to calculate using American PG&E company 69-bus system. The calculation results are given as Tab1.

Fig. 2. The block diagram of main program

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From the results of fault power flow, we can see that when the fault occurs in some branch, the voltage of the main line which the branch lies in drops a lot. When the fault occurs in the branch near the power supply, the voltage drop is smaller than the drop that occurs far from the power supply. Moreover, the calculation results with the polynomial load model are close to real situation when the fault occurs.

4 Conclusion In this paper, the fault power flow is researched. The forward-backward sweep method is used to calculate the fault power flow. The simulation research shows that the calculation results with the polynomial load model are proper in the practical application when the fault occurs. However, here is the elementary research of the fault power flow in this chapter. The method proposed will be further developed and improved.

References 1. Das, R., Sachdev, M.S., Sidhu, T.S.: A fault locator for radial sub-transmission and distribution lines. In: Proc. IEEE Power Eng. Soc. Summer Meeting, vol. 1, pp. 443–448. IEEE Press, New York (2000) 2. Laughton, M.A., Davies, M.W.H.: Numerical Techniques in Solution of Power System Load-Flow Problems. Proceedings of the institution of Electrical Engineers 111(9), 1575–1588 (1964) 3. Stott, B.: Review of Load-Flow Calculation Methods. Proceedings of IEEE 62(7), 916–929 (1974) 4. Wenyuan, L., Billinton, R.: Effect on bus load uncertainty and correlation in composite system adequacy evaluation. J. IEEE Trans. Power Syst. 6(4), 1522–1529 (1991) 5. Momoh, J.A., Ma, X.W., Tomsovic, K.: Overview and literature survey of fuzzy sets theory in power systems. J. IEEE Trans. Power Syst. 10(3), 1676–1690 (1995) 6. Reineri, C.A., Alvarez, C.: Load research for fault location in distribution feeders. J. Proc. Inst. Elect. Eng. Gen. Trans. Dist. 146(2), 115–120 (1999) 7. Lee, S.-J., Choi, M.-S., Kang, S.-H.: An intelligent and efficient fault location and diagnosis scheme for radial distribution systems. J. IEEE Trans. Power Delivery 19(2), 524–531 (2004) 8. Brown, R.E.: Electric Power System Reliability. Marcel Dekker, New York (2001) 9. Northeast Power Coordinating Council: Basic Criteria for Design and Operation of Interconnected Power Systems. October 26 (1990) 10. Sun, Q., Zhang, H., Zhang, T.: Fuzzy Flow Calculation in Power Distribution System and its Convergence. In: ISTAI 2008, pp. 2106–2110 (2008)

Research on Trajectory Control of Polishing Robot Based on Secondary Deceleration and Dynamic Model Tongying Guo, Languang Zhao, and Haichen Wang Shenyang Jianzhu University, Shenyang, China [email protected]

Abstract. In order to achieve non-collision and high precision trajectory control of polishing robot, this paper presents the trajectory control method based on secondary deceleration and dynamic model. Control system is designed using the method of computed torque based on PD feedback. Trajectory control simulation experiment is implemented, and experiment results show that robot end-effector can contact with the workpiece without oscillation, and can achieve unbiased trajectory control using the method of trajectory control based on secondary deceleration and dynamic model. Keywords: polishing robot; secondary deceleration; dynamic model; trajectory control.

1 Introduction In order to complete the polishing task, the traditional polishing process is completed mainly by hand, which is time consuming, inefficient and poor product consistency, lower accuracy. In addition, the work site noise and dust make environmental quality degradation, which will affect the health of operation workers. However, using automatic polishing technologies will increase labor productivity, ensure product quality, and solve the problem of shortage of skilled workers. Therefore, polishing robots researches have been widely developed [1-5]. The traditional trajectory planning from free space to constraint space is using T-shaped or S-shaped curve, so that when the robot end-effector contact with the workpiece, it will have greater contact force or oscillation. Trajectory control based on kinematic model is joint servo system which is composed of each joint servo loop. But in this case, coupling between joints generated by the centrifugal force, Coriolis force and gravity are disposed as external disturbances, which often can not meet the requirements of high precision control. It is better way to establish the robot dynamic model, considering the robot dynamic specialty. Computed torque control method is the method based on the robot dynamic model. According to the above analysis, this paper proposes trajectory planning method based on the secondary deceleration and dynamic model.

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Trajectory Planning Method Based on the Secondary Deceleration

In this paper, robot end-effector slowly contact with the workpiece using the secondary deceleration method, which can effectively solve the problem of oscillation. The trajectory planning method is as follows: In the base coordinate system, the of the robot end-effector is px , p y , pz , pose is α , β , γ , the initial position of end-effector is px = 0mm, p y = 0mm, pz = 0mm . Let the position and pose of robot end-effector has been perpendicular to the tangency plane of workpiece. Supposing the robot from point A to point B, it is shown as Fig.1. Fig. 2 shows the position, velocity and acceleration curves of the robot end-effector. S s5 s3 s1

s4 s2

s0 0

t t0

t1

t2

t3 t4 t5

t1

t2

t3 t4 t5

v v0

X

•

Z

A

Y

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•

v1 v2 0

a

t t0

a0

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Fig. 1. Illustration of robot moving from free-space to constraint-space

t1 t2

t3 t4 t5

Fig. 2. Curve of robot end-effector position, velocity and acceleration

From the figure, the robot end-effector can close to the surface of workpiece at slow speed using the method of secondary deceleration, so as to effectively suppress the occurrence of oscillation.

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3 Trajectory Control Based on Dynamic Model Computed torque control method is the use of the robot dynamic model, by solving the inverse dynamic to calculate the control torque, so that achieve approximate decoupling and linearization, therefore it is closely related to the dynamic model of the system. dynamic model is too complicated, computed torque control method is not conducive to be applied. As polishing robot dynamic model is no coupling between joints in this research, so using computed torque control method, polishing robot trajectory control can meet the system requirements of real-time control. Polishing robot dynamic equation can be abbreviated as:

D(q)q + G (q ) = T + J T F Where,

(1)

q , q and q ∈ R 5 , are respectively the joint position, velocity and accelera-

D(q ) ∈ R5×5 for the inertia matrix; G (q ) ∈ R5 for the gravity term; T ∈ R5 is the joint driving torque; F ∈ R n is the binding force of task space, which can be measured by force sensor in real time; J ∈ R 6×5 is the robot Jacobian.

tion;

Using two-level control strategy of servo compensation and linear compensation, let

T = αT ′ +β

(2)

Where, α= D(q) , β = G(q) − J F , then T

T ′ = q

(3)

Equation (3) is the linear decoupled system of the joint variables. Since the control system between input and output meet the linear relationship, the linear method can be used for system control. This research is using PD feedback method. That is, the PD feedback control law is

u = qd + K v e + K p e = T ′ Which

(4)

qd is the desired acceleration trajectory; K v and K p is positive definite gain

matrix; generally selected as the diagonal elements is positive diagonal matrix, so that decoupling; e = q − q , e = q − q , q is the joint expected trajectory, q the actual d

d

d

trajectory for the joints. Equation (4) is the servo compensator, which can reduce the model error and external disturbances on the system. By (3) and (4), may

e + kv e + k p e = 0

(5)

e →0. As long as the appropriate choice k and k p , then lim t →∞ v

According to equation (1) and (4), can be introduced equation (6).

T = D (q ) ⎡⎣ qd + K v e + K p e ⎤⎦ + G ( q ) − J T F

(6)

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4 Simulation For the actual project requirements, using the secondary deceleration trajectory planning method, in case of no modeling error, PID control based on kinematic model and computed torque control based on dynamic model is adopted, the joint trajectory control simulation experiment is implemented. Fig. 3 ~ Fig. 12 is joint trajectory and velocity tracking curve from joints 1 to joints 5.

Fig. 3. The figure of 1st joint trajectory control

Fig. 4. The figure of 1st joint velocity control

Fig. 5. The figure of 2nd joint trajectory control

Fig. 6. The figure of 2nd joint velocity control

Fig. 7. The figure of 3rd joint trajectory control Fig. 8. The figure of 3rd joint velocity control

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Fig. 9. The figure of 4th joint trajectory control Fig. 10. The figure of 4th joint velocity control

Fig. 11. The figure of 5th joint trajectory control Fig. 12. The figure of 5th joint velocity control

In the polishing robot dynamic model, gravity of joint 1 is larger proportion. Fig.3 and Fig. 4 shows the trajectory control based on kinematic model will be considered as interference terms, so there is a big trajectory error. However, computed torque control based on PD feedback can achieve linear gravity compensation of robot, so in the non-modeling error, the method can achieve unbiased robot trajectory tracking. But the actual existence of the system modeling errors, modeling errors mainly come from the dynamic modeling process, using some approximation algorithm, ignoring the impact of certain parts; the irregular component parts as a rule deal ; on uncertain factors such as external disturbances can not be precise mathematical model to represent the other. Using other compensation control method, the system does not model the interference and other factors into account, the next step of this research.

References 1. Santibanez, V., Kelly, R.: Global. Asymptotic Stability of a Tracking Fuzzy Controller for Robot Manipulators. IEEE Trans. on systems man and cybernetics (January 2004) 2. Guo, T.Y., Qu, D.K.: Research on Path Planning for Polishing Robot Based on Improved Genetic Algorithm. In: Proc. of the ROBIO conference, pp. 300–305 (2004) 3. Nagata, F., et al.: Polishing Robot Using a Joystick Controlled Teaching. Journal of Robotics and Mechanism 13(5), 517–525 (2002) 4. Wang, Y.T., et al.: Path Planning for Robot-Assisted Polishing Process. Proc. of the IEEE Int. Conf. on Robotics & Automation, 331–336 (2001) 5. Tsai, M.J., et al.: Development of an Automatic Mold Polishing System. In: Proc. of the IEEE Int. Con. on Robotic& Automation, pp. 3517–3522 (2003)

Security of Industrial Control System Peng Jie and Liu Li Information Engineering College, Nanchang University, Nan Chang 330031, China

Abstract. In the past, control networks were completely isolated from exterior attack with the use of proprietary control protocols running on specialized hardware and software. Since then, TCP/IP based systems have made their way into the industrial control system. Control networks based on TCP/IP provide better connectivity and remote access capabilities, making the industrial control system vulnerable to cyber threats such as computer virus, internet worm, manipulation of operational data which can result in the disruption of the entire control system. In this paper, security risks of the industrial control system based on control network are analyzed and the differences between the disruption of control performance and that of control function are presented. The countermeasures are put forward. Keywords: Industrial control; Cyber security; control network Ethernet.

； Virus ；

1 Introduction Security of Industrial networks has become increasingly prominent issue, primarily due to the following three reasons. First, industrial control systems and enterprise IT systems are jointed more and more closely. second, enterprise IT security measures can not be directly applied to the industrial control systems. Third, worms, viruses and hackers are becoming more powerful and pernicious. Therefore, the study on industrial control system security risks has been very urgent and meaningful.

2 Industrial Control System Adopts the Mainstream Technology Typically, the factory has three network layers: device layer, monitoring layer and management layer. For the monitoring layer, industrial Ethernet technology occupies the mainstream position. Along with the development and application of the real-time Ethernet technology, the industrial control system gradually realizes "Ethernet in the end”, as shown in Figure 1. In the monitoring layer, not only PLC and central controller are used in control system, operation stations with the Windows work for the optimization of system, advanced control, web servers, and database server and so on. The data can be got from different data sources and exchanged through the OPC technology and transmitted

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through the industrial Ethernet switches. with the integration and expansion of the automation system such as the FTP application, the application of B/S mode to monitor, the use of Internet for remote diagnosis, the application of the video and audio from industrial field to monitor data transmission, in the future the application of IPV6 to industrial control equipment system etc. The characteristics of industrial control system as a comprehensive mainstream information technology has became more apparent. Server Internet Ethernet

Gateway

Real-time Ethernet

PLC/Controller Real-time Ethernet

Fig. 1. Typical Industrial network

3 Threats to Industrial Control System Information Security The traits of industrial control system make the industrial control system meet not only traditional threats which aimed at commercial computer and network, but also those which would threaten the performance of control or even destroy control system function. 3.1 The Threats to Control Performance Typically, it can threaten the real-time performance of control system. A case in point is the industrial Ethernet which occupies a mainstream position in the monitoring layer. The network response time can reflect the real-time performance of the Industrial Ethernet. There are three main influencing factors for the network response time, including the source node processing in local system, the transmission on industrial Ethernet and destination node processing in the destination node system. Figure 2 shows the total time delay Tdelay can be divided into the following several parts: time delay in source node, time delay on network transmission and time delay in destination node. Source node time delay includes pretreatment time Tpre(the total of computation time Tscomp and encoding time Tscode) and the internal queuing time (node queue) Tn-queue, which is a one part of Twait and depends on the sum of transmitting data in source node and transmission condition.

Security of Industrial Control System

Depart queue

Start

from

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End

Last Bit Send

Bit

Last Bit Receive

Tprop Tframe Tpre

Twait

Source node

Ttx

Tdcode Tdcomp

Tpost

Network transmission

Destination node

Time Fig. 2. The diagram of industrial Ethernet response time

Network transmission time delay consists of transmitting time Ttx( the total delay time of sending frame delay Tframe and physical propagation delay) which depends on the size of the information, data transfer rate and the length of cable and the other part of the waiting time Twait, precisely the time of network congestion Tblock. Destination node delay time Tpost is the data processing time which is the sum of destination node decoding time and computing time. The total time delay can be expressed as follows: Tdelay = Tdest+Tsrc = Tpre + Twait + Ttx + Tpost = Tscomp + Tscode + Tn-queue + Tblock + Tframe + Tprop + Tdcode + Tdcomp Tpre

Twait

Ttx

Tpost

As seen in Fig 2, the computer virus, even if they don't cause operating system collapse, can occupy resources to make the delay in source node and destination node. Moreover, though LAN virus or network attacks can not make a paralyzing system, they can make the network transmission time delay through blockage. These uncertainties will destroy the real-time control performance.

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3.2 The Threats to the Function of Control System Typically, the threats can destroys and manipulates the Industrial control software, for example, the damage to the OPC software and real-time database. However, the more serious damage is the manipulation of industrial control software. On October 3rd 2010, National Computer Virus Emergency Response Center released information to remind users, especially large industrial department, to beware of a new type virus "Stuxnet". The virus can spread by mobile storage medium and LAN. The loopholes of Siemens control system (SIMATIC WinCC/Step7) become the way to infect data acquisition and Supervisory Control and Data Acquisition (SCADA). The purpose of the Stuxnet is to change the action of industrial production control system by modifying PLC, including the interception of the read/write request sent to PLC so as to judge whether the system is a potential attack target, the modification of existing code block and the rewriting of a new code block, and finally hiding PLC infection with the use of the Rootkit function so as to avoid PLC administrator or programmers detection. Step 7 software use library file s7otbxdx.dll to communicate with PLC. When Step7 program prepared to enter into the PLC, it will call the different routines of DLL files. For example, if a block of code need to use Step 7 to read out from PLC, then the routine s7blk_read will be invoked. The code in S7otbxdx.dll will enter into PLC, read the code and hand t it back to Step 7 program. As the Stuxnet is operated, it will make original file s7otbxdx.dLL renamed s7otbxsx.dll. Then, it will be replaced with the original DLL files, Stuxnet can intercept all orders from other access PLC. The s7otbxdx.dll modified by Stuxnet retains the original export list (export function 109), making the Stuxnet able to respond to all the same request. Most of the export orders will be transmitted to the real DLL(the file renamed was s7otbxsx.dll). As for The rest of the 16 export orders, they would not be simply forwarded, but intercepted by the DLL. The routines of code block to read, write and position in PLC are the intercepted order. By the interception of these requests, Stuxnet can modify data returned from PLC or sent to PLC without the notice of the administrator. Meanwhile, Stuxnet can use these routines to hide malicious code in PLC. Through the preliminary analysis, the following security threats are existing in the virus (1) The target of the virus is the special computer system used for data acquisition and monitoring control (2) Such data acquisition and monitoring control system, due to the importance and particularity their own functions, are often isolated from the Internet, making the system unable to update security promptly and in that case gives an opportunity of the virus infection. (3) Although at present the virus aims only at data acquisition and monitoring control system of Siemens, variations of the virus have potential threats to other special computer system.

4 The Security Strategy for Industrial Control System The development of industrial control system is dynamic. In other words, there is no absolute solution. We should formulate the information security strategy and select feasible protection technology for the system. This paper will make the preliminary discussion.

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(1) Cut down the operating system according to the application. Stuxnet worm virus attacks by taking advantage of the multiple loopholes of Windows system and WinCC system of SIMATIC. Five Microsoft loopholes are made use of to attack the system and four are already repaired by Microsoft official. On December 15, 2010, Microsoft released the "Windows scheduled local authority ascending loopholes" which have been repaired (announcement Numbers: MS10-092), and this is the last Windows loophole used by the "super factory" virus. With the repair of the first five holes, Stuxnet hazards have been settled. However there are many loopholes existing in the operating system, it is difficult for the industrial software to avoid threats. Therefore, it is better to cut down the operating system according to the application (2) The distributed firewall technology for the control system Develop and apply the distributed firewall technology for the industrial control system. Distributed firewall can overcome many traditional boundary firewall shortcomings. Compared with traditional boundary firewall, a security protection layer is added for the internal subnets. Make different configuration in terms of different needs of each service objects. During the configuration, the application of running should be fully considered. The distribution of network processing load overcomes the drawbacks of traditional firewall under the condition of a heavy load. In information network, distributed firewall rests in the host to implement the strategy. Similarly, we can develop the corresponding host firewall to use in the control system. What is different is that protection object of these host firewalls are the controller and some hosts used to execute control strategy such as the PLC, RTU, etc. (3) Risk assessment of industrial control systems One of the benefits of security risk assessment is that we can adopt corresponding security strategies and formulate solutions for the treatment of accidents. It is particularly useful to the security threats such as Stuxnet which can damage the function of control system. There are a lot of methods for the risk assessment of IT systems such as the method based on probability theory, the analytic hierarchy process method, fuzzy logic method and so on. The risk assessment of industrial control system should be based on a mature risk assessment method and focus on the unique requirements of control system to ensure the normal operation according to characteristics of the control system. For instance, if the control system can cause serious consequences of risk events, the assessment must be more "conservative". We should explore other feasible strategies such as the SSL and VPN technology, which have been researched in many papers.

，

5 Conclusions So far, the network attack that aims at industrial control system indicates that the security of the industrial control system should be paid attention to. The whole industrial network control system would generally be keep for 10 to 20 years in the field. So we should be able to judge the problems caused by various factors in the industrial control system, develop corresponding security strategies and make solutions for the treatment of accidents. In that case, the failure probability of the system and the loss will be always in the acceptable state.

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References 1. Shi, X.: Computer Network Technology. Higher Education Press, Beijing (2006) 2. Han, D.: Intrusion detection system and example analysis. Tsinghua University Press, Beijing (2002) 3. Peng, J., Ying, Q.: Industrial Ethernet security research. Instrumentation journal (1), 222–223 (2004) 4. Kang, J., Dai, G.: Security problems research of Industrial Ethernet control system. Information and Control 36(2), 245–249 (2007)

The Application of Automatic Control Theory in Software Engineering Wensong Hu, Xingui Yang, Min Zhu, and Yan Zhang School of software, Nanchang University, China [email protected] Abstract. At present, the methods of quantifying software process in software engineering have been very mature. Because the software process can be quantified, you can use the general principles of control theory and methods in software for the ways of automatic adjustment, coordination and optimal control by which the software process is in the range of stable equilibrium. This paper describes how to apply the control theory in software engineering. Keywords: Automatic Control Theory, Software Engineering quantitative, process feedback control.

1 Introduction In software engineering, software process improvement is to increase software development productivity, software quality assurance and effective means. We commonly use software process improvement methods, such as: CMM, ISO9000, ICE and so on, which the software Capability Maturity Model (SW-CMM) is a widely used software process capability assessment and improvement of the guiding framework, however, it pointed out that the CMM merely a "what should improvement”, it need to focus on "how to improve" in the practice of process improvement. Because of the application of quantitative analysis, software process improvement can be quantitatively predictable ways to track and control, thus ensuring the smooth implementation of process improvement and effective implementation. In the software process, the qualitative analysis and quantitative analysis are used in combination. It can’t do without CMM, ISO9000, ICE and other guidance for qualitative analysis and can’t do without control theory and other disciplines learn the principles and methods for quantitative analysis. The results of quantitative analysis of the interpretation of the software process are according to certain CMM, ISO9000, ICE and so on. In a word, automatically control theory principles and methods can be applied to software engineering and make the entire software process to the best, balanced way smoothly.

2 The Application of Automatic Control Theory in Software Engineering The control theory applied to software engineering is to study: 1. Studying the establishment of software process control methods and simulation models the actual method of software process M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 965–968. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2. Studying on the information and physical contact of software process among each internal factors and the information and material contact between software process and the external environment 3. Studying on the structural features of the software process, such as functionality, reliability, availability, efficiency, maintainability and portability and so on, in order to study the law of development, the process of formulating long-term software planning, and to make the right decision-making, provides a theoretical basis. 4. Studying on the regulation of the software process, control and coordination of the laws and methods, such as software process feedback control.Let’s give software process feedback control as an example.Software process feedback control After the software process has been quantificated, it is mainly related to the following three aspects, the quantification of progress, the quantification of quality, the quantification of risk, establishing the project baseline through the measurement process, thus the performance of software process improvement can be assessed.

Fig. 1. Software process feedback control

Baseline is formed by the collection and analysis throughout the project data, and to get the data from these reference points. Software process control is mainly to receive the input signal or error signal, give the amount of operation according to the control law to the implementation unit to operate the software process, make the software process has the required performance or status. Disturbance is the interference information which interferes and damage system in scheduled performance or the intended results, mainly including risk in software engineering. Getting the windage from the compare between the baseline and process result, then give it to the process controller to analyze in order to getting the amount of operation to the implementation unit to do something needed, software process improvement measurement results will be fed back to the control system and repeating Software process control, and so the cycle continues, and make the software process within the stable down.

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Select process Recognize product or process characteristics of present process performance

Select control mode

The process performance measures

Make sure and remove reasons

Measured value compare with Base value

result

The process is stable

Y

All measured values are whether in the range stated

N

Process is instability

Fig. 2. Software process feedback control

The following is feedback regulation of the entire block diagram of software process:

Fig. 3. Feedback regulation

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3 Conclusion Automatic Control is more than one Division of the technical disciplines. Its mission is to quantitatively analyses the relationship between the information transfer and conversion, then predict the behavior of the whole system based on relationship among these quantitative. As software engineering software process have more and more sophisticated quantitative methods, through the principle of automatic control theory and methods can provide guidance on software process improvement, but also can improve the risk, schedule, cost - benefit predicted in advance, so as to effectively process improvement to avoid the loss of control of resources and waste problems.

References 1. Yingguo, G.: Principle of Auto Control. Huanan Technology Press 2. Yang, S., Xu, L., Single, P.I.D.: Control of Inverted Pendulum. Control Engineering (5), 23–25 (2007) 3. Li, L.: Process Control Simulation System. Experimental Technology and Management 22(4), 81–83 (2005) 4. ISO/IEC JTCI/SC7/WG6, Information Technology Indicators and Measures[S] 5. Paulk, M.C., Curtis, B., Chrissis, M.B., Weber, C.V.: Capability Maturity Model for Software, version 1.1. CMU/SEI 93-TR-024 (1993) 6. CMMI Product Team: Capability Maturity Model® Integration(CMMI),Version 1.1. CMU/SEI-2002-TR-029 7. Park, R.E., Goethert, W.B., Florac, W.A.: Goal-Driven Software Measurement —A Guidebook. CMU/SEI-96-HB-002 (August 1996)

Portable MIT-BIH Physiological Signal Playback System Based on Android Wensong Hu, Li Ming, Min Zhu, and Linglin Xia School of software, Nanchang University, China [email protected]

.

Abstract This article introduces the one of three international common ECG databases—MIT-BIH Arrhythmia Database. Secondly, we introduce the platform of this system—android. Android is a complete operating environment based upon the Linux V2.6 kernel. It includes the operating system, user interface and applications. The main deployment target is smart phones. Finally, the application can read the data file which is collected from portable ECG device and display the ECG waveform on cellphone. Patients do not need to spend too much time to go to the hospital. We use these recordings of the MIT-BIH Arrhythmia Database to test and debug this application. Keywords: MIT-BIH Arrhythmia Database; ECG; android operating System.

1 Introduction According to the World Health Organization statistics show that the global number of deaths from heart disease each year as many as 700 million people. People are paying much more attention to healthcare. For the prevention and treatment of heart disease, in hospitals, there are many of the ECG monitoring instruments for treatment of heart disease. With the popularity of smart phones, it can be a practical and remote ECG monitoring instrument. MIT-BIH Arrhythmia Database is established by the Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology. It contains 48 half-hour excerpts of two-channel ambulatory ECG recordings. MIT-BIH Arrhythmia Database has become an internationally accepted arrhythmia database, also a standard test source. It uses a custom format In order to reduce the file size when stored to save storage space. Each record of the database contains three documents, namely the header file (extension .hea), data file (extension .dat), annotation file (extension .atr). Figure 1 shows a simplified view of the record. Header file A header file contains some information of the record, such as file name, the number of leads, the sampling frequency, the number of data points, storage format, , the patient's relevant information and so on. Storage mode is ASCII characters. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 969–974. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Record

Header file (.hea)

Data file (.dat)

Annotation file (.atr)

Fig. 1. The composing diagram of database recording

Data file The original data is stored in the data file by binary. The data format of data file is described in the header file. The uniform format of Arrhythmia database is “212”. "212" format is for the record of two signals (called signal-0 and signal-1). The two signals alternately store the data. Every three bytes stores one of data points of the two signals. These data points were sampled signals from the signal-0 and signal-1. Signal-0 consists of the first byte and the lower 4 bits of the second byte; signal-1 consists of the last byte and the higher 4 bits of the second byte. Figure 2 shows a simplified view of the storage mode.

Fig. 2. Illustration of MIT-BIH Arrhythmia Database data storage mode

Annotation file The annotation file is to record the results of doctor’s diagnosis. There are two main formats: MIT format and AHA format. MIT format is a compact format. In most cases, the length of each note takes two bytes, always used for online comments. In AHA format, a note occupies 16 bytes, it used for the exchange of documentation. MIT-BIH Arrhythmia Database uses the MIT format. Android is a mobile operating system running on the Linux kernel, and it includes rich functions. It was initially developed by Android Inc., a firm later purchased by Google, and lately by the Open Handset Alliance. It allows developers to write managed code in the Java language, controlling the device via Google-developed Java libraries. Android includes a set of C/C++ libraries used by various components of the

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Android system. These capabilities are exposed to developers through the Android application framework. Figure 3 shows the major components of the Android operating system.

Fig. 3. The major components of Android

The Android SDK provides the tools and APIs necessary to begin developing applications on the Android platform using the Java programming language. We develop this application in Eclipse. First of all, we need to install the JDK, Eclipse and download the Android SDK, then install the Android Development Tools (ADT) Plugin, and set the “Android” preferences in Eclipse to point to the SDK install location.

2 Collectivity Design The basic function of this system is to read the record of the MIT-BIH Arrhythmia Database and then dynamic display on the phone. Taking into account the screen size of the cellphone limits, we set 2 seconds to refresh the screen. Considering the phone built-in storage space constraints, the program defaults ECG data files stored in the SD card. Users select the header file of the record which we want to read, then the program open the header file to read. According the information of the header file, the program get the corresponding data file and annotation file. Since the data file contains two sets of leads (MLII and V5), we should choose which of the two signals waveform will be displayed before read the data file. Users can click a button on the phone screen to control the playback. Users can also look back the played ECG waveform. In the UI layer designing, the waveform display in screen is set Landscape mode that is better for display. As different phone's screen has different size and resolution, program should be run through APIs provided by Android SDK to obtain parameters of the phone's screen, and

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then draws the UI such as the coordinate system and ECG waveform based on the parameters. The following codes show how to get parameters of screen: (ECGPlayer.java) /**Get parameters of screen*/ DisplayMetrics dm = new DisplayMetrics(); this.getWindowManager().getDefaultDisplay().getMetrics(dm); if (dm.heightPixels > dm.widthPixels) { /**Portrait mode*/ Height_of_Screen = dm.heightPixels; Width_of_Screen = dm.widthPixels; } else { /**Landscape mode */ Height_of_Screen = dm.widthPixels; Width_of_Screen = dm.heightPixels; } In the Android application development, the layout of the user-interface has been written in a XML file, and then the setContentView() method was called in current activity, which is inherited from the parent class--Activity. In order to achieve modular design, we create a class named CoordinateSystem to manage the drawing. Class ECGPlayer is responsible to read the ECG file. After completion of UI design and the choice of the user indicate which group of leads, the program start to read data file. Refer to the MIT provides special tool--DB Browser, the vertical axis of coordinate system was then divided into 7 spaces. According to the width of the phone’s screen, we can get the width of each grid, and then we can compute the number of grids in the horizontal axis. The following codes show these. (CoordinateSystem.java) /**Get the size of a grid*/ CS_Interval = (int) (((ECGPlayer.Width_of_Screen - (ECGPlayer.Width_of_Screen / 8)) / CS_Row) / 5); CS_Interval = CS_Interval * 5; /**Compute the number of grids in the horizontal axis*/ CS_Col = (int) (ECGPlayer. Height_of_Screen / CS_Interval);

Fig. 4. Interface of player. (Run in a emulator)

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The following content takes the MLII load of the record 100 as example. To achieve dynamic displaying, we create a thread named drawcurve to read data, according to the rules related to file data, and update the interface. And then create a handler named handler_play to send message to the message queue of the thread at set time. In this way, we can achieve the desired effect. Here, we introduce to you the class Handler. A Handler allows you to send and process message and runnable objects associated with a thread's message queue. Each Handler instance is associated with a single thread and that thread's message queue. When you create a new Handler, it is bound to the thread / message queue of the thread that is creating it -- from that point on, it will deliver messages and runnables to that message queue and execute them as they come out of the message queue. In the header file we can read the number of data points and the sampling rate. According to this information, we can compute the total sampling time and the number of data points contained within each grid, and then we set the thread reading a fixed amount of data points. Refer to the storage mode of data files mentioned above, the following codes read the signal of MLLII load and achieve the desired effect. (ECGPlayer.java) public void run() { for (int i = 0; i < Points_of_Grid; i++) { try { if ((b = in.read(data)) != -1) { temp = (short) ((data[1] & 0x0f)); m_point = (short) ((short) (temp * Math.pow(2, 8)) + (short) (data[0] & 0xff)); value = 0.005f * m_point; position[i] = 2 * value * CoordinateSystem.CS_Interval; } else { in.close(); handler_play.removeCallbacks(this); } } catch (IOException e) { e.printStackTrace(); } } Points points = new Points(); points.setM_point(position); m_screen_list.add(points); cs.drawCurve(position); handler_play.postDelayed(drawcurve, 200); } We add the drawCurve() method in the class CoordinateSystem. In this method, we call the invalidate() method which calls the onDraw() method at some point in the future. In onDraw() method, we draw waveform through the drawPath() method of class Canvas.

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In order to achieve view past waveform, we add an ArrayList object to save the data read every time. When we want to read the past waveform, the program will read the data from the saved ArrayList object.

3.

Conclusion and Outlook

The system runs successfully and could show the ECG waveforms very well. Compared with the display result of DB Browser provided by MIT, we show that in the following figure. (Figure5).

Fig. 5(a). Display result of DB Browser

Fig. 5(b). Display result of this system

At the present time, the system implements dynamic display and playback functions. With the rapid development of network and the superiority of network applications in the Android platform, we want to expand the system capability to enable mobile phone able to send ECG files through the network to the hospital’s electronic medical record system, at the same time, the application can automatically sent a text message to remind doctor to diagnose. In this way, doctors can look over ECG files in the electronic medical record system on their personal computer. After diagnosing the patient’s disease, the electronic medical record system can automatically sent back diagnostic messages to patient’s cellphone. Doing so would greatly facilitate the communication between doctors and patients. Believe that further efforts in developing functions of the system will be more perfect, and it would play a significant role in real life.

References 1. MIT-BIH Arrhythmia Database, http://www.physionet.org/physiobank/database/mitdb/ 2. Hu, C.: Mobile webserver to the Android platform. In: 2010 International Colloquium on Computing, Communication, Control, and Management, CCCM 2010 (2010) 3. Yan, Z.: MIT-BIH Arrhythmia Database Signal Generator Base on MSP430. Progress in Biomedical Engineering 30(3) (2009)

The Application of Network Model on Power Station Min Zhu, Wensong Hu, and Yan Zhang School of Software, Nanchang University, Jiangxi, China [email protected]

Abstract. This paper introduces an application of fuzzy neural network theory applied in relay protection. This new method combines the artificial neural network with fuzzy system, which makes full use of the strong structural knowledge express ability of fuzzy logic as well as self-learning and direct quantitative data processing ability of neural network. Hence the robustness and self-learning of FZZ are improved. At the end of this paper, we use a relay protection working case to certify the new method. Keywords: power station, protection, neural network.

1 Introduction With the fast development of information technology, human can easily collect and store data. The growth of data management technology, especially the rise of Internet, promotes the commercial and government affairs informationize. Thus it generates a lot of data exponentially. In order to manage those data, large database are widely used in both commercial and science areas. Though the progress that database technology made make it more and more convenient to collect or store data, the explosive growth of data size is far beyond the understanding of human beings. It is difficult to use traditional database management systems and analysis method to find the hidden information from the data. Therefore a new intelligent technology, data mining, comes into birth. Data mining means the progress that extracts the certain knowledge of interest from data in large databases. This knowledge is implicit and unknown which is extracted in the form as the Concept, Rule, Regularity and Pattern. In general, as shown in Fig.1, data mining extract the data from data warehouse and store them in the data warehouse or data market. There are several data mining algorithm including rough set, fuzzy set, cluster and decision tree. Because of the data characteristics, those methods are not suitable in some case research. If the neural network technology can be used for data mining, we can solve those problems on this issue by virtues of the nonlinear processing and the noises capabilities of neural network.

2 Fuzzy Neural Network 2.1 Fuzzy System Fuzzy system is based on fuzzy set theory and it does fuzzy measure, fuzzy recognition, fuzzy reasoning and fuzzy control to complicated things. In classical fuzzy theory, the M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 975–980. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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characteristic function only allows access two values “1” and “0”, while fuzzy sets theory has extended the characteristic function to a range of [0,1]. Fuzzy algorithm identification is a way to change opaque recognition algorithm into a transparent pattern recognition algorithm by using related fuzzy set theory algorithms. It is suitable for the expression of that vague and qualitative knowledge, because its reasoning mode is more similar to human thinking. However, fuzzy systems lack self-learning ability. 2.2 Neural Network Neural network is a method that simulates the human beings image intuitive thinking. It is a parallel processing network presented with simplification, induction and refining according to biological neurons and neural network features based on the research on biological neural network. Initially the neural network applications in data mining has not got good views, mainly because the neural network has its defects such as complex structure, poor interpretability and long training time. But neural network has the high affordability of noise data and low error rate. After a variety of network training and optimization algorithm are proposed in succession, especially the various network contact pruning algorithm and rule extraction algorithm, the use of neural network in data mining is more and more popular in the real research. However, the traditional artificial neural networks are generally use non-time-varying way to process information, which is difficult to reflect the cumulative effect in actual projects. It can not resolve the continuous complex problem of nonlinear time-varying systems well. Here we introduce fuzzy theory to conquer those problems. 2.3 Fuzzy Neural Network From the discussion above, we can see that if we combine the fuzzy system with neural network, we can get the new system of better performance named fuzzy neural network with both the uncertain information processing ability of fuzzy system and self-learning of neural networks. The principle structure of fuzzy neural network system is shown as figure 2.

Fig. 1. Process of Data Mining

Fig. 2. Structure principle of FNN

3 Algorithm of FNN Structure of FNN. Figure 3 shows the structure of fuzzy neural system model. There are five layers in FNN system.

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Fig. 3. Layers of FNN

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Fig. 4. Membership function

The first layer is input layer. The nodes in this layer are input nodes, which are directly connected with each component of input vector. It can transmit the input directly to the next layer.

f = uil And a = f

(1)

The second layer is membership degree layer. The neuron indicates the membership function of linguistic variables.

(u f =−

2 i

− mij

σ

2 ij

)

2

And

a = ef

(2)

The third layer is the rules nodes layer that represents the fuzzy logic rules. In this layer, each node is an IF part of potential fuzzy rule. Thus, all the nodes in layer three compose a fuzzy rule library.

(

)

f = min u13 , u23 ,⋅ ⋅ ⋅, u 3p And a = f

(3)

The fourth layer is also terminology nodes layers like the neuron in the second layer. p

f = ∑ ui4 And a = min (1, f )

(4)

i =1

The fifth layer is output layer, where the node has two running mode: one for feeding the training data to network from top to down; while the other for transmitting the decision signal from down to up and clearing with de-fuzzy.

f = ∑ wij5ui5 = ∑ (mijσ ij ) ui5 And a = Where

1

∑σ

5 ij i

u

(5)

mij and σ ij represent the central value and width of function respectively. 5

5

Self Organizing Learning Algorithm. In this stage, the algorithm is used to determine the membership degree function and detect the fuzzy logic rules.

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The center m of membership degree function is determined by Kohonen characteristic diagram by organizing learning.

x(t ) − mclosest (t ) = min{ x(t ) − mi (t ) } 1≤ i ≤ k

mclosest (t + 1) = mclosest (t ) + α (t )[x(t ) − mclosest (t )] mi (t + 1) = mi (t ) When mi (t ) ≠ mclosest (t )

(6) (7) (8)

The width is calculated by the following formula

⎛ m − mj 1 n ⎡ E = ∑ ⎢ ∑ ⎜⎜ i 2 i =1 ⎢i∈N N ⎝ σ i ⎣

2 ⎤ ⎞ ⎟⎟ − r ⎥ ⎥⎦ ⎠

2

(9)

Supervised Learing Algorithm. The goal of this step is to adjust the parameters of membership degree function to obtain the optimization output, that is, minimize the objective function error.

E=

∧ 1⎡ ( ) (t )⎤⎥ − y t y ⎢ 2⎣ ⎦

2

(10)

Since it is in supervised learning stage, the calculation process is from the bottom to up. After the detailed procedure, the adaptive rule should be

σ ij (t + 1) = σ ij (t ) − η

∂E f i 2(ui − mij ) e ∂ai σ ij3

(11)

The convergence rate is faster than the general inversion algorithm.

4 Case Simulation Here we use a series of data to certify the affectivity of the method. Table 1 shows 300 source data from XXX power station supervision team. The relay protection will work when the current or voltage error reach to a certain degree. We use 200 source data to train the network and the rest data to certify the correctness of the fuzzy neural network. The two inputs are power current error and power voltage error while the relay protection action is the output. Thus we construct a FZZ with two inputs and one output vector. According to the experience and expert system, we change the power current error into three fuzzy variables with fuzzy language rule. They are high, medium and low. Hence the center value of membership function

m11 , m12 , m13 is 90, 60 and 30

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respectively. Correspondingly, the center value of membership function of power voltage error m21 , m22 , m23 is 80, 40 and 10 respectively. Width of the membership function is 0.1. Table 1. Source Data ……. Power current difference Power voltage difference Relay protection probability

1

2

3

4

5

3.58

-7.2

-6.7

5.6

-0.63

……

2.19

-3.0

3.5

3.1

-3.7

……

48

61

55

63

32

……

The two inputs are set as subset {S , M , L} to indicate low, medium and high. The membership function formulas are

S =e

−k1

(χ − δ1 )2 2σ 2

M = e −k 2 L = e −k 3

(12)

(χ − δ 2 )

2

2σ 2

(13)

(χ − δ 3 )2 2σ 2

(14)

The membership functions are shown as following. Set 0.1 as the threshold value of the ratio of sample to total data, we can get the following fuzzy rules. Table 2. Fuzzy Rules

Power current error 100% Power voltage error 100% Relay protection

Rule 1

Rule 2

Rule 3

Rule 4

Rule 5

S

L

L

M

M

S

S

M

L

L

S

S

M

M

L

The rest 100 data verify that the correctness of this fuzzy neural network is 92.3%, which determines the effectiveness of those fuzzy rules.

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5 Conclusion This paper present a data mining method based on fuzzy neural network theory, which combines the fuzzy system with artificial neural network. FZZ makes full use of the strong structural knowledge express ability of fuzzy logic as well as self-learning and direct quantitative data processing ability of neural network. Hence the robustness and self-learning of FZZ are improved. The knowledge hidden in database is stored in the network weight by constructing FZZ and training the sample. After clipping the network, the main knowledge is shown and then we obtain the more accurate fuzzy rules. At the end of this paper, we use a relay protection working format to certify the correctness of data mining method on FZZ.

References 1. Ling, L.: Fuzzy neural network for rural power dam short. Agricultural Research 2, 210–212 (2010) 2. Qiongzhi, L.: knowledge discovery and data mining based fuzzy neural network. Berjing chemistry university master paper, pp. 31–42 (March 2003) 3. Yifan, W.: A data mining algorithm on fuzzy neural network. Journal of Chinese university of electric power 17, 27–30 (2002) 4. Xiao, P.: Data mining study based on fuzzy neural network. Microelectronics and computer 22, 48–54 (2005) 5. Yunhui, L.: Construction of improved fuzzy neural network method. Jounral of huaqiao university 31, 256–259 (2010) 6. Zhang, Z., Chen, C.: Research and realization of time synchronization system based on distributing algorithm. Telecommunications for Electric Power System (May 2005)

The Application of Algorithm in Power Station Min Zhu, Wensong Hu, and Yan Zhang School of Software, Nanchang University, Jiangxi, China [email protected]

Abstract. This paper presents a new mathematic model which is based on wavelet neural network theory algorithm. This model thrives from wavelet analysis theory and is applied in power load prediction. The author analyzes power load in any dimension and collects it effectively with wavelet analysis method. At the end of this paper, the authors analyze the model parameters and discuss various factors deeply. Keywords: algorithm, power station, neural network, prediction.

1 Introduction The production and application of electric power have their specialties. First, it is difficult for the engineers to sore large amount of electric power. Secondly, various power users needs to electric power change in every moment. Thus the power generation system should closely follow the system load changes in dynamic equilibrium. In other words, the system should fully play the equipments capacities and make the whole system stable and rum effectively. Otherwise the power quality and even the system security will be damaged. Therefore, prediction technique of electric power load come into birth and make the whole system run well. The key problem of load prediction is prediction mathematic model or prediction technique. The traditional mathematic models use current mathematical expressions to describe the problem. The disadvantages of those models are lack of adaptive ability and enough robustness. The non-mathematic model prediction method based on neural network brings a new way to settle the mathematic model problems. Wavelet neural network (WNN) is based on wavelet transform and forward multi-layer (BP) neural network structure of the new neural network model. It has been proved that neural network theory has more information extraction and approximation, fault tolerance than classical multilayer neural network. To the same study task, wavelet network structure is simpler and has faster convergence speed. It is suitable to setup the power load prediction model.

2 Wavelet Neural Network 2.1 WNN Model The structure of wavelet neural network model is similar to BP model. And the difference is that hidden layer activation function is wavelet transform function. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 981–985. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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The input of the n network is

th

sample is

X n = {xin }, i = 1,2 ⋅ ⋅ ⋅ L . The output of

Y n = { ykn }, k = 1,2 ⋅ ⋅ ⋅ S . The corresponding target output is

D n = {d kn }, k = 1,2 ⋅ ⋅ ⋅ S . There, n = 1,2 ⋅ ⋅ ⋅ N , while N means total number of samples. S is output layer unit number and L is for the input layer unit number. The input layer activation function is the linear transformation (output = input). The middle layer is wavelet transform layer (with cell number M), and the output of the j unit is

(the input of the j

weight between the j

th

th

wavelet unit is − b j

i th unit of input layer. ukj indi-

th

cates the connection weight between the k units of output layer and the middle layer. Thus the output that based on current network parameters is:

n

Ykn = ∑ u kj Ψ j =1

∑V j =1

ji

wavelet

/ a j ). V jk indicates the connection

units of middle layer and the

L

th

j th unit in

X in − b j aj

aj

k = 1,2 ⋅ ⋅ ⋅ S ; n = 1,2 ⋅ ⋅ ⋅ N

(1)

The error function is:

E=

1 2N

∑ ∑ (Y N

S

n k

− D kn

n = 1 k =1

)

2

(2)

Here we select Ψ ( x ) as Morlet wavelet which is the cosine-modulated gaussian wave with the high resolution both in frequency domain and time domain.

Ψ ( x ) = cos (1 . 75 )exp −

x2 2

(3)

2.2 WNN Structure Because wavelet neural network can realize a special nonlinear transformation in hidden layers, the learning ability of wavelet network can be viewed as the approaches to multi-dimensional functions. Thus we can setup the accurate mode by using wavelet neural network. WNN is a multi-input single-output feed forward neural network. As shown in Fig.1, the whole WNN is composed of input layer, hidden layer and output layer.

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Fig. 1. Structure of WNN

2.3 Algorithm Training Procedure of WNN The procedures of WNN training can be classified as five steps as following. Step 1: Network initialization. In this step, the dilation factor of wavelet function a j , displacement factor

b j and network connection weight are initialized at random.

Step 2: Samples classification. Samples are classified as training sample and text sample. Step 3: Output prediction. Set the training sample as the input to the network and predict the network output. Step 4: Weight correction. Network weight and wavelet function parameters are corrected according to the error e. Step 5: End discrimination. If it is not the end of the process, then go back to step 3.

3

Case Study

The load data in this case is offered by Jiangxi XXX Power Supply Bureau. We use MATLAB language to write the program and then train the network. 3.1 Original Data Analysis The power load data is the time series with the unit as month. This time series shows that monthly load is enhanced by comparing with the data of the same month in previous years. Meanwhile, with the season alternation, monthly load indicates some certain fluctuation tendency together with the nearest months. Thus we choose both the horizontal history load and vertical history load as the input neuron of wavelet neural network. 3.2 Parameter Determination It is essential to determine the wavelet neural network structure and training sample parameters before we use wavelet neural network to predict the power load. Sample

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parameters are selected with the consideration of user custom, user ratio, weather, user structure and so on. Those factors ensure that the sample parameters can describe the figures of power load change and that provide accurate original parameter characterizations to prediction result. In this case, we choose weather and data as the two main input of this WNN model. So there are six neurons in input layer. They are monthly highest temperature, monthly lowest temperature, monthly average temperature, humidity, precipitation. Output is the monthly power load. The neurons in hidden layer is determined by the formula

n = m + 1 + a . Where m=5, a=2, and n=5. so the structure of wavelet neural network is 5-5-1, that is, there are five nodes in input layer, five nodes in hidden layer and one node in output layer. 3.3 Prediction Result On the condition that the maximum squared error is 0.0001, after 1000 steps calculation, the prediction result is shown in following table. In contrast, we also use the traditional BP algorithm to predict the power load. Table 1. Prediction result

Month

Actual load [M kwh]

BP network model

Wavelet neural network model

Relative Error 100% 8.367

Prediction load 506.37

Relative Error 100% 1.763

January

515.46

Prediction load 472.33

February

475.96

430.00

9.657

465.16

2.27

March

505.83

461.14

8.835

512.37

-1.293

April

480.27

441.79

8.013

474.67

1.165

May

467.71

423.07

9.545

458.58

1.951

June

458.65

420.74

8.265

445.45

2.879

July

494.63

451.85

8.649

496.32

-0.342

August

503.94

457.76

9.163

504.07

-0.026

September

515.19

470.07

8.757

509.71

1.064

October

513.35

471.60

8.132

504.44

1.736

November

568.13

522.56

8.021

558.63

1.672

December

649.47

588.97

9.316

639.81

1.487

From the prediction result above, it is shown that compared with BP network, wavelet neural network has more prediction accuracy with shorter training period.

4 Conclusion In this paper, wavelet neural network model is presented based on wavelet theory and neural network theory. The result of the case model shows that WNN can overcome the

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shortcomings of general artificial neural network (ANN) which is low training speed and inaccurate model structure. The problems that need further research are pointed out that the advantage of wavelet neural network depends on the amount and uniformity of input sample data. In real case, it is hard to meet this criterion. Therefore, there is negative impact in case study. Further research needs to be pointed out in these conditions. At the end of this paper, we use a relay protection working format to certify the correctness of data mining method on FZZ.

References 1. Jing, Z.: Application of neural network and fuzzy theory in short-term load forecaseting. Procceedings of the CSU-EPSA 22, 129–133 (2010) 2. Yaohua, W.: Application of neural network in power system load forecaseting. China Rural Water and Hydropower 4, 131–133 (2009) 3. Raphael, A., Monica, B.: Human resources management processes:a value-creating source of competitive advantage. European Management Journal 17(2), 174–181 (1999); Nicole, R.: Title of paper with only first word capitalized. J. Name Stand. Abbrev. (in press) 4. Jiwen, X.: Method of Temperature Measurement Using Image Color Based on Wavelet Neural Network. Jounal of Instrumentation 25, 372–375 (2004) 5. H.G.: High performance pipelined architecture for measurement and monitoring of multiple sensor signals. IEEE Trans. Inst. and Magn. 6, 808–814 (2002) 6. Zhang, Z., Chen, C.: Research and realization of time synchronization system based on distributing algorithm. Telecommunications for Electric Power System (May 2005)

The Application of IP/UDP Protocols in 51 Microcontrollers Wensong Hu, Yuyuan Zhu, Min Zhu, and Ke Zuo School of software, Nanchang University, China [email protected] Abstract. This article introduces the way to apply the IP/ARP/ICMP/UDP protocols to the embedded system based on the 8051 microcontrollers, so it can communicate with the host by UDP packet. In the system, the author chooses RTL8019 integrated chip to work as the network card and writes the program by C language. Keywords: network communication; IP/UDP; 8051 microcontroller; RTL8019.

1 Introduction In this experiment, the hardware platform of the embedded system is based on SST89E516RD2 which is a 8051 core microcontroller and it control the RTL8019 microchip to implements the function of network communication. In this experiment, the communication procedure between the embedded system and the host is based on IP/ARP/ICMP/UDP protocol which is part of the TCP/IP protocol family. The embedded system is server, while host is client. The flow chart of the embedded system’s communication mode is showed as figure 1.

Fig. 1. The communication mode of the embedded system M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 987–991. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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2. Detailed Design 2.1 The Process Procedure of the UDP Packet from the Host The process procedure for the UDP packet from the host is showed as Figure 2. First the program initializes the network card and global variables and then checks the communication port weather a packet is coming. When finding a packet, the program will receive the packet and judge the type of its data section. If it is an IP packet, the program abstracts the IP head and verifies weather the packet is received right. If verifying ok, the program will renew the IP address of the host. On the next step, the program will judge the type of data section in the IP packet, if the type is UDP, the program goes forward. Abstract UDP packet from the IP packet and verify it, then the program renew the port of the host. On the next, it will abstract the destination port from the UDP packet, if the destination port is right, the program abstract the command string and respond to the host.

Fig. 2. The process procedure of the UDP packet from the host

2.2 Send UDP Packet to the Host Before sending the UDP packet to the host, the program must get the begin position of the data, the length of the data and destination IP address, then pass these information to the UDP handling function, which will allocate memory for the new packet and fill it. Then the function calculates the check sum and fills the UDP head with it. On the next

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step, the UDP packet is passed to the IP handling function which will fill the IP head for the packet. Before the program fills the Ethernet head for the packet, it must find the hardware address of the destination or the gateway. If the ARP resolution runs successfully, the program will go to next step, or it will set a wait flag for the packet and return. On the next, the packet is sent to the Ethernet handling function. It will fill the Ethernet head with the hardware address and other information. The flow chart of this procedure is showed below.

Fig. 3. Sending a packet to the host

2.3 Process Procedure of the ARP Resolution As we all know, the reason why a packet on the hardware link can reach its destination is that it contain the destination’s hardware address. But when we program, we use the IP address, which is a logical address. So on the link layer, we must know the corresponding hardware address to the IP address. General a machine which can link to the network contains an ARP cache table in its RAM and renew it from time to time. By ARP resolution, we fine the hardware address corresponding to the IP address for the new packet. First the program gets the IP address of the destination and compares it with the system’s. If the host is in the same network of the embedded system, the

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program goes next, or it uses the gateway’s IP address instead of the destination IP address. Then the program searches the IP address in the ARP cache table. If finding the IP in a record, it will abstract the hardware address from the record and return, or sending an ARP broadcast packet to query other machines and set a wait flag for the packet, then return either.

Fig. 4. Process procedure of the ARP resolution

2.4 Process Procedure for the PING Query As we all know, when we want to test the link of two machines, we use the PING command. PING command uses the ICMP protocol whose packet is contained in the IP packet (see Fig.4.2 understand better). When the program gets an ICMP packet, first it checks the check sum, if right, program goes next. Then judging the message type, if type is 3, program return error; or if type is 8, it states that the packet is a query packet, program goes next; or give the packet to other handling function. On the next the program allocates memory for the new ICMP packet, filling head and data, calculating check sum. At last the new packet is sent to the IP sending function.

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Fig. 5. process procedure for the PING query

3 Conclusion Under strict test, we find that if more than 200 UDP packets were sent to the net link, the microcontroller may not reply timely. But in most case, it can finish its actions rightly. As more and more portable and embedded devices were used in our lift, I think this technology will be widely used.

References 1. Hu, C.: Mobile webserver to the Android platform. In: 2010 International Colloquium on Computing, Communication, Control, and Management, CCCM 2010 (2010) 2. Ohsmann, M.: Short Course 8051/8032 Microcontrollers and Assembler. Elector electronics publishing (1994) 3. Ayala, K.: The 8051 Microcontroller. Delmar cengage learning (2004)

A Collaborative Filtering Recommendation Algorithm Based on User Information*,** Daner Chen Zhejiang Textile & Fashion College, Ningbo 315211, P.R. China [email protected]

Abstract. As the rapid growth and wide application of Internet, the amount of information is increasing more quickly than people’s ability to process it. To help people to find useful information, personalized filtering technique emerges. Collaborative filtering is one successful personalized filtering technology, and is extensively used in many fields. But traditional collaborative filtering recommendation algorithm has the problem of sparsity, which will influence the efficiency of prediction, and collaborative filtering algorithms only consider users’ rating similarity. They do not consider users’ information similarity. Aiming at the problem of data sparsity for personalized filtering systems, a collaborative filtering recommendation algorithm based on user information is given. This method uses the user information similarity technology to fill the vacant ratings where necessary at first, and then uses collaborative filtering approach to produce recommendations. Keywords: personalized; recommender system; collaborative filtering; user information; similarity.

1 Introduction With the development of Internet technology, the total network resources are also exploding. In order to easily and quickly get the information in huge amounts of data, recommendation system came into being. It is recommended based on the user's interests consistent with the object of user interest, also known as personalized recommendation system. Researchers have proposed a variety of recommended methods: content-based recommendation, collaborative filtering and hybrid recommendation and so on. Collaborative filtering is a commonly used technique to reduce information overload, which has become a major tool in a personalized recommendation system. Traditional collaborative filtering is called user-based collaborative recommendation, and the basic idea is: by comparing the similarity between users to recommend resources. The recommended approach has the problem of poor scalability, efficiency with the recommended number of users, the increase in the number of items which significantly reduced. *

A Project Supported by Ningbo Training Base of Textile and Fashion (Grant No. JD090312). ** A Project Supported by Ningbo advanced textile technology and apparel CAD Laboratory (Grant No. 2009ZDSYS-A-003). M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 993–998. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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Collaborative filtering determines the nature of the recommendation to obtain satisfactory results must be accurate user information, which requires a large number of users based on the basis of the data. Although in different applications, the data will be different, but the current offered personalized recommendation service system saves the user most of the basic background information. Many users are willing to provide their own Web site name, sex, age, educational background and interests of less sensitive information. In order to alleviate the problem of data sparsity for personalized filtering systems, in this paper, a collaborative filtering recommendation algorithm based on user information is presented. This method uses the user information similarity technology to fill the vacant ratings where necessary at first, and then uses collaborative filtering approach to produce recommendations.

2 The Collaborative Filtering Recommendation Algorithm Based on User Information User information-based collaborative filtering algorithm is the basic idea of the traditional collaborative filtering before making use of existing information to build a user no longer sparse matrix. This method, complete the predicted value of zero in the original matrix. Had filled matrix of collaborative filtering recommendations will greatly enhance the accuracy. Also, compared to traditional collaborative filtering algorithms, the new algorithm combines the predicted value of zero user information, the additional data to the user collaborative filtering to obtain semantic similarity and the level of information the user is associated. 2.1 User Information 1. Age. Different needs of different age groups; the performance of interest is also different. Such as the choice of television programs, children like to watch cartoons, young people like to watch films on campus, young people like to watch romantic films, middle-aged people like to watch live film, and the elderly sometimes like to watch some documentaries. People of different ages, because of its experience different levels of comprehension of life is different, so its like the object will be some differences in the level of class. Different items by age level may be some differences, so the division of age can ask an expert to give criteria for the classification. Taking into account the community at different ages given different responsibilities to different individuals because of the responsibilities entrusted to different periods show different psychological characteristics, which divided the ages, the ages should be combined with the division of his career. 2. User gender. In many ways, different gender categories are to select different items. For example, while women like to show emotion, while men are more like cops and robbers film.

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3. Awareness. As the user issues the appropriate understanding of professional degree is in the appropriate level. On this basis, that different occupation reflects different life course of things the same occupations are more likely to have the same understanding of angle. According to the user's understanding of his career, we estimate the degree of which can be divided into different levels of. 4. Community. Geographical area is a common cause of life interaction between people, and this is a key factor. In general, users will be up for the online features of the first three elements, the first three factors in this selection as a factor in identifying the characteristics of the user characteristics. 2.2 Similarity of User Information User information can use a variety of different types of properties to be described, generally include continuous numerical attributes, binary attributes and classification attributes. To calculate the similarity of user information to deal with mixed type of property records. Calculate the similarity between users background information, the following formula: n

SIM1( p, q) = ∑[s im(upk , uqk )w(uk )] k =1

n

∑w(u ) =1 k=1

k

Where n is the dimension of the property, sim (Upk, Uqk) is a user p and user q on the similarity in the properties of Uk, w (Uk) is the weight of attribute Uk. Users p and q in the similarity of background information on the SIM1(p, q) is equal to the similarity of all attributes on the weighted sum of different attributes in accordance with the following rules on the calculation of similarity: 1) If Uk is the value of property, calculated using the following similarity:

sim ( Upk , Uqk ) =

1 | upk − uqk | +1

2) If Uk is a binary attribute, calculated using the following similarity:

⎧1 if Upk=Uqk sim ( Upk , Uqk ) = ⎨ ⎩0 if Upk!=Uqk

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3) If Uk is the classification of property, consider all the potential semantic relationship between values, build a domain knowledge-based semantic classification tree, the tree leaf node is a different attribute values, the structure shown in Figure 1.

Fig. 1. Structure level of user attribute

The similarity between attribute values from the corresponding leaf node in the tree structure on the position of the following formula:

sim ( Upk , Uqk ) =

depth( father(upk , uqk )) min[depth(upk ), depth(uqk )]

One father (Upk, Uqk) is the property value Upk and Uqk leaf node in the tree corresponding to the common parent, depth (u) is a node u of depth, root depth is 0. The above equation shows that, at a deeper level of semantic value of different attributes associated with a higher similarity. 2.3 User Rating Similarity Pearson’s correlation measures the linear correlation between two vectors of ratings, as shown in following.

SIM 2(i, j) =

∑

c∈Iij

∑

c∈Iij

(Ric − Ai )(Rjc − Aj ) 2

(Ric − Ai )

∑

c∈Iij

( Rjc − Aj )

2

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Where Ric is the rating of the item c by user i, Ai is the average rating of user i for all the co-rated items, and Iij is the items set both rating by user i and user j. 2.4 Recommendation Flow The recommendation algorithm is shown as figure 2.

Fig. 2. Recommendation flow

2.5 Produce Recommendation The rating of the target user u to the target item t is as following: c

Put = Au +

∑ (R i =1

− Ai ) * sim (u , i )

it c

∑ sim(u , i ) i =1

3 Conclusions With the rapid growth and wide application of Internet, the amount of information is increasing more quickly than people’s ability to process it. To help people to find

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useful information, personalized filtering technique emerges. Collaborative filtering is one successful personalized filtering technology, and is extensively used in many fields. But traditional collaborative filtering recommendation algorithm has the problem of sparsity, which will influence the efficiency of prediction, and collaborative filtering algorithms only consider users’ rating similarity. They do not consider users’ information similarity. Aiming at the problem of data sparsity for personalized filtering systems, a collaborative filtering recommendation algorithm based on user information is given. This method uses the user information similarity technology to fill the vacant ratings where necessary at first, and then uses collaborative filtering approach to produce recommendations.

References 1. Gao, F., Xing, C., Du, X., Wang, S.: Personalized Service System Based on Hybrid Filtering for Digital Library. Tsinghua Science and Technology 12(1), 1–8 (2007) 2. Huang, Q., Ouyang, W.: Fuzzy collaborative filtering with multiple agents. Journal of Shanghai University (English Edition) 11(3), 290–295 (2007) 3. Alpert, S.R., Karat, J., Karat, C.-M., Brodie, C., Vergo, J.G.: User Attitudes Regarding a User-Adaptive eCommerce Web Site. User Modeling and User-Adapted Interaction 13, 373–396 (2003) 4. Vozalis, M.G., Margaritis, K.G.: Using SVD and demographic data for the enhancement of generalized Collaborative Filtering. Information Sciences 177, 3017–3037 (2007) 5. Zhao, L., Hu, N.J., Zhang, S.: Algorithm design for personlalized recommendation systems. Journal of computer research and development 39(18) (2002) 6. Lekakos, G., Giaglis, G.M.: Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors. Interacting with Computers 18, 410–431 (2006) 7. Lekakos, G., Giaglis, G.M.: A hybrid approach for improving predictive accuracy of collaborative filtering algorithms. User Model User-Adap. Inter. 17, 5–40 (2007) 8. Degemmis, M., Lops, P., Semeraro, G.: A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation. User Model User-Adap. Inter. 17, 217–255 (2007) 9. Yu, L., Liu, L., Li, X.: A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce. Expert Systems with Applications 28, 67–77 (2005) 10. Herlocker, J.: Understanding and Improving Automated Collaborative Filtering Systems. Ph.D. Thesis, Computer Science Dept., University of Minnesota (2000)

The Personalized Recommendation Algorithm Based on Item Semantic Similarity*,** Yulong Ying Zhejiang Textile & Fashion College, Ningbo 315211, P.R. China [email protected]

Abstract. Everyday there are masses of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. Personalized recommendation is the process to alleviative the problem. Collaborative filtering is one of the most popular technologies in the personal recommendation system. As the user rating matrix becoming extremely sparsity, traditional collaborative filtering recommendation algorithm calculates similarity between items using the rating data, and it does not consider the semantic relationship between different items, thus recommendation quality is very poor. To solve this problem, the paper combines the item semantic similarity and the item rating similarity, which takes into account the influence of item semantic and user rating to enhance the item-based collaborative filtering. The personalized collaborative filtering recommendation algorithm combining the item semantic similarity and item rating similarity can mitigate the sparsity problem in the electronic commerce recommender systems. Keywords: personalized recommendation; algorithm; collaborative filtering; item semantic similarity.

1 Introduction With the rapid development of Internet and wide application of e-commerce, there has brought great wealth of information resources, and has also become an important source of access to information. However, as the people to enjoy the convenience of the Internet at the same time, generally feeling like being submerged in the vast sea of information, information overload and other issues are also increasingly serious. Recommender system came into being, which is based on the user's characteristics, the object of recommendations to meet user requirements. So far in the recommendation system, collaborative filtering recommendation algorithm is the most successful technology. Collaborative filtering techniques commonly used nearest neighbor techniques, the use of information to calculate the user's historical preferences of the distance between the users and then use the target user's nearest neighbors weighted evaluation of user evaluation of the value of items to predict the target user's preference for a *

Programs Supported by Ningbo Natural Science Foundation (Grant No. 2010A610118). ** A Project Supported by Ningbo Training Base of Textile and Fashion (Grant No. JD090312). M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 999–1004. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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particular item. According to this preference, it can be recommended to the target user. Collaborative filtering algorithm, as the main memory-based collaborative filtering recommendation algorithm, can be divided into user-based and item-based collaborative filtering recommendation algorithms. Collaborative filtering based on user ratings similar to the nearest neighbor based on ratings data to target users have recommended. Item-based collaborative filtering depends on the similarity of the items to determine recommendation. Sparse data and new items the problems are the most prominent problems in the item-based collaborative filtering system. As in sparse data, it is difficult to find one set of nearest neighbor users, on the other hand the cost of similarity calculations will be great. Also, because the data is very sparse, in the formation of the target user when the user sets the nearest neighbor, to live in will cause the loss of information, resulting in reduced effects recommended. In a new item for the first time when it did because there is no evaluation of the user, so a simple collaborative filtering can not predict their score and recommendation. Also, since the early emergence of new item and users fewer, the recommended accuracy is relatively poor. To solve this problem, the paper combines the item semantic similarity and the item rating similarity, which takes into account the influence of item semantic and user rating to enhance the item-based collaborative filtering.

2 The Item Semantic Similarity Item semantic information exists in the item properties contact the item and the item’s status, the relationship between each other and item meta-information and so on. For an example, the article of the property there is a certain level of semantic relations. One of several articles or articles with citations in the relationship reference each other, these articles do not link to the article than that of other similar large. In addition, the article categories, keywords, are also similar to the impact of the article. Therefore, the full use of the semantic information, combined with domain knowledge and user's subjective evaluation of the value of the similarity calculation can project a more objective and more accurate, and ultimately improve the performance of recommendation systems. 2.1 Item Semantic Hierarchy In the collaborative filtering system, finding the nearest neighbor is accurate, which directly affect the recommended results. The need of finding the nearest neighbor is the similarity calculated by the user. The similarity of traditional collaborative filtering method, such as the Cosine similarity and Pearson coefficients are based on user ratings intersection of item, the user rates items are expressed as the n-dimensional Euclidean space vector, n is the number of items, and assuming independence among items. In this paper, we can consider the item semantic hierarchy, and then use the item semantic information to produce recommendation. Then consider the film classification system in the movie site on the film score and user situation shown in Figure 1.

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Fig. 1. Item semantic hierarchy

When two users score less common item scores or no common items, and if the similarity with the traditional method of calculating the similarity between users, the similarity will be very small or zero. This just created in the system when the most prominent, because the system had just been established, only a few small user evaluation of the item, the extreme sparse data allows users to item a common score less, then the user can not find the nearest to produce collaborate filtering recommendation. 2.2 Calculating the Item Semantic Similarity The philosophical ontology concept from the composition of said applications in ontology terms constitute the basic terminology and relationships, and relationships with the expansion of new vocabulary terms and the basic rules of the organic combination. Figure 1 depicts a classification of the film in the field of ontology. The hierarchy will use the concept of calculating the similarity between instances and predict the user does not score the item score. In general, the smaller the distance between the tree body, the greater the similarity between instances. In information theory, random events from any information, defined as the number of events on the probability of the negative. Extended to the ontology design ontology the set of all concepts O, for every concept O, p (O) is getting the concept O and its sub-concepts are examples of the possibility, then the concept of self-definition of information O is:

I (O) = −(log p (O))

In information theory, mutual information formula is:

I (o1 , o2 ) = log( p(o1 , o2 )) − (log( p(o1 )) + log( p(o2 )))

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It is a measure of the strength of a physical match. If a variable characterization of o1 on o2 have meaning, then the o1 and o2 of the mutual information larger. Extended to the body, the two concepts o1 and o2 the degree of similarity between, depending on the size of the mutual information between them. Taking into account the parent of the two concepts can be characterized by their common concepts, concepts o 1, o 2 can be the degree of similarity between the definitions of the following formula:

sim11(o1 , o2 ) = k × max[log( p (oi ))] − (log( p (o1 )) + log( p (o2 ))) where oi ∈ p (o1 , o2 ) Where, P (o1, o2) is the parent of the concept of joint ownership of the collection. It is customary that the similarity of the range should be 2 to a smaller positive integers, for which, in Max [log (p (ci))] multiplied by the coefficient of the former k, k smaller, sim11 value smaller; At the time, can guarantee the value of not less than 0; therefore generally take k = 2, and calculated the degree of similarity in the form of rewritten as follows:

sim1(o1 , o2 ) = where

2 × max[log( p (oi ))] log( p (o1 )) + log( p (o2 ))

ci ∈ p (c1 , c2 )

3 The Personalized Recommendation Algorithm Combining Item Semantic Similarity and Item Rating Similarity 3.1 Item Rating Similarity Sim2 (ip, iq) is the similarity between Item iq and item ip. There are several ways, mainly cosine similarity, the adjusted cosine similarity, the correlation similarity and so on. Cosine similarity: The similarity between the cosines of the angle between vectors through the measure. m

∑

JG JG s im 2 ( i p , i q ) = c o s ( i p , i q ) =

j =1

m

∑

j =1

R jp × R jq

( R jp ) 2 ×

m

∑

(R

j =1

jq

)2

(2) The adjusted cosine similarity: In the cosine similarity measure does not consider the problem of different users rating scale, modified cosine similarity measure the user by subtracting the average score of the project to improve the defects. We have a matrix R (m, n) in the ip and iq are the project score line, that is, the project ip and iq scores of users have set will be defined as U sim 2 ( i p , i q ) =

∑ (R j ∈U

∑ (R j ∈U

jp

jp

− A j )( R jq − A j )

− A j )2

∑ (R j ∈U

jq

− A j )2

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(3) The correlation similar: According to the correlation coefficient to measure the similarity between items, in order to calculate the fair, the similarity measure should also be made in the evaluation of the two projects the user set U in Calculated.

sim 2(i p , iq ) =

∑ (R j∈U

∑ (R j∈U

jp

jp

− A p )( R jq − Aq )

∑ (R

− Ap )2

j∈U

jq

− Aq ) 2

3.2 Combining Similarity Evaluation of resources and semantic similarity of the final synthesis of the resources can be simple linear combinations.

SIM (i p , iq ) = asim1(i p , iq ) + (1 − a ) sim 2(i p , iq ) Where: SIM (ip, iq) for the combined items of p and q between the combination of similarity; a is the weight parameter, a = 0, the combination of the similarity of SIM (ip, iq) = sim2 (ip , Iq), the traditional item-based collaborative filtering algorithm. The a values can be selected by experiment, which determines the similarity between the two weights. 3.3 Produce Recommendation By calculating the combining similarity, we choose the most similar to the target item and their predicted weight. k

R ut =

∑ (R j =1

uj

× SIM ( i j , it ))

k

∑ SIM (i j =1

j

, it )

4 Conclusions Everyday there are masses of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. Personalized recommendation is the process to alleviative the problem. Collaborative filtering is one of the most popular technologies in the personal recommendation system. As the user rating matrix becoming extremely sparsity, traditional collaborative filtering recommendation algorithm calculates similarity between items using the rating data, and it does not consider the semantic relationship between different items, thus recommendation quality is very poor. To solve the sparsity problem, in this paper, we present a personalized collaborative filtering recommendation algorithm combine the item semantic similarity and the item rating similarity, which takes into account the influence of item semantic and user rating to enhance the item-based collaborative filtering.

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References 1. Breese, J., Hecherman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI 1998), pp. 43–52 (1998) 2. Herlocker, J.: Understanding and Improving Automated Collaborative Filtering Systems. Ph.D. Thesis, Computer Science Dept., University of Minnesota (2000) 3. Resnick, P., Varian, H.R.: Recommender Systems. Special issue of Communications of the ACM 40(3) (1997) 4. Schafer, J.B., Konstan, J., Riedl, J.: Recommender Systems in E-Commerce. In: Proceedings of ACM E-Commerce 1999 conference (1999) 5. Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Analysis of Recommendation Algorithms for E-Commerce. In: Proceedings of the ACM EC 2000 Conference, Minneapolis,MN, pp. 158–167 (2000) 6. Lekakos, G., Giaglis, G.M.: Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors. Interacting with Computers 18, 410–431 (2006) 7. Lekakos, G., Giaglis, G.M.: A hybrid approach for improving predictive accuracy of collaborative filtering algorithms. User Model User-Adap. Inter. 17, 5–40 (2007) 8. Degemmis, M., Lops, P., Semeraro, G.: A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation. User Model User-Adap. Inter. 17, 217–255 (2007) 9. Alpert, S.R., Karat, J.O., Karat, C.-M., Brodie, C., Vergo, J.G.: User Attitudes Regarding a User-Adaptive eCommerce Web Site. User Modeling and User-Adapted Interaction 13, 373–396 (2003) 10. Papagelis, M., Plexousakis, D.: Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Engineering Application of Artificial Intelligence 18, 781–789 (2005) 11. Lee, J.-S., Olafsson, S.: Two-way cooperative prediction for collaborative filtering recommendations. Expert Systems with Applications (2008), doi:10.1016/ j.eswa.2008.06.106 12. Cheung, K.-W., Tian, L.: Learning User Similarity and Rating Style for Collaborative Recommendation. Information Retrieval 7, 395–410 (2004) 13. Ahn, H.J.: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences 178, 37–51 (2008)

Application Research of Four-Branch Antenna Design by Improved Orthogonal Multi-objective Evolutionary Algorithm Jincui Guo, Xiaojuan Zhao, and Jinxin Zou Faculty of Mechanical & Electronic Information, China University of Geosciences, Wuhan, 430074, China [email protected], [email protected]

Abstract. In this paper, the optimization of four-branch antenna is studied by improved orthogonal multi-objective evolutionary algorithm. The multi-objective evolutionary algorithm can be used to search the large space for the optimal solution, but the orthogonal design method can be used to find a potential good solution which may be located near the proper position of the optimal solution. And the orthogonal design method is a kind of very good sampling method, the method can be used to solve the larger dimension of the dynamic optimization problems, it also improves the performance of the antenna. The NEC software is used as numerical calculation method in this article. The design results of the four-branch antenna show the accuracy and efficiency of the method. Keywords: evolution algorithms; orthogonal design; multi-objective optimization.

1 Introduction During the modern scientific theory and practice, there are many problems related to optimization and adaptive. Except for some simple case, it is powerless for people to solve the large complex system optimization and adaptive problems. In this paper, we consider many actual problems which should optimize to the met in antenna design, and the application of general design method is difficult to get a satisfactory results we need to develop an antenna design software of strong optimization ability. The traditional antenna is designed by professional and technical personnel. It is often hard to describe mathematical equations. The complexity of improved orthogonal multi-objective evolutionary algorithm is lower than the traditional during the simulation of antenna design. The speed and precision of improved orthogonal multi-objective evolutionary algorithm is high. For large complex antenna system optimization and adaptive problem, the improved algorithm provides a powerful help for satellite antenna design problem of precision, especially in deep space exploration areas [1]. In recent decades, with the further research of evolution algorithm theory and the application field, evolution algorithm was also introduced to aerospace sector. Especially a titled “Evolutionary software automatically designs antenna” news occurred in NASA in 2004, it reported the antenna automatic design software which based on M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 1005–1011. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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evolutionary algorithm of NASA Ames research center. This evolution software technique provides some advantages such as wide frequency band, high gain, high efficiency, small volume, light weight, easy to make and so on for the design of ST5(Space Technique 5) [2]. Many domestic scholars also use intelligent algorithm for the optimization design of the antenna electromagnetic field in many years, and also obtain a lot achievements: Tsinghua University successfully applied genetic algorithm to the optimization of micro-strip antennas, the bandwidth from 5% to 16.6%, and obtained double-frequency, the double-frequency ratio is 1:1.31, and also has similar lines polarization [3].In addition, the university of electronic science and technology [4], science and technology university of China [5], Nanjing university of aeronautics [6], Wuhan university [7], Beijing university of technology [8], Nanjing postal college [9], Shanghai jiaotong university [10] also have good results in genetic algorithm.

2 Antenna Modeling 2.1 Coding of Antenna According to the branch shape of each antenna, four-branch antenna uses tree branch coding [11, 12]. An antenna begins at the root of trees, also is one of the initial origin feeder, the positive direction along the Z axis, and in very node, it gets a branch in growth under a branch, and then in the current position and direction, it depends a branch of growth on changing the current direction through the rotation. In this design, four times are allowed. 2.2 Model of Antenna The antenna model is shown in Figure 1. The coding sequence is: (R, F0, x1, y1, z1, x2, y2, z2, x3, y3, z3, x4, y4, z4), where, R is the radius of wires, F0 is the length of the feed wire, both are fixed in this evolution. The following parts represent the end coordinates of the branch in the first quadrant, separately.

Fig. 1. Antenna model of four-branch

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3 Orthogonal Design Method This design is quoted an example to introduce the basic concept of experimental design method [13]. The output of vegetables depends on 1) temperature, 2) the amount of fertilizer, 3) soil PH value. One of the three experimental factors has three possible values, as shown in Table 1. Table 1. Each factor has three possible values Level value

temperature

Level1

o

20 C

Level2

o

25 C

Level3

o

factors the amount of fertilizer

soil PH value

2

6

2

7

2

8

100g/m

150 g/m

30 C

200 g/m

In order to get the maximum output, we need to find the best level combination. In the above, there are 3 x 3 x 3 = 27, therefore, it has 27 experiments. Generally, when the experiment system has N factors and each factor has Q values, there are QN experiments. When N and Q are enough large, experiments are not to be done. Therefore, we hope to experiment a smaller, typical combination. The orthogonal design is created for this purpose. It provides a series of orthogonal array for different factors N and different level number Q. LM(QN) means N factors and Q level number of an orthogonal array, here “L” means Latin square, “M” means the combination numbers of level. LM(QN) have M line, means M combinations. The orthogonal array LM(QN), only need to be chose M combinations to do experiments, here, M usually less than QN greatly. We can get potential solution through the orthogonal array combination by statistical method. The statistic method computing the target of each factor in every level, then through this target, evaluate the influence of each factor in every level, finally combine the best level of combination as a potential optimal solution. The orthogonal table we used is shown in Table 2. Table 2. Orthogonal table -1 -1 -1 -1 -1 -1 -1 -1 -1 …

-1 -1 -1 0 0 0 1 1 1 …

-1 -1 -1 0 0 0 1 1 1 …

-1 -1 -1 0 0 0 1 1 1 …

-1 0 1 -1 0 1 -1 0 1 …

-1 0 1 -1 0 1 -1 0 1 …

-1 0 1 -1 0 1 -1 0 1 …

-1 0 1 0 1 -1 1 -1 0 …

-1 0 1 1 -1 0 0 1 -1 …

-1 0 1 0 1 -1 1 -1 0 …

-1 0 1 1 -1 0 0 1 -1 …

-1 0 1 0 1 -1 1 -1 0 …

-1 0 1 1 -1 0 0 1 -1 …

Where, ‘-1’ in the orthogonal table means a decrease of Δ (0.3mm) from the evolutionary results, ‘1’ means an increase of Δ (0.3mm) from the evolutionary results, and ‘0’ means keeping the evolutionary results unchanged.

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4 Improved Orthogonal Multi-objective Evolutionary Algorithm Orthogonal multi-objective evolutionary algorithm can affect algorithm output. In this paper, orthogonal design method is not used in global space but in the space of father body which is randomly selected from the groups, largely avoid the question above, improving the orthogonal multi-objective evolutionary algorithm. Step1 Select two father body m1 and m2 from groups randomly; Step2 Select a target that is statistic optimized in orthogonal design method from K targets randomly; Step3 Use orthogonal design method in the m1, m2 subspace, experiments Q, J determined by type:

P = (QJ −1) (Q−1) ≥ N ，Q≥3，Q is prime. Step4 S1 is the minimum value of the target function during the QJ experiment, S2 is the potential solution for follow type: b = {k j | Δ k j j = min{Δ1 j , Δ 2 j ,..., Δ Qj }}

　

　

Step5 Output S1, S2.

5 The Design Results and Comparison The design requirements of the four-branch include [14]: 1) Transmit frequency: 8470MHz; receive frequency: 7209.125MHz; right-hand polarization; 2) VSWR≤1.2 at transmit frequency, VSWR≤1.5 at receive frequency, Input impedance: 50Ω; 3) Gain Pattern >0dBic, with 40°<θ<80°, 0°<φ<360° (including receive and transmit); 4) Geometry size: Diameter<15.24cm, Height<15.24cm, the mass of antenna <165g. After evolution, 27 experiments calculated by Ansoft HFSS [15] software, the coordinates of the antenna branch in the first quadrant is shown in Table 3: Table 3.

Among them, the first column represents the first section, it shows the length of the feeder of antenna, and then the 12 column is representing x, y, z three coordinates heft of four section branches in the first quadrant.

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Fig. 2. The right-hand circular polarization gain after the multi-objective evolutionary algorithm, F = 8.470GHz (X-axis is θ, Y-axis is the dB value of right-hand circular polarization gain)

Fig. 3. The right-hand circular polarization gain after the improved orthogonal multi-objective evolutionary algorithm, F = 8.470GHz (X-axis is θ, Y-axis is the dB value of right-hand circular polarization gain)

Fig. 4. The right-hand circular polarization gain after the multi-objective evolutionary algorithm, F = 7.209125GHz(X-axis is θ, Y-axis is the dB value of right-hand circular polarization gain)

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Fig. 5. The right-hand circular polarization gain after the improved orthogonal multi-objective evolutionary algorithm, F = 7.209125GHz(X-axis is θ, Y-axis is the dB value of right-hand circular polarization gain)

From Figure 2,3,4,5, we know the simulation results of the antenna gain by Ansoft HFSS respectively. The gains have been largely improved after the improved multi-objective evolutionary algorithm. The four-branch antenna can be applied to Satellite communications fields directly.

6 Conclusions In this paper, there are three innovation points: Firstly, the improved orthogonal multi-objective evolutionary algorithm is a new algorithm of solving optimization; Secondly, we first use the new algorithm for antenna structure to be automatic simulation optimization; Thirdly, the complex is lower than traditional manual antenna. The improved orthogonal multi-objective evolutionary algorithm can search local spaces further and refiner. The simulation results validate the feasibility of this method for the design of four-branch antenna.

References 1. Zeng, S.Y., Kang, L.S., Ding, L.X.: An Orthogonal Multi-objective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints. Evolutionary Computation 12(1), 77–98 (2004) 2. Linden, D.S., Altshuler, E.E.: Evolving Wire Antennas Using Genetic Algorithms: A Review. In: The First NASA/DoD Workshop on Evolvable Hardware, Pasadena, California, July 1999, pp. 225–233 (1999) 3. Yangfan, Zhang, X.: Genetic algorithms in the application of micro-strip antenna optimization. Journal of electronics 28(9), 91–95 (2000) 4. Xiao, S., Wang, B.: The genetic algorithm based on micro-strip reconfigurable antenna design. Journal of university of electronic science and technology 2(33), 137–141 (2004) 5. Liuhao: The genetic algorithm in array antenna informs the application of beam comprehensive. J. Waves science journal 17(5), 539–548 (2002)

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6. Shenji, Han, L., Lv, J.: Parallel genetic algorithms in airborne broad-spectral-bandwidth antenna mask design research on the application of aviation 126(12), 158–161 (2005) 7. Wei, Z., Gao, H., Ke, H.: A modified genetic algorithm for high frequency, the optimization design of the broadband loading antenna. J. Waves science journal 19(3), 343–347 (2004) 8. Wang, H., Gao, B., Liu, R., Fanbin: Genetic algorithms in the waveguide slot array antenna design. The application of electronic journals 30(3), 376–380 (2002) 9. Ren, H.: The genetic algorithm in array antenna comprehensive application of orientation image design. J. Waves science journal 11(4), 62–67 (1998) 10. Chen, W., Li, B., Xietao: Wing of the resonant frequency formula microstrip antenna and its application revision. Shanghai jiaotong university journals 39(4), 653–665 (2005) 11. Shihui, Zeng, S., Chenguang: Based on orthogonal design, dynamic robust problem solving the new algorithm. Computer application and research 25(3), 336–339 (2008) 12. Zeng, S., Cai, Z., Zhangqing, Kang, L.: Evaluation of approximate Pareto frontier diversity a new method. Journal of software 19(6), 1301–1308 (2008) 13. Deb, K.: An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering 186(2-4), 311–338 (2000) 14. Xu, J., Zeng, S., Wangping, Yan, J.: Four armed branch antenna automatic coding scheme of design. Journal of micro computer information 23(7-3), 218–219 (2007) 15. Hou, W., Shao, J.: Application of Ansoft HFSS in teaching experiment of antenna. Digital communication, 87–89 (2009)

Research on Aviation Forewarning Systems Based on Business Intelligence Liu Yongjun1, Hu Qing2, and Ruan Wenjuan3 School of Management, Wuhan University of Technology, 430070,Wuhan, China [email protected], [email protected]

Abstract. In order to make an accurate aviation forewarning from multi-data sources of mass data analysis, this paper presents aviation forewarning solutions based on business intelligence. The scheme of aviation analysis is based on the current status aviation forewarning, and it will provide an overall business intelligence framework in the aviation forewarning area of Air Traffic Control Center. With the difficulties in ETL implementation process, the paper will give a concrete ETL process design and implementation plan. Keywords: Aviation forewarning; Business intelligence; ETL.

1

Introduction

Aviation is an industry full of high technology and high risk. The aviation catastrophe which occurred in 1977 brings the aviation forewarning to human beings’ attention. With a high developing speed in aviation industry , more and more people have investigated in developing a effective way to prevent such disasters repeat again .As a result , aviation security forewarning system, aviation disaster forewarning system and aviation weather forewarning system etc. appear one after another. For there are too many factors to take into account, we should excavate many more useful information from lots of operating data. However, the organization of these operating data is faced up with operations rather than management. It’s hard to imagine the difficulty and cost in the process of excavating useful information. Besides, as the basic operating system becomes more and more perfect, these information will represent such features: (1) The data structure is heterogeneous. (2) The data base is different. (3) The data is stored distributedly. (4) The data is copied into different medium. (5) The amount of data is large. According the preceding fact, this paper we will analyze the existed problems in order to put the business intelligence in the aviation forewarning system and provide the whole framework of aviation forewarning system. Acquainted with business intelligence, we will recognize the importance of the ETL implement. Just in this way, we can find the problems concerned and existed and give a specific implement plan. M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 1013–1019. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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The Current Research Status of Aviation Forewarning

Researches on aviation forewarning fist started in the 1980th. The primary researches on aviation forewarning are as follows: In 2001, Fan Luo, Lian She and Bichong Gu [1]made a general research on the framework of Civil Aviation forewarning system, and raised that operating model of Civil Aviation traffic disaster management system. In 2002, SKYTTNER L [2]raised the theory that supervise and forewarn human being’s living environment. In 2004, FINK A,SIEBE, KUHLE J P[4] raised the mode of forewarning decision supporting system. In 2007, Ke Li[6] made a research on Civil Aviation disaster forewarning decision system based on the data house ware and OLAP technology. According to the data source research of air traffic control center, we find that only overwhelmed the following difficulties can we make an accurate forewarning. (1)Take full advantage of the data from air traffic control center. (2)Strengthen the analysis function of relevant forewarning system. (3)Integrate the aviation forewarning data wholly and organically. At present, with several years developing the operating system and electronic construction, the aviation industry has accumulated abundant historical data and has an excellent data base. Taking advantage of ripe business intelligence technology to integrate the existing historical data and using multi-dimension analysis technologies from multi-angle and multi-gradation to analyze and measure , we can present the analysis outcome in a more abundant ,direct and imaginable way to the aviation forewarning predictors , and the result will provide a scientific basis and guarantee for making decisions.

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Business Intelligence Framework

The data source of the aviation forewarning is primarily from airline companies、 Air traffic control centers and airports. How to accumulate the data from there different sources to analyze and make a research to achieve a goal which can make an accurate forewarning .But the three data sources are independent from each other .So this paper is primarily to make a research on the Air Traffic Control Center and to give the business intelligence framework. It will be a good reference for the airports and airline companies. 3.1

The Aviation Forewarning of Business Intelligence Framework of the Air Traffic Control Center

The paper is to make research on the basic framework of the business intelligence, with the practical situation of the air traffic control center, and it will give the Air traffic Control Center’s business intelligence framework figure .From the figure ,we will see that the ETL dealing locates in the basic situation, and that it serves directly data warehouse and business data storage.

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Fig. 1. BI overall framework figure based on the airline forewarning From the figure ,the analysis of the data is built on the data warehouse. Therefore we need to extract ,clean ,convert and upload the data source to build a well-framework data warehouse. But these operations are achieved by the ETL process. The purpose of the ETL primarily reflects the following there points:

⑴ Solve the problem of the scattered data.

Base on the basic research of airline traffic control center, these data is scattered in different operating system such as weather forewarning, safety forewarning ,disaster forewarning and other systems .But the choice of database among the systems , data definition ,data storage etc. may be different .ETL technology can accumulate the scattered data according to the theme needs of the data warehouse.

⑵Solve the problem of the dirty data.

It just says that the variety of the data source leads to the inconsistency of data, repeatability and potential content paradox. So we need to accumulate, clean and convert the scattered and dirty data to get an expression of uniform data, getting rid of the repeating data and recognize the paradox data by using ETL process.

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⑶ Make building data center convenient

Data warehouse is faced up with the data application among the whole aircraft industry. But the collection of aviation forewarning information must be built on large amount of data. Using the ETL to integrate, clean, check, convert and associate the data, we will build a uniform and expanded business intelligence platform framework to realize the integrated storage and management on the area of aviation forewarning .

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Plan for the ETL Implementation Process

In the implement process of the business intelligence project, the dealing process will occupy 60%-80% of the whole business intelligence project’s time span. ETL plays as a connecting link between the preceding and the following , and the result of the ETL will affect the final result of the BI project .So the built of the platform of ETL dispose and efficiency improvement are issues that must be taken into account. This section will unit the practical situation of aviation forewarning to give the scheme plan of the ETL implement process. 4.1

Build ETL Uniform Dispose Platform

After analyzing and making a research on the various operating systems of the current aviation forewarning system, we find that every operating system has a dependent ETL dispose platform. While add a new operating system ,we need to build a ETL dispose platform .Doing so leads to the waste of human resource and financial resource ,with every operating systematic data independent ,and it can’t achieve to share information better .So raise a uniform ETL dispose platform as showing in the figure 4-1. The platform build ETL dispose blocks, to realize the management of the whole events, to control schemed tasks and to distribute resources. I t realizes the forewarning information’s sharing , mostly reducing the waste of human resources and financial resources ,conducing ETL dispose personnel to learn from each other in order to improve specialized skills. What is the most important is to supervise and control the aviation forewarning system by using the platform. That will lay a good foundation for building the aviation forewarning business intelligence system . 4.2

Optimize the Implement Process

For the data warehouse system that can store large amount of information , the pressure of efficiently dispose of ETL dealing with information is quite high. How to improve the dealing efficiency has significant importance for the forewarning system to provide timeliness information .The optimization of ETL implement process is a permanent theme. In the practical ETL process of building airline business intelligence system ,we will optimize from five aspects as follows: Reduce I/O operation as much as possible， When the amount of information is large, database or ETL operating tools will read-in and write-out discs frequently, and it will store, read-in and write-out thousands of temporary files . For the database or the ETL implement tools, I/O operation always limits the improvement of ETL implement. Methods to reduce I/O operations are primarily: operation logic optimization and

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ODS System Database

Safety Forewarning

Weather Forewarning

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Disaster forewarning

ODSSystemETLApplication

Safety Forewarning System ETL Application

Disaster forewarning system ETL Application

Weather Forewarning System ETL Application

UniformETLDisposeBlock Events Managem ent Block

Tasks Schemed Block

Tasks Impleme ntBlock

Resource Distributio nBlock

QualityControlonEnterprise level Supervisory ControlBlock

Data source’s Quality ControlBlock

ETL Dispose Process Quality’s ControlBlock

AirlineForewarningDataWareHouse

Fig. 2. Uniform ETL Dispose Framework

procedure logic optimization. For the database, we can reduce the times of visiting the tables, use table association to supplant correlated subquery, reduce vernier usage and operate information in the RAM, etc. For the optimization of ETL dispose tool, we can avoid work’s landing ground as much as possible and avoid the gotten information to be located to the same disc space, and distribute more I/O resources for the data that is frequently read and written. Improve reading and writing speed, For the work that have large amount of data and easy dealing logic ,reading and writing the work will occupy more than 50% of the whole time .And for many files that are read-in at the same time ,the file that takes the

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longest time will seriously affect the whole time of the work .So when choose or design ETL dispose tools ,we should take the parallel processing of reading data into account. For example ,when we use the DATASTAGE to read in files that only few fields attend processing process, we consider to use Complex to read-in and write-out data. Strengthen the parallel processing, Parallel processing can fully take advantage the hardware resource and improve the work’s operating efficiency. Both database and ETL dispose tool can support parallel processing such as the venire in the ORACLE database , pipe technology and data partition technology of DATASTAGE .And we can realize the tasks’ parallelism (the parallelism among works) on the uniform ETL dealing platform . To determine the degree of parallelism needs to test repeatedly according to the fact, in this way we will set a rational degree of parallelism and make the relevant parallel scheduling plan . Optimize database performance, We can optimize the database from index and partition, etc. For index, make sure that the sub query in SELECT ,UPDATE and Delete sentences should select less than 20% table lines .We should build index on tables that are indexed with frequent delete operations so that space segments in the index affect the function .Of course we still need to consider to optimize the PL/SQL sentences to improve the dealing speed during the database operation . Optimize DATASTAGE performance, For optimizing the DATASTAGE tool, we can consider to distribute the space rationally, set tool parameters reasonably, strengthen the DATASTAGE tuning and adjust system parameter etc. For example, distribute more I/O access as many as possible; Set tool parameters according to the systematic practical situation; for every parameter of a particular work , partition method , buffer form, and node number in the process should be turned better .Adjusting some systematic parameters is to make the tool perform better.

5

Summarize

Accurate aviation forewarning has significant importance to make sure of pilots and passengers’ property. This paper makes a research on the current development of the aviation forewarning ,finding the difficulties that the aviation forewarning has to overcome and raising the idea of aviation forewarning business intelligence system .And it gives a plan for the implement of ETL in the aviation forewarning business intelligence system by analyzing the key technologies of ETL. Putting business intelligence into the area of aviation forewarning is primarily to accumulate the relevant forewarning data effectively so that we can make an accurate prediction on the hazards and disasters. This paper provides the Air Traffic Control Center with the airline waning business intelligence framework, and it can be applied in the airports and airlines .

Acknowledgment This research was supported by ”National Nature and Science Fundamental Research Funds”: “Research on Risk Mechanism of Aviation Transportation and Forewarning Decision Supporting Systems” (70971104).

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References 1. Luo, F., She, L., Bichong, G.: Cival Aviation Traffic Disaster Forewarning Management Systemic Framework Discuss. Beijing University of Astronauts and Astronautics Journal (Social Science Edition) 14(4), 33–36 (2001) 2. Skyttner, L.: Monitoring and early warning systems—a design for human survival. Kybernetes 31(2), 220–245 (2002) 3. Perry, R.W.: Incident management systems in disaster management. Disaster Prevention and Management 12(5), 405–412 (2003) 4. Fink, A., Siebe, Kuhle, J.P.: How scenarios support strategic early warning processes. Foresight 6(3), 173–185 (2004) 5. Hu, H., Xu, B., Xu, B.: An Agent-based Framework for Intelligent and Dynamic Business Information Retrieval. In: Workshop on Intelligent Information Technology Application, pp.77–81 (December 2007) 6. Li, K.: Cival Aviation Disasters Forewarning Decision Supporting Systems[J. Southwest Jiaotong University Journal 42(4), 511–515 (2007) 7. Zhang, S.: Research onAir Traffic Control Center Forewarning Management Information System. School of Management,Wuhan University of Technology, Hubei (2007) 8. Zhang, N., Jia, Z., Shi, Z.: Research on ETL Technology in the Data warehouse. Computer Engineering and Application 24(3), 213–216 (2002) 9. Luo, F., She, L., Bichong, G.: Airline Company’s Traffic Disaster Forewarning Indexes System. Wuhan University of Technology Journal (Traffic Science and Engineering Edition) 28(3), 361–364 (2004)

The Need for Teleradiology System in Medical Remote-Diagnosis Process Mohammad H. Al-Taei1, Ziyad T. Abdul-Mehdi1, and Subhi H. Hamdoon2 1 Information Technology Department, Sohar Applied Science College, Sohar - PO Box 135 PC 311, Sultanate of Oman [email protected], [email protected] 2 Information Technology Department, Sur Applied Science College, Sur, Sultanate of Oman [email protected]

Abstract. Teleradiology makes remote diagnosis possible and applicable with great accuracy due to the need and importance of radiographs in the diagnostic process. Teleradiology and Telediagnosis form an integral part of the telemedicine concepts. Teleradiology application and its services represent the main and important sources of information necessary for diagnostic information. This article highlights the position, importance, and need of Teleradiology in the Telemedicine services; it also discusses this need that creates faster diagnosis. The description of a proposed teleradiological system which can be used in the Internet hospital for remote diagnosis process is discussed in this article. The scenario of such system that explains the role of radiologist in the diagnosis process was explained. The needed equipments and methods of transmission are mentioned. Keywords: Telemedicine, Teleradiology, diagnosis system, medical information.

1 Introduction Teleradiology has been a forerunner in developing telemedicine systems and eHealth services. Teleradiology is most effective as part of a larger telemedicine network. The most important sources of diagnostic information are the radiographs which can be interpreting and diagnosing by the radiologist who resides in a distance place from the patient and transmitted by Teleradiology system to the physicians in the hospitals. This article highlights the position of Teleradiology in the spectrum of telemedicine services. Teleradiology is becoming a mature technology because of advances in imaging technology, database design and communications infrastructure and capabilities. It plays a large role in the globalization of medicine; it is the transmission of X-ray pictures or various scans such as a CT scan across distances from one location to another (tele-imagining) in order to create faster diagnosis. Important aspect in tele-imaging is the database question, comprehensive database development is the most difficult and expensive technology for tele-imaging. [1] In some cases, Teleradiology is not only bringing medicine to the underserved, it is relieving rural hospitals of the burden of attracting their own radiologists. This article M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 1021–1026. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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illustrates the services that provided by this application and its communication aspects; a simple prototype for Teleradiology system that used to cooperate between local and regional hospitals in disease diagnosing is explained with scenario for the role of radiologist in the Telediagnosis process. The equipment used for the information of such system and its transmission methods are also discussed.

2 Teleradiology Components and Operation Teleradiology as a stand-alone technique has a proven place in modern medicine; it depends on many components and several operations. Teleradiology components include image capture, data formatting, image storage, display workstation, communication networks, user friendliness software, image compression, and image security. Teleradiology have several operation models, from the simplest off-hour reading, application service provider (ASP), Web-based, to more complex models like PACS (Picture Archiving and Communication System) and Teleradiology combined and enterprise level using grid computing technology. Several important issues to be considered in Teleradiology are the tradeoff between Teleradiology technologies and operation, image data security, and medical–legal concern [2].

3 Services and Communication Aspects Teleradiology application and its services are required to increase the availability and diagnosing physician from a remote location, it is associated with other facets of telemedicine [3]. Teleradiology is neither a single medical device nor an instrument; instead, it is a system integration of various imaging devices using communication technology and system software with operations connecting multiple imaging centers and expert centers together. Services of telemedicine are many and distributed in all its applications, in Teleradiology and through some of the present Teleradiology systems we found several tangible benefits for this applications includes the reduced time and costs of traveling and films, better management of patients, wider delivery of services and intangible benefits like faster management of medical problems, equitable access to specialist opinion and reduced length of stay [4]. For some applications, synchronous communication is either unnecessary or inadequate. For x-ray and other still-image information specialized workstations have been developed for an acquisition, transfer and remote viewing on computer screens [6].

4 Telediagnostic Needs Telediagnosis systems are one of the important applications of telemedicine. In which patient can make diagnosis for his disease remotely by a specific specialist without traveling to his place. The most necessary requirements for such diagnosis are the radiological images that always played an important role in medical diagnosis, and that explain many abnormal points in patient's body. These can be achieved by the Teleradiological systems that can also be used for remote consultation and

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interpretation of images and allowing real-time interaction between physicians. Teleradiology is suitable for providing radiological services to remote medical facilities and 90% of physicians depend on radiograph images in their diagnosis.

5 Current Teleradiological Systems There has been tremendous growth of Teleradiology companies both in the United States and abroad. One of the largest is located in India and has a five-story building in India. Vesta brings you Teleradiology solutions and services, providing 24x7x365 access to our highly qualified Board-Certified radiologists through a secure virtual private network (VPN). Vesta Teleradiology is a complete end-to-end radiology solution. We integrate seamlessly with your technology. Our radiologists are all US Board certified, many with sub-specialty certifications that provide coverage 24 x 7 x 365. [7]. According to Col. Poropatich, one of the largest applications for telemedicine in Iraq is Teleradiology. The primary use of Teleradiology in Iraq is to move CAT scan images from Baghdad or Balad to Landstuhl Regional Medical Center in Germany so the images can be archived. The field hospital in Bagran, Afghanistan, established a high-bandwidth satellite connection with Landstuhl Regional Medical Center in Germany, supporting Combat radiology support in Bagran is equipped for x-ray, ultrasound, and CT [8]. Kalyanpur is the only Indian radiologist providing nighthawk service to the U.S. He receives an average of 25 emergency scans per day from several U.S. hospitals. The reach of Teleradiology extends to the high seas, although most experts agree that the maritime Teleradiology is still limited. It does allow the transmission of radiographs from a ship anywhere in the world via satellite and Internet links for immediate expert radiological opinion and advice.

6 Teleradiology System Prototype Telediagnosis system between the patient and GP/ Physician often depends in its diagnosing on the radiological investigation, so the requester (GP/physician) refers the patient to the nearest radiological unit to the patient to take the investigation. After investigation the radiological provider will use the Teleradiology system to make the Interpretation and return back the report to the requester. The scenario of this system includes the roles requester, radiographer and radiologist summarized in figure (1). The requester sends a radiological services order to the provider in the local hospital. The provider evaluates the request, and proposes alternative investigation time. After selecting the time by patient who then sees the provider and perform the investigation. After the investigation, the provider (radiographer) will request the radiologist interpretation remotely by transferring the digitized images to the radiologist in the regional hospital for interpretation. Radiological report will be send back to the requester either through the provider or directly.

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6.1 Teleradiological Equipment The hardware and other equipment for Teleradiology systems are dependent on the type of images to be transmitted. Any Teleradiological system must consist of a personal computer (PC), and local hospital that conducted by Teleradiological system must be equipped with a PC and devices for digitizing films and documents (scanner) as well as the investigation equipment for x-ray, CT,MRI and others. Regional hospital must be equipped with high resolution monitor. Teleradiology workstation, for acquiring, transmitting and display high quality images are next class of equipment.

Fig. 1. Teleradiological System Scenario

6.2 Transmission of Teleradiology Information Image transmission depends primarily on high-speed electronic communication pipelines such as ATM (asynchronous transfer mode) or gigabit networks that enable rapid transmission of digital images without loss of content or resolution [9]. Information used in Teleradiology is the images that acquired from radiological modalities that include x-ray, magnetic resonance (MR), computer tomography (CT), ultrasound, nuclear medicine (NM) and others.

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X-ray films are digitized with laser scanners, while images from digital imaging modalities such as (MR) and (CT) are available directly in digital. Digitized still images are easily transmitted between PCs; Video signal may be digitized into a series of still images, which can be transmitted between computers. This information can be transmitted in two modes synchronous (cooperative) or asynchronous. More recently, clinicians have started to use Internet for transmission because of its universal protocol and low cost bandwidth. So, the types of system services and its telecommunication aspects are listed in the following table1: Table 1. (Teleradiological services and communication aspects)

Teleradiological Services - Remote diagnosis ordinary x-ray picture - Remote diagnosis computer Tomography picture. - Remote diagnosis ultrasonic Picture. - Radiological remote consultation.

Telecommunication Aspects -Digitized still pictures/data Communication. - Data communication. - Interactive video/audio. -Interactive video/audioCommunication.

7 Conclusion Teleradiology is currently one of the most widely utilized telemedicine applications. It makes telediagnosis possible and applicable in great accuracy. Without radiology services, rural hospitals will tell you they cannot keep their doors open. It is a unique combination of digital data network and computer and their function is the electronic transmission of radiographs or radiological images from one site to another. These systems can be implemented as a special network or public network like Internet, the very low cost bandwidth. Teleradiology systems can improve emergency service coverage and integrate multi hospital / clinics health care provider consortiums. The network of the prototype in this article illustrates the cooperation between local and regional hospitals in radiological services, which help physician in diagnosing and makes it a lot easier to provide better patient care and benefits both patients and radiologists.

References 1. Elsayed, K.M.A.M., Tohme, W.G., Chris, Y.: WuTeleradiology/Telepathology requirements and implementation. Seo Journal of Medical Systems 1 (1977), 35 (2011) 2. Pysher, L., Harlow, C.: Teleradiology using low-cost consumer-oriented computer hardware and software Centaur Health System/St. Thomas More Hospital, Canon City, CO 81212, USA 3. Shirtley, C.G.S.: Teleradiology: Present and future. ADF health 2 (April 2001) 4. Hayward, T., Mitchell, J.: The cost-effectiveness of Teleradiology at the women’s Hospital in Adelaide. Journal of Telemedicine and Telecare 6(suppl.), 23–25 (2000)

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5. Vitanen, J., Sund, T., et al.: Nodic Teleradiology development. Computer methods and Programs in Biomedicine 37, 273–277 (1992) 6. Nymo, B.J.: Telemedicine. Journal of Telektronikk 1, 93 (1993) 7. Vesta Teleradiology system, http://vestarad.com/ 8. Page, D.: Teleradiology readies for war (March 2003), http://www.dimag.com/pacsweb/printer_friendly/ ?articleID=47901795 9. Page, D.: Demand for medical services in remote areas and staffing shortages push radiology to the ends of the earth (2003) http://home.earthlink.net/~douglaspage/id47.html )

The Clustering Methods Based on the Most Similar Relation Diagram* Yan Yu1, Wei Hong Xu2, Yu Shan Bai1, and Min Zhu2 1

College of Education, Tianjin Normal University, Tianjin, China [email protected] 2 College of Computer Science, Sichuan University, Chengdu, China [email protected]

Abstract. The MSRD (Most Similar Relation Diagram) of a dataset is a diagram in which each datum is linked to its MSD (Most Similar Data) and each MSRG (Most Similar Relation Group) is linked to its MSG (Most Similar Group). In essence, the MSRD of a dataset is the visualization of the most similar relations of the data. A clustering method based on the MSRD usually includes two stages: drawing the MSRD of the dataset and selecting some links in the MSRD to cut off. In this paper, we present a package of methods for the later stage and prove that the clustering based on the MSRD can detect both the spherical and non-spherical clusters simultaneously and has the capacity to distinguish the linearly inseparable clusters in high dimensional datasets. Keywards: Data mining, clustering algorithm, the most similar relation diagram (MSRD), the most similar data (MSD), the most similar group (MSG).

1 Introduction As an important tool, cluster analysis has become an active research subject in data mining and has been extensively studied in many fields such as pattern recognition [1], customer segmentation [2], similarity search [3] and trend analysis [4] . Till now, thousands of clustering algorithms have been published and some new algorithms still continue to appear [5]. In recent years, ensemble methods [6], Semi-supervised method [7], methods for clustering large-scale datasets, Multi-way clustering method [8] as well as kernel and spectral methods [9] are developed notably. Recently, Yan Yu et al. presented a clustering method based on the MSRD (Most Similar Relation Diagram) [10]. It is valuable for it can generate more receivable and reasonable partitions than the available methods can provide on some datasets. The MSRD is a diagram in which each datum is linked to its MSD (Most Similar Data) and each MSRG (Most Similar Relation Group) is linked to its MSG (Most Similar Group). In essence, the MSRD of a dataset is the visualization of the similarity structure of the dataset in high-dimension vector space, and partly preserves the topology structure and, most importantly, the most similar relations between data *

This work is supported by the Science and Technology Department, Sichuan Provence, China (grant 2009JY0038).

M. Ma (Ed.): Communication Systems and Information Technology, LNEE 100, pp. 1027–1034. springerlink.com © Springer-Verlag Berlin Heidelberg 2011

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and natural data-groups embedded in the dataset. Therefore, the MSRD of a dataset can be the good basis for clustering the dataset. The clustering of a dataset based on the MSRD consists of two stages: drawing the MSRD of the dataset and determining where to cut the MSRD into clusters. The MSRD of a dataset is unique no matter what procedure is followed, but there may be variety of choices for cutting the MSRD into clusters so as to obtain partitions with different features. In literature [10], a procedure of drawing the MSRD has been presented and described in detail, but for the second stage, i.e. the approaches of determining where to cut the MSRD into clusters are not studied sufficiently. In this paper we present a package of methods for selecting the links to cut off and perform some of the methods on synthesized and real dataset.

2 The Clustering Algorithm Definition 1: Suppose: (1) |X|= is a dataset; (2) dij=|| Xi-Xj || is the dissimilarity between Xi and Xj (||•|| denotes Euclidean distance; Xi, Xj |X|). For Xh∈|X| ( h=1, 2, …, N; h≠i, j), if dij≤dih=|| Xi-Xh ||, Xj is the Most Similar Datum (MSD) of Xi. According to definition 1, each datum has at least one MSD in a dataset, but it is not true that a datum must be the MSD of another datum. Some data is not the MSD of any datum.

∈

Definition 2: Suppose: (1) a dataset |X| is partitioned into K groups (|X|= ); (2) Gi∩Gj =Φ (Gi, Gj∈|X|; i, j=1,2,…,K; i≠j); (3) dpq= minp,q(||XipXjq||) (||•|| denotes Euclidean distance; Xip Gi; Xjq Gj; p=1,2, …,I; q=1,2, …, J; I and J are the number of data in Gi and Gj respectively). For Gh = {Xh1, Xh2, …, XhH} (h=1,2, …, K; h≠i,j; H is the number of data in Gh), if dpq≤duv=minu,v(||Xiu-Xhv||) (Xiu∈Gi; Xhv∈Gh; u=1,2, …,I; v=1,2, …,H), Gj is the Most Similar Group (MSG) of Gi. dpq is defined as the dissimilarity between Gi and Gj. Xip and Xjq are called the most similar data between Gi and Gj. According to definition 2, any data group has at least one MSG in a partition, but some data groups are not the MSG of any group. The clustering algorithm based on the MSRD involves two stages: (1) drawing the MSRD of the dataset; (2) determining where to cut the MSRD into clusters.

∈

∈

2.1 Drawing the MSRD of the Dataset The procedure of drawing the MSRD of a dataset is as follows: • Compute the dissimilarities of each datum with others and find the MSDs of every datum following the definition of the MSD of a datum. • Link every datum to its MSD with “→” letting the arrow head towards the MSD. For instance, if datum A is the MSD of datum B, we link datum A and B as A←B. If datum C and D is the MSD of each other, we use a double headed arrow to link the two data as C D. By doing so, all data are linked to compose a certain number of data groups. We call these data groups the 1st level MSRGs of the dataset, and denote them with Gi(1), i=1, 2, …, n(1); n(1) is the number of 1st level MSRGs.

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• According to the definition of MSG, find the MSGs of every 1st level MSRG and the most similar data between groups. • Link every 1st level MSRG to its MSG through linking the most similar data between the two MSRGs with “→” letting the arrow head towards the datum in the MSG. By doing so, the 1st level MSRGs are merged into a certain number of 2nd level MSRGs. We denote them by Gi(2), i=1, 2, …, n(2), n(2) is the number of 2nd level MSRGs. • Iterate such process to merge the lower level MSRGs into higher level MSRGs until all MSRGs are aggregated into one group, which is called the final MSRG. We call every arrow in the MSRD a link. The arrow linking a datum to its MSD is called a 1st level link. Similarly, the arrow linking a lth MSRG to its MSG is called the (l+1)th level links, and l is called as the link level of the link. The dissimilarity between the linked two data is also called the link length di. In this paper, the Euclidean distance is used to measure the dissimilarity, so the link length equals to the distance between the linked two data. • Write the link length di and link level li beside each link as follows: or Here, the o.22 is the link length and the (2) denotes that the link level is 2. For simplicity, the link level of the 1st level is neglected. As an example, we synthesized a simple two-dimension dataset including 20 data called dataset 1. Fig.1 (a) is the coordinate graph of dataset 1. The numerals in Fig.1 (a) are the ID numbers of some data. The MSRD of dataset 1 drawn following the procedure above is shown in Fig.1 (b). Comparing Fig.1 (a) with (b), the relation between a dataset and its MSRD can be seen.

(a)

(b)

Fig. 1. (a) The coordinate graph of dataset 1; (b) The MSRD of dataset 1

From Fig.1 (b) it can be seen that data 1~8 are linked together to form a 1st level group, G1(1), by 1st level links; data 9~16 are linked to form another 1st level group G2(1); data 17 and 18 composed G3(1); data 19 and 20 composed G4(1). Then, G1(1) and G4(1)are linked by the 2nd level link, 0.73(2), to form a 2nd level group G1(2); G2(1) and G3(1) are linked to form G2(2). At last, the two 2nd MSRGs are linked by the 3rd level link, 0.30(3), to form a 3rd level group G1(3), which is the highest level MSRG of all the MSRGS and the final MSRG in dataset 1.

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2.2 Determining Where to Cut the MSRD into Clusters By cutting off a certain number of links in the MSRD, a partition of the dataset can be generated. Cutting different links can obtain different partitions. There may be variety of methods for determining where to cut the MSRD into clusters relying on the factors being taken into consideration such as the number of the clusters, the similarity between the clusters, the densities of the clusters, the shape of the clusters, the uniformity of the partition, etc. In this paper, we present a package of methods for determining where to cut the MSRD into Clusters. • The link length method (d method). Cut off some of the longest links of all links, the dataset can be divided into a certain number of clusters. • The link level method (l method). Cut off some of the highest level links of all links, a partition of the dataset can be obtained. • The relative link length method (r method). The relative link length ri of link i is calculated as follows: Assume a fragment in a MSRD of a dataset is as Fig. 2 (a). For simplicity, we use short lines instead of the arrows in the MSRD to denote the links. di, dj and dk are the link length between the linked two data. The relative link length ri of each link in the MSRD is calculated according to formula (1).

(a)

(b)

(c)

Fig. 2. The fragment cases in MSRD: (a) there are links at both ends of link i; (b) there is no link at one end of link i; (c) there are multiple links at one end or two ends of link i

ri=

(di-dj)+(di-dk)

if (di-dj)>0 and (di-dk)>0

(di-dj)

if (di-dj)>0 and (di-dk)≦0

0

if (di-dj)≦0 and (di-dk)≦0

(1)

In the case of Fig. 2 (b), take dk=∞. In the case of Fig. 2 (c), take dk=( dl+dh)/2. Substitute each di in the MSRD with ri, the MSRD is converted into r-MSRD. Fig.3 (a) is the r-MSRD of dataset 1. In the r-MSRD, the short line is used as the link instead of the arrow; no matter the arrow is single or double one. Compare the rMSRDs in Fig.3 (a) with the MSRD in Fig.1 (b), it can be seen that some link lengths are increased and some are reduced. Cut off some links with the longest relative link length r of all the link lengths, a partition of the dataset can be obtained. • The uniformity method (u method). The uniformity ui of link i is calculated according to formula (2) ui=(Ni1Ni2)/(Ni1+Ni2)

(2)

The Clustering Methods Based On the Most Similar Relation Diagram

1031

Ni1 and Ni2 are the number of data in the two clusters respectively produced if link i is cut off. Cut off some links with the high uniformities of the links in the MSRD, the dataset can be grouped into several clusters. • The weighted link length method (w method). There are several different submethods of w-method, such as wi=lidi, wi=uidi, wi=liri, wi=uiri, wi=uilidi, wi=uiliri. Accordingly, the methods are called ld-method, ud-method, lr-method, ur-method, ulr-method etc. respectively. Substituting each link length di in the MSRD with wi, the MSRD is converted into w-MSRD. Now we call the link length d, link level l, link uniformity u, relative link length r, the weighted link length ld, lr, ud, ur and ulr as the link parameters.

(a) the r-MSRD

(b) d-method

(d) u-method

(c) l-, r-, lr- ur- and ulr-methods

(e) ud-method

Fig. 3. (a) The r-MSRD of the dataset 1; (b), (c), (d), (e) The 4-cluster partition of the dataset 1 based on different methods

Obviously, partitions with different features can be obtained by selecting different methods. For example, the d-method guarantees that the gaps between the generated clusters are as big as possible. It is interesting to notice that the partition generated by dMSRD method is all the same with that generated by Hierarchical simple method. Fig.3 (b) is the partition of dataset 1 generated by d-method of MSRD as well as by Hierarchical simple method. The l-method can guarantee the lower level MSRG not be divided. It also can produce more uniform partition then the d-method, See Fig.3 (c). The u-method can provide partition with high uniformity, See Fig.3 (d). Clustering based on the rmethod can guarantee the data points in a manifold to be in the same cluster, see Fig.3 (c). The w-methods take double or triple factors into consideration so that it can generate partitions with multi-features. Fig. 3 (e) is a partition based on the ud-MSRD method, it is more uniform than (b), but the gaps between some clusters are narrower than that of (b). These arguments support that the MSRD method is a reach clustering method for it can generate variety of partitions with different features, some of which are not be able to generate by K-means or Hierarchical, such as the partition in Fig.3 (c). It is

1032

Y. Yu et al.

necessary to declare that the partition in Fig. 3 (c) can be generated by l-, r-, lrur- and ulr- methods of MSRD does not mean that these method always produce the same partitions for any dataset. It is worthy to mention the ulr-method for it is valid and effective and can generate reasonable partitions in clustering some datasets with complex structures. So we will use this method in clustering the synthesized dataset 2 and the two real dataset, Iris and Breast-cancer, in the following parts of this paper.

3 The Clustering of a Synthesized Dataset For testing our algorithm, a tough dataset containing both spherical and non-spherical clusters is synthesized. We call this dataset as dataset 2. The coordinate graph of the data is shown in fig.4 (a). It is a two dimensional dataset including 83 data points which composes two spirals (S1, including datum 1~19; S2, including datum 20~38) and two spherical groups (S3, including datum 39~47; S4, including datum 48~83)

(a) the coordinate graph

(b) by ulr-MSRD

(c) by K-means

(d) by Single

(e) by Ward

Fig. 4. (a) The of dataset 2; (b) ,(c), (d) ,(e) The clustering result of dataset 2 by different methods

The partition generated by ulr-method and r-method of MSRD are the same and is shown in Fig.4 (b). It can be seen that the two spirals are separated while also the two spherical groups are being separated. It is noticeable that such a partition, which seems to be more reasonable than the others, is not able to be generated by simple Kmeans and the seven methods of Hierarchical. One of the partitions by simple Kmeans is shown in Fig.4 (c), and the partition by Ward method of Hierarchical is shown in Fig.4 (e). The partitions generated by the Average method, Complete method, Weighted method, Centroid method and Median method are not much different from the partition generated by Word method (Fig.4 (e)) and are not submitted here. The partition obtained by Hierarchical simple method is shown in Fig.4 (d), which is the same with that produced by d-method of MSRD.

4 The Clustering of the Real Dataset In this section we report the clustering results by our algorithm on the UCI datasets, Iris and Brest-cancer.

The Clustering Methods Based On the Most Similar Relation Diagram

1033

4.1 About the Co-position Data If the dissimilarity between datum A and datum B is Zero, then datum A is the coposition datum of datum B, and datum B is the co-position datum of datum A. In real dataset many data have co-position data if the Euclidean distance is used to measure the similarities. Before drawing the MSRD, the co-position data of a datum are removed to leave a concise dataset. After the concise dataset is clustered, the removed data are re-added to the clusters their co-position datum belongs to. 4.2 About the Dataset The Iris includes 150 data with 4 attributes belonging to 3 categories called Sitosa, Versicolor and Virginica respectively. There are 50 data in every category. 3 coposition data are removed and the concise dataset includes 147 data. The Breast-cancer includes 684 normal data in two classes, class 2 and class 4. The class 2 includes 445 data and the class 4 includes 239 data. 235 co-position data are removed. The concise dataset includes 448 data, in which 212 data belong to class 2, 236 data belong to class 4. 4.3 The Clustering Results The clustering results generated by the ulr-method on both Iris and Breast-cancer are given in Table 2 and Table 3 together with the clustering results obtained by simple K-means, Single and Ward method of Hierarchical for comparison. The Nj in Table 2 ad 3 is the number of the data in the category or class j labelled by the dataset provider; Ni is the number of the data in the cluster i generated by the clustering method; Nij is the number of the data belong to the category j being partitioned into cluster i. Table 2 shows that the partition of Iris generated by ulr-MSRD method is better than that generated by K-means and Word of Hierarchical. It should be noticed that the partition generated by ward is the best of the partitions generated by the seven methods of Hierarchical. For the Brest-Cancer, the best one of the results generated by the seven methods of Hierarchical is generated by Ward also (see Table 3). The partition generated by ulr-MSRD is a little worse than that by Ward but much better than K-means. These clustering results demonstrated that the MSRD method has the capacity in clustering high dimension dataset with linearly inseparable clusters. Table 1. The clustering results of Iris by four different methods Dataset

K-means

Single

Ward

ulr-MSRD

Categories

Nj

Nj

Ni,j

Nj

Ni,j

Ni

Ni,j

Ni

Ni,j

1

50

50

50

50

50

50

50

50

50

2

50

62

48

1

0

64

49

47

46

3

50

38

36

99

49

36

35

53

49

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Y. Yu et al. Table 2. The clustering results of Brest-Cancer by four different methods Dataset

K-means

Single

Ward

ulr-MSRD

Class 2

Nj 445

Ni 454

Ni,j 305

Ni 683

Ni,j 445

Ni 447

Ni,j 423

Ni 422

Ni,j 413

4

239

230

90

1

1

236

235

262

229

5 Conclusion This paper demonstrated that the clustering based on the MSRD can detect both the spherical and non-spherical clusters simultaneously and has the capacity to partition the high dimension datasets with linearly inseparable clusters. Furthermore, the clustering method based on the MSRD is more universal than the existing methods for it can adapt clusters with different structures, and is richer than the existing methods for it can provide multiple partitions with different features for users to choose.

Acknowledgments The authors would like to thank Professor Pilian He in advance for his helpful suggestion to improve this paper. We are grateful to the editors and anonymous reviewers for their constructive comments.

References 1. Sehgal, Desai, U.B.: 3D object recognition using Bayesian geometric hashing and pose clustering. Pattern Recognition 36(3), 765–780 (2003) 2. Boone, D.S., Roehm, M.: Retail segmentation using artificial neural networks. Internal Journal of Research in Marketing 19(3), 287–301 (2002) 3. Castelli, V., Thomasian, A., Li, C.-S.: CSVD: clustering and singular value decomposition for approximate imilarity search in high-dimensional spaces. IEEE Transactions on Knowledge and Data Engineering 15(3), 671–685 (2003) 4. Popescul, A., Flake, G.W., Lawrence, S., Ungar, L.H., Giles, C.L.: Clustering and identifying temporal trends in document databases. In: Proc. of IEEE Advances in Digital Libraries 2000 (ADL 2000), Washington, DC, May 22–24, pp. 173–182 (2000) 5. Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognition Letters 31, 651–666 (2010) 6. Fred, A., Jain, A.K.: Data clustering using evidence accumulation. In: Proc. Internat. Conf. Pattern Recognition, ICPR (2002) 7. Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press, Cambridge (2006) 8. Ron, El-Yaniv, Ran, McCallum, Andrew: Multi-way distributional clustering via pairwise interactions. In: Proc. 22nd Internat. Conf. Machine Learning, pp. 41-48 (2005) 9. Filippone, M., Camastrab, F., Masulli, F., Rovetta, S.: A survey of kernel and spectral methods for clustering. Pattern Recognition 41, 176–190 (2008) 10. Yu, Y., Bai, Y.S., Xu, W.H., et al.: A Clustering Method Based on the Most Similar Relation Diagram of Datasets. In: 2010 IEEE International Conference on Granular Computing, San Jose, California, August 14-16, pp. 598–603 (2010)

Author Index

Abdul-Mehdi, Ziyad T. 1021 Aeenmehr, Amin 309 Al-Taei, Mohammad H. 1021 Angel, Araujo Jose 525 Asli, Rahebeh Niaraki 31

Deng, Xiaojun 541 Ding, Yuanming 133 Dong, Haiguang 117 Dong, Tao 921 Dun, Yueqin 563

Bai, Yu Shan 1027 Baoji, Ma 141 Binbin, Rao 609, 617 Botao, Zhang 23

Fan, Hui 487 Fan, Rong 171 Fang, Gang 651 Fathi, Adel 911 Feng, Jun 187 Feng, Linwei 563 Feng, Wei 497 Fong, Li-Wei 455 Frischer, Robert 433

Cao, Bisheng 283 Cao, Xiaoyan 795 Chang, Meng-Chou 577 Changzhen, Hu 411 Chao, Jen-Ai 515 Che, Xuezhe 811 Che, Yanqiu 697, 705 Chen, Chong 625 Chen, Daner 993 Chen, Dao-ping 369 Chen, Dongchao 211 Chen, Liang 833 Chen, Shuwang 639 Chen, Xiaodong 859 Chen, Xue-bo 243 Chen, Yi Qun 673 Cheng, Ke 827 Chin, Jiung-Bin 79 Chun-Jung, Chen 549, 557 Cong, Peng 867, 875 Cui, Guohua 833 Dazhong, Ma 945 Deng, Bin 697, 705 Deng, Junmin 681

Ganji, Davood D. 819 Gao, Dandan 361 Gao, Rui 479 Gao, Yang 601 Ge, Wei 1, 15 Gorji-Bandpy, Moﬁd 819 Gu, Cheng-yu 227 Gu, Haiyun 103, 111 Gu, Yujiong 211 Guan, Xiaohan 235 Guo, Jincui 1005 Guo, Tongying 953 Guo, Wanyou 361 Hamdoon, Subhi H. 1021 Han, Chunxiao 697, 705 Han, Xiaoming 773 Hao, Xiaoguang 487 He, Jun 1

1036

Author Index

Hong, Tao 179 Honglian, Li 87 Hong-yun, Liao 761 Hsieh, Ling-Feng 79 Hu, Dongping 833 Hu, Lingyan 729 Hu, Wensong 965, 969, 975, 981, 987 Hu, Yao-hui 95 Hu, Yihua 827 Hua, Huang 867, 875 Hua, Qing-yi 187 Hua, Shungang 163 Huang, Juying 379 Huang, Qi 859 Huang, Xianlian 171 Huang, Yi-Cheng 515 Ignacio, Garate Jose

525

Jabraeil Jamali, Mohammad Ali Janckulik, Dalibor 439 Jia, Cui 509 Jia, Xiongwei 395 Jiang, Dong 295 Jiang, Huiyan 479 Jiang, Qiuxin 163 Jianguo, Zhou 945 Jianwei, Sun 411 Jie, Liang 411 Jie, Peng 959 Jin, Gui 361 Jin, Tiezheng 211 Jin, Zhi-jian 227 Jing, Bin 259 Jing, Fan 87 Jing, Zhongguo 283 Junjun, Zhang 219 Kang, Jinsong Kasik, Vladimir Kong, Yu 563, Koonsanit, Kitti Krejcar, Ondrej Kuo, Yen-Ting

41 195 569 463 433, 439, 447 577

Le-ping, Bu 585 Le-ping, Pu 593 Li, Cuihuan 325 Li, Deliang 639 Li, Dongming 937

911

Li, Haitao 781 Li, Haiyun 259, 379, 681 Li, Hua 345 Li, Junyi 657 Li, Lan 353 Li, Lijun 827 Li, Liu 959 Li, Qian 187 Li, Qiufeng 251 Li, Wenfang 117 Li, Xia 585, 593 Li, Xiaoli 289 Li, Xingfu 267 Li, Xinying 795 Li, Ying-Hao 515 Li, Zhaoxia 395 Li, Zhongpeng 235 Li-ming, Wang 585, 593 Li-tian, Liu 761 Lian, Li 503, 509 Liang, Guangdong 753 Liang, Guoyan 601 Liang, Jintao 387 Liang, Ming 289 Liang, Zhe 645 Liao, Mei 533 Lin, Haibo 839 Lin, Lin 203 Liping, Wang 633 Liu, Junbo 625 Liu, Li 601 Liu, Shenglan 657 Liu, Xiaohu 937 Liu, Xiaojie 479 Liu, Xinning 15 Liu, Zhixiang 157 Lu, Changbo 667 Lu, Guangshan 753 Lu, Weiyuan 157 Lv, Donghuan 267 Lv, Guo-qiang 95 Lv, Yecheng 419 Ma, Fang-Yue 275 Ma, Hongxing 171 Ma, Shihai 601 Ma, Xiaodong 471 Ma, Xing 171 Mao, Xuming 427 Miguel, de Diego Jose

525

Author Index Min, Yue 569 Ming, Li 969 Mirza, Muhmmad J. 401 Mo, Tai-ping 303 Mostoﬁ, Mehdi 819 Mu, Chunyang 171 Muller, Chris 57 Ning, Tingting 157 Ning, Xu 361 Niu, Wei 187 Niu, Yichuan 41 Ouyang, Ming-san 645 Ouyang, Xin-yu 243 Pan, Jin 487 Pashaei, Javad 911 Peng, Shusheng 773 Peng, Yang-Yang 275 Perumal, Logah 893 Pires, Nuno M.M. 921 Qiantai, Yan 769 Qian, Xiuqing 157 Qiao, Zizhi 395 Qin, Mingxin 361 Qing, Hu 1013 Qingrong, Wang 845 Qingtian, Han 503, 509 Qiuye, Sun 945 Qu, Dian 259 Qu, Yan-liang 49, 71 Rao, Binbin 625 Ren, Zhizheng 211 Renli 789 Ruilan, Zheng 609 Sano, Akira 133 Sha, Zhenghui 387 Shan, Weiwei 15 Shao, Xiangxin 795 Shaohui, Wang 149 Shen, Fenglong 811 Shi, Chunchao 697, 705 Shi, Lihua 251 Shi, Yanbin 753 Shijie, Li 609 Shujin, Chen 219 Shuqiang, Zhang 23 Sina, Alireza 309

Songhorzadeh, Maryam Stankus, Martin 195 Su, Xiaofang 625 Sui, Wen-Quan 275 Sun, Lulu 657 Sun, Yan 125 Sun, Youchao 427 Sun, Yufeng 471 Tan, Pengliu 541 Tian, Chenwei 601 Wan, Zhou 283 Wang, Chao 361 Wang, Cheng 937 Wang, Chunyan 259 Wang, Chunying 795 Wang, Feng-hua 227 Wang, Haichen 953 Wang, Hao 187 Wang, Jiang 697, 705 Wang, Jian-hua 303 Wang, Jianhui 811 Wang, Kai 419 Wang, Li 721 Wang, Liangying 295 Wang, Ligong 853 Wang, Qianping 295 Wang, Tao 203 Wang, Xiao-Ying 275 Wang, Yan 133 Wang, Youhua 317, 325 Wei, Gao 903 Wei, Rao 885 Wei, Xile 697, 705 Wen, Jin 689 Wenjuan, Ruan 1013 Wu, Cheng 1 Wu, Chunsheng 497 Wu, Hao 379, 681 Wu, Juan 95 Wu, Junyun 803 Wu, Mu-Chen 79 Wu, Tianhao 533 Wu, Yi Lin 673 Wuzhuang, Li 769 Xia, Linglin 969 Xia, Naiyong 7 Xiang-long, Chen 761

31

1037

1038

Author Index

Xiao, Jing 569 Xiaofang, Su 609, 617 Xie, Jia 387 Xie, Ningxiang 419 Xie, Zhen 15 Xin, Hui 729 Xin, Yong 729 Xing, Jianbing 395 Xiong, Chunhua 667 Xiong, Xin 283 Xu, Bo 729 Xu, Honghua 497 Xu, Jia 361 Xu, Jian 729 Xu, Lin 361 Xu, Shuhui 117 Xu, Wei Hong 1027 Xu, Zhangsui 639 Xue, Huijie 497 Xue, Ye 827 Yan, Huagang 157 Yang, Shenggang 289 Yang, Xiaoguang 317, 325 Yang, Xiaolin 803 Yang, Xingui 965 Yang, Zhaochu 921 Yang, Zhiming 827 Yang, Zilong 497 Yi, Zhang 503 Yin, Aihua 833 Yin, Jianping 803 Yin, Jing 95 Yin, Xuesong 859 Ying, Shao 585, 593 Ying, Xuan 87 Ying, Yulong 999 Yonghong, Cui 845 Yongjun, Liu 1013 Yong-min, Feng 761 Yongsheng, Zeng 617 You, Jun 117 Yu, Henry 57 Yu, Nan 203 Yu, Yan 1027 Yuan, Feng 585, 593 Yuan, Guihong 259 Yuan, Haiying 781 Yue, Xiaoming 487 Yun-kui, Xiao 761

Zhai, Yong 179 Zhan, Changan 203 Zhang, An 753 Zhang, Bo 929 Zhang, Changchang 929 Zhang, Haijun 929 Zhang, Housheng 7 Zhang, Jun 227 Zhang, Lei 921 Zhang, Li 563 Zhang, Ling 171 Zhang, Quanhong 251 Zhang, Shu-hua 737, 745 Zhang, Wenjun 713 Zhang, Yan 965, 975, 981 Zhang, Zhimeng 487 Zhao, Guangyan 471 Zhao, Haitao 427 Zhao, Junwei 427 Zhao, Languang 953 Zhao, Mengnan 1 Zhao, Min 251 Zhao, Shengdun 387 Zhao, Tie-shi 49, 71 Zhao, Xiaojuan 1005 Zhao, Xijun 179 Zhao, Yanlei 7 Zhaoyan 789 Zheng, Guangyu 773 Zhong, Jie 187 Zhong, Qing 163 Zhongqiu, Yu 23 Zhongxu, Li 945 Zhou, Jian 827 Zhou, Lin 427 Zhou, Rui 295 Zhou, Ru Qi 673 Zhou, Shilin 803 Zhou, Shunyuan 345 Zhou, Weibin 259 Zhou, Xue guang 125 Zhou, Yan 795 Zhou, Youjie 667 Zhu, Cheng-jie 645 Zhu, Min 965, 969, 975, 981, 987, 1027 Zhu, Ming-hua 333 Zhu, Yuyuan 987 Zou, Jinxin 1005 Zuo, Ke 339, 987

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