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controlling the load-balance. The actual performance of the model remains to be proved by experiment. Ⅳ SUMMARY AND OUTLOOK There are a lot of research results of P2P network nowadays. But there are some problems needs to be resolved such as how to finding correct resources effectively and controlling the number of the messages in searching process. Beside this, the problem of churn and semantic query is key problem of distributed unstructured P2P and how to select supernodes effectively and how to do while supernodes are disable. ACKNOWLEDGMENT This work is supported by Economic Commence Market Application Technology Foundation Grant by 2007gdecof004. REFERENCES: [1] Dou wen,etc. A Recommendation-Based Peer to Peer Trust Model. [J] Journal of Software, 2004, 15(4):571~583. [2] V. Kalogeraki,etc. A Local Search Mechanism for P2P Networks. In CIKM, 2002. [3] Fox G. Peer-to-Peer networks. [J]Computing in Science & Engineering. 2001.3(3):75~77. [4] Huang Daoyin,etc. A Distributed P2P NetworkModel Based on Active Network. [J] Journal of Software, 20 04,15(7):1081~1089. [5] Xu Fei, etc. Design of Distributed Storage System on Peer-to-Peer Structure. [J] Journal of Software, 2004, 15(2):268~277. [6] Mihajlo A, Jovanovice B S. Modeling Lager-scale Pe er-to-Peer Network Architecture[C], In: International C onference on Commumication Technology (ICCT2003) Proceeding,2003.1222~1232. [7] Wang Qingbo, etc. An Infrastructure for Attribute Ad dressable P2P Network: Barnet. [J] Journal of Softwar e, 2003,14(8):1481~1488. [8] Ma Yan. Design and Implementation of DoubleID Sp ace Based Peer-to-Peer File System. [J] Computer En gineer, 2005,31(10):12~19. [9] B. Yang H. Garcia-Molina. Efficient search in Peer-to-Peer net works. Proceedings of ICDCS'02, 2002. [10] Q. Lv, etc. Search and replication in unstructured P2P network s, Proceedings of the 16th ACM International Conference on Supercomputing, 2004. [11] Xiong Wei,etc. A Structured Peer to Peer File Sharing Model with Non-DHT Searching Algorithm.[J] Journal of Computer Research and Develop 2009, 46(3): 415~424 [12] Z. Zhuang,etc. "Hybrid Periodical Flooding in Unstructured P2P Networks". Proceedings of International Conference on Parallel Processing, 2003. [13] Stoica I,Morris R,Karger D,et al.Chord:A scalable peer-to-peer lookup service for Internet applications.[C] Proc of the ACM SIGCOMM 2001. New York:ACM,2001:149-160. [14] Stoica I,Morris R,Karger D,etc.Chord:A scalable peer to peer lookup service for Internet applications. [C] Proc of the ACM SIGCOMM 2001. New York: ACM, 2001: 149-160. [15] Yue Guangxue, etc. An Efficient Virtual Co-Allocation Service Model in P2P Networks.[J] Journal of Computer Research and Develop. 2008,45(11):1928~1938. 226
ISBN 978-952-5726-09-1 (Print) Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10) Jinggangshan, P. R. China, 2-4, April. 2010, pp. 227-229 Emotion Recognition of EMG Based on BP Neural Network Xizhi Zhu School of Physical & Electronics Engineering, Taizhou University, Taizhou, China zxztzc@126.com Abstract— This paper presents the emotion recognition of BP neural network classifier. Experiment analyzes wavelet transform of surface Electromyography (EMG) to extract the maximum and minimum multi-scale wavelet coefficients firstly. And then we enter the two kinds of structural feature vector classifier for emotion recognition. The experimental results show that both classifiers shows a effective recognition on the four kinds of emotions, joy, anger, sadness, pleasure, etc. Also the experimental results show that feature vector extracted by wavelet transform can characterize emotional patterns, and it has a stronger emotional recognition effect than the traditional classify method. Index Terms—Surface Electromyography(EMG)Signal; Emotional Recognition; Wavelet Transform; BP Neural Network I. INTRODUCTION The study of emotion recognition has an important significance in understanding human emotions in the role of human intelligence. In daily life, human intelligence not only shows in the normal rational thinking and logical reasoning ability, but also in the normal emotional capabilities. In computer science, this ability to promote the establishment of a friendly man-machine interface is of great significance. As the deepening of affective computing, the request for emotion recognition [1, 2] technology will be correspondingly enhanced. The MIT media experiment team led by Professor Picard collected a 20-day physiological signal from eight kinds of emotions of an actor when performing deliberately, to extract the value of demographic characteristics using way of Fisher Projection [2] . At last they get 83% of the emotion recognition rate [3] . In Augsburg University, Germany, Johannes Wagner and others got approximately 80% of the emotion recognition rate on a subject's evoked in the music under joy, anger, sadness, pleasure of the four kinds of EMG signal by the use of physical K-nearest neighbor, linear discriminant function and multi-layer in 25 days [4] . Among them, Yang Ruiqing and Liu Guangyuan led the feature selection by the use of discrete binary particle swarm optimization (BPSO), proving that the method of BPSO on physiological signal for feature selection is feasible [5] . Also Niu Xiaowei and Liu Guangyuan selected the most representative characteristics of the corresponding optimal combination of emotional states by using the genetic algorithms, proving that the optimization problem, the genetic feature selection algorithm being used to identify © 2010 ACADEMY PUBLISHER AP-PROC-CS-10CN006 227 the choice of the combinational optimal portfolio of the physiological signal recognition optimal emotional characteristics, is feasible [6] . In this paper, the method of Wavelet Transform is used in surface EMG aiming for non-stationary features of surface EMG signal in order to extract more effective, reliable, robust signal characteristics. This will help improve the recognition rate of the surface EMG. Also this paper uses the surface EMG signal with objective data for six-scale decomposition of surface EMG with the method of wavelet transform and extract the maximum of multi-scale wavelet coefficients, constructing 14- dimensional feature vector, then we input into the BP neural network for emotion pattern recognition. II. RECOGNITION METHODS OF EMOTIONAL MOTION BP neural network is a multi-level error feedback network proposed by Rumelhart and Mc Clelladn in 1985. It uses the difference between the actual output and the desired output to correct the connection weight of the network and the threshold of each node in each layer from back to front. BP neural network contains an input layer, a middle layer (hidden layer) and an output layer. There is a full connectivity between the upper and lower layers and no connections between neurons in each layer. For the input signal, it needs to spread towards to hidden layer nodes and transformed by the function, then transmit the input signal of hidden layer nodes to the output layer nodes. Usually, the transfer function of BP neural network is Sigmoid Type differentiable function, which can achieve arbitrary non-linear mapping between the input and output, so BP network has been widely applied in pattern recognition, function approximation and other areas [7, 8] . The three nodes of the BP network is represented as: Input layer Hidden layer Output layer Figure 1. BP neural network structure
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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Ⅲ. AN IMPROVED DNA ALGORITHM FOR
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Ⅴ.CONCLUSION REMARKS DNA computer
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II. RELATED WORK A. Mobile Service
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special services. SOAP is used to b
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detection methods of DDoS attacks m
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Figure 1. An analysis of the partit
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distance of view point. Given that
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Ⅳ. EVALUATION OF BLENDED LEARNING
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A. Data Preparing To generate our t
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indirect causes, and the logical re
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B. Evaluation We have evaluated thi
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Combinational logic unit failures i
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TABLE I. DESCRIPTION OF PROPOSITION
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Figure 2. The process of the invers
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Figure 9. (a)the original image.(b)
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module, communication module, apper
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After the comprehensive performance
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system testing can be seen that the
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III. AUTONOMIC RESOURCE ALLOCATION
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Keyboard event Application User mod
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SQL Azure will eventually include a
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TABLE II. OPERATIONAL EMPLOYEE TABL
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A. Object-relational Type To audit
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to execution time. Also, DBA would
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Liping Chen .......................
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