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2013 IEEE 4th International Conference on Software Engineering and service Science

作者： Lei Gu The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University School of Computer Science and Technology Nanjing University of Posts and Telecommunication

ISBN: (纸本)9781467349970

The Locality-weight fuzzy c-means clustering method has been presented *** this approach can improve the clustering accuracies,it often gains the unstable clustering results because some random samples are employed for the initial *** this paper,an initialization method based on the core clusters is used for the locality-weight fuzzy c-means *** core clusters can be formed by constructing the σ-neighborhood graph and their centers are regarded as the initial centers of the locality-weight fuzzy c-means *** investigate the effectiveness of our approach,several experiments are done on three *** results show that our proposed method can improve the clustering performance compared to the previous locality-weight fuzzy c-means clustering.

关键词： the initialization clustering methods core clusters locality-weight fuzzy c-means neighborhood graph

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Method on the fault detection and diagnosis for the railway turnout based on the current curve of switch machine

Method on the fault detection and diagnosis for the railway ...

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2nd International Conference on Mechanical Engineering, Industrial Electronics and Informatization, MEIEI 2013

作者： Mo, Xue Mei Fang, Yu Yang, Yun Guo The Key Laboratory of Embedded System and Service Computing Ministry of Education China Shanghai Railway Telecommunication Equipment Ltd Co. Shanghai China

ISBN: (纸本)9783037858905

This paper proposes a method of the fault detection and diagnosis for the railway turnout based on the current curve of switch machine. Exact curve matching fault detection method and SVM-based fault diagnosis method are adopted in the paper. Based on envelope and morpheme match algorithm, exact curve matching method is used to match the detected current curve with the reference curve so as to predict whether the curve would have fault or not. Moreover, the SVM-based fault diagnosis method is used to make sure that the fault conditions could be diagnosed intelligently. Finally, the experimental results show that the proposed method can accurately identify the turnout fault status in the conversion process, and the accuracy rate in the diagnosis of the fault location is above 98%, which verify the effectiveness of the method in the fault detection and diagnosis. © (2013) Trans Tech Publications, Switzerland.

关键词： Fault detection

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Cough signal recognition with gammatone cepstral coefficients

Cough signal recognition with gammatone cepstral coefficient...

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2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013

作者： Liu, Jia-Ming You, Mingyu Li, Guo-Zheng Wang, Zheng Xu, Xianghuai Qiu, Zhongmin Xie, Wenjia An, Chao Chen, Sili Key Laboratory of Embedded System and Service Computing Department of Control Science and Engineering Tongji University Shanghai 201804 China Department of Respiratory Medicine Shanghai 200065 China

ISBN: (纸本)9781479910434

Cough Recognition is a valuable classification problem in healthcare. Generally, feature representation contributes a lot to the overall classifying performance. In this paper, a novel feature extraction method, Gammatone Cepstral Coefficients (GTCC), is investigated for cough recognition. The accuracy of GTCC comparing with MFCC is evaluated on a designed cough dataset following a 10 fold cross-validation schemes. Considering the imbalance of that dataset, weighted SVM is applied as the base classifier. The results indicate that GTCC surpass MFCC in modeling cough signals. With combination of GTCC and MFCC, a better performance is achieved. This paper provides a better feature representation prototype in cough recognition. © 2013 IEEE.

关键词： Feature extraction

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An enhanced two-factor user authentication in wireless sensor networks

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Telecommunication systems 2013年第1期55卷 105-113页

作者： Yuan, Jian-Jun Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China

Since wireless sensor networks (WSN) are often deployed in an unattended environment and sensor nodes are equipped with limited computing power modules, user authentication is a critical issue when a user wants to access data from sensor nodes. Recently, M.L. Das proposed a two-factor user authentication scheme in WSN and claimed that his scheme is secure against different kinds of attack. Later, Khan and Alghathbar (K-A) pointed out that Das’ scheme has some security pitfalls and showed several improvements to overcome these weaknesses. However, we demonstrate that in the K-A-scheme, there is no provision of non-repudiation, it is susceptible to the attack due to a lost smart card, and mutual authentication between the user and the GW-node does not attained. Moreover, the GW-node cannot prove that the first message comes from the user. To overcome these security weaknesses of the K-A-scheme, we propose security patches and prove our scheme.

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Real-Time Communication Method for mHealth Base on Extended XMPP Protocol

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Communications and Network 2013年第3期5卷 590-595页

作者： Chenjie Shi Yu Fang The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Considering characteristic of mHealth communication and problems of existing methods, this paper presents a real-time communication method for mHealth based on extended XMPP protocol. The method can maintain the role status efficiently and reduce data latency during the communication process. Meanwhile, it can be extended flexibly to meet increasing communication demands of mHealth services. Furthermore, a system framework is presented to support telemonitoring scene. Finally, system implementation and feasibility tests verify the effectiveness of the method and framework.

关键词： mHealth XMPP Protocol Real-Time Communication

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Research on Fault Diagnosis of Tennessee Eastman Process Based on KPCA and SVM

Research on Fault Diagnosis of Tennessee Eastman Process Bas...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Ke Zhang Kun Qian Yi Chai Yi Li Jianhuan Liu State Key Laboratory of Power Transmission Equipment & System Security and New Technology College of Automation Chongqing University Chongqing P.R. China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education College of Automation Chongqing University Chongqing P.R. China

ISBN: (纸本)9781479970063

Based on principal component analysis (PCA) and support vector machine (SVM), a new method for the fault diagnosis of TE Process is proposed. The fault recognition based on kernel principal component analysis (KPCA) is analyzed and SVM is employed as a classifier for fault classification. To establish a more efficient SVM model, genetic algorithm (GA) is used to determine the optimal kernel parameter γ and penalty parameter C of SVM with the highest accuracy and generalization ability. The classification accuracy of this GA-SVM approach is tested by real data of TE Process and compared with some other related methods such as artificial neural network. The experimental results indicate that the classification accuracy of this GA-SVM is more superior than that of some artificial neural network.

关键词： Support vector machines Kernel Genetic algorithms Principal component analysis Testing Training data Accuracy

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Using more initial centers for the seeding-based semi-supervised K-harmonic means clustering

Using more initial centers for the seeding-based semi-superv...

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2012 International Conference on Emerging Technologies for Information systems, computing, and Management, ICM 2012

作者： Gu, Lei Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China School of Computer Science and Technology Nanjing University of Posts and Telecommunication Nanjing China

ISBN: (纸本)9781461470090

In the initialization of the traditional semi-supervised k-means, the mean of some labeled data belonging to one same class was regarded as one initial center and the number of the initial centers is equal to the number of clusters. However, this initialization method using a small amount of labeled data also called seeds which are not appropriate for the semi-supervised k-harmonic means clustering insensitive to the initial centers. In this paper, a novel semi-supervised k-harmonic means clustering is proposed. Some seeds with one same class are divided into several groups and the mean of all data is viewed as one initial center in every group. Therefore, the number of the initial centers is more than the number of clusters in the new method. To investigate the effectiveness of the approach, several experiments are done on three datasets. Experimental results show that the presented method can improve the clustering performance compared to other traditional semi-supervised clustering algorithms. © 2013 Springer Science+Business Media New York.

关键词： Harmonic analysis

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Symptom selection for multi-label data of inquiry diagnosis in traditional Chinese medicine

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Science China(Information Sciences) 2013年第5期56卷 238-250页

作者： SHAO Huan LI GuoZheng LIU GuoPing WANG YiQin School of Computer Engineering and Science Shanghai University Department of Control Science and Engineering Key Laboratory of Ministry of Education for Service Computing and Embedded SystemsTongji University Laboratory of Information Access and Synthesis of TCM Four Diagnosis Shanghai University of Traditional Chinese Medicine

In traditional Chinese medicine(TCM) diagnosis,a patient may be associated with more than one syndrome tags,and its computer-aided diagnosis is a typical application in the domain of multi-label learning of high-dimensional *** is common that a great deal of symptoms can occur in traditional Chinese medical diagnosis,which affects the modeling of diagnostic *** selection entails choosing the smallest feature subset of relevant symptoms,and maximizing the generalization performance of the *** present there are rare researches on feature selection on multi-label data.A hybrid optimization technique is introduced to symptom selection for multi-label data in TCM diagnosis in this paper,and modeling is made by means of four multi-label learning algorithms like k nearest neighbors,*** compare the performance of the algorithm with the current popular dimension reduction algorithms like MEFS(embedded feature selection for multi-Label learning),MDDM(multi-label dimensionality reduction via dependence maximization) on the UCI Yeast gene functional data set and an inquiry diagnosis dataset of coronary heart disease(CHD).Experimental results show that the algorithm we present has significantly improved the *** particular,the improvement on the average precision for the classifier is up to 10.62% and 14.54%.Syndrome inquiry modeling of CHD in TCM is realized in this paper,providing effective reference for the diagnosis of CHD and analysis of other multi-label data.

关键词： multi-label learning feature selection high-dimensionality inquiry of traditional Chinese medicine coronary heart disease

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Use the core clusters for the initialization of the clustering based on one-class support vector machine

Use the core clusters for the initialization of the clusteri...

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4th International Conference on Advances in Swarm Intelligence, ICSI 2013

作者： Gu, Lei Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 200092 China School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing 210023 China

ISBN: (纸本)9783642387142

The clustering method based on one-class support vector machine has been presented recently. Although this approach can improve the clustering accuracies, it often gains the unstable clustering results because some random datasets are employed for its initialization. In this paper, a novel initialization method based on the core clusters is used for the clustering algorithm based one-class support vector machine. The core clusters are gained by constructing the neighborhood graph and they are regarded as the initial datasets of the clustering algorithm based one-class support vector machine. To investigate the effectiveness of the proposed approach, several experiments are done on four datasets. Experimental results show that the new presented method can improve the clustering performance compared to the previous clustering algorithm based on one-class support vector machine and k-means approach. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Support vector machines

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An evolutionary semi-supervised subtractive clustering method by seeding

An evolutionary semi-supervised subtractive clustering metho...

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2012 International Conference on Information Technology and Software Engineering, ITSE 2012

ISBN: (纸本)9783642345210

A semi-supervised subtractive clustering has been proposed recently. However, it performance depends greatly on the choice of the parameters of the mountain function and only proper parameters enable the clustering method to produce a better effect. In this paper, an evolutionary semi-supervised subtractive clustering method by seeding is presented. The novel approach uses the genetic algorithms for tuning the relative parameters of the subtractive clustering by minimizing the Davies Bouldin clustering validity index regarded as the fitness function. To investigate the effectiveness of our approach, several experiments are done three real datasets. Experimental results show that our proposed method can improve the clustering performance significantly compared to other two traditional semi-supervised clustering algorithms. © 2013 Springer-Verlag.

关键词： Genetic algorithms

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