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International Conference on Communication and Signal Processing

作者： S. Deivalakshmi P. Palanisamy Gayatri Vishwanathan the Electronics and Communication Engineering Department National Institute of Technology Tiruchirappalli TN 620015 India

ISBN: (纸本)9781467348652

Segmentation of the contents of document images into text and non-text regions is an essential pre-processing step for applications such as document analysis and classification, as well as OCR. This paper presents a novel technique to segment the document image into text and non-text regions using a combination of Wavelet-based Gray Level Co-Occurrence Matrix (GLCM) features and k-means clustering. A comparison between the performances of different wavelets in document image segmentation is also performed and tabulated. The technique was tested on a number of scanned article images from the MediaTeam Document Database and results show a marked improvement over the already existing method based on GLCM features.

关键词： Discrete Wavelet Transform Document Image Segmentation GLCM Features k-means clustering algorithm

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Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

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Physics Procedia 2012年 25卷 911-916页

作者： Lijun Yang Jimin Zhang College of Automotive Engineering Tongji University Shanghai China Railway &Urban Mass Transit Research Institute Tongji University Shanghai China

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks’ hidden layer computed with k-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

关键词： Railway vehicle Brake anti-slide control RBF neural network k-means clustering algorithm Multi-step prediction

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Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

Prediction Study on Anti-Slide Control of Railway Vehicle Ba...

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2012 International Conference on Solid State Devices and Materials Science(SSDMS 2012)

作者： Lijun Yang Jimin Zhang College of Automotive Engineering Tongji University Railway &Urban Mass Transit Research Institute Tongji University

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks' hidden layer computed with k-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

关键词： Railway vehicle Brake anti-slide control RBF neural network k-means clustering algorithm Multi-step prediction

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An extended k-means technique for clustering moving objects

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EGYPTIAN INFORMATICS JOURNAL 2011年第1期12卷 45-51页

作者： Ossama, Omnia Mokhtar, Hoda M. O. El-Sharkawi, Mohamed E. Cairo Univ Fac Comp & Informat Cairo Egypt

k-means algorithm is one of the basic clustering techniques that is used in many data mining applications. In this paper we present a novel pattern based clustering algorithm that extends the k-means algorithm for clustering moving object trajectory data. The proposed algorithm uses a key feature of moving object trajectories namely, its direction as a heuristic to determine the different number of clusters for the k-means algorithm. In addition, we use the silhouette coefficient as a measure for the quality of our proposed approach. Finally, we present experimental results on both real and synthetic data that show the performance and accuracy of our proposed technique. (C) 2011 Faculty of Computers and Information, Cairo University. Production and hosting by Elsevier B. V. All rights reserved.

关键词： clustering moving objects Moving objects databases Mining moving object trajectories k-means clustering algorithm

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Portable measurement system for the objective evaluation of the spasticity of hemiplegic patients based on the tonic stretch reflex threshold

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MEDICAL ENGINEERING & PHYSICS 2011年第1期33卷 62-69页

作者： kim, keo Sik Seo, Jeong Hwan Song, Chul Gyu Chonbuk Natl Univ Sch Elect & Informat Engn Ctr Adv Image & Informat Technol Jeonju 561756 Jeonbuk South Korea Chonbuk Natl Univ Dept Rehabil Med Sch Med Jeonju 561712 Jeonbuk South Korea

The clinical scales used for the evaluation of the spasticity have some drawbacks, in spite of their simplicity and ease of assessment, and their inter- and intra-rater reliability remains controversial. The aim of this study is to develop a portable system for the objective and reliable evaluation of the spasticity based on the k-means clustering of the tonic stretch reflex threshold (TSRT) and to compare the discrimination performance of the level of spasticity determined by our method with that by the conventional modified Ashworth scale (MAS). Fifteen hemiplegic patients (7 males and 8 females, age: 63.5 +/- 15.6) participated in the study. The average and standard deviation values of the TSRTs were 127.9 +/- 1.6, 121.8 +/- 1.5 and 117.9 +/- 1.3 in groups G1, G2 and G3, respectively, and there were significant differences between the TSRTs of each group (p<0.05). Also, our groups classified by the criteria of the TSRT had a strong negative correlation (r=-0.95, r(2) = 0.90, p<0.05) between the level of spasticity and TSRTs. These results demonstrated that our system could be clinically more useful for the quantitative and reliable discrimination of the spasticity than the conventional MAS. Crown Copyright (C) 2010 Published by Elsevier Ltd on behalf of IPEM. All rights reserved.

关键词： k-means clustering algorithm Portable system Spasticity Tonic stretch reflex threshold

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Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm

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kNOWLEDGE-BASED SYSTEMS 2011年第3期24卷 378-385页

作者： Shen, Wei Guo, Xiaopen Wu, Chao Wu, Desheng Univ Toronto RiskLab Toronto ON M5S 3G3 Canada N China Elect Power Univ Sch Business & Adm Beijing 102206 Peoples R China Univ Waterloo Waterloo ON N2L 3G1 Canada Reykjavik Univ Menntavegur 1 IS-101 Reykjavik Iceland

Stock index forecasting is a hot issue in the financial arena. As the movements of stock indices are non-linear and subject to many internal and external factors, they pose a great challenge to researchers who try to predict them. In this paper, we select a radial basis function neural network (RBFNN) to train data and forecast the stock indices of the Shanghai Stock Exchange. We introduce the artificial fish swarm algorithm (AFSA) to optimize RBF. To increase forecasting efficiency, a k-means clustering algorithm is optimized by AFSA in the learning process of RBF. To verify the usefulness of our algorithm, we compared the forecasting results of RBF optimized by AFSA, genetic algorithms (GA) and particle swarm optimization (PSO), as well as forecasting results of ARIMA. BP and support vector machine (SVM). Our experiment indicates that RBF optimized by AFSA is an easy-to-use algorithm with considerable accuracy. Of all the combinations we tried in this paper, BIAS6 + MA5 + ASY4 was the optimum group with the least errors. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Artificial fish swarm algorithm Radial basis function neural network k-means clustering algorithm Data mining Shanghai Stock Exchange Index

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Research on the Text clustering algorithm based on Latent Semantic Analysis and Optimization

Research on the Text Clustering Algorithm based on Latent Se...

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2011 IEEE International Conference on Computer Science and Automation Engineering(CSAE 2011)

作者： Wang Chun-hong Nan Li-Li Ren Yao-Peng Computer Science and Technology ,Yun cheng University, Yun Cheng,044000

The text clustering based on Vector Space Model has problems, such as high-dimensional and sparse, unable to solve synonym and polyseme etc. And meanwhile, k-means clustering algorithm has shortcomings, which depends on the initial clustering center and needs to fix the number of clusters in advance. Aiming at these problems, in this paper, a text clustering algorithm based on Latent Semantic Analysis and Optimization is proposed. This algorithm can not only overcome the problems of Vector Space Model, but also can avoid the shortcomings of k-means algorithm. And compared with the text clustering algorithm based on Latent Semantic Analysis and the text clustering algorithm based on Vector Space Model and optimization, our algorithm is proved which can preferably improve the effect of text clustering, and upgrade the precision ratio and recall ration of text.

关键词： text clustering Vector Space Model Latent Semantic Analysis clustering optimization k-means clustering algorithm

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Applying Hybrid kEPSO clustering to Web Pages 10

Applying Hybrid KEPSO Clustering to Web Pages

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48th ACM Annual Southeast Regional Conference (SE)

作者： Moh, Teng-Sheng Sabnis, Ameya San Jose State Univ Dept Comp Sci San Jose CA 95192 USA

ISBN: (纸本)9781450300643

Various optimization methods are used along with the standard clustering algorithms to make the clustering process simpler and quicker. In this paper we propose a new hybrid technique of clustering known as k-Evolutionary Particle Swarm Optimization (kEPSO) based on the concept of Particle Swarm Optimization (PSO). The proposed algorithm uses the k-means algorithm as the first step and the Evolutionary Particle Swarm Optimization (EPSO) algorithm as the second step to perform clustering. The experiments were performed using the clustering benchmark data. This method was compared with the standard k-means and EPSO algorithms. The results show that this method produced compact results and performed faster than other clustering algorithms. Later, the algorithm was used to cluster web pages. The web pages were clustered by first cleaning the unnecessary data and then labeling the obtained web pages to categorize them.

关键词： clustering algorithms k-means clustering algorithm Particle Swarm Optimization Data clustering Evolutionary Particle Swarm Optimization algorithm

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Internet Public Opinion Hotspot Detection Research Based on k-means algorithm

Internet Public Opinion Hotspot Detection Research Based on ...

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1st International Conference on Swarm Intelligence

作者： Liu, Hong Li, Xiaojun Zhejiang Gongshang Univ Coll Comp & Informat Engn Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9783642134975

Internet is becoming a spreading platform for the public opinion. It is important to grasp the internet public opinion (IPO) in time and understand the trends of their opinion correctly. Text mining plays a fundamental role in a number of information management and retrieval tasks. This paper studies internet public opinion hotspot detection using text mining approaches. First, we create an algorithm to obtain vector space model for all of text document. Second, this algorithm is combined with k-means clustering algorithm to develop unsupervised text mining approach. We use the proposed text mining approach to group the internet public opinion into various clusters, with the center of each representing a hotspot public opinion within the current time span. Through the result of the experiment, it shows that the efficiency and effectiveness of the algorithm using.

关键词： Internet public opinion k-means clustering algorithm vector space model text classification

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Optimal Locations of Remote Radio Units in CoMP Systems for Energy Efficiency

Optimal Locations of Remote Radio Units in CoMP Systems for ...

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IEEE 72nd Vehicular Technology Conference Fall 2010

作者： Zhang, Congqing Zhang, Tiankui Zeng, Zhimin Cuthbert, Laurie Xiao, Lin Beijing Univ Posts & Telecommun Sch Informat & Telecommun Engn Beijing 100088 Peoples R China Univ London Sch Elect Engn & Comp Sci London WC1E 7HU England

ISBN: (纸本)9781424435746

In coordinated multi-point transmission (CoMP) systems, the optimal remote radio unit (RRU) location is analyzed theoretically and a RRU location design scheme for energy efficiency in practical scenarios is given. An average minimum access distance criterion is given for RRU location optimization. By minimizing the average distance between users and RRU, the optimal RRU distribution can be obtained when users are located uniformly in the cell. Taking into account the fact that user distribution will not be completely uniform in a practical environment, the k-means clustering algorithm is used to get the optimized RRU deployment in a practical user distribution. Simulation results show that the uplink transmission power can be greatly reduced with the RRU optimized location design in both the uniform and non-uniform user distribution.

关键词： CoMP energy efficiency RRU location optimization site design k-means clustering algorithm

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