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15th World Multi-conference on Systematics, cybernetics and Informatics (WMScI 2011)

作者： Park, Yong Kuk Lee, Min Goo Jung, Kyung Kwon Yoo, June Jae Kim, Hyeong-Seok Korea Technol Inst U Embedded Convergence Res Ctr Seongnam Si 463816 South Korea Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea

In this paper, a fuzzy c-means clustering algorithm is proposed to determine the optimum deployment of sensor nodes. It is for a given application space to improve energy efficiency and reduce cost. We performed simul... 详细信息

ISBN: (纸本)9781936338290

关键词： Sensor Nodes Deployment fuzzy c-means algorithm

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fuzzy clustering optimized with genetic algorithms: Application for hybrid speech recognition system 4

Fuzzy clustering optimized with genetic algorithms: Applicat...

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4th International conference on control, Decision and Information Technologies (coDIT)

作者： Lazli, Lilia Boukadoum, Mounir Mohamed, Otmane Ait Univ Quebec ETS Dept Elect Engn Montreal PQ Canada Univ Quebec UQAM Dept Comp Sci Montreal PQ Canada Concordia Univ Dept Elect Engn & Comp Sci Montreal PQ Canada

ISBN: (纸本)9781509064656

In this paper, we report experimental results of hybrid system using Hidden Markov Models/Multi-Layer Perceptron (HMM/MLP) model as acoustic model and based on the fuzzy c-means (FcM) clustering with optimization with Genetic algorithm (GA). In this context, we use the result of FcM clustering as initial population of GA, this allows training the GA with a population of empirically generated chromosomes and not randomly initialized. Our results on speech recognition tasks show an increase in the estimates of the posterior probabilities of the correct words after training. We demonstrate the effectiveness of the proposed clustering approach in large-vocabulary speaker-independent continuous speech recognition with regard to the three baseline systems : Discrete HMM, hybrid HMM/MLP with K-means and FcM clustering.

关键词： Speech clustering fuzzy c-means algorithm Genetic algorithms Hybrid HMM/MLP recognition system

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Output convergence and international trade: Time-series and fuzzy clustering evidence for New Zealand and her trading partners, 1950-1992

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Journal of International Trade and Economic Development 2005年第1期14卷 93-114页

作者： Giles, David E.A. Department of Economics University of Victoria Victoria BC V8W 2Y2 PO Box 1700 Canada

Using historical time-series data, we test for convergence and common trends in real per capita output for New Zealand and her four major trading partners. Both bivariate and multivariate time-series methods are used, and we also implement the fuzzy c-means clustering algorithm as a new method for detecting convergence. The results of our time-series analysis accord with earlier studies - we find limited evidence of (only bivariate) convergence, but ample evidence of a small number of common trends. In contrast, our fuzzy clustering analysis reveals very strong evidence of a particular form of output convergence when the five trading countries are considered as a group. © 2005 Taylor & Francis Group Ltd.

关键词： cointegration common trends convergence Economic growth fuzzy c-means algorithm fuzzy sets International trade Pattern recognition

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clustering Functional MRI Patterns with fuzzy and competitive algorithms 6th

Clustering Functional MRI Patterns with Fuzzy and Competitiv...

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6th International Symposium on computational Modeling of Objects Represented in Images - Fundamentals, Methods and Applications (compIMAGE)

作者： Vergani, Alberto Arturo Martinelli, Samuele Binaghi, Elisabetta Univ Insubria Varese Italy

ISBN: (纸本)9783030208059;9783030208042

We used model-free methods to explore the brain's functional properties adopting a partitioning procedure based on cross-clustering. We selected fuzzy c-means (FcM) and Neural Gas (NG) algorithms to find spatial patterns with temporal features and temporal patterns with spatial features. We applied these algorithms to a shared fMRI repository of face recognition tasks. We matched the classes found and our results of functional connectivity analysis with partitioning of BOLD signal signatures. We compared the outcomes using the just acquired model-based knowledge as likely ground truth, confirming the role of Fusiform Brain Regions. In general, partitioning results show a better spatial clustering than temporal clustering for both algorithms. In the case of temporal clustering, FcM outperforms Neural Gas. The relevance of brain sub-regions related to face recognition were correctly distinguished by the algorithms and the results are in agreement with the current neuroscientific literature.

关键词： fMRI Partitive clustering fuzzy c-means algorithm Neural Gas algorithm

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Remote sensing image classification based on dot density function weighted FcM clustering algorithm

Remote sensing image classification based on dot density fun...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Liu, Xiaofang Li, Xiaowen Zhang, Ying Yang, cunjian Xu, Wenbo Li, Min Luo, Huanmin Univ Elect Sci & Technol China Inst Geospatial Informat Sci & Technol Chengdu 610054 Peoples R China

ISBN: (纸本)9781424412112

Based on the uncertainty and fuzziness of remote sensing images, a dot density function weighted fuzzy c-means (WFcM) clustering algorithm is proposed to carry out the fuzzy classification or the hard classification of remote sensing images. First, the algorithm considering data spatial distributing information and classification fuzziness is described. fuzzy c-means algorithm is an unsupervised fuzzy classification method. clustering precision of the algorithm is affected by its equal partition trend for data sets, which leads that the optimal solution of the algorithm may not be the correct partition in the data set of which cluster sample numbers are difference greatly. In order to overcome this drawback, a dot density function WFcM algorithm is proposed in this paper. The method has not only overcome the limitation of FcM to certain extent, but also been favorable convergence. Then the WFcM algorithm would be compared with the K-means algorithms by experiments in LANDSAT TM image. Finally classification result of the algorithms is analyzed systematically, and the experiment result shows the WFcM algorithm can improve classification accuracy for remote sensing images.

关键词： remote sensing image classification fuzzy c-means algorithm weighted fuzzy c-means algorithm dot density functiont

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On fuzzy clustering for Heavy-Tailed Data 5

On Fuzzy Clustering for Heavy-Tailed Data

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5th Iranian Joint congress on fuzzy and Intelligent Systems (cFIS)

作者： Taheri, S. Mahmoud Mohammadpour, A. Atiyah, Israa Univ Tehran Coll Engn Fac Engn Sci Tehran Iran Amirkabir Univ Technol Fac Math & Comp Sci Tehran Iran

ISBN: (纸本)9781509040087

The fuzzy c-means method is investigated to cluster the heavy tailed data by using some measures of distance. A comparison study is provided based on time and precision. The results show that when using the Euclidean distance, the time required is less than if we used Manhattan distance, but the precision is higher when using the Manhattan distance.

关键词： cluster analysis heavy-tailed distribution fuzzy c-means algorithm

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Dental Fluorosis Segmentation Using Enhanced Quantum-Inspired fuzzy clustering algorithm 37

Dental Fluorosis Segmentation Using Enhanced Quantum-Inspire...

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37th International Technical conference on circuits/Systems, computers and communications (ITc-cScc)

作者： Petaitiemthong, Natchapon Auephanwiriyakul, Sansanee Theera-Umpon, Nipon Kongpun, chatpat Chiang Mai Univ Grad Sch Fac Engn Comp Engn Dept Chiang Mai Thailand Chiang Mai Univ Biomed Engn Inst Fac Engn Comp Engn Dept Chiang Mai Thailand Chiang Mai Univ Biomed Engn Inst Fac Engn Elect Engn Dept Chiang Mai Thailand Minist Publ Hlth Intercountry Ctr Oral Hlth Dept Hlth Bangkok Thailand

ISBN: (数字)9781665485593

ISBN: (纸本)9781665485593

Dental fluorosis is occurred when there is a highly exposure to high concentration of fluoride in the teeth development stage. This phenomenon occurs in many parts of the world. To help the health policy makers developing the prevention and treatment plans, a manual or automatic imagebased dental fluorosis classification system is needed. However, to develop a good classification system, a well-segmented dental image is also needed. In this paper, we developed a system that can segment dental digital images using the enhanced quantum-inspired fuzzy c-means clustering (EQIE-FcM). The segmentation result shows that the system can yield a reasonable segmentation.

关键词： Dental fluorosis fuzzy c-means algorithm Quantum-inspired clustering Multi-prototypes

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WATER BODIES IDENTIFIcATION FROM MULTISPEcTRAL IMAGES USING GABOR FILTER, FcM AND cANNY EDGE DETEcTION METHODS

WATER BODIES IDENTIFICATION FROM MULTISPECTRAL IMAGES USING ...

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International conference on Information communication and Embedded Systems (IcIcES)

作者： Vignesh, T. Thyagharajan, K. K. SA Engn Coll Dept Comp Sci & Engn Madras Tamil Nadu India RMD Engn Coll Madras Tamil Nadu India

ISBN: (纸本)9781509061358

Water bodies identification using multispectral images is a very useful application of image processing. This paper proposed a novel method for water bodies identification from multispectral images using Gabor filter, fuzzy c-means and canny edge detection algorithm. Gabor filter is a combination of lowpass filter and bandpass filter. This two filters extracting the importance features from satellite images. From the extracted features fuzzy c-means algorithm clustered the various land use and land cover classes. Finally water bodies are identified from land use and land cover classes with the use of canny edge detection methods. The proposed approach was experimented with the use of Landsat7, Landsat-8 satellite images. Our experimental results proved that proposed methods provides better result for water identification with high efficiency.

关键词： Gabor filter fuzzy c-means algorithm canny edge detection land use and land cover classification feature extraction

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Research on Data Processing for condition Monitoring of Wind Turbine Based on Hadoop Platform 14

Research on Data Processing for Condition Monitoring of Wind...

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IEEE International conference on Mechatronics and Automation (IcMA)

作者： Wang, Hongjun Zhao, Shaowei Zhao, Hui Yue, Youjun Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Complic Binshui Xidao 391 Tianjin Peoples R China Tianjin Agr Univ Jinjing Rd 22 Tianjin Peoples R China

ISBN: (纸本)9781509067596

The status monitoring data of wind turbines have large, multi-source, heterogeneous, complex and rapid growth of large data characteristics. The existing data processing methods are difficult to guarantee efficiency when handling massive amounts of data, and may miss the best time to troubleshoot. How to deal with the monitoring data more efficiently is of great significance to the accurate judgment of the fault. This paper proposes the use of cloud platform to deal with massive data to improve efficiency. Firstly, the state monitoring model of wind turbine is put forward. Then, the fuzzy c means clustering algorithm is introduced, and the algorithm process is realized by MapReduce model. Finally, the experiment is carried out with Hadoop platform, using distributed database HBase to store data, and using distributed programming framework MapReduce to calculate data. It is found that with the increase of the data volume and the number of nodes, the cloud platform is able to store and calculate data at a faster speed.

关键词： Hadoop fuzzy c-means algorithm Fusion technology Wind power

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AUTOMATIc SYSTEM FOR BRAIN MRI ANALYSIS USING A NOVEL cOMBINATION OF fuzzy RULE-BASED AND AUTOMATIc cLUSTERING-TEcHNIQUES

AUTOMATIC SYSTEM FOR BRAIN MRI ANALYSIS USING A NOVEL COMBIN...

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conference on Image Processing - Medical Imaging 1995

作者： HILLMAN, GR cHANG, cW YING, H KENT, TA YEN, J UNIV TEXAS MED BRANCH DEPT PHARMACOL GALVESTON TX 77550 USA

ISBN: (纸本)0819417823

Analysis of magnetic resonance images (MRI) of the brain permits the identification and measurement of brain compartments. These compartments include normal subdivisions of brain tissue, such as gray matter, white matter and specific structures, and also include pathologic lesions associated with stroke or viral infection. A fuzzy system has been developed to analyze images of animal and human brain, segmenting the images into physiologically meaningful regions for display and measurement. This image segmentation system consists of two stages which include a fuzzy rule-based system and fuzzy c-means algorithm (FcM). The first stage of this system is a fuzzy rule-based system which classifies most pixels in MR images into several known classes and one `unclassified' group, which fails to fit the predetermined rules. In the second stage, this system uses the result of the first stage as initial estimates for the properties of the compartments and applies FcM to classify all the previously unclassified pixels. The initial prototypes are estimated by using the averages of the previously classified pixels. The combined processes constitute a fast, accurate and robust image segmentation system. This method can be applied to many clinical image segmentation problems. While the rule-based portion of the system allows specialized knowledge about the images to be incorporated, the FcM allows the resolution of ambiguities that result from noise and artifacts in the image data. The volumes and locations of the compartments can easily be measured and reported quantitatively once they are identified. It is easy to adapt this approach to new imaging problems, by introducing a new set of fuzzy rules and adjusting the number of expected compartments. However, for the purpose of building a practical fully automatic system, a rule learning mechanism may be necessary to improve the efficiency of modification of the fuzzy rules.

关键词： IMAGE SEGMENTATION fuzzy c-means algorithm fuzzy RULE-BASED SYSTEM MAGNETIc RESONANcE IMAGES

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