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conference on Data Analysis and Modeling for Process control

作者： Hong, SJ May, GS Yamartino, J Skumanich, A Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)0819452912

Modular neural networks (MNNs) are investigated as a tool for modeling process behavior and fault detection and classification (FDc) using tool data in plasma etching. Principal component analysis (PcA) is initially employed to reduce the dimensionality of the voluminous multivariate tool data and to establish relationships between the acquired data and the process state. MNNs are subsequently used to identify anomalous process behavior. A gradient-based fuzzy c-means clustering algorithm is implemented to enhance MNN performance. MNNs for eleven individual steps of etch runs are trained with data acquired from baseline, control (acceptable), and perturbed (unacceptable) runs, and then tested with data not used for training. In the fault identification phase, a 0% of false alarm rate for the control runs is achieved.

关键词： advanced process control fault detection and classification principal component analysis modular neural networks fuzzy c-means algorithm

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Application of immunological memory to the color classification of tiles

Application of immunological memory to the color classificat...

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congress on Evolutionary computation (cEc)

作者： Liu, D Zhu, XF S China Univ Technol Coll Automat Sci & Engn Guangzhou 510640 Peoples R China

ISBN: (纸本)0780378040

Based on mechanism that the vertebrate immune system remembers the antigen it has met before by retaining in the body some memory cells, an algorithm is proposed to search for the representative of the data set by generating its memory cells. The algorithm is first tested on a two-dimensional data set with three cluster centers to see if the memory cells built could really be representative. Then it is applied to a real world application, where the fuzzy c-means algorithm (FcM) is adopted to classify the tiles into different clusters according to the color similarity. The feature vectors extracted from the tile images act as the antigen and the memory cells generated are regarded as the initial cluster centers. Its performance was compared with that obtained with randomly initialized centers. By this algorithm, the number of clusters does not require to be pre-defined. The convergence speed and clustering accuracy of FcM are also improved.

关键词： pattern clustering image classification genetic algorithms image colour analysis immunological memory cells tiles color classification vertebrate immune system antigen fuzzy c-means algorithm cluster centers

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fuzzy block truncation coding

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OPTIcAL ENGINEERING 2002年第12期41卷 3161-3167页

作者： Boudraa, AO Beghdadi, A Dehalk, SMR Iordache, R Ecole Navale IRENav F-29200 Brest France Univ Paris 13 F-93430 Villetaneuse France ENST Dept Image F-75634 Paris France Tampere Univ Technol Inst Signal Proc FIN-33101 Tampere Finland

Block truncation coding (BTc) is a well known lossy compression scheme. Due to its low complexity and easy implementation, BTc has gained wide interest in its further development and application for image compression. Based on simple thresholding, BTc retains sharp edges and thus leads to artifacts such as the staircase effect. The second problem encountered in BTc is the splitting of homogeneous regions, which produces false contours. In this work a fuzzy approach of BTc to avoid truncating homogeneous blocks and to preserve smooth edges in two-cluster blocks is proposed. Each image block, viewed as a fuzzy set, is segmented into two clusters using a fuzzy clustering algorithm. The block is then encoded by modified fuzzy weighted means of the two clusters. Initialization strategies of the fuzzy clustering algorithm and a contextual quantization method are proposed. Experimental results show an improvement of visual quality of reconstructed images and peak signal-to-noise ratio when compared to BTc, economical BTc (EBTc), absolute moment BTc (AMBTc), and a minimum mean square error quantizer (MMSEQ). computation time required by AMBTc, EBTc, and fuzzy BTc methods are reported. (c) 2002 Society of Photo-Optical Instrumentation Engineers.

关键词： image processing image coding block truncation coding fuzzy clustering fuzzy c-means algorithm contextual quantization

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Segmentation techniques for tissue differentiation in MRI of Ophthalmology using fuzzy clustering algorithms

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MAGNETIc RESONANcE IMAGING 2002年第2期20卷 173-179页

作者： Yang, MS Hu, YJ Lin, KcR Lin, ccL Chung Yuan Christian Univ Dept Math Chungli 32023 Taiwan Nanya Inst Technol Dept Management Informat Syst Chungli Taiwan Natl Yang Ming Univ Med Sch Ophthalmol Taipei 112 Taiwan

This paper presents MRI segmentation techniques to differentiate abnormal and normal tissues in Ophthalmology using fuzzy clustering algorithms. Applying the best-known fuzzy c-means (FcM) clustering algorithm, a newly proposed algorithm, called an alternative fuzzy c-mean (AFcM), was used for MRI segmentation in Ophthalmology. These unsupervised segmentation algorithms can help Ophthalmologists to reduce the medical imaging noise effects originating from low resolution sensors and/or the structures that move during the data acquisition. They may be particularly helpful in the clinical oncological field as an aid to the diagnosis of Retinoblastoma, an inborn oncological disease in which symptoms usually show in early childhood. For the purpose of early treatment with radiotherapy and surgery, the newly proposed AFcM is preferred to provide more information for medical images used by Ophthalmologists. comparisons between FcM and AFcM segmentations are made. Both fuzzy clustering segmentation techniques provide useful information and good results. However, the AFcM method has better detection of abnormal tissues than FcM according to a window selection. Overall, the newly proposed AFcM segmentation technique is recommended in MRI segmentation. (c) 2002 Elsevier Science Inc. All rights reserved.

关键词： image segmentation magnetic resonance image (MRI) fuzzy c-means algorithm alternative fuzzy c-means algorithm

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A feature weighted FcM clustering algorithm based on evolutionary strategy

A feature weighted FCM clustering algorithm based on evoluti...

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4th World congress on Intelligent control and Automation

作者： Li, J Gao, XB Ji, HB Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)0780372689

fuzzy c-means (FcM) algorithm is one of effective methods for fuzzy cluster analysis, which has widely used in unsupervised pattern classification. To consider the different contribution of each dimensional feature of the given samples to be classified, this paper presents a novel FcM clustering algorithm based on feature weighted. With clustering validity function as criterion, the proposed algorithm optimizes the weight matrix using evolutionary strategy and obtains better result than the traditional one, which enriches the theory of FcM-type algorithms. The test experiment with real data of IRIS demonstrates the effectiveness of the novel algorithm.

关键词： cluster analysis fuzzy c-means algorithm evolutionary strategy

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Linguistic Valued Association Rules

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Systems Science and Systems Engineering 2002年第4期11卷 409-413页

作者： LU Jian-jiang, QIAN Zuo-pingInstitute of communications Engineering, PLA University of Science & Technology, Nanjing 210016, china Institute of Communications Engineering PLA University of Science & Technology Nanjing 210016 China

Association rules discovering and prediction with data mining method are two topics in the field of information processing. In this paper, the records in database are divided into many linguistic values expressed with normal fuzzy numbers by fuzzy c-means algorithm, and a series of linguistic valued association rules are generated. Then the records in database are mapped onto the linguistic values according to largest subject principle, and the support and confidence definitions of linguistic valued association rules are also provided. The discovering and prediction methods of the linguistic valued association rules are discussed through a weather example last.

关键词： data mining fuzzy c-means algorithm linguistic valued association rules

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fuzzy clustering based on K-nearest-neighbours rule

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fuzzy SETS AND SYSTEMS 2001年第2期120卷 239-247页

作者： Zahid, N Abouelala, O Limouri, M Essaid, A Univ Mohammed V AGDAL Fac Sci Lab Concept & Syst Rabat Morocco

In cluster analysis, several algorithms have been made for partitioning a set of objects into c natural clusters. in general, this problem is formulated as being an objective function optimization approach. However, it is known that the function being minimized is nonconvex and hence it may lead to convergence to many local minima, i.e., to different partitions. Thus, the clustering is repeated with different initializations hoping that some runs will lead to the global minimum. Therefore, the performance of these algorithms depends largely on good choice of these initializations. The most widely used algorithm using this function is called fuzzy c-means algorithm (FcMA). Zn this paper a new algorithm is proposed to carry out fuzzy clustering without a priori assumptions on initial guesses. This algorithm is based on two-layer clustering strategy. During the first layer, the K-nearest-neighbours decision rule is used. Then, to achieve an optimal partition, the second layer involves one iteration of FcMA. The performance of the proposed algorithm and that of FcMA have been tested on six data sets. The results obtained show that the new algorithm possesses a number of advantages over the FcMA. (c) 2001 Elsevier Science B.V. All rights reserved.

关键词： cluster analysis fuzzy c-means algorithm K-nearest-neighbours decision rule fuzzy sets pattern recognition

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Genetic fuzzy entropy-constrained vector quantization

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JOURNAL OF THE cHINESE INSTITUTE OF ENGINEERS 2001年第3期24卷 369-377页

作者： Hwang, WJ chine, cF Hong, SL Chung Yuan Christian Univ Dept Elect Engn Chungli 320 Taiwan

A novel variable-rare vector quantizer (VQ) design algorithm using both generic and fuzzy clustering techniques is presented. The algorithm, termed genetic fuzzy entropy-constrained VQ (GFEcVQ) design algorithm, has a superior rate-distortion performance than that of the existing variable-rate VQ design algorithms. The algorithm utilizes fuzzy clustering technique to enhance the rate-distortion performance for the VQ design. In addition, a novel genetic algorithm is employed to ensure the robustness of the performance against the selection of initial parameters. Simulation results demonstrate that the FEcVQ can be an effective alternative for the design of variable-rate VQs.

关键词： fuzzy clustering vector quantization image compression fuzzy c-means algorithm

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Adaptive digital watermarking using fuzzy clustering technique

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IEIcE TRANSAcTIONS ON FUNDAMENTALS OF ELEcTRONIcS cOMMUNIcATIONS AND cOMPUTER ScIENcES 2001年第8期E84A卷 2052-2060页

作者： Lou, Dc Yin, TL Natl Def Univ Chung Cheng Inst Technol Dept Elect Engn Tao Yuan 33509 Taiwan

In this paper a novel adaptive digital watermarking approach based upon human visual system model and fuzzy clustering technique is proposed. The human visual system Model is utilized to guarantee that the watermarked image is imperceptible. The fuzzy clustering approach has been employed to obtain the different strength of watermark by the local characters of image. In our experiments, this scheme allows us to provide a more robust and transparent watermark.

关键词： watermarking human visual system spread-spectrum communications DcT fuzzy c-means algorithm

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Methods for robust clustering of epileptic EEG spikes

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IEEE TRANSAcTIONS ON BIOMEDIcAL ENGINEERING 2000年第7期47卷 857-868页

作者： Wahlberg, P Lantz, G Univ Lund Dept Elect Engn & Comp Sci Signal Proc Grp S-22100 Lund Sweden Univ Hosp HUG Dept Neurol Human Brain Mapping Lab CH-1211 Geneva 14 Switzerland

We investigate algorithms for clustering of epileptic electroencephalogram (EEG) spikes. Such a method is useful prior to averaging and inverse computations since the spikes of a patient often belong to a few distinct classes. Data sets often contain outliers, which makes algorithms with robust performance desirable. We compare the fuzzy c-means (FcM) algorithm and a graph-theoretic algorithm. We give criteria for determination of the correct level of outlier contamination. The performance is then studied by aid of simulations, which show good results for a range of circumstances, for both algorithms. The graph-theoretic method gave better results than FcM for simulated signals. Also, when evaluating the methods on seven real-life data sets, the graph-theoretic method was the better method, in terms of closeness to the manual assessment by a neurophysiologist. However, there was some discrepancy between manual and automatic clustering and we suggest as an alternative method a human choice among a limited set of automatically obtained clusterings. Furthermore, we evaluate geometrically weighted feature extraction and conclude that it is useful as a supplementary dimension for clustering.

关键词： clustering electrode geometry epileptic EEG spikes fuzzy c-means algorithm graph theory k-components localization of foci outlier contamination robustness

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