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classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method

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cOGNITIVE NEURODYNAMIcS 2013年第6期7卷 477-494页

作者： Zhang, Jian-Hua Peng, Xiao-Di Liu, Hua Raisch, Joerg Wang, Ru-Bin E China Univ Sci & Technol Dept Automat Shanghai 200237 Peoples R China E China Univ Sci & Technol Inst Cognit Neurodynam Shanghai 200237 Peoples R China Tech Univ Berlin Control Syst Grp D-10587 Berlin Germany Max Planck Inst Dynam Complex Tech Syst Syst & Control Theory Grp D-39106 Magdeburg Germany

The human operator's ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human-automation interaction. The fuzzy c-mean (FcM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FcM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety-critical human-machine cooperative systems.

关键词： Operator functional state fuzzy c-means algorithm Psychophysiological measures Feature extraction Pattern classification

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An new initialization method for fuzzy c-means algorithm

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fuzzy OPTIMIZATION AND DEcISION MAKING 2008年第4期7卷 409-416页

作者： Zou, Kaiqi Wang, Zhiping Hu, Ming Dalian Univ Technol Coll Informat Engn Univ Key Lab Informat Sci & Ingn Dalian 116622 Peoples R China

In this paper an initialization method for fuzzy c-means (FcM) algorithm is proposed in order to solve the two problems of clustering performance affected by initial cluster centers and lower computation speed for FcM. Grid and density are needed to extract approximate clustering center from sample space. Then, an initialization method for fuzzy c-means algorithm is proposed by using amount of approximate clustering centers to initialize classification number, and using approximate clustering centers to initialize initial clustering centers. Experiment shows that this method can improve clustering result and shorten clustering time validly.

关键词： fuzzy c-means algorithm Grid Density

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Applications of Partition based clustering algorithms: A Survey

Applications of Partition based Clustering Algorithms: A Sur...

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IEEE International conference on computational Intelligence and computing Research (IccIc)

作者： Dharmarajan, A. Velmurugan, T. Bharathiar Univ Ctr Res & Dev Coimbatore 046 Tamil Nadu India DG Vaishnav Coll Res Dept Comp Sci Madras 106 Tamil Nadu India

ISBN: (纸本)9781479915941;9781479915958

Data mining is one of the interesting research areas in database technology. In data mining, a cluster is a set of data objects that are similar to one another with in a cluster and are different to the entities in the former clusters. clustering is the efficient method in data mining in order to process huge data sets. The core methodology of clustering is used in many domains like academic result analysis of institutions. Also, the methods are very well suited in machine learning, clustering in medical dataset, pattern recognition, image mining, information retrieval and bioinformatics. The clustering algorithms are categorized based upon different research phenomenon. Varieties of algorithms have recently occurred and were effectively applied to real-life data mining problems. This survey mainly focuses on partition based clustering algorithms namely k-means, k-Medoids and fuzzy c-means In particular, they applied mostly in medical data sets. The importance of the survey is to explore the various applications in different domains.

关键词： clustering algorithm fuzzy c-means algorithm k-means algorithm k-Medoids algorithm

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A Novel fuzzy clustering Method with No Outliers Influence

A Novel Fuzzy Clustering Method with No Outliers Influence

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2nd International conference on Mechatronics and Applied Mechanics (IcMAM2012)2nd International conference on Mechatronics and Applied Mechanics (IcMAM2012)

作者： Kao, Li-Jen Huang, Yo-Ping Hwa Hsia Inst Technol Dept Comp Sci & Informat Engn New Taipei City 23568 Taiwan Natl Taipei Univ Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9783037856512

fuzzy c-means (FcM) clustering algorithm can be used to classify hand gesture images in human-robot interaction application. However, FcM algorithm does not work well on those images in which noises exist. The noises or outliers make all the cluster centers towards to the center of all points. In this paper, a new FcM algorithm is proposed to detect the outliers and then make the outliers have no influence on centers calculation. The experiment shows that the new FcM algorithm can get more accurate centers than the traditional FcM algorithm.

关键词： fuzzy c-means algorithm noise detection outlier detection

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A Thorough Analysis of the Suppressed fuzzy c-means algorithm

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13th Iberoamerican congress on Progress in Pattern Recognition, Image Analysis and Applications

作者： Szilagyi, Laszlo Szilagyi, Sandor M. Benyo, Zoltan Sapientia Hungarian Sci Univ Transylvania Fac Tech & Human Sci Targu Mures Romania

ISBN: (纸本)9783540859192

Suppressed fuzzy c-means (s-FcM) clustering was introduced in [Fan, J. L., Zhen, W. Z., Xie, W. X.: Suppressed fuzzy c-means clustering algorithm. Patt. Recogn. Lett. 24, 1607-1612 (2003)] with the intention of combining the higher speed of hard c-means (HcM) clustering with the better classification properties of fuzzy c-means (FcM) algorithm. They added an extra computation step in to the FcM iteration, which created a competition among clusters: lower degrees of memberships were diminished according to a previously set suppression rate, while the largest fuzzy membeship grew by swallowing all the suppressed parts of the small ones. Suppressing the FcM algorithm was found successful in the terms of accuracy and working time, but the authors failed to answer a series of important questions. In this paper we attempt to clarify the view upon the optimality and the competitive behavior of s-FcM via analytical computations and numerical analysis.

关键词： fuzzy c-means algorithm suppressed fuzzy c-means algorithm competitive clustering alternating optimization

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An Efficient Genetic algorithm with fuzzy c-means clustering for Traveling Salesman Problem

An Efficient Genetic Algorithm with Fuzzy c-Means Clustering...

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IEEE congress on Evolutionary computation (cEc)

作者： Yoon, Jong-Won cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

ISBN: (纸本)9781424478354

Genetic algorithms (GA) are one of effective approaches to solve the traveling salesman problem (TSP). When applying GA to the TSP, it is necessary to use a large number of individuals in order to increase the chance of finding optimal solutions. However, this incurs high evaluation costs which make it difficult to obtain fitness values of all the individuals. To overcome this limitation we propose an efficient genetic algorithm based on fuzzy clustering which reduces evaluation costs with minimizing loss of performance. It works by evaluating only one representative individual for each cluster of a given population, and estimating the fitness values of the others from the representatives indirectly. A fuzzy c-means algorithm is used for grouping the individuals and the fitness of each individual is estimated according to membership values. The experiments were conducted with randomly generated cities, and the performance of the method was evaluated by comparing to other GAs. The results showed the usefulness of the proposed method on the TSP.

关键词： genetic algorithm fitness evaluation fuzzy clustering fuzzy c-means algorithm

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fuzzy clustering of mechanisms

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SADHANA-AcADEMY PROcEEDINGS IN ENGINEERING ScIENcES 2012年第5期37卷 539-556页

作者： Ghosh, Amitabha Pratihar, Dilip Kumar Amarnath, M. V. V. Dittrich, Guenter Mueller, Jorg Indian Inst Technol Dept Mech Engn Kharagpur 721302 W Bengal India Bengal Engn & Sci Univ Howrah 711103 India Rhein Westfal TH Aachen Inst Getriebetech & Maschinendynam D-52062 Aachen Germany

During the course of development of Mechanical Engineering, a large number of mechanisms (that is, linkages to perform various types of tasks) have been conceived and developed. Quite a few atlases and catalogues were prepared by the designers of machines and mechanical systems. However, often it is felt that a clustering technique for handling the list of large number of mechanisms can be very useful, if it is developed based on a scientific principle. In this paper, it has been shown that the concept of fuzzy sets can be conveniently used for this purpose, if an adequate number of properly chosen attributes (also called characteristics) are identified. Using two clustering techniques, the mechanisms have been classified in the present work and in future, it may be extended to develop an expert system, which can automate type synthesis phase of mechanical design. To the best of the authors' knowledge, this type of clustering of mechanisms has not been attempted before. Thus, this is the first attempt to cluster the mechanisms based on some quantitative measures. It may help the engineers to carry out type synthesis of the mechanisms.

关键词： clustering mechanisms fuzzy c-means algorithm entropy-based algorithm genetic algorithm self-organizing maps

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A fuzzy c-means approach for regionalization using a bivariate homogeneity and discordancy approach

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JOURNAL OF HYDROLOGY 2011年第3-4期401卷 231-239页

作者： Sadri, S. Burn, D. H. Univ Waterloo Dept Civil & Environm Engn Waterloo ON N2L 3G1 Canada

In stochastic analysis for droughts, such as frequency or trend analysis, the absence of lengthy records typically limits the reliability of statistical estimates. To address this issue, "regional" or "pooled" analysis approach is often used. The main contribution of this study is to create regions based on bivariate criteria rather than univariate ones;the two variables are severity and duration. The methodology is applied to hydrological records of 36 unregulated flow monitoring sites in the canadian "prairie" provinces of Alberta, Saskatchewan and Manitoba. Our criteria for a hydrological "region" to be suitable are that it should be homogeneous, that it should not be discordant, and that it should not be too small. Tests for homogeneity and non-discordancy are traditionally based on univariate L-moment statistics;for example there have been several applications of univariate L-moments to bivariate drought analysis by simply ignoring one of the variables. Instead, we use multivariate L-moments, also known as L-comoments. The approach uses site characteristics and a fuzzy clustering approach, called fuzzy c-means (FcM), to form the initial regions (clusters) and adjusts initial clusters based on partial or fuzzy membership of each site to other clusters to form final clusters that meet the criteria of homogeneity, lack of discordancy, and sufficient size. We also estimate return periods using a bivariate copula method. (c) 2011 Elsevier B.V. All rights reserved.

关键词： Drought Regional drought frequency analysis Bivariate homogeneity Bivariate discordancy fuzzy c-means algorithm Regionalization

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Performance research of Gaussian function weighted fuzzy c-means algorithm

Performance research of Gaussian function weighted fuzzy C-m...

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conference on Pattern Recognition and computer Vision

作者： Liu, Xiaofang Li, Xiaowen Yang, chun He, Binbin Zhang, Ying Univ Elect Sci & Technol China Inst Geospatial Informat Sci & Technol Chengdu 610054 Peoples R China Sichuan Univ Dept Comp Sci & Technol Zigong 643000 Peoples R China Sichuan Univ Dept Engn Management Zigong 643000 Peoples R China

ISBN: (纸本)9780819469526

fuzzy c-means (FcM) algorithm is a fuzzy pattern recognition method. clustering precision of the algorithm is affected by its equal partition trend for data set of large discrepancy of each class samples number, and the optimal clustering result of the algorithm mightn't be a right partition in this case. In order to overcome this disadvantage, a Gaussian function Weighted fuzzy c-means (WFcM) algorithm is proposed, which the weighted function is produced by a Gaussian function calculating dot density of each sample. To certain extent, the WFcM algorithm has not only overcome the limitation of equal partition trend in fuzzy cmeans algorithm, but also been favorable convergence and stability. The calculation of the weighted function and the choice of sample dot density range restriction value for the algorithm are both objective. When partially supervised information obtained from a few labeled samples is introduced to the WFcM algorithm, the classification performance of the WFcM algorithm is further enhanced and the convergent speed of objective function is further accelerated.

关键词： fuzzy c-means algorithm weighted fuzzy c-means algorithm partially supervised and weighted fuzzy c-means algorithm Gaussian function pattern recognition fuzzy clustering

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A modified fuzzy c-means algorithm for MR brain image segmentation

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4th International conference on Image Analysis and Recognition

作者： Szilagyi, Laszlo Szilagyi, Sandor M. Benyo, Zoltan Budapest Univ Technol & Econ Dept Control Engn & Informat Technol Budapest Hungary Sapienta Hungarian Sci Univ Fac Tech & Human Sci Targu Mures Romania

ISBN: (纸本)9783540742586

Automated brain MR image segmentation is a challenging pattern recognition problem that received significant attention lately. The most popular solutions involve fuzzy c-means (FcM) or similar clustering mechanisms. Several improvements have been made to the standard FcM algorithm, in order to reduce its sensitivity to Gaussian, impulse, and intensity non-uniformity noises. This paper presents a modified FcM-based method that targets accurate and fast segmentation in case of mixed noises. The proposed method extracts a scalar feature value from the neighborhood of each pixel, using a context dependent filtering technique that deals with both spatial and gray level distances. These features are clustered afterwards by the histogram-based approach of the enhanced FcM algorithm. Results were evaluated based on synthetic phantoms and real MR images. Test experiments revealed that the proposed method provides better results compared to other reported FcM-based techniques. The achieved segmentation and the obtained fuzzy membership values represent excellent support for deformable contour model based cortical surface reconstruction methods.

关键词： fuzzy c-means algorithm image segmentation noise elimination feature extraction. context dependent filter magnetic resonance imaging

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