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 satis...
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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 fuzzyc-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.
In this paper an initialization method for fuzzyc-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...
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In this paper an initialization method for fuzzyc-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.
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 th...
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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 fuzzyc-means In particular, they applied mostly in medical data sets. The importance of the survey is to explore the various applications in different domains.
fuzzyc-means (FcM) clustering algorithmcan 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 ...
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ISBN:
(纸本)9783037856512
fuzzyc-means (FcM) clustering algorithmcan 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 algorithmcan get more accurate centers than the traditional FcM algorithm.
Suppressed fuzzyc-means (s-FcM) clustering was introduced in [Fan, J. L., Zhen, W. Z., Xie, W. X.: Suppressed fuzzyc-meansclustering algorithm. Patt. Recogn. Lett. 24, 1607-1612 (2003)] with the intention of combin...
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ISBN:
(纸本)9783540859192
Suppressed fuzzyc-means (s-FcM) clustering was introduced in [Fan, J. L., Zhen, W. Z., Xie, W. X.: Suppressed fuzzyc-meansclustering 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 fuzzyc-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.
Geneticalgorithms (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 ...
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ISBN:
(纸本)9781424478354
Geneticalgorithms (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 geneticalgorithm based on fuzzyclustering 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.
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...
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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.
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 "...
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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 fuzzyclustering approach, called fuzzyc-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.
fuzzyc-means (FcM) algorithm is a fuzzy pattern recognition method. clustering precision of the algorithm is affectedby its equal partition trend for data set of large discrepancy of each class samples number, and th...
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ISBN:
(纸本)9780819469526
fuzzyc-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 fuzzyc-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 fuzzycmeansalgorithm, 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.
Automated brain MR image segmentation is a challenging pattern recognition problem that received significant attention lately. The most popular solutions involve fuzzyc-means (FcM) or similar clustering mechanisms. S...
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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 fuzzyc-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.
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