In the paper, we present an efficient method to solve the piecewise constant Mumford-Shah (M-S) model for two-phase image segmentation within the level set framework. A clustering algorithm is used to find approximate...
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In the paper, we present an efficient method to solve the piecewise constant Mumford-Shah (M-S) model for two-phase image segmentation within the level set framework. A clustering algorithm is used to find approximately the intensity means of foreground and background in the image, and so the M-S functional is reduced to the functional of a single variable (level set function), which avoids using complicated alternating optimization to minimize the reduced M-S functional. Experimental results demonstrated some advantages of the proposed method over the well-known Chan-Vese method using alternating optimization, such as robustness to the locations of initial contour and the high computation efficiency.
In RSSI (Received Signal Strength Indicator)-based communication distance estimation of mobile wireless sensor network localization, RSSI is assumed to exponential attenuation with increment of communication distance ...
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In RSSI (Received Signal Strength Indicator)-based communication distance estimation of mobile wireless sensor network localization, RSSI is assumed to exponential attenuation with increment of communication distance in ideal radio propagation models, which is invalid due to the uncertainty of RSSI data in real communication environment, resulting in considerable error of communication distance estimation. Moreover, dynamic distance estimation demands a high efficiency of computation for the continual generation of RSSI data stream in the mobile node. This paper develops a new dynamic communication distance estimation method using uncertain interval data stream clustering, named as DDEUDSC (Dynamic Distance Estimation method using Uncertain Data Stream clustering). First, statistical information of RSSI data is used to represent the RSSI-D mapping relationship in terms of interval data. Then we consider the data pattern composed of some consecutive cluster centers, and apply it in our uncertain RSSI data stream clustering algorithm to estimate the dynamic communication distance. Finally, RSSI data streams in three typical communication environments are conducted for experiments. The experimental results show the proposed method is an effective way to improve RSSI-D estimation precision in RSSI data stream with uncertainty and dynamics characteristic. (C) 2014 Elsevier Ltd. All rights reserved.
A robust validity index for fuzzy c-means (FCM) algorithm is proposed in this paper. The purpose of fuzzy clustering is to partition a given set of training data into several different clusters that can then be modele...
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A robust validity index for fuzzy c-means (FCM) algorithm is proposed in this paper. The purpose of fuzzy clustering is to partition a given set of training data into several different clusters that can then be modeled by fuzzy theory. The FCM algorithm has become the most widely used method in fuzzy clustering. Although, there are some successful applications of FCM have been proposed, a disadvantage of FCM is that the number of clusters must be predetermined. After clustering, it is often necessary to evaluate the fitness of the results obtained by FCM. Such assessment techniques are called cluster validity. In this paper, a new cluster validity index is proposed to evaluate the fitness of clusters obtained by FCM and four examples show the results of proposed index have good performances than other cluster validities.
According to the characteristic of hybrid electric bulldozer operating under various working conditions, clustering algorithm is used to monitor working conditions of hybrid electric bulldozer. In this study, firstly ...
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According to the characteristic of hybrid electric bulldozer operating under various working conditions, clustering algorithm is used to monitor working conditions of hybrid electric bulldozer. In this study, firstly -the pound hardware and software of remote monitoring system is developed and A large number of historical data are collected and stored in the database. Secondly, clustering algorithm is improved by using strong robustness of ant colony algorithm. Then a new definition of outlier data mining based on ant colony clustering is put forward by combining clustering analysis and some parameters of ant colony algorithm such as p(ij)(t), according to characteristics of the data point number and distance between data point and center point in clusters. In the end, the Outlier data mining is successfully realized by program code and a large amount of historical data of hybrid electric bulldozer are analyzed. Using the proposed algorithm in this paper, the outlier abnormal cluster can be easily found and realize early warning and operation optimization of hybrid electric bulldozer.
Compressed sensing （CS） algorithm enables sampling rates significantly under classical Nyquist rate without sacrificing reconstructed image quality. It is known that, a great number of images have many similar areas...
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Compressed sensing （CS） algorithm enables sampling rates significantly under classical Nyquist rate without sacrificing reconstructed image quality. It is known that, a great number of images have many similar areas which are composed by the same number of grayscale or color. A new CS scheme, namely clustering compressed sensing （CCS）, was proposed for image compression, and it introduces clustering algorithm onto framework of CS based on similarity of image blocks. Instead of processing the image as a whole, the image is firstly divided into small blocks, and then the clustering algorithm was proposed to cluster the similar image blocks. Afterwards, the optimal public image block in each category is selected as the representative for transmission. The discrete wavelet transform （DWT） and Gaussian random matrix are applied to each optimal public image block to obtain the random measurements. Different from equal measurements, the proposed scheme adaptively selects the number of measurements based on different sparsity of image blocks. In order to further improve the performance of the CCS algorithm, the unequal-CCS algorithm based on the characteristics of wavelet coefficients was proposed as well. The low frequency coefficients are retained to ensure the quality of reconstructed image, and the high frequency coefficients are compressed by the CCS algorithm. Experiments on images demonstrate good performances of the proposed approach.
As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algori...
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As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is *** method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target ***,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is *** combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD *** the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.
Customer clustering is an essential step to reduce the complexity of large-scale logistics network optimization. By properly grouping those customers with similar characteristics, logistics operators are able to reduc...
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Customer clustering is an essential step to reduce the complexity of large-scale logistics network optimization. By properly grouping those customers with similar characteristics, logistics operators are able to reduce operational costs and improve customer satisfaction levels. However, due to the heterogeneity and high-dimension of customers' characteristics, the customer clustering problem has not been widely studied. This paper presents a fuzzy-based customer clustering algorithm with a hierarchical analysis structure to address this issue. Customers' characteristics are represented using linguistic variables under major and minor criteria, and then, fuzzy integration method is used to map the sub-criteria into the higher hierarchical criteria based on the trapezoidal fuzzy numbers. A fuzzy clustering algorithm based on Axiomatic Fuzzy Set is developed to group the customers into multiple clusters. The clustering validity index is designed to evaluate the effectiveness of the proposed algorithm and find the optimal clustering solution. Results from a case study in Anshun, China reveal that the proposed approach outperforms the other three prevailing algorithms to resolve the customer clustering problem. The proposed approach also demonstrates its capability of capturing the similarity and distinguishing the difference among customers. The tentative clustered regions, determined by five decision makers in Anshun City, are used to evaluate the effectiveness of the proposed approach. The validation results indicate that the clustered results from the proposed method match the actual clustered regions from the real world well. The proposed algorithm can be readily implemented in practice to help the logistics operators reduce operational costs and improve customer satisfaction levels. In addition, the proposed algorithm is potential to apply in other research domains. Published by Elsevier Ltd.
作者：
Ye, JunShaoxing Univ
Dept Elect & Informat Engn 508 Huancheng West Rd Shaoxing 312000 Zhejiang Peoples R China
clustering plays an important role in data mining, pattern recognition, and machine learning. Single-valued neutrosophic sets (SVNSs) are useful means to describe and handle indeterminate and inconsistent information ...
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clustering plays an important role in data mining, pattern recognition, and machine learning. Single-valued neutrosophic sets (SVNSs) are useful means to describe and handle indeterminate and inconsistent information that fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single-valued neutrosophic information, this article proposes single-valued neutrosophic clustering methods based on similarity measures between SVNSs. First, we define a generalized distance measure between SVNSs and propose two distance-based similarity measures of SVNSs. Then, we present a clustering algorithm based on the similarity measures of SVNSs to cluster single-valued neutrosophic data. Finally, an illustrative example is given to demonstrate the application and effectiveness of the developed clustering methods.
In this paper, a new method for the detection of switching time is proposed for discrete-time linear switched systems, whose switching mechanism is unknown. The switching instant estimation problem consists to predict...
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In this paper, a new method for the detection of switching time is proposed for discrete-time linear switched systems, whose switching mechanism is unknown. The switching instant estimation problem consists to predict the mode switching for discrete behavior from a finite set of input–output data. First, the proposed method use a clustering and classification approach define the number of submodels and the data repartition. Then, by the use of statistical learning approach, we define the linear boundary separator of each validity region. Finally, a technique of detection given an explicitly estimation of switching time. A numerical example was reported to evaluate the proposed method.
In this study, the clustering method was applied to improve the usage of effective rainfall (ER) for irrigating rice paddy in the region managed by the TaoYuan Irrigation Association (TIA) of Taiwan. A total of 16 rai...
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In this study, the clustering method was applied to improve the usage of effective rainfall (ER) for irrigating rice paddy in the region managed by the TaoYuan Irrigation Association (TIA) of Taiwan. A total of 16 rainfall stations and rainfall data from 1981 to 2000 were used. A traditional area-weighted method (Thiessen polygons method) and an optimal clustering model of ER were evaluated and compared. The optimal clustering model of ER comprised self-organizing map (SOM), k-means (KM), and fuzzy c-means (FCM) clustering algorithms. To obtain optimal clustering data of ER, the clustering groups from two to five of SOM, KM, and FCM algorithms were determined using root-mean-squared-error. The results show that three algorithms with group numbers from two to five are adopted for the monthly optimal clustering model in different months. However, for the annual optimal model, 12 sub-models are assessed and then compared. The results show that the SOM clustering with groups three was the optimal model for annual ER. The optimal clustering model of ER provides a new procedure step in preparation of the irrigation scheduling for the TIA, and the amount irrigation water waste can be reduced from 770.1 to 22.3 mm/year. The planned ER using the optimal clustering model significantly improves the irrigation water use efficient in agricultural water management.
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