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Realisation of a cluster-based protocol using fuzzy c-means algorithm for wireless sensor networks

IET WIRELESS SENSOR SYSTEMS 2013年第3期3卷 163-171页

作者： Hoang, Duc chinh Kumar, Rasjesh Panda, Sanjib Kumar Natl Univ Singapore Dept Elect & Comp Engn 4Engn Dr 1 Singapore 117576 Singapore

In this study, real-time design and implementation of an energy-efficient cluster-based protocol for wireless sensor networks (WSNs) are presented. The formation of suitable clusters is of prime importance to balance energy usage by sensor nodes within each cluster of the cluster-based WSNs. This leads to energy savings for the sensor nodes resulting in longer network lifetime. To obtain this objective, the fuzzy c-means (FcM) clustering algorithm is incorporated in the protocol. The protocol is realised on a hardware test-bed with the support of the embedded operating system, TinyOS. Experimental results obtained from a scale-down laboratory based test-bed with up to 50 sensor nodes are provided to illustrate the efficacy of the WSNs using the proposed protocol and compared with the well-known cluster-based protocols such as Low Energy Adaptive clustering Hierarchy. It has been shown that the FcM protocol is able to achieve better organisation of the network and thus can extend its lifetime under varying operating conditions and with different number of nodes.

关键词： embedded systems energy conservation fuzzy set theory operating systems (computers) organisational aspects pattern clustering protocols wireless sensor networks wireless sensor networks real-time design energy-efficient cluster-based protocol energy usage balance sensor nodes cluster-based WSN energy savings fuzzy c-means clustering algorithm FcM clustering algorithm hardware test-bed embedded operating system TinyOS scale-down laboratory-based test-bed cluster-based protocols FcM protocol

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Linguistic Dynamic Analysis for Single Lane's Traffic Status Based on FcM

Linguistic Dynamic Analysis for Single Lane's Traffic Status...

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International conference on Informative and cybernetics for computational Social Systems (IccSS)

作者： Wang Jie Mo Hong Zhu Fenghua Changsha Univ Sci & Technol Coll Elect & Informat Engn Changsha Hunan Peoples R China State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9781479947522

The results of traditional traffic status analysis are mostly single values, whose accuracy can't be determined;fuzzy c-means clustering (FcM) algorithm based on fuzzy theory can calculate the clustering center of plenty data quickly and easily;linguistic dynamic systems could describe the dynamic rules of complex systems in the language level. In this paper, membership functions are decided by FcM;result of a specific time period taken as one example is obtained;it's discussed that linguistic dynamic analysis of traffic status in different period within a day by the same method.

关键词： fuzzy c-means clustering algorithm linguistic dynamic systems traffic flow

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fuzzy c-means clustering algorithm with unknown number of clusters for symbolic interval data

Fuzzy c-means clustering algorithm with unknown number of cl...

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SIcE Annual conference

作者： chen-chia chuang Jin-Tsong Jeng chih-Wen Li Department of Electrical Engineering National I-Lan University Ilan Taiwan Department of Computer Science and Information Engineering National Formosa University Taiwan

ISBN: (纸本)9784907764296

In this study, the concepts of competitive agglomeration clustering algorithm is incorporated into fuzzy c-means (FcM) clustering algorithm for symbolic interval-values data. In the proposed approach, called as IFcMwUNc clustering algorithm, the problems of the unknown clusters number and the initialization of prototypes in the FcM clustering algorithm for symbolic interval-values data are overcome and discussed. Due to the competitive agglomeration clustering algorithm possess the advantages of the hierarchical clustering algorithm and the partitional clustering algorithm, IFcMwUNc clustering algorithm can be fast converges in a few iterations regardless of the initial number of clusters. Moreover, it is also converges to the same optimal partition regardless of its initialization. Experiments results show the merits and usefulness of IFcMwUNc clustering algorithm for the symbolic interval-values data.

关键词： Symbolic interval-values data competitive agglomeration clustering algorithm fuzzy c-means clustering algorithm

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Linguistic Dynamic Analysis for Single Lane's Traffic Status Based on FcM

Linguistic Dynamic Analysis for Single Lane's Traffic Status...

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2014 International conference on Informative and cybernetics for computational Social Systems(IccSS2014)

作者： Wang Jie Mo Hong Zhu Fenghua College of Electrical &Information Engineering Changsha University of Science &Technology State Key Laboratory of Management and Control for Complex System

The results of traditional traffic status analysis are mostly single values,whose accuracy can’t be determined;fuzzy c-means clustering(FcM)algorithm based on fuzzy theory can calculate the clustering center of plenty data quickly and easily;linguistic dynamic systems could describe the dynamic rules of complex systems in the language *** this paper,membership functions are decided by FcM;result of a specific time period taken as one example is obtained;it’s discussed that linguistic dynamic analysis of traffic status in different period within a day by the same method.

关键词： fuzzy c-means clustering algorithm linguistic dynamic systems traffic flow

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comparison of clustering algorithms on generalized propensity score in observational studies: a simulation study

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JOURNAL OF STATISTIcAL cOMPUTATION AND SIMULATION 2013年第12期83卷 2206-2218页

作者： Tu, chunhao Jiao, Shuo Koh, Woon Yuen Univ New England Coll Pharm Portland ME 04103 USA Fred Hutchinson Canc Res Ctr Div Publ Hlth Seattle WA 98109 USA Univ New England Dept Math Sci Biddeford ME 04005 USA

In observational studies, unbalanced observed covariates between treatment groups often cause biased inferences on the estimation of treatment effects. Recently, generalized propensity score (GPS) has been proposed to overcome this problem;however, a practical technique to apply the GPS is lacking. This study demonstrates how clustering algorithms can be used to group similar subjects based on transformed GPS. We compare four popular clustering algorithms: k-means clustering (KMc), model-based clustering, fuzzy c-means clustering and partitioning around medoids based on the following three criteria: average dissimilarity between subjects within clusters, average Dunn index and average silhouette width under four various covariate scenarios. Simulation studies show that the KMc algorithm has overall better performance compared with the other three clustering algorithms. Therefore, we recommend using the KMc algorithm to group similar subjects based on the transformed GPS.

关键词： fuzzy c-means clustering algorithm generalized propensity score k-means clustering algorithm model-based clustering algorithm observational studies partitioning around medoids algorithm

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Ejecting Outliers to Enhance Robustness of fuzzy cluster Ensemble

Ejecting Outliers to Enhance Robustness of Fuzzy Cluster Ens...

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IEEE International conference on Systems, Man, and cybernetics (SMc)

作者： Kao, Li-Jen Huang, Yo-Ping Hwa Hsia Inst Technol Dept Comp Sci & Informat Engn Taipei 23568 Taiwan Natl Taipei Univ Technol Dept Elect Engn Taipei 10608 Taiwan

ISBN: (纸本)9781479906529

clustering analysis provides significant contributions to healthcare or medical service. However, relying only on one set of clusters obtained from employing a clustering algorithm, such as fuzzy c-means algorithm (FcM), with an arbitrary initialization may be not robust and accurate in data clustering. The cluster ensemble, the concept of combining multiple clusters produced by a cluster algorithm with several different initializations, can improve the robustness problem. When the outliers were taken into the ensemble may lead the final cluster ensemble to inaccurate results. Thus, outliers should be removed before merging different clusters. In this paper, an adapted FcM algorithm is proposed to detect and remove the outliers. The cluster ensemble framework will employ this adapted FcM algorithm to generate multiple sets of clusters by giving different initialization parameters. Then, a pairwise approach is used to combine those outlier-free clusters. The experimental results verify that the final clusters obtained from the proposed cluster ensemble framework are more robust.

关键词： cluster ensembles fuzzy c-means clustering algorithm outliers

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A Method for Fish Diseases Diagnosis Based on Rough Set and FcM clustering algorithm

A Method for Fish Diseases Diagnosis Based on Rough Set and ...

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3rd International conference on Intelligent System Design and Engineering Applications (ISDEA)

作者： Xu Miao-jun Zhang Jian-ke Li Hui Ningbo Dahongying Univ Ningbo Zhejiang Peoples R China Zhejiang Ocean Univ Sch Math Phys & Informat Sci Zhoushan Peoples R China

ISBN: (纸本)9780769549231;9781467348935

In order to achieve the rapid and mass diagnosis of fish diseases, it is proposed to set up a new and efficient model which closely connects rough set and fuzzy c-means(FcM) clustering algorithm. First, the rough set was used for access to knowledge, that is, the typical cases of fish diseases were regarded as sample room for the formation of the decision-making table of the "symptoms - disease";next, based on rough set of simplified method of knowledge, redundant properties and samples were removed;then, the fine performance of FcM clustering algorithm was used to analyze clustering;and finally fish diseases classification rules were formed. The model integrated the strong extracting capabilities of rough set and the excellent classifying ability of FcM clustering algorithm, and proved experimentally to be efficient in classification and rapid in fish diseases diagnosis.

关键词： rough set fuzzy c-means clustering algorithm fish disease diagnosis attributive condition symptoms set

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Ejecting Outliers to Enhance Robustness of fuzzy cluster Ensemble

Ejecting Outliers to Enhance Robustness of Fuzzy Cluster Ens...

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IEEE International conference on Systems, Man, and cybernetics

作者： Li-Jen Kao Yo-Ping Huang Department of Computer Science and Information Engineering Hwa Hsia Institute of Technology Taipei Taiwan 23568 Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan 10608

ISBN: (纸本)9781479906505

关键词： cluster ensembles fuzzy c-means clustering algorithm outliers

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Overhead Power Line Detection from UAV Video Images

Overhead Power Line Detection from UAV Video Images

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19th International conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Yang, Tang Wen Yin, Hang Ruan, Qiu Qi Han, Jian Da Qi, Jun Tong Yong, Qing Wang, Zi Tong Sun, Zeng Qi Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Techno Beijing 100044 Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Beijing Jiaotong Univ Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9780473204853;9781467316439

currently, unmanned aerial vehicles (UAVs) are applied to routine inspection tasks of electric distribution networks. As an important information source, machine vision attracts much attention in the area of the UAV's autonomous control. To this end, real-time algorithms are studied in this paper to detect the power lines in the UAV video images. First, video images are converted into binary images through an adaptive thresholding approach. Then, Hough Transform is used to detect line candidates in the binary images. Finally, a fuzzy c-means (FcM) clustering algorithm is used to discriminate the power lines from the detected line candidates. The properties of power lines are used to remove the spurious lines, and the length and slope of the detected lines are used as features to establish the clustering data set. Experimental results show that the algorithms proposed are effective and able to tolerate noises from complicated terrain background and various illuminations.

关键词： power line detection UAV image binarization Hough Transform fuzzy c-means clustering algorithm

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Novel Shannon's Entropy Based Segmentation Technique for SAR Images

Novel Shannon's Entropy Based Segmentation Technique for SAR...

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6th International conference on Information Processing (IcIP 2012)

作者： Samanta, Debabrata Sanyal, Goutam Department of CSE National Institute of Technology West Bengal 713209 India

ISBN: (纸本)9783642316852

Segmentation of SAIL image plays an imperative function in analysis of huge amount of satellite data. The good recital of recognition algorithms based on the quality of segmented image. In case of SAIL image, it is one of the most complicated and challenging tasks in image processing, and determines the quality of the final results of the analysis. The capability of SAR image is to penetrate cloud cover to predict the weather condition at any particular instant of time. Image data can also be used to classify the land, forest, hills, oceans etc. In this paper a novel methodology has been carried out to segment a SAR images based on Shannon's definition of information entropy. Since entropy is a statistical measure of randonmess that can be used to characterize the texture of the input image. The basic concept is that the background remains informatively poor, whereas the objects carry relevant information. This method preserves the details, highlights edges, and decreases random noise;all of this is done in one calculation.

关键词： fuzzy c-means clustering algorithm Shannon's Entropy Probability Shannon's Filter

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