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fuzzy Entropy clustering Approach to Evaluate the Reliability of Emergency Logistics System

Energy Procedia 2012年 16卷 278-283页

作者： Bengang Gong Xiang Chen Chaozhong Hu School of Management Engineering Anhui Polytechnic University Wuhu 241000 China

Because of burstiness, weak economy, time-uncertain of emergency logistics system, the paper constructs the operation model and its evaluation index system for emergency logistics system. In view of the imperfection of subjective determining index weight method in traditional evaluation, the index weight coefficients are determined by information entropy method of combining subjective judgment with objective evaluation. Then the fuzzy clustering and information entropy to determine the weights are used into emergency logistics system reliability evaluation. Finally the paper obtains a specific solution steps based on an example.

关键词： Emergency logistics system Reliability evaluation fuzzy clustering algorithm Information entropy method

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A modified Sage-Husa adaptive Kalman filter for state estimation of electric vehicle servo control system

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ENERGY REPORTS 2022年 8卷 20-27页

作者： Wang, Xingyu Wang, Anna Wang, Dazhi Xiong, Yingjie Liang, Bingxue Qi, Yufei Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Bluelight Dr Technol Co Ltd Shenyang 110179 Peoples R China China North Vehicle Res Inst Beijing 100072 Peoples R China

Kalman filter algorithm is widely used in state estimation of electric vehicles (EV) servo system as dynamic estimation of power system. In order to improve the estimation effect of noise interference in engineering practice, a scheme of Modified Sage-Husa adaptive Kalman filter (MSHAKF) combined with fuzzy clustering algorithm is proposed. The scheme processes the experimental data in a robust and adaptive way through the combination of constantly changing fuzzy clustering and MSHAKF, so as to better represent the dynamic characteristics of EV. The simulation results show that this method has higher estimation accuracy than the traditional method on the premise of unknown disturbance and noise characteristics in the system, and is more in line with the practical application of EV. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 2021 The 2nd International Conference on Power Engineering, ICPE, 2021.

关键词： Sage-Husa adaptive Kalman filter fuzzy clustering algorithm Servo system Electric vehicle Uncertain noise identification

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fuzzy classification maximum likelihood algorithms for mixed-Weibull distributions

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SOFT COMPUTING 2008年第10期12卷 1013-1018页

作者： Hung, Wen-Liang Chang, Yen-Chang Chuang, Shun-Chin Natl Hsinchu Univ Educ Grad Inst Comp Sci Hsinchu Taiwan Natl Hsinchu Univ Educ Dept Appl Math Hsinchu Taiwan Natl Hsinchu Univ Educ Comp & Network Ctr Hsinchu Taiwan

In this paper we propose an efficient algorithm based on Yang's (fuzzy Sets Syst 57:365-337, 1993) concept, namely the fuzzy classification maximum likelihood (FCML) algorithm, to estimate the mixed-Weibull parameters. Compared with EM and Jiang and Murthy (IEEE Trans Reliab 44:477-488, 1995) methods, the proposed FCML algorithm presents better accuracy. Thus, we recommend FCML as another acceptable method for estimating the mixed-Weibull parameters.

关键词： fuzzy class model EM algorithm fuzzy clustering algorithm mixed-Weibull distribution

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Research on global coupling analysis and approximate decoupling of quality characteristics in the early design stage

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2021年第4期235卷 599-610页

作者： Chen, Yifan Ran, Yan Wang, Zhichao Zhang, Genbao Chongqing Univ Coll Mech Engn Shazheng St Chongqing 400044 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Shazheng St Chongqing 400044 Peoples R China Chongqing Univ Arts & Sci Coll Mech Engn Chongqing Peoples R China

Due to the existence of coupling relationships, quality characteristics form a variation propagation process, which increases the risk possibility of product quality. In order to improve the robustness of quality characteristics, this paper proposes a fuzzy clustering-based key quality characteristics decoupling planning considering risk criticality. Firstly, based on the design structure matrix, a modular correlation matrix of key quality characteristics was established to quantify the coupling relationships among them. Secondly, according to the variation characteristics of quality characteristics, the variation propagation model is constructed to identify the potential quality risk. Thirdly, the fuzzy clustering algorithm is used to obtain the optimal control sequence of key quality characteristics. Finally, the computerized numerical control machine tool is taken as an actual case, the effectiveness and superiority of this method are verified by the comparison of the numerical result and method. This method provides a new indicator system solution for the coupling analysis of product design.

关键词： Key quality characteristics design structure matrix variation propagation model fuzzy clustering algorithm decoupling planning

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Fuzziness Modeling of Polarized Scattering Mechanisms and PolSAR Image Classification Using fuzzy Triplet Discriminative Random Fields

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第7期57卷 4980-4993页

作者： Song, Wanying Li, Ming Zhang, Peng Wu, Yan Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China Xidian Univ Collaborat Innovat Ctr Informat Sensing & Underst Xian 710071 Shaanxi Peoples R China

Dominant scattering mechanism (DSM) obtained by Freeman decomposition is significant for polarimetric synthetic aperture radar (PolSAR) image classification. To preserve the purity of scattering characteristics, it restricts pixels in a scattering category to be classified with other pixels in the same scattering category. However, due to the speckle and the limited image resolution, it is difficult to obtain the DSMs of some pixels, which are defined as the fuzziness of polarized scattering mechanisms. Therefore, we first consider a particular-and pertinent-auxiliary field, and then propose the fuzzy triplet discriminative random fields (FTDF) model to describe the fuzziness of polarized scattering mechanisms, thus categorizing the scattering mechanisms into four classes: surface scattering, double-bounce scattering, volume scattering, and mixed scattering. The pixels in the first three categories are with specific DSMs, and the FTDF model introduces an exponential kernel distance to combine the multiple features of PolSAR data into classification. For the pixels in the mixed scattering, FTDF introduces a fuzzy clustering algorithm regularized by Kullback-Leibler information to consider the fuzzy DSMs, thus enhancing the classification. Then the furriness modeling of polarized scattering mechanisms can guide the classification of PolSAR images. The experimental results on real PolSAR images demonstrate the effectiveness of the FTDF model, and illustrate that it can improve the classification accuracy, and simultaneously preserve the purity of scattering mechanisms.

关键词： Classification fuzzy clustering algorithm kernel k-means (KKM) polarimetric synthetic aperture radar (PolSAR) triplet discriminative random fields (TDF)

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Enhanced fuzzy partitions vs data randomness in FCM

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JOURNAL OF INTELLIGENT & fuzzy SYSTEMS 2014年第4期27卷 1639-1648页

作者： Jiang, Yizhang Chung, Fu-Lai Wang, Shitong Jiangnan Univ Sch Digital Media Wuxi Jiangsu Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

IFP-FIM and GIFP-FCM are two typical enhanced fuzzy clustering algorithms in which the rationale of fuzzy clustering and its robustness to noise and/or outliers are enhanced by making the maximal fuzzy membership of each data point belonging to a cluster become as big as possible and other fuzzy memberships of this point belonging to all other clusters become as small as possible. In this study, a new finding will be revealed that their enhanced fuzzy partitions can be equivalently achieved by factitiously disturbing the given dataset using a random noise and then applying the proposed noise-resistant fuzzy clustering algorithm NR-FCM to the dataset with factitiously added random noise. NR-FCM is designed as an intermediate step for us to observe this finding. The virtue of this finding exists in that it indeed helps us witness from an alternative perspective that fuzziness of fuzzy partitions in fuzzy clustering and data randomness can be collaborative and even mutually transformable rather than competitive. Our several experimental results verify the above claim.

关键词： fuzzy clustering algorithm noise-resistant penalty term fuzzy partitions equivalence

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Research on sports video image based on fuzzy algorithms

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2019年第May期61卷 105-111页

作者： Chang, Wu-Yeh Univ Taipei Dept Ball Sports 101Sec 2Zhongcheng Rd Taipei 111 Taiwan

With the rapid development of network and multimedia technology, a large number of sports and national fitness information are stored in various fitness guidance systems in the form of video and pictures. In order to better promote public fitness and facilitate learning and viewing, sports video has a variety of needs of editing, segmentation and integration. Aiming at the shortcomings of current sports video image segmentation methods, such as rough segmentation results and high spatial distortion rate, a sports video image segmentation method based on fuzzy clustering algorithm is proposed. This paper introduces the basic theory of fuzzy clustering algorithm, establishes second-order fuzzy attributes with normal distribution and gray value by means of time-domain difference images, assigns the fuzzy attribute S membership function, then performs fuzzy clustering on time-domain difference images, and obtains the segmentation results of moving video images by edge detection. The experimental results show that the method has high spatial accuracy, good noise iteration performance and low spatial distortion rate, and can accurately segment complex moving video images to obtain high-definition images. (C) 2019 Published by Elsevier Inc.

关键词： fuzzy clustering algorithm Image segmentation Membership Motion video

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Robust fuzzy C-Means clustering with Self- Tuning Non Local Spatial Information for Brain Magnetic Resonance Image Segmentation

Robust Fuzzy C-Means Clustering with Self- Tuning Non Local ...

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International Conference on Education, Law and Humanities (ELH’2013);International Conference on Civil and Environmental Engineering (CEE’2013);International Conference on Chemical, Mining and Metallurgical Engineering (CMME’2013);International Conference on Information Technology and Computer Systems Engineering (ITCSE’2013);Int’l Conference on Industrial, Manufacturing, Automation and Mechanical Engineering (IMAME’2013);International Conference on Electrical, Electronics and Instrumentation Engineering (EEIE’2013)

作者： Saeid Fazli Saeed Fathi Ghiri Research Institute of Modern Biological Techniques University of Zanjan Department of Electrical Engineering University of Zanjan

A Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions （GIFPCM） as a fuzzy clustering algorithm does not achieve a satisfactory performance in image segmentation on noisy images. This is due to not taking into account any spatial information contained in the image. In order to solve this problem, a novel robust fuzzy c-means algorithm with self-tuning spatial information （RFCMSI） is proposed in this paper. In the proposed method, a novel non local adaptive spatial constraint term is used to modify the objective function of GIFPCM. The characteristic of this technique is that the self-tuning spatial parameter for each pixel is designed to make the non local spatial information of each pixel play a positive role in guiding the noisy image segmentation. Segmentation experiments on synthetic and real images, especially brain magnetic resonance （MR） images, are performed to assess the performance of an RFCMSI in comparison with GIFPCM and fuzzy c-means clustering algorithms with local spatial constraint. Experimental results show that the proposed method is robust to noise in the image and more effective than the other existing algorithms.

关键词： Image segmentation fuzzy clustering algorithm Non local spatial constraint Adaptive spatial parameter Brain magnetic resonance (MR) image.

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Spatial improved fuzzy c-means clustering for image segmentation

Spatial improved fuzzy c-means clustering for image segmenta...

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2011 International Conference on Electronic & Mechanical Engineering and Information Technology(EMEIT 2011)(2011年机电工程与信息技术国际会议)

作者： Feng Zhao Licheng Jiao School of communication and information engineering Xi'an University of Posts and Telecommunication Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China

The generalized fuzzy c-means clustering algorithm with improved fuzzy partition (GFCM) is a new modified version of the fuzzy c-means clustering algorithm (FCM). GFCM under appropriate parameters can converge more rapidly than FCM. However, GFCM, similar to FCM, is sensitive to noise in normal graylevel images. In order to overcome this problem, a novel fuzzy segmentation algorithm called spatial improved fuzzy c-means clustering algorithm (IFCMS) is proposed in this paper. In IFCM S, a spatial constraint term is introduced into the objective function of GFCM, and the center and membership function update equations are also presented. Experiments on synthetic and synthetic aperture radar (SAR) images, show that the proposed method behaves well in segmentation performance and speed.

关键词： Image segmentation fuzzy clustering algorithm Spatial information Synthetic aperture radar (SAR) image

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Using machine learning to trace the pollution sources of disinfection by-products precursors compared to receptor models

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 914卷 169671页

作者： Xiao, Yuan Ma, Shunjun Yang, Shumin He, Huan He, Xin Li, Cheng Feng, Yuheng Xu, Bin Tang, Yulin Tongji Univ Key Lab Urban Water Supply Water Saving & Water En Minist Water Resources Shanghai 200092 Peoples R China Shanghai Pudong Water Grp Shanghai 201300 Peoples R China Tongji Univ Thermal & Environm Engn Inst Sch Mech Engn 1239 Siping Rd Shanghai 200092 Peoples R China

To increase the efficiency of managing backup water resources, it is critical to identify and allocate pollution sources. Source apportionment of dissolved organic matter (DOM) was investigated in our work. Parallel factor analysis (PARAFAC) and the Spearman correlation analysis were used for source identification. After that, a newly hybrid model applying the fuzzy c -means and support vector regression (FCM-SVR) was employed for source apportionment compared to receptor models. The results demonstrated that the FCM-SVR model exhibited excellent generalization, and only required standardization and normalization as pre-processing steps for dataset. According to the results, microbial sources played a key role (28.1 %) in the formation potential of disinfection byproducts (DBPFPs). Additionally, shipping marine sources exhibited a substantial contribution (21.2 %) to DBPFPs. The prediction accuracy of DBPFPs was matched or exceeded receptor models, and the R2 of DOC (0.884) was significantly high. Therefore, we recommend the FCM-SVR model combined with PARAFAC to trace the source of DBPFPs as its significant effectiveness in source identification, source apportionment, and prediction accuracy, possessing the potential for further applicability in tracking more organic compounds. Environmental implication: The disinfection byproducts precursors in water sources, which were thought to be hazardous materials in this study, are proved to be chlorinated into carcinogenic disinfection byproducts (DBPs) during drinking water treatment, However, the source apportionment methods of DBPs are not well developed compared to other inorganic matter, e.g., heavy metals and ammonia nitrogen. We proposed a new FCM-SVR model to trace the source of DBPs, which required easier pre-treatment and resulted a better source apportionment and prediction accuracy. As a result, it could provide a different prospect and useful management advices to trace the source of DBPs.

关键词： Source apportionment Parallel factor analysis Machine learning fuzzy clustering algorithm Supporting vector regression

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