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32nd Chinese Control Conference (CCC)

作者： Zhao Xiao-qiang Zhou Jin-Hu Yang Jia-Min Lanzhou Univ Tech Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Key Lab Gansu Adv Control Ind Proc Lanzhou 730050 Peoples R China

ISBN: (纸本)9781479900305

fcm algorithm is sensitive to initial clustering centers and noise data which easily fall into local optimum that cannot reach global optimum. To solve this problem, a fuzzy clustering algorithm of data mining based on IWO is proposed in this paper. We introduce invasive weed optimization with strong robustness and global optimization ability to find the optimal solution as the initial clustering centers for fcm algorithm, and finally obtain the global best optimal solution by using fcm algorithm to initialize the cluster centers, which effectively overcomes the shortcomings of fcm algorithm. Compared with the results of using genetic algorithm, particle swarm optimization algorithm optimal fcm algorithm, the simulation results of this paper's proposed algorithm is more accurate and better clustering efficiency.

关键词： Data Mining fcm algorithm Invasive Weed Optimization

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Designing reward and penalty scheme in performance-based regulation for electric distribution companies

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IET GENERATION TRANSMISSION & DISTRIBUTION 2012年第9期6卷 893-901页

作者： Simab, M. Alvehag, K. Soder, L. Haghifam, M. R. Islamic Azad Univ Sci & Res Branch Dept Elect Engn Fars Iran Royal Inst Technol KTH Div Elect Power Syst Stockholm Sweden Tarbiat Modares Univ Fac Elect & Comp Engn Tehran Iran

A reward and penalty scheme (RPS) in performance-based regulation (PBR) penalises companies for providing poor reliability and rewards them for providing good reliability. In this study, an algorithm is presented to obtain the parameters of RPS for each electric company by using data envelopment analysis (DEA) and fuzzy c-means clustering (fcm). This algorithm is based on system average interruption duration index (SAIDI) and customers' value of interruptions. fcm algorithm is applied to find similar distribution companies and cluster companies into different clusters. The best performer in each cluster is utilised as a benchmark for other companies and DEA is used to set a quality target for each electric distribution company. The performance of the proposed algorithm is demonstrated in a case study to design RPS for Iranian electricity distribution companies. The results of the algorithm include DEA efficiency score, parameters of RPS and financial risk assessment.

关键词： power distribution reliability reliability fcm algorithm PBR fuzzy set theory Reliability electricity supply industry DEA efficiency score data envelopment analysis Iranian electricity distribution companies performance-based regulation Power system management, operation and economics Combinatorial mathematics reward and penalty scheme SAIDI system average interruption duration index RPS fuzzy c-means clustering

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Forecasting of Return of Stocks Portfolio Based in Fuzzy c-Means algorithm and Fuzzy Transform

Forecasting of Return of Stocks Portfolio Based in Fuzzy c-M...

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IEEE Conference on Computational Intelligence for Financial Engineering and Economics (CIFEr)

作者： Aguiar, Renato A. Centro Universitário FEI Brazil

ISBN: (纸本)9781467318037

The aim of this paper is to investigate the trend of the return of a portfolio formed randomly or for any specific technique. The approach is made using two techniques fuzzy: fuzzy c-means (fcm) algorithm and the fuzzy transform, where the rules used in the fuzzy transform arise from the application of the fcm algorithm. The results show that the proposed methodology is able to predict the trend of the return of a stock portfolio, as well as the tendency of the market index. Real data of the financial market are used from 2004 until 2007.

关键词： fcm algorithm Fuzzy Transform Overreaction Financial Ratios

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Facial Skin Segmentation with Varying Illumination Correction for Evaluation of Facial Surgery Systems

Facial Skin Segmentation with Varying Illumination Correctio...

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IEEE Symposium on Industrial Electronics and Applications (ISIEA)

作者： Bakhshali, Mohamad Amin Shamsi, Mousa Golzarfar, Amir Sahand Univ Technol Dept Elect Engn Tabriz Iran

ISBN: (纸本)9781467330053;9781467330046

Human face has a fundamental role in the appearance of the person. Today, the importance of facial surgeries is clear for everyone. Thus, there is a need for the appropriate and accurate facial skin segmentation in order to extract different features. Thus far, many methods have been presented for detecting skin under different illumination conditions. In this paper, an algorithm was proposed which was capable of accurate skin segmentation by modifying varying illumination in color images. The proposed algorithm was robust to determine the accurate border of facial skin in the color images taken from front and side perspectives and its 94.34% accuracy on the tested data indicated its efficiency relative to other algorithms including standard EM algorithm.

关键词： Color Facial Skin Segmentation EM algorithm Evaluation of Facial Surgery fcm algorithm Varying Illumination

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Study on PMV Index Forecasting Method Based on Fuzzy C-means Clustering

Study on PMV Index Forecasting Method Based on Fuzzy C-means...

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International Conference on Manufacturing Science and Technology (ICMST 2011)

作者： Zhang, Chun-cheng Chen, Xiang-guang Xu, Yuan-qing Beijing Inst Technol Sch Chem Engn & Environm Beijing 100081 Peoples R China Beijing Inst Technol Sch Life Sci Beijing Peoples R China

ISBN: (纸本)9783037852958

In order to improve the forecasting accuracy of indoor thermal comfort, the basic principle of fuzzy c-means clustering algorithm (fcm) and support vector machines (SVM) is analyzed. A kind of SVM forecasting method based on fcm data preprocess is proposed in this paper. The large data sets can be divided into multiple mixed groups and each group is represented by a single regression model using the proposed method. The support vector machines based on fuzzy c-means clustering algorithm (fcm+SVM) and the BP neural network based on fuzzy c-means clustering algorithm (fcm+BPNN) are respectively applied to forecast PMV index. The experimental results demonstrate that the fcm+SVM method has better forecasting accuracy compared with fcm+BPNN method.

关键词： fcm algorithm Support Vector Machines PMV Index BP

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Segmentation of MRI Brain Images by Incorporating Intensity Inhomogeneity and Spatial Information Using Probabilistic Fuzzy C-Means Clustering algorithm

Segmentation of MRI Brain Images by Incorporating Intensity ...

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International Conference on Communications, Devices and Intelligent Systems (COOlS)

作者： Adhikari, Sudip Kumar Sing, J. K. Basu, D. K. Nasipuri, M. Saha, P. K. Neotia Inst Technol Management & Sci Dept Comp Sci & Engn Kolkata India Univ Jadavpur Dept Comp Sci & Engn Kolkata India Univ Iowa Dept Elect & Comp Engn Iowa City IA USA

ISBN: (纸本)9781467346986;9781467346993

Segmentation of magnetic resonance imaging (MRI) brain images is an important task to analyze tissue structures of a human brain. Due to improper image acquisition systems, MRI images are generally corrupted by intensity inhomogeneity (IIH) or intensity nonuniformity (INU). Conventional methods try to segment MRI images using only spatial information about the distribution of pixel intensities and are highly sensitive to noise and the IIH or INU. This paper presents a method to segment MRI brain images by considering the INU and spatial information using fuzzy C-means (fcm) clustering algorithm. Firstly, the INU of MRI brain image is corrected using fusion of Gaussian surfaces. The individual Gaussian surface is estimated independently over the different homogeneous regions by considering its center as the center of mass of the respective homogeneous region. Secondly, the IIH corrected image is segmented using probabilistic fcm algorithm, which considers spatial features of image pixels. The experiments using 3D synthetic phantoms and real-patient MRI brain images reveal that the proposed method performs satisfactorily.

关键词： MRI images INU or bias field fcm algorithm

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基于遗传算法与模糊聚类算法相结合的变压器故障诊断研究

基于遗传算法与模糊聚类算法相结合的变压器故障诊断研究

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中国电机工程学会电力系统自动化专业委员会2012年学术交流会

作者：尹涛刘倩倩国网电科院安徽继远电网技术有限责任公司

模糊C 均值聚类(fcm)算法对初始化敏感、容易陷入局部最优的缺陷,遗传算法具有较强的全局寻优能力,能够克服该缺陷.将两者相结合对变压器故障进行诊断,通过实例分析,该算法能有效地对样本进行聚类,并具有较高的判定正确率.

关键词： fcm algorithm genetic algorithm transformer fault diagnosis

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An Improved Fuzzy C-Means Clustering algorithm and Application in Meteorological Data

An Improved Fuzzy C-Means Clustering Algorithm and Applicati...

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International Conference on Materials Science and Technology

作者： Li, Hongfei Wang, Fuling Zheng, Shijue Gao, Li Cent China Normal Univ Dept Comp Sci Wuhan Hebei Peoples R China

ISBN: (纸本)9783037850152

The fuzzy clustering algorithm is sensitive to the m value and the degree of membership. Because of the deficiencies of traditional fcm clustering algorithm, we made specific improvement. Through the calculation of the value of m, the amendments of degree of membership to the discussion of issues, effectively compensate for the deficiencies of the traditional algorithm and achieve a relatively good clustering effect. Finally, through the analysis of temperature observation data of the three northeastern province of china in 2000, the reasonableness of the method is verified.

关键词： fcm algorithm weight exponent membership degree cluster validity

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Automatic Location of the Talairach Cortical Landmarks from T2-weighted MR Images

Automatic Location of the Talairach Cortical Landmarks from ...

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International Conference on Materials, Mechatronics and Automation (ICMMA 2011)

作者： Fu, Yili Zhang, GuangCai Chang, Qiuyue Wang, Shuguo Han, Xianwei Harbin Inst Technol State Key Lab Robot & Syst Harbin 150006 Peoples R China

ISBN: (纸本)9783037850176

For labeling the T2-weighted MR images using human brain atlas, it is prerequisite to the foundation of the Talairach space for T2W MR images, and the basic condition to found Talairach space is the location of Talairach cortical landmarks from T2W MR images. A method to locate the Talairach cortical landmarks from T2W MR images is proposed, it consists of three aspects: Firstly, determine the planes including the six cortical landmarks;segment the planes based on fuzzy C-means clustering algorithm, gray level projection, watershed algorithm, region merging, thresholding, and morphologic operations;locate the cortical landmarks from the segmented planes. The algorithm has been validated quantitatively with 20 T2W MR images data sets. The mean errors of the Talairach cortical landmarks were below 1.00 mm. It took about 8 seconds for identifying them on P4 3.0 GHz. This fast, robust algorithm is potentially useful in clinic and for research.

关键词： Talairach cortical landmarks T2-weighted MR image fcm algorithm Watershed algorithm region merging thresholding

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Trend of Return of Stocks Portfolio:An Approach Using Fuzzy c-Means algorithm and Fuzzy Transform

Trend of Return of Stocks Portfolio:An Approach Using Fuzzy ...

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2011 World Congress on Engineering and Technology(CET 2011)

作者： Renato Aparecido Aguiar Dept. of Electrical Engineering Centro Universitário da FEI So Paulo Brazil

关键词： fcm algorithm Fuzzy Transform Overreaction Financial Ratios.

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