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A New McP Method of Wind Speed Temporal Interpolation and Extrapolation considering Wind Speed Mixed Uncertainty

ENERGIES 2017年第8期10卷 1231页

作者： Liu, Xiao Lai, Xu Zou, Jin Wuhan Univ Sch Water Resource & Hydro Elect Engn Wuhan 430072 Hubei Peoples R China China Southern Power Grid Elect Power Res Inst Guangzhou 510080 Guangdong Peoples R China

In this paper, a missing wind speed data temporal interpolation and extrapolation method in the wind energy industry was investigated. Given that traditional methods have previously ignored part of mixed uncertainty of wind speed, a concrete granular computing method is constructed and a new Measure-correlate-Predict (McP) method of wind speed data temporal interpolation and extrapolation considering all mixed uncertainties is proposed, based on granular computing theory by adopting the cloud model method, support vector regression method, artificial neural network, genetic algorithm, and fuzzy c-means clustering algorithm as tools. The importance of considering mixed wind speed uncertainty and the suitability of using granular computing method are illustrated, and wind speed mixed uncertainty analysis is implemented, then, recommended values and estimation tools for wind speed measurement uncertainty and combined uncertainty are provided. An interpolation case of two practical meteorological sites in central Southern china was used to implement and validate the method proposed in this paper. The following conclusions are reached: (a) by using the method proposed in this paper, mixed uncertainty of wind speed can be considered, comparing to other McP methods used for purposes of comparison, a better estimation of the wind speed is provided, and most evaluation metrics employed in this analysis were superior to other methods, that is to say, the accuracy of the wind resource assessment improved, and the risks of wind farm construction were reduced;(b) granular computing method is suitable for the issue of wind speed data interpolation and extrapolation considering wind speed mixed uncertainty;(c) mixed uncertainty of wind speed can be divided into three levels, and recommended values of granularity are minimum interval of records, 0.3-0.8 m/s, and 1-3 m/s, respectively.

关键词： measure-correlate-predict method mixed uncertainty of wind speed granular computing theory cloud model support vector regression neural network genetic algorithm fuzzy c-means clustering algorithm

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3D reconstruction method based on contour features

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Journal of Beijing Institute of Technology 2016年第3期25卷 301-308页

作者： HAN Bao-ling ZHU Ying LUO Qing-sheng XU Bo ZHANG Tian School of Mechanical Engineering Beijing Institute of Technology Beijing 100081 China School of Optoelectronics Beijing Institute of Technology Beijing 100081 China School of Mechatronical Engineering Beijing Institute of Technology Beijing 100081 China

To guarantee the accuracy and real-time of the 3D reconstruction method for outdoor scene,an algorithm based on region segmentation and matching was ***,on the basis of morphological gradient information,obtained by comparing color weight gradient images and proposing a multi-threshold segmentation,scene contour features were extracted by a watershed algorithm and a fuzzy c-means clustering ***,to reduce the search area,increase the correct matching ratio and accelerate the matching speed,the region constraint was established according to a region＇s local position,area and gray characteristics,the edge pixel constraint was established according to the epipolar constraint and the continuity ***,by using the stereo matching edge pixel pairs,their 3D coordinates were estimated according to the binocular stereo vision imaging *** results show that the proposed method can yield a high stereo matching ratio and reconstruct a 3D scene quickly and efficiently.

关键词： gradient map watershed algorithm fuzzy c-means clustering algorithm region con-straint contour matching 3D reconstruction

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Research on Feature Extraction and clustering of Virtual Learning community Network Learning

Research on Feature Extraction and Clustering of Virtual Lea...

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International conference on Information System and Artificial Intelligence (ISAI)

作者： cheng, Yan Xie, Jian-hua Tan, Ping-fei Yang, Zhi-ming Tongji Univ Post Doctoral Mobile Stn Comp Sci & Technol Shanghai 200092 Peoples R China Jiangxi Normal Univ Coll Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China

ISBN: (纸本)9781509015856

Virtual learning community is a kind of learning environment based on network is a new type of learning organization. However, Virtual Learning community in the teaching data is often messy, fragmentary, it's value is often difficult to be detected and reasonable to use data mining techniques to deal with data will give us a analysis to study the effect of get twice the result with half the effort. Personalized teaching has always been a difficult point in the network teaching research, and the extraction and analysis of the characteristics of the learner's learning is the basis of personalized teaching. The purpose of this paper is through the acquisition learning in virtual learning community of learning behavior record and analysis learners learning characteristics, through the establishment of learning feature vectors, using fuzzy c-means algorithm for mining learners common characteristics. The common characteristics of individual learners as a research object of the transformation to the learner that clustering center feature vector as the research object, so that learning features simplified. characteristics similar to the learner to divide into a group, formulated by the relevant experts in the field of practical teaching strategies and learning tasks, for each group are for teaching, personalized teaching, so as to improve the learning efficiency. Development of individualized instruction.

关键词： virtual learning community fuzzy c-means clustering algorithm learning characteristics personalized teaching

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fuzzy c-means clustering recognition in ultrasonic fault detection in bonding steel plate and rubber material

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MATERIALS RESEARcH INNOVATIONS 2014年第Sup2期18卷 286-289页

作者： Liu, X. Zhou, R. Inner Mongolia Univ Coll Elect Informat Engn Hohhot 010010 Peoples R China

The article elaborates the use of ultrasonic in testing the quality of the bonding of a steel plate and rubber. We also analyse information such as the echo signal's attenuation coefficient, harmonic amplitude and frequency, and extract the signal energy, signal duration and the product of singular peak value and quantity, which are regarded as the characteristic values. We use the fuzzy c-means clustering algorithm to make a quantitative analysis on steel plate and rubber bonding quality. The results show that the recognition correct rate of the algorithm is 97.5%, and this method can effectively classify and identify the quality in bonding steel plate and rubber material.

关键词： fuzzy c-means clustering algorithm Ultrasonic detection Echo signal

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A NOVEL ULTRA-SHORT TERM LOAD FOREcASTING METHOD BASED ON LOAD TREND AND fuzzy c-means clustering ALGORIHM

A NOVEL ULTRA-SHORT TERM LOAD FORECASTING METHOD BASED ON LO...

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International conference on Power System Technology (Powercon)

作者： Zhang Yi Zhang Feng Zhu Bingquan Zhejiang Univ Water Resources & Elect Power Hangzhou 310018 Zhejiang Peoples R China Stae Grid Zhejiang Elect Power Co Hangzhou 310007 Zhejiang Peoples R China

ISBN: (纸本)9781479950324

According to the requirement of ultra-short term load forecasting in a certain provincial power grid and on the basis of fully analyzing the load characteristics, a novel ultra-short term load forecasting method based on the trend of power load fluctuation and fuzzy c-means clustering algorithm is proposed. In this method the identification and correction of pseudo data are integrated into the load forecasting process. This method is accurate and practicable;the practical application of this method shows that its error analysis index is much better than that from other ultra-short load forecasting method being used in the certain provincial power grid.

关键词： Energy management system (EMS) Ultra-short term load forecasting Load trend fuzzy c-means clustering algorithm Power system dispatching

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comparative assessment of efficiency for content based image retrieval systems using different wavelet features and pre-classifier

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MULTIMEDIA TOOLS AND APPLIcATIONS 2015年第24期74卷 11595-11630页

作者： chowdhury, Manish Kundu, Malay Kumar Indian Stat Inst Machine Intelligence Unit Kolkata 108 India

Recently, content Based Image Retrieval (cBIR) has emerged as an active research area having applications in various fields. There exist several states-of-the art cBIR systems that uses both spatial and transform features as input. However, as hardly any details study reported so far on the effectiveness of different transform domain features in cBIR paradigm. This motivates the current article where we have presented extensive comparative assessment of five different transform domain features considering various filter combinations. Three different feature representation schemes and three different classifiers have been used for this purpose. Extensive experiments on four widely used benchmark image databases (Oliva, caltech101, caltech256 and MIRFlickr25000) were conducted to determine the best combination of transform, filters, feature representation and classifier. Furthermore, we have also attempted to discover the optimal features from the best combinations using maximal information compression index (MIcI). Both qualitative and quantitative evaluations show that the combination of Least Square Support Vector Machine (LSSVM) as a classifier and the statistical parametric framework based reduced feature representation in Non-Subsampled contourlet Transform (NScT) with "pyrexc" and "sinc" filters gives the best retrieval performances.

关键词： content based image retrieval fuzzy c-means clustering algorithm Multi-resolution analysis Multi-scale geometric analysis classification

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Application of principal component analysis and a fuzzy c-means clustering algorithm to wear debris morphology classification

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PROcEEDINGS OF THE INSTITUTION OF MEcHANIcAL ENGINEERS PART J-JOURNAL OF ENGINEERING TRIBOLOGY 2009年第J7期223卷 1059-1066页

作者： Wu, J. P. Yan, X. P. Jin, Z. M. Tipper, J. L. Yuan, c. Q. Zhou, X. c. Wuhan Univ Technol Reliabil Engn Inst Wuhan 430063 Hubei Peoples R China Univ Leeds Div Microbiol MBE Leeds W Yorkshire England

The application of principal component analysis (PcA) and fuzzy c-means clustering algorithm to the classification of ultrahigh molecular weight polyethylene (UHMWPE) wear debris from artificial joints has been described in this article. Wear particles were extracted and isolated from peri-prosthetic tissues collected during revision surgery, which was revised for loosening. The implant life of the hip prosthesis was 12 years. The particles were examined by scanning electron microscopy. Digitized particle images were analysed on a computer by specially developed software 'Image-Pro Plus'. The following 19 numerical descriptors were used to characterize the particles: particle area, length, width, perimeter, boundary fractal dimension, and shape parameters such as form factor, roundness, convexity, aspect ratio, and others. PcA algorithm was applied to reduce the amount of parameters to simplify the following calculation. Furthermore, main factors and important parameters such as mean diameter, equivalent circle diameter, and perimeter were found out by PcA. However, c-means clustering algorithm was applied to classify the UHMWPE wear debris into 4-7 clusters. The Xie-Beni index was introduced to determine the optimal number of clusters and illuminate the clustering validity The result of the calculation indicates that five clusters is the optimal clustering number. The feature of the debris in each cluster is also described in this article.

关键词： ultrahigh molecular weight polyethylene wear debris artificial joints principal component analysis fuzzy c-means clustering algorithm

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A New RBF Neural Network with GA-based fuzzy c-means clustering algorithm for SINS Fault Diagnosis

A New RBF Neural Network with GA-based Fuzzy C-Means Cluster...

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21st chinese control and Decision conference

作者： Liu, Zhide chen, Jiabin Song, chunlei Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China

ISBN: (纸本)9781424427239

In this paper, a new radial basis function (RBF) neural network with fuzzy c-means clustering algorithm based on genetic algorithm (GA) is proposed for the fault diagnosis of gyroscopes and accelerometers of strapdown inertial navigation system (SINS). The fuzzy c-means algorithm (FcM) tends to fall into the local optimum. The fuzzy c-means clustering algorithm combined with GA (FGA) obtains the global optimal cluster centers. FGA is used to provide the optimal cluster centers for RBF neural network, and a second order learning algorithm is used to train the parameters and weights of RBF neural network. Experimental results show that the proposed RBF neural network with FGA quickly converges and effectively improves the diagnostic accuracy rate of SINS fault diagnosis.

关键词： Radial basis function neural network Fault diagnosis Strapdown inertial navigation system fuzzy c-means clustering algorithm Genetic algorithm

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Application of principal component analysis and a fuzzy c-means clustering algorithm to wear debris morphology classification

Application of principal component analysis and a fuzzy C-me...

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5th china International Symposium on Tribology/1st International Tribology Symposium of IFToMM

关键词： ultrahigh molecular weight polyethylene wear debris artificial joints principal component analysis fuzzy c-means clustering algorithm

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Multi-model direct generalised predictive control for automatic train operation system

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IET INTELLIGENT TRANSPORT SYSTEMS 2015年第1期9卷 86-94页

作者： Wen, Shuhuan Yang, Jingwei Rad, Ahmad B. Hao, Pengcheng Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China Simon Fraser Univ Sch Engn Sci Surrey BC V3T 0A3 Canada

The authors propose a novel multi-model direct generalised predictive control based on predictive function control (PFc) algorithm for automatic train operation system. The proposed method facilitates autonomous driving of a train through a given guidance trajectory. Firstly, they present a multi-model architecture based on fuzzy c-means clustering algorithm. In order to obtain the optimal number of sub-linear models, they apply Xie-Beni cluster validity index. In this regards, the multi-model set is established off-line. Secondly, the proper sub-linear model is selected as the predictive model by using switching performance index at each time slot. The control variables are calculated by direct generalised predictive controller based on PFc. The control algorithm is simple, and can reduce the on-line computation time by directly identifies the unknown parameters in the controller. It can avoid recursively solving the Diophantine equations. The calculation of compensation value becomes simple by introducing PFc. Finally, simulation results are provided to show the effectiveness of the proposed scheme.

关键词： predictive control trajectory control fuzzy set theory pattern clustering performance index time-varying systems railways rail traffic control multimodel direct generalised predictive control automatic train operation system predictive function control algorithm PFc algorithm autonomous train driving trajectory guidance multimodel architecture fuzzy c-means clustering algorithm sublinear models Xie-Beni cluster validity index sublinear model switching performance index online computation time Diophantine equations compensation value

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