检索结果-南通市图书馆

Abnormal State Detection of OLTc Based on Improved fuzzy c-means clustering

在线全文

学校读者我要写书评

暂无评论

chinese Journal of Electrical Engineering 2023年第1期9卷 129-141页

作者： Hongwei Li Lilong Dou Shuaibing Li Yongqiang Kang Xingzu Yang Haiying Dong School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou 730070China School of New Energy and Power Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

An accurate extraction of vibration signal characteristics of an on-load tap changer(OLTc)during contact switching can effectively help detect its abnormal ***,an improved fuzzy c-means clustering method for abnormal state detection of the OLTc contact is ***,the wavelet packet and singular spectrum analysis are used to denoise the vibration signal generated by the moving and static contacts of the ***,the Hilbert-Huang transform that is optimized by the ensemble empirical mode decomposition(EEMD)is used to decompose the vibration signal and extract the boundary spectrum ***,the gray wolf algorithm-based fuzzy c-means clustering is used to denoise the signal and determine the abnormal states of the OLTc *** analysis of the experimental data shows that the proposed secondary denoising method has a better denoising effect compared to the single denoising *** EEMD can improve the modal aliasing effect,and the improved fuzzy c-means clustering can effectively identify the abnormal state of the OLTc *** analysis results of field measured data further verify the effectiveness of the proposed method and provide a reference for the abnormal state detection of the OLTc.

关键词： On-load tap changer singular spectrum analysis Hilbert-Huang transform gray wolf optimization algorithm fuzzy c-means clustering

New two-dimensional fuzzy c-means clustering algorithm for image segmentation

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of central South University of Technology 2008年第6期15卷 882-887页

作者：周鲜成申群太刘利枚 School of Information Science and Engineering Central South University School of computer and Electronic Engineering Hunan University of Commerce

To solve the problem of poor anti-noise performance of the traditional fuzzy c-means (FcM) algorithm in image segmentation, a novel two-dimensional FcM clustering algorithm for image segmentation was proposed. In this method, the image segmentation was converted into an optimization problem. The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixels described by the improved two-dimensional histogram. By making use of the global searching ability of the predator-prey particle swarm optimization, the optimal cluster center could be obtained by iterative optimization, and the image segmentation could be accomplished. The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%. The proposed algorithm has strong anti-noise capability, high clustering accuracy and good segment effect, indicating that it is an effective algorithm for image segmentation.

关键词： image segmentation fuzzy c-means clustering particle swarm optimization two-dimensional histogram

ALLIED fuzzy c-means clustering MODEL

在线全文

学校读者我要写书评

暂无评论

Transactions of Nanjing University of Aeronautics and Astronautics 2006年第3期23卷 208-213页

作者：武小红周建江南京航空航天大学信息科学与技术学院江苏大学电气信息工程学院中国镇江212013

A novel model of fuzzy clustering, i.e. an allied fuzzy c means （AFcM） model is proposed based on the combination of advantages of fuzzy c means （FcM） and possibilistic c means （PcM） clustering. PcM is sensitive to initializations and often generates coincident clusters. AFcM overcomes this shortcoming and it is an ex tension of PcM. Membership and typicality values can be simultaneously produced in AFcM. Experimental re- suits show that noise data can be well processed, coincident clusters are avoided and clustering accuracy is better.

关键词： fuzzy c-means clustering possibilistic c means clustering allied fuzzy c-means clustering

Optimization Deployment of Temperature Measurement Points for cZ Silicon Single crystal Growth Equipment Based on Improved fuzzy c-means clustering

在线全文

学校读者我要写书评

暂无评论

Optimization Deployment of Temperature Measurement Points fo...

fuzzy c-means text clustering based on topic concept sub-space

第43届中国控制会议

作者： Weichao Huang Siyuan cao Ding Lui School of Automation and Information Engineering Xi'an University of Technology Shannxi Key Laboratory of complex System control and Intelligent Information Processing

The inverse heat transfer method is an effective inversion algorithm for solving the heat transfer at non-measurable places of the study object. However, the current inverse heat transfer inversion method has the problems of low accuracy and difficult selection of temperature measurement point locations. To solve this problem, this paper proposes an optimization algorithm for measurement point locations based on improved fuzzy c-means clustering(IFcM). The method first generates the initial clustering center through a specific density function and introduces the Mahalanobis distance to solve the problem that the traditional fuzzy c-means clustering results depend on the initial clustering center and the results are unstable. Then, an optimized deployment algorithm based on IFcM temperature measurement points is proposed and compared with other mean value clustering algorithms. Finally, the optimal temperature measurement position on the crystal surface is selected by the IFcM temperature measurement point optimization deployment algorithm on the cZ(czochraski)silicon single crystal growth equipment, and the inversion results of the randomly selected temperature measurement point scheme and the optimized temperature measurement point scheme are compared by the real-time inversion algorithm based on the dynamic matrix control multi-boundary heat flux, and the results of the optimized temperature measurement point scheme are obtained to be better than that of the randomly selected temperature measurement point scheme.

关键词： fuzzy c-means clustering IFcM Temperature measurement point optimization Inverse heat transfer

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Journal of Southeast University(English Edition) 2007年第3期23卷 439-442页

作者：吉翔华陈超邵正荣俞能海中国科学技术大学多媒体计算与通信教育部-微软重点实验合肥230027 中国科学技术大学图书馆合肥230027

To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TcS2FcM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision.

关键词： TcS2FcM topic concept space fuzzy c-means clustering text clustering

A fuzzy c-means based User clustering Method for Demand Response Regulation

在线全文

学校读者我要写书评

暂无评论

A Fuzzy C-means based User Clustering Method for Demand Resp...

The cuckoo search Algorithm Based on fuzzy c-mean clustering 36

第40届中国控制会议

作者： Liang Li Liang Wang Hongwei MA Peidong HAN Ziyan Li Ye Tian School of Automation Beijing Institute of Technology

The smart grid provides a good chance for the application of demand response technology, which could improve the renewable energy consumption and system stability. This paper proposes a fuzzy c-means based User clustering Method for Demand Response Regulation. fuzzy c-means clustering method is adopted to find the user set which has the closest load characteristics with the renewable energy generation. This user set has the best probability to consume renewable energy. Then,particle swarm algorithm is used to adjust the electric price, which could slightly change the electric consumption behavior of the above user set. Finally, a higher renewable energy consumption rate could be achieved under a stable and economical condition.

关键词： fuzzy c-means clustering Demand Response curve Similarity Particle Swarm Optimization

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

The Cuckoo search Algorithm Based on Fuzzy C-mean Clustering

Recognition of Blast Furnace Gas Flow center Distribution Based on Infrared Image Processing

第36届中国控制会议

作者： Lijun Yu Zequan Dong Hui Wang Ying Ding college of Automation Harbin Engineering University

ISBN: (纸本)9781538629185

cuckoo Search is a meta-heuristic algorithm proposed in recent *** evaluation strategy is used in pair solution,while the quality and convergence rate of solutions decrease for the interdimensional interference when solving high-dimensional functions *** order to supplement this defect,this work proposed to cluster similar components among individuals with fuzzy c-means clustering and then update the evaluation by ***,the new solution to the current solution can be improved by accepting with greedy *** simulation results show that the improved cS can effectively increase the quality and convergence rate of ***,with the increase of dimensions,this advantage becomes more and more *** comparison between other improved cS and meta-heuristic algorithm shows that the improved algorithm has large advantage in solving high-dimensional functions.

关键词： cuckoo Search fuzzy c-means clustering Greedy strategy High-dimensional functions optimization

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

Journal of Iron and Steel Research International 2016年第3期23卷 203-209页

作者： Lin SHI You-bin WEN Guang-sheng ZHAO Tao YU School of Mathematics and Physics and Biological Engineering University of Science and Technology of Inner Mongolia School of Materials and Metallurgy University of Science and Technology of Inner Mongolia

To address the problems about the difficulty in accurate recognition of distribution features of gas flow center at blast furnace throat and determine the relationship between gas flow center distribution and gas utilization rate,a method for recognizing distribution features of blast furnace gas flow center was proposed based on infrared image processing,and distribution features of blast furnace gas flow center and corresponding gas utilization rates were categorized by using fuzzy c-means clustering and statistical methods.A concept of gas flow center offset was *** results showed that,when the percentage of gas flow center without offset exceeded 85%,the average blast furnace gas utilization rate was as high as 41%;when the percentage of gas flow center without offset exceeded50%,the gas utilization rate was primarily the center gas utilization rate,and exhibited a positive correlation with no center offset degree;when the percentage of gas flow center without offset was below 50% but the sum of the percentage of gas flow center without offset and that of gas flow center with small offset exceeded 86%,the gas utilization rate depended on both the center and the edges,and was primarily the edge gas utilization *** method proposed was able to accurately and effectively recognize gas flow center distribution state and the relationship between it and gas utilization rate,providing evidence in favor of on-line blast furnace control.

关键词： infrared image processing gas flow center recognition gas utilization rate fuzzy c-means clustering

Intelligent diagnosis of the solder bumps defects using fuzzy c-means algorithm with the weighted coefficients

在线全文

学校读者我要写书评

暂无评论

Science china(Technological Sciences) 2015年第10期58卷 1689-1695页

作者： LU XiangNing SHI TieLin WANG SuYa LI Li Yi SU Lei LIAO GuangLan School of Mechanical & Electrical Engineering Jiangsu Normal University School of Materials Science and Engineering Georgia Institute of Technology State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology

Solder bump technology has been widely used in electronic packaging. With the development of solder bumps towards higher density and finer pitch, it is more difficult to inspect the defects of solder bumps as they are hidden in the package. A nondestructive method using the transient active thermography has been proposed to inspect the defects of a solder bump, and we aim at developing an intelligent diagnosis system to eliminate the influence of emissivity unevenness and non-uniform heating on defects recognition in active infrared testing. An improved fuzzy c-means(FcM) algorithm based on the entropy weights is investigated in this paper. The captured thermograms are preprocessed to enhance the thermal contrast between the defective and good bumps. Hot spots corresponding to 16 solder bumps are segmented from the thermal images. The statistical features are calculated and selected appropriately to characterize the status of solder bumps in FcM clustering. The missing bump is identified in the FcM result, which is also validated by the principle component analysis. The intelligent diagnosis system using FcM algorithm with the entropy weights is effective for defects recognition in electronic packages.

关键词： solder bump fuzzy c-means clustering feature weighting principal component analysis