The implementation of content-based image retrieval(CBIR)mainly depends on two key technologies:image feature extraction and image feature *** this paper,we extract the color features based on Global Color Histogram(G...
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The implementation of content-based image retrieval(CBIR)mainly depends on two key technologies:image feature extraction and image feature *** this paper,we extract the color features based on Global Color Histogram(GCH)and texture features based on Gray Level Co-occurrence Matrix(GLCM).In order to obtain the effective and representative features of the image,we adopt the fuzzy mathematical algorithm in the process of color feature extraction and texture feature extraction *** we combine the fuzzy color feature vector with the fuzzy texture feature vector to form the comprehensive fuzzy feature vector of the image according to a certain *** feature matching mainly depends on the similarity between two image feature *** this paper,we propose a novel similarity measure method based on k-Nearest Neighbors(kNN)and fuzzy mathematical algorithm(SBkNNF).Finding out the k nearest neighborhood images of the query image from the image data set according to an appropriate similarity measure *** the k similarity values between the query image and its k neighborhood images to constitute the new k-dimensional fuzzy feature vector corresponding to the query *** using the k similarity values between the retrieved image and the k neighborhood images of the query image to constitute the new k-dimensional fuzzy feature vector corresponding to the retrieved *** the similarity between the two kdimensional fuzzy feature vector according to a certain fuzzy similarity algorithm to measure the similarity between the query image and the retrieved *** experiments are carried out on three data sets:WANG data set,Corel-5k data set and Corel-10k data *** experimental results show that the outperforming retrieval performance of our proposed CBIR system with the other CBIR systems.
With the rapid development of intelligent substation, the continuous progress of the condition-based maintenance of intelligent substation has already become very urgent. Status evaluation is the main stage of conditi...
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ISBN:
(纸本)9781509064151;9781509064144
With the rapid development of intelligent substation, the continuous progress of the condition-based maintenance of intelligent substation has already become very urgent. Status evaluation is the main stage of condition-based maintenance. Its purpose is to determine the health status of the equipment, and be able to timely maintenance before the failure occurred. The paper focuses on the research on the status evaluation methods. This paper makes clear the scope of the evaluation and establishes a evaluation system of secondary system in intelligent substation on condition of the existing standards and research. It uses fuzzy mathematical algorithm to establish the evaluation algorithm model. Meanwhile, it introduces two new fuzzy sets to evaluate its status more accurately. This paper also uses the method of BP artificial neural network to determine the weight of each kind of evaluation index, and finally it completes the evaluation of the secondary system in intelligent substation.
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