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- W2368958843 abstract "A new method is presented, using infrared image features and radial basis probabilistic neural network (RBPNN) for insulator contamination grades detection of natural contaminated insulators under different humidity. An amended alpha filter and an image segmentation method based on the histogram trough of the insulator image are adopted to preprocess the insulator infrared image. Experiments are designed under different humidity, four features which can represent the contamination grades of the insulator are extracted from the segmented image, they are the mean temperature of the image background, the highest temperature of the insulator surface, the mean temperature of the insulator surface, the temperature variance of the insulator surface, RBPNN is designed to map the relation between the infrared image features of the insulator under different humidity and the contamination grades; then RBPNN is trained to recognize the contamination grades. A method integrated gradient algorithm and random algorithm is applied to decide the centers of hidden nodes、the width control parameters and the weights matrix of the RBPNN . Experiments results indicate this method is an effective approach for the detection of the insulator contamination grades." @default.
- W2368958843 created "2016-06-24" @default.
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- W2368958843 date "2006-01-01" @default.
- W2368958843 modified "2023-09-25" @default.
- W2368958843 title "Contamination Grades Recognition of Insulators Under Different Humidity Using Infrared Image Features and RBPNN" @default.
- W2368958843 hasPublicationYear "2006" @default.
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