| Pd Pattern Recognition Based on Linear Discriminant Analysis in GIS |
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| 作者姓名： | ZHANG Xiao-xing TANG Ju SU Cai-xin XU Zhong-rong ZHOU Qian |
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| 作者单位： | Key Laboratory of High Voltage Engineering and Electrical New Technology, Ministry of Education, Chongqing University, Chongqing 400030, China |
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| 摘 要： | According to the character of PD in GIS, the authors design four kinds of GIS defection models. The GIS gray intensity images are constructed based on mass specimens gathered by the ultra - high frequency and high speeds systems, Aiming at the PD characteristics and its defections, A PCA-FDA method is put forward based on PD images. The principal component analysis is employed to condense the dimension of PD images, then the optimal sets of statistically uncorrelated discriminant vectors are extracted, and the minimum distance classifier is constructed as classifier. The identified results show that this method can effectively elevated the discrimination of the four kinds of defects in GIS PD.
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| 关 键 词： | GIS PD pattern recognition Linear Discriminant Analysis principal component analysis |
| 收稿时间： | 6/8/2006 12:00:00 AM |
| 修稿时间： | 6/8/2006 12:00:00 AM |
Pd Pattern Recognition Based on Linear Discriminant Analysis in GIS |
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| ZHANG Xiao-xing,TANG Ju,SU Cai-xin,XU Zhong-rong,ZHOU Qian.Pd Pattern Recognition Based on Linear Discriminant Analysis in GIS[J].Storage & Process,2006(10):1-4. |
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| Authors: | ZHANG Xiao-xing TANG Ju SU Cai-xin XU Zhong-rong ZHOU Qian |
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| Institution: | Key Laboratory of High Voltage Engineering and Electrical New Technology, Ministry of Education, Chongqing University, Chongqing 400030, China |
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| Abstract: | According to the character of PD in GIS, the authors design four kinds of GIS defection models. The GIS gray intensity images are constructed based on mass specimens gathered by the ultra - high frequency and high speeds systems, Aiming at the PD characteristics and its defections, A PCA-FDA method is put forward based on PD images. The principal component analysis is employed to condense the dimension of PD images, then the optimal sets of statistically uncorrelated discriminant vectors are extracted, and the minimum distance classifier is constructed as classifier. The identified results show that this method can effectively elevated the discrimination of the four kinds of defects in GIS PD. |
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| Keywords: | GIS PD pattern recognition Linear Discriminant Analysis principal component analysis |
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