A method to classify the signals from artificially prepared defects in GIS using the decision tree method

被引：2

作者：

Hirose, H ^{[1
]}

Ohhata, T ^{[1
]}

Kotou, Y ^{[1
]}

Matsuda, S ^{[1
]}

Hikita, M ^{[1
]}

Nishimura, T ^{[1
]}

Ohtsuka, S ^{[1
]}

Matsumoto, S ^{[1
]}

Tsuru, S ^{[1
]}

Ichimaru, J ^{[1
]}

机构：

[1] Kyushu Inst Technol, Fukuoka, Japan

来源：

PROCEEDINGS OF THE 2005 INTERNATIONAL SYMPOSIUM ON ELECTRICAL INSULATING MATERIALS, VOLS, 1-3 | 2005年

关键词：

D O I：

10.1109/ISEIM.2005.193523

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

On-line diagnosing of GIS (Gas Insulated Switchgears) requires the pattern classification and identification of signals that are emitted from GIS. To classify the patterns correctly, substantial data sets that are emitted by artificially mimicked defects in GIS are needed. Applying the neural networks to the data sets, in general, identification methods of defects in GIS have widely been developed. Some identification system shows a good success such that the misclassification rate is reduced to below 5%; the key features in identification, however, are not obviously revealed in neural networks systems because of nonlinear network structures. The decision tree method that classifies the signals using the feature rules in plain graphical representations can explains the classification rules in clear forms. We applied the decision tree classification method to the signals emitted from the signals by artificially prepared defects in GIS, and find that the method shows a good classification rates over 95% which are comparable to that in neural networks. We also discuss the robustness from noise, and compare the results of the misclassification rates by the two methods.

引用

页码：885 / 888

页数：4

共 10 条

[1] Spatially adaptive wavelet thresholding with context modeling for image denoising [J].