Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Symposium on Electrical Insulating Materials |
| Pages | 885-888 |
| Number of pages | 4 |
| Volume | 3 |
| Publication status | Published - 2005 |
| Externally published | Yes |
| Event | 2005 International Symposium on Electrical Insulating Materials, ISEIM 2005 - Kitakyushu Duration: 2005 Jun 5 → 2005 Jun 9 |
Other
| Other | 2005 International Symposium on Electrical Insulating Materials, ISEIM 2005 |
|---|---|
| City | Kitakyushu |
| Period | 05/6/5 → 05/6/9 |
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ASJC Scopus subject areas
- Engineering(all)
- Materials Science(all)
Cite this
A method to classify the signals from artificially prepared defects in GIS using the decision tree method. / Hirose, H.; Ohhata, T.; Kotou, Y.; Matsuda, S.; Hikita, M.; Nishimura, T.; Ohtsuka, S.; Matsumoto, Satoshi; Tsuru, S.; Ichimaru, J.
Proceedings of the International Symposium on Electrical Insulating Materials. Vol. 3 2005. p. 885-888.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A method to classify the signals from artificially prepared defects in GIS using the decision tree method
AU - Hirose, H.
AU - Ohhata, T.
AU - Kotou, Y.
AU - Matsuda, S.
AU - Hikita, M.
AU - Nishimura, T.
AU - Ohtsuka, S.
AU - Matsumoto, Satoshi
AU - Tsuru, S.
AU - Ichimaru, J.
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=25644452937&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=25644452937&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:25644452937
VL - 3
SP - 885
EP - 888
BT - Proceedings of the International Symposium on Electrical Insulating Materials
ER -