Evaluation of machine learning result for metal identification

被引：0
|
作者：
Okawa, Shinnosuke [1 ]
Tashiro, Kunihisa [1 ]
Wakiwaka, Hiroyuki [1 ]
Nakamura, Yoshihiro [2 ]
Machida, Kazutoshi [2 ]
机构：
[1] School of Engeneering, Shinshu University, 4-17-1, Wakasato, Nagano,380-8553, Japan
[2] Fuji Electric Co., Ltd., 1-11-2, Osaki, Shinagawa-ku, Tokyo,141-0032, Japan
来源：
IEEJ Transactions on Fundamentals and Materials | 2021年 / 141卷 / 04期
关键词：
Machine learning;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The purpose of this study is improvement of metal identification performance with step response. Feature values are maximum derivative current and its reaching time, these values depend on lift-off in the range of 0.5-1.5 mm. As a result of metal identifications, decision tree is the fastest and highest accuracy in 4 machine learning models. When increasing training samples, calculation time of all models are increasing, and accuracies are saturated 100 samples. When comparing between data whose lift-off is from 0.5 to 1.5 mm and data that fixed lift-off, classification accuracy in data fixed lift-off is improve than one in data not fixed lift-off. © 2021 The Institute of Electrical Engineers of Japan.
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页码：233 / 238
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