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.

引用

页码：233 / 238

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