材料の種類を考慮した転移学習による破面分類-Reference-Cited by-同舟云学术

材料の種類を考慮した転移学習による破面分類

Published:2023-05-15 Issue:5 Volume:72 Page:376-383
ISSN:0514-5163
Container-title:Journal of the Society of Materials Science, Japan
language:en
Short-container-title:J. Soc. Mat. Sci., Japan

Author:

UESUGI Tokuteru¹,KONDOU Mitsuaki²,HAMADA Naoyuki³,KITA Shunsuke³,HIRATA Tomotake³

Affiliation:

1. Graduate School of Informatics, Osaka Metropolitan University

2. School of Knowledge and Information Systems, Osaka Prefecture University

3. Osaka Research Institute of Industrial Science and Technology

Publisher

Society of Materials Science, Japan

Subject

Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,General Materials Science

Link

https://www.jstage.jst.go.jp/article/jsms/72/5/72_376/_pdf

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3. 3) M. Fukushima, T. Uesugi, M. Tsujikawa, S. Tsutsumi, K. Ogawa, K. Sawada and K. Nakamoto, "Prediction of Mg yield rate of graphite spheronization by machine learning using quantile loss", Journal of Japan Foundry Engineering Society, Vol. 94, pp.69-75 (2022).

4. 4) T. Mochizuki, T. Uesugi and Y. Takigawa, "Prediction system for solid solubility Limits of Ag-, Cu-, Al-, and Mg-based alloys using artificial neural networks and first-principles calculations", Materials Transactions, Vol. 61, pp.2083-2090 (2020).

5. 5) K. Komai, K. Minoshima and M. Koyama, "Development of diagnostic expert system for environmentally assisted cracking (EXENAC) and importance evaluation of knowledge in inference", Transactions of the Japan Society of Mechanical Engineers Series A, Vol. 57, pp.188-194 (1991).

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