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Prediction of Current-Dependent Motor Torque Characteristics Using Deep Learning for Topology Optimization
Title: | Prediction of Current-Dependent Motor Torque Characteristics Using Deep Learning for Topology Optimization |
Authors: | Aoyagi, Taiga Browse this author | Otomo, Yoshitsugu Browse this author | Igarashi, Hajime Browse this author →KAKEN DB | Sasaki, Hidenori Browse this author | Hidaka, Yuki Browse this author | Arita, Hideaki Browse this author |
Keywords: | Convolutional neural networks | CNNs | deep learning | DL | permanent magnet motor | topology optimization | TO |
Issue Date: | Sep-2022 |
Journal Title: | IEEE Transactions on Magnetics |
Volume: | 58 |
Issue: | 9 |
Start Page: | 1 |
End Page: | 4 |
Publisher DOI: | 10.1109/TMAG.2022.3167254 |
Abstract: | In this study, we propose a fast topology optimization (TO) method based on a deep neural network (DNN) that predicts the current-dependent motor torque characteristics using its cross-sectional image. The trained DNN is shown to provide the current condition that provides the maximum torque under the assumed motor control method. The proposed method helps perform TO with a reduced number of field computations while maintaining a high search capability. |
Rights: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Type: | article (author version) |
URI: | http://hdl.handle.net/2115/87028 |
Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 五十嵐 一
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