SASAKI Hidenori

ササキヒデノリ | SASAKI Hidenori

Affiliation and department：
Job title： Lecturer
Homepage URL (1)： https://sasaki-lab.ws.hosei.ac.jp/wp/

Research field (2)： Electrical power engineering , Design engineering

Research keywords (5)：モータ , 機械学習 , 電気機器 , トポロジー最適化 , 自動設計

Research theme for competitive and other funds (6)：

2023 - 2025 Development of Magnet-Saving, High-Efficiency Synchronous Motors Using Asymmetric Magnetic Poles
2024 - 2025 強化学習を用いたモータ磁気回路設計の自動化に関する研究
2024 - 2025 深層学習・トポロジー最適化等を活用した回転機設計技術の開発
2023 - 2025 磁気シミュレーションとビッグデータを駆使した電気機器の新奇構造開発
2022 - 2025 モータ構造と特性の関係抽出を用いた対話型自動トポロジー最適化の実現

Show all

Papers (23)：

Hidenori Sasaki, Kazuhisa Iwata, Takahiro Sato, Yuki Sato. Prediction of Motor Characteristic Maps via Deep Operator Networks for Topology Optimization. IEEE Transactions on Magnetics. 2024. 1-1
Hidenori Sasaki, Kazuhisa Iwata, Takahiro Sato, Yuki Sato. Motor Characteristics Map Prediction Using Deep Operator Neural Networks. CEFC 2024 - 21st IEEE Biennial Conference on Electromagnetic Field Computation. 2024
Yuki Sato, Hirokazu Matsumoto, Akito Maruo, Takahiro Sato, Hidenori Sasaki. Fast Analysis and Design for 3D-Structured Magnetic Components Using Surrogate Model from Transfer Learning. CEFC 2024 - 21st IEEE Biennial Conference on Electromagnetic Field Computation. 2024
Tatsuya Yamaguchi, Yuki Kuroda, Yoshifumi Okamoto, Hidenori Sasaki, Koji Fujiwara. Acceleration of Waveform Control for Measurement of Magnetic Hysteresis Based on Single Sheet Tester Using Neural Network. CEFC 2024 - 21st IEEE Biennial Conference on Electromagnetic Field Computation. 2024
Kazuhisa Iwata, Hidenori Sasaki, Hajime Igarashi, Daisuke Nakagawa, Tomoya Ueda. Parameter and Topology Optimization Method for IPM Motors using Multimodal Neural Network. CEFC 2024 - 21st IEEE Biennial Conference on Electromagnetic Field Computation. 2024

ｍore...

MISC (40)：

Hidenori SASAKI. Efficiency Improvement in Motor Development Through the Integration of Deep Learning and Topology Optimization. The Journal of The Institute of Electrical Engineers of Japan. 2023. 143. 10. 628-631
佐藤孝洋, 佐藤佑樹, 佐々木秀徳. An Investigation on Construction of Response Surface Using Deep Operator Network. 電気学会研究会資料(Web). 2023. SA-23-058-068/RM-23-057-067
佐々木秀徳, 岩田和久, 佐藤孝洋, 佐藤佑樹. An Investigation on Motor Torque Map Prediction using Deep Operator Networks with Convolutional Processing. 電気学会研究会資料(Web). 2023. SA-23-069-080/RM-23-068-079
岩田和久, 佐々木秀徳, 五十嵐一, 中川大輔, 上田智哉. A study on the High-Speed Inverse Analysis Method of Electrical Device Structures Using Deep Learning and Topology Optimization. 電気学会研究会資料(Web). 2023. SA-23-069-080/RM-23-068-079
佐々木秀徳, 五十嵐一. Accelerating Topology Optimization for Electric Motors with Deep Learning. 電気学会全国大会講演論文集(CD-ROM). 2023. 2023

ｍore...

Patents (11)：

ｍore...

Books (2)：

電気学会技術報告第1547号「電磁界解析の先進応用技術」
電気学会 2023
Accurate Performance Evaluation Technology for Rotating Machines by Electromagnetic Field Analysis
2022

Lectures and oral presentations (77)：

グラフニューラルネットワークを用いた埋込磁石型同期モータの特性高精度予測に関する基礎検討
(MAGDA2024 2024)
ベイズ最適化を用いた同期モータのトポロジー最適化に関する基礎検討
(MAGDA2024 2024)
トポロジー最適化と深層学習によるシミュレーションベースモータ設計支援
(第2回「電機システム×AI」ワークショップ 2024)
Motor-LIME: Interpretation Method for Motor Design Based on Deep Learning
(21st International IGTE Symposium 2024 2024)
Predicting Torque Characteristics of Synchronous Reluctance Motos Using Swin Transformer
(21st International IGTE Symposium 2024 2024)

ｍore...

Education (3)：

2019 - 2021 Hokkaido University
2016 - 2018 Hokkaido University Graduate School of Information Science and Technology Division of Systems Science and Informatics
2012 - 2016 Hokkaido University School of Engineering Department of Electronics and Information Engineering

Professional career (1)：

Work history (2)：

Committee career (10)：

Show all

Awards (4)：

Association Membership(s) (4)：

IEEE , 日本AEM学会 , 日本シミュレーション学会 , 電気学会

※ Researcher’s information displayed in J-GLOBAL is based on the information registered in researchmap. For details, see here.

Return to Previous Page