Automatic Design System With Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor-Reference-Cited by-同舟云学术

Automatic Design System With Generative Adversarial Network and Convolutional Neural Network for Optimization Design of Interior Permanent Magnet Synchronous Motor

Published:2023-03 Issue:1 Volume:38 Page:724-734
ISSN:0885-8969
Container-title:IEEE Transactions on Energy Conversion
language:
Short-container-title:IEEE Trans. Energy Convers.

Author:

Shimizu Yuki¹^ORCID,Morimoto Shigeo²^ORCID,Sanada Masayuki²^ORCID,Inoue Yukinori²

Affiliation:

1. College of Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan

2. Graduate School of Engineering, Osaka Metropolitan University, Sakai, Osaka, Japan

Funder

JST, ACT-X, Japan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Link

http://xplorestaging.ieee.org/ielx7/60/10050374/09896140.pdf?arnumber=9896140

Reference29 articles.

1. Transfer Learning Through Deep Learning: Application to Topology Optimization of Electric Motor

2. Deep Learning and Reduced Models for Fast Optimization in Electromagnetics

3. Using Machine Learning to Reduce Design Time for Permanent Magnet Volume Minimization in IPMSMs for Automotive Applications

4. Generative adversarial nets;goodfellow;Proc Adv Neural Inf Process Syst,0

5. Torque Performance Improvement of Permanent Magnet Arc Motor Based on Two-Step Strategy

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