Masahiro Kishi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Masahiro Kishi has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Shinji Wakao
Waseda University
2 shared publications Hiroaki Makino
Toshiba Corporation
1 shared publication- Katsutoku Takeuchi
Infrastructure Systems R&D Center, Toshiba Infrastructure Systems & Solutions Corporation
1 shared publication Koji Fujiwara
Doshisha University
1 shared publicationMakoto Matsushita
Toshiba Corporation
1 shared publicationNoboru Murata
Waseda University
1 shared publication
Latest Publications
- 1 October 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Magnetics
- Vol. 62 (10) , 7205108
Warm-Start Level Set Optimization of Synchronous Reluctance Motors via Objective-Based Latent Space Shaping
This paper proposes a warm-start design optimization method based on potential-shaped latent representations learned by a variational autoencoder (VAE). The latent space is structured so that objective function values correspond to a quadratic potential, concentrating high-performance designs near the origin without explicit latent-space optimization. Candidate designs are generated by sampling the latent space around the origin and subsequently refined using level-set-based optimization. The effectiveness of the proposed method is demonstrated through rotor flux-barrier shape optimization of a synchronous reluctance motor.
- 1 October 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Magnetics
- Vol. 62 (10) , 7205108
Warm-Start Level Set Optimization of Synchronous Reluctance Motors via Objective-Based Latent Space Shaping
This paper proposes a warm-start design optimization method based on potential-shaped latent representations learned by a variational autoencoder (VAE). The latent space is structured so that objective function values correspond to a quadratic potential, concentrating high-performance designs near the origin without explicit latent-space optimization. Candidate designs are generated by sampling the latent space around the origin and subsequently refined using level-set-based optimization. The effectiveness of the proposed method is demonstrated through rotor flux-barrier shape optimization of a synchronous reluctance motor.