Zobrazeno 1 - 10
of 345
pro vyhledávání: '"Hajime Igarashi"'
Autor:
Akito Maruo, Hajime Igarashi
Publikováno v:
IEEE Access, Vol 11, Pp 67230-67239 (2023)
Uncertainties caused by material variation can significantly impair the characteristics of devices. Therefore, it is important to design devices whose performance is not significantly damaged even when material variations occur. Robust optimization s
Externí odkaz:
https://doaj.org/article/530ffd632208435aae945c62715a8210
Publikováno v:
Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol 17, Iss 3, Pp JAMDSM0038-JAMDSM0038 (2023)
Topology optimization (TO), which is a design optimization technique that does not require design parameters, has been attracting attention. TO has a high degree of freedom and can obtain a novel design shape suitable for desired purposes. However, t
Externí odkaz:
https://doaj.org/article/389c8077a1c54b3ab51ad3927ab0cd24
Publikováno v:
IEEE Access, Vol 10, Pp 60814-60822 (2022)
This paper proposes a new method for accurately predicting rotating machine properties using a deep neural network (DNN). In this method, the magnetic field distribution over a cross-section of a rotating machine at a fixed mechanical angle is used a
Externí odkaz:
https://doaj.org/article/156d7cca182a420f978f297c1dd301be
Publikováno v:
Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol 16, Iss 5, Pp JAMDSM0049-JAMDSM0049 (2022)
As a solution for sequence optimization, and to obtain a solution that is strong robustness to variations, a multi-objective robust design optimization (MORDO) and robust genetic algorithm (GA) are applied to a multi-objective optimization problem. A
Externí odkaz:
https://doaj.org/article/669cd11577274f81adf89f988e1bec45
Publikováno v:
IEICE Communications Express. 12:316-321
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::740559aad75f285dbf7f507fa7cb4ddc
https://doi.org/10.1587/comex.2022xbl0177
https://doi.org/10.1587/comex.2022xbl0177
Publikováno v:
IEEE Transactions on Magnetics. 59:1-4
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a737ee04869754c5cf553c1be18849f
https://doi.org/10.1109/tmag.2023.3245056
https://doi.org/10.1109/tmag.2023.3245056
Autor:
Hayaho Sato, Hajime Igarashi
Publikováno v:
IEEE Transactions on Magnetics. 59:1-4
This study proposes a novel topology optimization (TO) method for permanent magnet (PM) motors based on a variational autoencoder (VAE) and a neural network (NN). The VAE is trained to embed various shapes generated from the TO into the latent space.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::157106e61b6af17333942747ade43260
https://doi.org/10.1109/tmag.2023.3242288
https://doi.org/10.1109/tmag.2023.3242288
Publikováno v:
International Journal of Applied Electromagnetics and Mechanics. 71:S275-S282
The resistance and inductance of an inductor working at high frequencies depend on frequency because of eddy currents caused by the skin and proximity effects. In addition, the stray capacitance among winding coils would give significant influence. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::825a5e7b353d13509404a65001241f80
https://doi.org/10.3233/jae-220160
https://doi.org/10.3233/jae-220160
Publikováno v:
IEEE Transactions on Magnetics. 58(9):1-4
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::550e2c6deeeb43bae440a5074c0c54da
http://hdl.handle.net/2115/87028
http://hdl.handle.net/2115/87028
Publikováno v:
IEEE Transactions on Magnetics. 58:1-4
This article proposes a novel 2.5-D multi-phase topology optimization method using a Gaussian basis function for permanent magnet motors. The design region in the rotor was sliced into cylindrical layers; the 2-D topology optimization was performed f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8aba90152216ea4885fce2afc63702b9
https://doi.org/10.1109/tmag.2022.3171558
https://doi.org/10.1109/tmag.2022.3171558