A Data-Driven Inductor Modeling Technique Using Parametric Circuit Simulation and Deep Learning-Reference-Cited by-同舟云学术

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A Data-Driven Inductor Modeling Technique Using Parametric Circuit Simulation and Deep Learning

Published:2023-11 Issue:11 Volume:59 Page:1-5
ISSN:0018-9464
Container-title:IEEE Transactions on Magnetics
language:
Short-container-title:IEEE Trans. Magn.

Author:

Motomatsu Takehiro¹^ORCID,Koga Takahiro²^ORCID,Shigei Noritaka¹^ORCID,Yamaguchi Masahiro³^ORCID,Itagaki Atsushi⁴,Ishizuka Yoichi²^ORCID

Affiliation:

1. Graduate School of Science and Engineering, Kagoshima University, Kagoshima, Japan

2. Graduate School of Engineering, Nagasaki University, Nagasaki, Japan

3. New Industry Creation Hatchery Center, Tohoku University, Sendai, Japan

4. Ryowa Electronics Company Ltd., Sendai, Japan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/20/10294220/10195260.pdf?arnumber=10195260

Reference11 articles.

1. A fractal model of anomalous losses in ferromagnetic materials

2. Large-signal characterization technique for power electronics magnetic components by deep learning with double pulse test measurement image;koga;Proc Joint MMM-INTERMAG,2022

3. High frequency magnetics: Black magic, art or science? Magnetics core loss;ridley,0

4. Ferrite core loss for power magnetic components design

5. Triple Pulse Test (TPT) for Characterizing Power Loss in Magnetic Components in Analogous to Double Pulse Test (DPT) for Power Electronics Devices

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