Feasibility Study on Machine Learning-based Method for Determining Self-and Mutual Inductance-Reference-Cited by-同舟云学术

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Feasibility Study on Machine Learning-based Method for Determining Self-and Mutual Inductance

Published:2022-07-20 Issue: Volume: Page:
ISSN:
Container-title:2022 International Seminar on Intelligent Technology and Its Applications (ISITIA)
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Author:

Stillig Javier¹,Parspour Nejila¹,Ewert Daniel²,Jung Thomas Josef²

Affiliation:

1. Institute of Electrical Energy Conversion University of Stuttgart Pfaffenwaldring 47,Stuttgart,D-70569

2. Corporate Research Robert Bosch GmbH Robert-Bosch-Campus 1,Renningen,D-71272

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9855197/9855201/09855321.pdf?arnumber=9855321

Reference28 articles.

1. Computational cost reduction in extracting inductance

2. A solenoidal basis method for efficient inductance extraction

3. CHIME

4. Fast Multicore FastHenry and a Tetrahedral Modeling Method for Inductance Extraction of Complex 3D Geometries

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1. A stacking machine‐learning based method for accelerating magnetic coupler design with ferrite cores in inductive power transfer applications;International Journal of Circuit Theory and Applications;2024-05-24

2. Machine Learning Based Design of Magnetic Coupler for Wireless Power Transfer;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19

3. Ultra-Fast Power Module Inductance Estimation using Convolutional Neural Networks;2023 IEEE Energy Conversion Congress and Exposition (ECCE);2023-10-29

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