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Machine Learning Based Metal Object Detection for Wireless Power Transfer Using Differential Coils

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/84953

Title: Machine Learning Based Metal Object Detection for Wireless Power Transfer Using Differential Coils
Authors: Gong, Yunyi Browse this author
Otomo, Yoshitsugu Browse this author
Igarashi, Hajime Browse this author →KAKEN DB
Keywords: wireless power transfer
metal object detection
differential coils
machine learning
Issue Date: 2022
Publisher: Japan Society for Simulation Technology
Journal Title: Journal of Advanced Simulation in Science and Engineering
Volume: 9
Issue: 1
Start Page: 20
End Page: 29
Publisher DOI: 10.15748/jasse.9.20
Abstract: This paper presents the machine learning-based detection of foreign metal object for the wireless power transfer device including differential coils. To test the proposed method, the differential voltages are computed using finite element method for about 1500 cases with and without an aluminum cylinder at driving frequency of 85 kHz considering misalignment between the primal and secondary coils. It has been shown that gradient boosting decision tree and random forests classifier have the accuracy over 90% when input voltages and differential voltages are inputted together.
Rights: © 2022 Japan Society for Simulation Technology
Type: article (author version)
URI: http://hdl.handle.net/2115/84953
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 龔 云羿(Gong, Yunyi)

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