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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Sensorless metal object detection for wireless power transfer using machine learning
| Title: | Sensorless metal object detection for wireless power transfer using machine learning |
| Authors: | Gong, Yunyi Browse this author | | Otomo, Yoshitsugu Browse this author | | Igarashi, Hajime Browse this author →KAKEN DB |
| Keywords: | Machine learning | | Metal object detection | | Wireless power transfer | | Support vector machines |
| Issue Date: | 14-Apr-2022 |
| Publisher: | Emerald Group Publishing |
| Journal Title: | COMPEL : The international journal for computation and mathematics in electrical and electronic engineering |
| Volume: | 41 |
| Issue: | 3 |
| Start Page: | 807 |
| End Page: | 823 |
| Publisher DOI: | 10.1108/COMPEL-03-2021-0069 |
| Abstract: | Purpose This study aims to realize a sensorless metal object detection (MOD) using machine learning, to prevent the wireless power transfer (WPT) system from the risks of electric discharge and fire accidents caused by foreign metal objects. Design/methodology/approach The data constructed by analyzing the input impedance using the finite element method are used in machine learning. From the loci of the input impedance of systems, the trained neural network (NN), support vector machine and naive Bayes classifier judge if a metal object exists. Then the proposed method is tested by experiments too. Findings In the test using simulated data, all of the three machine learning methods show high accuracy of over 80% for detecting an aluminum cylinder. And in the experimental verifications, the existence of an aluminum cylinder and empty can are successfully identified by a NN. Originality/value This work provides a new sensorless MOD method for WPT using three machine learning methods. And it shows that NNs obtain high accuracy than the others in both simulated and experimental verifications. |
| Rights: | © 2021, Emerald Publishing Limited. This AAM is provided for your own personal use only. It may not be used for resale, reprinting, systematic distribution, emailing, or for any other commercial purpose without the permission of the publisher |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/84955 |
| 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)
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Submitter: 龔 云羿(Gong, Yunyi)
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