Deep Neural Network-Based Design of Planar Coils for Proximity Sensing Applications
保存先:
| 出版年: | Sensors vol. 25, no. 14 (2025), p. 4429-4453 |
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| 第一著者: | |
| その他の著者: | , , |
| 出版事項: |
MDPI AG
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| タグ: |
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| 抄録: | This study develops a deep learning procedure able to identify a planar coil geometry, given the desired magnetic field map. This approach demonstrates its capability to discover suitable coil designs that produce desired field characteristics with high accuracy and efficiency. The generated coils show strong agreement with target magnetic fields, enabling manufacturers to achieve simpler structures and improved performance. This method is suitable for inductive proximity sensors, wireless power transfer systems, and electromagnetic compatibility applications, offering a powerful and flexible tool for advanced planar coil design. |
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| ISSN: | 1424-8220 |
| DOI: | 10.3390/s25144429 |
| ソース: | Health & Medical Collection |