Deep Neural Network-Based Design of Planar Coils for Proximity Sensing Applications
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| Publicat a: | Sensors vol. 25, no. 14 (2025), p. 4429-4453 |
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| Autor principal: | |
| Altres autors: | , , |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/s25144429 |2 doi | |
| 035 | |a 3233261828 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231630 |2 nlm | ||
| 100 | 1 | |a Lalla Abderraouf |u Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy; paolo.dibarba@unipv.it (P.D.B.); eve.mognaschi@unipv.it (M.E.M.) | |
| 245 | 1 | |a Deep Neural Network-Based Design of Planar Coils for Proximity Sensing Applications | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Simulation | ||
| 653 | |a Physics | ||
| 653 | |a Deep learning | ||
| 653 | |a Magnetic fields | ||
| 653 | |a Printed circuit boards | ||
| 653 | |a Optimization | ||
| 653 | |a Neural networks | ||
| 653 | |a Heating | ||
| 653 | |a Data processing | ||
| 653 | |a Numerical analysis | ||
| 653 | |a Design | ||
| 653 | |a Geometry | ||
| 653 | |a Product development | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Di Barba Paolo |u Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy; paolo.dibarba@unipv.it (P.D.B.); eve.mognaschi@unipv.it (M.E.M.) | |
| 700 | 1 | |a Hausman Sławomir |u Institute of Electronics, Lodz University of Technology, Al. Politechniki 8, 93-590 Lodz, Poland; slawomir.hausman@p.lodz.pl | |
| 700 | 1 | |a Mognaschi Maria Evelina |u Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy; paolo.dibarba@unipv.it (P.D.B.); eve.mognaschi@unipv.it (M.E.M.) | |
| 773 | 0 | |t Sensors |g vol. 25, no. 14 (2025), p. 4429-4453 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233261828/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233261828/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233261828/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |