High-Precision and Robust DNN Model for Predicting Quality Factor of WPT-Oriented Slotted Ground Resonators
Dautov K. Tolebi G. Hashmi M.S. Jarndal A. Almajali E. Nauryzbayev G.
2025Institute of Electrical and Electronics Engineers Inc.
IEEE Access
2025#1336647 - 36657 pp.
Machine learning (ML) has emerged as an effective approach for optimizing circuit design and bringing a paradigm shift in the development of wireless power transfer (WPT) systems. Being the main building blocks of near-field WPT, the slotted ground plane (SGP) resonators with a high quality factor (Q) enhance power transfer efficiency. However, it is pertinent to note that the resonator size, slot shape, and location result in distinct Q outcomes. Therefore, this work delves into the use of ML for predicting Q of SGP resonators. It can be predicted through a deep learning approach, owing to its capacity to learn from the implicit associations between input and output data. Hence, a deep neural network (DNN) model was designed using 20006 data files generated by electromagnetic (EM) simulations. DNN demonstrated its effectiveness, achieving an accuracy of 99.26%, thereby outperforming other benchmark ML models. Furthermore, the model proved its robustness in predicting Q of variously sized resonators and showed 98.3% accuracy. Subsequently, this enables anticipating the Q metric of scaled resonators without the need for exhaustive EM simulations. The predicted Q values were supported through experimental measurements. Finally, the SGP resonators were aptly employed to exhibit the near-field WPT system.
Average comparative error (ACE) , deep neural network (DNN) , machine learning (ML) , magnetic resonant coupling (MRC) , resonator , slotted ground plane (SGP) , wireless power transfer (WPT)
Text of the article Перейти на текст статьи
University of Sharjah, Research Institute of Sciences and Engineering (RISE), Sharjah, United Arab Emirates
Nazarbayev University, School of Engineering and Digital Sciences, Astana, 010000, Kazakhstan
University of Sharjah, Department of Electrical Engineering, Sharjah, United Arab Emirates
University of Sharjah
Nazarbayev University
University of Sharjah
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026