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Author: search.filters.author.Bennia, FatimaSubject: search.filters.subject.Biomedical implants

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    Wireless Power Transfer Optimization Using Meta-heuristic Algorithms
    (IEEE, 2024) Bennia, Fatima; Boudouda, Aimad; Nafa, Fares
    The importance of Wireless Power Transfer (WPT) technology in biomedical implants to mitigate the risk of regeneration has increased significantly in recent years. WPT systems are dependent on key parameters such as the coupling coefficient (K), quality factor (Q), and mutual inductance (M), which play a crucial role in determining power transfer efficiency. These parameters are closely related to the geometric characteristics of the coils involved. Therefore, this study explores various meta-heuristic algorithms to search for optimal parameters that maximize power transfer efficiency. The initial results demonstrate that these algorithms perform well across different iterations. To confirm these findings, the study conducted comprehensive validation using Ansys Maxwell software to verify the optimal values obtained through optimization.
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    Optimal design of wireless power transfer coils for biomedical implants using machine learning and meta-heuristic algorithms
    (Springer Nature, 2024) Bennia, Fatima; Boudouda, Aimad; Nafa, Fares
    The classical methods for optimizing wireless power transfer (WPT) systems using mathematical equations or finite element methods can be time-consuming and may only sometimes yield optimal designs. In order to overcome this challenge, this paper introduces a novel approach integrating machine learning techniques with meta-heuristic methods to design and optimize a miniaturized, high-efficiency WPT receiving coil for biomedical applications. The objective is to achieve dimensions below 20 mm, a depth of 30 mm within the tissue, and a frequency of 13.56 MHz. Our approach leverages a neural network (NN) model to predict efficiency based on geometric coil parameters, eliminating the need for complex equations. The NN was trained on a dataset generated via finite element method simulations. We employ two meta-heuristic algorithms, the genetic algorithm and the coyote optimization method, to find optimal parameters that maximize efficiency. Our NN model demonstrates exceptional accuracy, exceeding 97%. Furthermore, the proposed WPT coil design approach enhances transfer efficiency by up to 76%, significantly reducing computation time compared to classical methods. Finally, we validate our results using finite element simulation with Ansys Maxwell 3D.