Abstract
Wireless power transfer (WPT) is an appropriate method of delivering power without connecting wires to multiple devices. To further increase WPT performance, metamaterials’ extraordinary properties, such as electromagnetic field focusing, have been used successfully. Normally, metamaterial properties depend on multiple parameters. Several metamaterial designs require a significant amount of time to complete numerical simulation. In this work, we propose a rapid design square-spiral metamaterial method using an artificial neural network (ANN). When ANN is used, the results show an accuracy of 97.4% and a collective mean square error (MSE) less than 0.7 × 10–3. For synthesizing the design parameters, the results show an accuracy of 95.6% and the MSE less than 7 × 10–3. Besides, the computation time of 1000 samples can be reduced 93 × 103 times compared to the HFSS simulation.
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Acknowledgements
This work was supported by Hanoi University of Mining and Geology and by Thu Dau Mot University.
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Nguyen, B.H., Hoang, QD., Huynh, L.N.T. (2023). Rapid Design of Square-Spiral Metamaterial for Enhanced Wireless Power Transfer Applications Using Artificial Neural Networks. In: Nguyen, T.D.L., Lu, J. (eds) Machine Learning and Mechanics Based Soft Computing Applications. Studies in Computational Intelligence, vol 1068. Springer, Singapore. https://doi.org/10.1007/978-981-19-6450-3_12
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DOI: https://doi.org/10.1007/978-981-19-6450-3_12
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