High-efficiency and compact WPT receiving antenna design using simulation optimization method

Kim, Yanghyun; Seo, Chulhun

doi:10.1177/0020720921989014

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…The initial predictions were then inserted into a K-nearest neighbors (KNN) model, which reported the final prediction of 0.00456 mean squared error in the case of a triangular-shaped pin-fed patch antenna. To increase the transmission efficiency of antennas used in wireless power transmission, ML has become a reliable assistive technology [50].…”

Section: Design Optimization and Synthesismentioning

confidence: 99%

Application of Machine Learning in Electromagnetics: Mini-Review

et al. 2021

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As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

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Section: Design Optimization and Synthesismentioning

confidence: 99%