Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna

We designed the wire monopole antenna bent at three points by applying a machine learning technique to achieve a good impedance matching characteristic. After performing the deep neural network (DNN)-based training, we validated our machine learning model by evaluating mean squared error and R-squar...

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Autores principales:	Jaeyul Choo, Pho Thi Ha Anh, Yong-Hwa Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning deep neural network bent monopole antenna impedance matching Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/07422380960943528cab54991dc2947c
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spelling	oai:doaj.org-article:07422380960943528cab54991dc2947c2021-11-25T16:38:49ZMachine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna10.3390/app1122108292076-3417https://doaj.org/article/07422380960943528cab54991dc2947c2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10829https://doaj.org/toc/2076-3417We designed the wire monopole antenna bent at three points by applying a machine learning technique to achieve a good impedance matching characteristic. After performing the deep neural network (DNN)-based training, we validated our machine learning model by evaluating mean squared error and R-squared score. Considering the mean squared error of about zero and R-squared score of about one, the performance prediction by the resulting machine learning model showed a high accuracy compared with that by the numerical electromagnetic simulation. Finally, we interpreted the operating principle of the antennas with a good impedance matching characteristic by analyzing equivalent circuits corresponding to their structures. The accomplished works in this research provide us with the possibility to use the machine learning technique in the antenna design.Jaeyul ChooPho Thi Ha AnhYong-Hwa KimMDPI AGarticlemachine learningdeep neural networkbent monopole antennaimpedance matchingTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10829, p 10829 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning deep neural network bent monopole antenna impedance matching Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	machine learning deep neural network bent monopole antenna impedance matching Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Jaeyul Choo Pho Thi Ha Anh Yong-Hwa Kim Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna
description	We designed the wire monopole antenna bent at three points by applying a machine learning technique to achieve a good impedance matching characteristic. After performing the deep neural network (DNN)-based training, we validated our machine learning model by evaluating mean squared error and R-squared score. Considering the mean squared error of about zero and R-squared score of about one, the performance prediction by the resulting machine learning model showed a high accuracy compared with that by the numerical electromagnetic simulation. Finally, we interpreted the operating principle of the antennas with a good impedance matching characteristic by analyzing equivalent circuits corresponding to their structures. The accomplished works in this research provide us with the possibility to use the machine learning technique in the antenna design.
format	article
author	Jaeyul Choo Pho Thi Ha Anh Yong-Hwa Kim
author_facet	Jaeyul Choo Pho Thi Ha Anh Yong-Hwa Kim
author_sort	Jaeyul Choo
title	Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna
title_short	Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna
title_full	Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna
title_fullStr	Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna
title_full_unstemmed	Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna
title_sort	machine learning technique to improve an impedance matching characteristic of a bent monopole antenna
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/07422380960943528cab54991dc2947c
work_keys_str_mv	AT jaeyulchoo machinelearningtechniquetoimproveanimpedancematchingcharacteristicofabentmonopoleantenna AT phothihaanh machinelearningtechniquetoimproveanimpedancematchingcharacteristicofabentmonopoleantenna AT yonghwakim machinelearningtechniquetoimproveanimpedancematchingcharacteristicofabentmonopoleantenna
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Machine Learning Technique to Improve an Impedance Matching Characteristic of a Bent Monopole Antenna

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