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dc.contributor.authorSharma, Yashika
dc.contributor.authorChen, Xi
dc.contributor.authorWu, Junqiang
dc.contributor.authorZhou, Qiang
dc.contributor.authorZhang, Hao Helen
dc.contributor.authorXin, Hao
dc.date.accessioned2022-03-24T22:11:37Z
dc.date.available2022-03-24T22:11:37Z
dc.date.issued2022
dc.identifier.citationSharma, Y., Chen, X., Wu, J., Zhou, Q., Zhang, H. H., & Xin, H. (2022). Machine Learning Methods-based Modeling and Optimization of 3-D-Printed Dielectrics around Monopole Antenna. IEEE Transactions on Antennas and Propagation.en_US
dc.identifier.issn0018-926X
dc.identifier.doi10.1109/tap.2022.3153688
dc.identifier.urihttp://hdl.handle.net/10150/663783
dc.description.abstractIn this paper, we propose using new Machine Learning (ML)-based optimization methods, as an alternative to traditional optimization methods, for complex antenna designs. This is an efficient methodology to tackle computational challenges, as it is capable of handling a large number of design parameters and is more efficient as well as informative. The proposed technique is applied for modeling gain performance in the principal plane of a monopole antenna when its radiation properties are modified by placing spatially dependent dielectric material around it. Using the proposed methodology, the dielectric constant values are mapped to the gain pattern of the design. We use two ML techniques for this purpose, namely, Gaussian Process (GP) regression and Artificial Neural Network (ANN). Once each of these models is obtained, they are further used for estimating the dielectric constant values that can suggest optimal directions to modify gain patterns for single-beam and multiple-beam patterns rather than the conventional omnidirectional pattern of a monopole antenna. The performance of this technique is compared with heuristic optimization techniques such as genetic algorithms. The proposed method proves to be quite accurate in spite of being a high-dimensional non-linear problem. A prototype of a monopole design with three-beam gain pattern is fabricated and tested. The measurement results agree well with the simulation results. The proposed methodology can provide useful and scalable optimization tools for computationally intensive antenna design problems.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCopyright © 2021 IEEE.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subject3D printingen_US
dc.subjectAntenna radiation patternsen_US
dc.subjectAntennasen_US
dc.subjectArtificial neural networksen_US
dc.subjectComputational modelingen_US
dc.subjectDielectric constanten_US
dc.subjectGaussian Processen_US
dc.subjectNeuronsen_US
dc.subjectOptimizationen_US
dc.titleMachine Learning Methods-based Modeling and Optimization of 3-D-Printed Dielectrics around Monopole Antennaen_US
dc.typeArticleen_US
dc.identifier.eissn1558-2221
dc.contributor.departmentDepartment of Electrical and Computer Engineering, University of Arizonaen_US
dc.contributor.departmentDepartment of Systems and Industrial Engineering, University of Arizonaen_US
dc.contributor.departmentDepartment of Physics, University of Arizonaen_US
dc.identifier.journalIEEE Transactions on Antennas and Propagationen_US
dc.description.noteImmediate accessen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleIEEE Transactions on Antennas and Propagation
dc.source.beginpage1
dc.source.endpage1
refterms.dateFOA2022-03-24T22:11:38Z


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