東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back to Search results for [ null ]

Switch To: Labeled | MARC Mode | ISBD

Machine Learning and Additive Manufa...

Sharma, Yashika.

Linked to FindBook

Google Book

Amazon

博客來

Machine Learning and Additive Manufacturing Based Antenna Design Techniques.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning and Additive Manufacturing Based Antenna Design Techniques./
Author:	Sharma, Yashika.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	171 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-10, Section: A.
Contained By:	Dissertations Abstracts International81-10A.
Subject:	Electrical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27742849
ISBN:	9781658471169

Machine Learning and Additive Manufacturing Based Antenna Design Techniques.
Sharma, Yashika.

Machine Learning and Additive Manufacturing Based Antenna Design Techniques. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 171 p.

Source: Dissertations Abstracts International, Volume: 81-10, Section: A.

Thesis (Ph.D.)--The University of Arizona, 2020.

This item is not available from ProQuest Dissertations & Theses.

This dissertation investigates the application of machine learning (ML) techniques to additive manufacturing (AM) technology with the ultimate goal of tackling the universal antenna design challenges and achieving automated antenna design for a broad range of applications.First, we investigate the implementation and accuracy of few modern machine learning techniques including, least absolute shrinkage and selection operator (lasso), artificial neural networks (ANN) and k-nearest neighbor (kNN) methods, for antenna design optimization for antennas. The automated techniques provide an efficient, flexible, and reliable framework to identify optimal design parameters for a reference dual-band double T-shaped monopole antenna to achieve favorite performance in terms of its dual bandwidth. We first provide a brief background for these techniques and then explain how these techniques can be used to optimize the performance of the referenced antenna. Then the accuracy of these techniques is tested by doing a comparative analysis with HFSS simulations as well.After obtaining encouraging results from the primitive work mentioned above, we implement ML techniques for the optimization of a more complex 3D-printed slotted waveguide antenna. The design has more design parameters that are be tuned and, also multiple performance parameters, including bandwidth, realized gain, sidelobe level, and back lobe level, are optimized. This is a higher-dimensional and non-linear problem. Hence, we use an artificial neural network for this work. Next, we demonstrate the advantages and challenges of using ML techniques compared to heuristic optimization techniques. We apply ML techniques first for 'modeling' that refers to prediction of the performance curve (e.g., reflection coefficient w.r.t. frequency, gain plots in a given plane, etc.) for a given design of antenna with particular set of design parameters and then use it for obtaining 'optimization' results that refers to searching the value of the design parameters that can give optimized results for a particular goal (e.g., specific frequency band of operation, maximum gain, minimum sidelobe level, etc.). To explain modeling using ML-techniques, we use two antenna examples in this work, first is the modeling of the reflection coefficient curve with respect to frequency for a planar patch antenna when its shape changes from square to circular and second is the modeling of gain response of a monopole antenna when it is loaded with 3D-printed dielectric material. To explain the optimization process, we use the behavioral model obtained in the second antenna example, and find the design parameter values that are capable of providing single-beam, and multiple-beam radiation. The performance of ML is compared with a heuristic technique like genetic algorithm for this work and the benefits of using ML over GA are mentioned in this work. One of the prototypes that can provide a 3-beam radiation pattern is manufactured and its fabrication process and measurement results are also presented in this work. The ultimate goal of this research work is to overcome universal antenna design challenges and achieving automated antenna design for a broad range of applications. With this work, ML models are built to find the relationship between design parameters and antenna performance parameters analytically, thus requiring only analytical calculations instead of time-consuming numerical simulations for different design goals. This is useful for applications such as IoT, which involve a large number of antenna designs with different goals and constraints. ML techniques help build such behavioral models for antennas automatically from data which is beneficial for fully exploring the vast design degrees of freedom offered by AM.

ISBN: 9781658471169Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Antenna

Machine Learning and Additive Manufacturing Based Antenna Design Techniques.
LDR:05022nmm a2200385 4500 001 2283704
005 20211115071628.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9781658471169
035 $a (MiAaPQ)AAI27742849
035 $a AAI27742849
040 $a MiAaPQ $c MiAaPQ
100 1 $a Sharma, Yashika. $3 3562722
245 1 0 $a Machine Learning and Additive Manufacturing Based Antenna Design Techniques.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 171 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-10, Section: A.
500 $a Advisor: Xin, Hao.
502 $a Thesis (Ph.D.)--The University of Arizona, 2020.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation investigates the application of machine learning (ML) techniques to additive manufacturing (AM) technology with the ultimate goal of tackling the universal antenna design challenges and achieving automated antenna design for a broad range of applications.First, we investigate the implementation and accuracy of few modern machine learning techniques including, least absolute shrinkage and selection operator (lasso), artificial neural networks (ANN) and k-nearest neighbor (kNN) methods, for antenna design optimization for antennas. The automated techniques provide an efficient, flexible, and reliable framework to identify optimal design parameters for a reference dual-band double T-shaped monopole antenna to achieve favorite performance in terms of its dual bandwidth. We first provide a brief background for these techniques and then explain how these techniques can be used to optimize the performance of the referenced antenna. Then the accuracy of these techniques is tested by doing a comparative analysis with HFSS simulations as well.After obtaining encouraging results from the primitive work mentioned above, we implement ML techniques for the optimization of a more complex 3D-printed slotted waveguide antenna. The design has more design parameters that are be tuned and, also multiple performance parameters, including bandwidth, realized gain, sidelobe level, and back lobe level, are optimized. This is a higher-dimensional and non-linear problem. Hence, we use an artificial neural network for this work. Next, we demonstrate the advantages and challenges of using ML techniques compared to heuristic optimization techniques. We apply ML techniques first for 'modeling' that refers to prediction of the performance curve (e.g., reflection coefficient w.r.t. frequency, gain plots in a given plane, etc.) for a given design of antenna with particular set of design parameters and then use it for obtaining 'optimization' results that refers to searching the value of the design parameters that can give optimized results for a particular goal (e.g., specific frequency band of operation, maximum gain, minimum sidelobe level, etc.). To explain modeling using ML-techniques, we use two antenna examples in this work, first is the modeling of the reflection coefficient curve with respect to frequency for a planar patch antenna when its shape changes from square to circular and second is the modeling of gain response of a monopole antenna when it is loaded with 3D-printed dielectric material. To explain the optimization process, we use the behavioral model obtained in the second antenna example, and find the design parameter values that are capable of providing single-beam, and multiple-beam radiation. The performance of ML is compared with a heuristic technique like genetic algorithm for this work and the benefits of using ML over GA are mentioned in this work. One of the prototypes that can provide a 3-beam radiation pattern is manufactured and its fabrication process and measurement results are also presented in this work. The ultimate goal of this research work is to overcome universal antenna design challenges and achieving automated antenna design for a broad range of applications. With this work, ML models are built to find the relationship between design parameters and antenna performance parameters analytically, thus requiring only analytical calculations instead of time-consuming numerical simulations for different design goals. This is useful for applications such as IoT, which involve a large number of antenna designs with different goals and constraints. ML techniques help build such behavioral models for antennas automatically from data which is beneficial for fully exploring the vast design degrees of freedom offered by AM.
590 $a School code: 0009.
650 4 $a Electrical engineering. $3 649834
650 4 $a Electromagnetics. $3 3173223
650 4 $a Communication. $3 524709
653 $a Antenna
653 $a Gaussian process
653 $a lasso
653 $a Machine learning
653 $a Optimization
690 $a 0544
690 $a 0607
690 $a 0459
710 2 $a The University of Arizona. $b Electrical & Computer Engineering. $3 1018545
773 0 $t Dissertations Abstracts International $g 81-10A.
790 $a 0009
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27742849