将散射参数转换为复介电常数的机器学习方法

IF 0.9 4区 工程技术 Q4 ENGINEERING, CHEMICAL
Robert Tempke, Liam A Thomas, Christina Wildfire, D. Shekhawat, T. Musho
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引用次数: 2

摘要

摘要:本文研究了利用人工神经网络来确定由VNA散射参数测量得到的复杂介电材料特性。该研究利用有限元方法综合生成数据来训练神经网络。神经网络使用监督学习方法进行训练,并使用实验测量数据进行验证。频率范围为0.1 ~ 13.5 GHz,介电常数实部为1 ~ 100,虚部为0 ~ 0.2。与现有的逆方法相比,这种建模方法降低了不确定性。这种方法证明了一个通用的框架,可以用于转换实验或计算导出的散射参数到复杂的介电常数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning approach to transform scattering parameters to complex permittivities
Abstract This study investigates the application of artificial neural networks to determine the complex dielectric material properties derived from experimental VNA scattering parameter measurements. The study utilizes a finite element approach to synthetically generate data to train the neural network. The neural network was trained using a supervised learning approach and validated using experimental measurement data. The frequency range of interest was between 0.1 and 13.5 GHz with the real part of the dielectric constants ranging from 1 − 100 and the imaginary part ranging from 0 − 0.2. This modelling approach decreases the uncertainty when compared to existing inverse approaches. This approach demonstrates a general framework that can be used for converting experimental or computational derived scattering parameters to complex permittivities.
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来源期刊
Journal of Microwave Power and Electromagnetic Energy
Journal of Microwave Power and Electromagnetic Energy ENGINEERING, CHEMICAL-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
2.50
自引率
6.70%
发文量
21
期刊介绍: The Journal of the Microwave Power Energy (JMPEE) is a quarterly publication of the International Microwave Power Institute (IMPI), aimed to be one of the primary sources of the most reliable information in the arts and sciences of microwave and RF technology. JMPEE provides space to engineers and researchers for presenting papers about non-communication applications of microwave and RF, mostly industrial, scientific, medical and instrumentation. Topics include, but are not limited to: applications in materials science and nanotechnology, characterization of biological tissues, food industry applications, green chemistry, health and therapeutic applications, microwave chemistry, microwave processing of materials, soil remediation, and waste processing.
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