A novel method for extracting and optimizing the complex permittivity of paper-based composites based on an artificial neural network model

IF 4.4 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
ChenBin Xia, JunYi Shen, ShaoWei Liao, Yi Wang, ZhengSheng Huang, Quan Xue, Min Tang, Jin Long, Jian Hu
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引用次数: 0

Abstract

Measuring the complex permittivity of ultrathin, flexible materials with a high loss tangent poses a substantial challenge with precision using conventional methods, and verifying the accuracy of test results remains difficult. In this study, we introduce a methodology based on a back-propagation artificial neural network (ANN) to extract the complex permittivity of paper-based composites (PBCs). PBCs are ultrathin and flexible materials exhibiting considerable complex permittivity and dielectric loss tangent. Given the absence of mature measurement methods for PBCs and a lack of sufficient data for ANN training, a mapping relationship is initially established between the complex permittivity of honeycomb-structured microwave-absorbing materials (HMAMs, composed of PBCs) and that of PBCs using simulated data. Leveraging the ANN model, the complex permittivity of PBCs can be extracted from that of HMAMs obtained using standard measurement. Subsequently, two published methods are cited to illustrate the accuracy and advancement of the results obtained using the proposed approach. Additionally, specific error analysis is conducted, attributing discrepancies to the conductivity of PBCs, the homogenization of HMAMs, and differences between the simulation model and actual objects. Finally, the proposed method is applied to optimize the cell length parameters of HMAMs for enhanced absorption performance. The conclusion discusses further improvements and areas for extended research.

基于人工神经网络模型的提取和优化纸基复合材料复介电常数的新方法
使用传统方法测量具有高损耗正切的超薄柔性材料的复介电常数对精度提出了巨大挑战,而且验证测试结果的准确性仍然十分困难。在本研究中,我们介绍了一种基于反向传播人工神经网络(ANN)的方法,用于提取纸基复合材料(PBC)的复介电常数。纸基复合材料是一种超薄柔性材料,具有相当高的复介电常数和介电损耗正切。由于缺乏成熟的 PBC 测量方法,也缺乏足够的数据进行 ANN 训练,因此利用模拟数据初步建立了蜂窝结构微波吸收材料(HMAM,由 PBC 组成)的复介电常数与 PBC 的复介电常数之间的映射关系。利用 ANN 模型,可以从使用标准测量方法获得的 HMAM 的复介电常数中提取 PBC 的复介电常数。随后,引用了两种已发表的方法,以说明使用所提方法获得的结果的准确性和先进性。此外,还进行了具体的误差分析,将差异归因于 PBC 的电导率、HMAM 的均质化以及模拟模型与实际物体之间的差异。最后,提出的方法被应用于优化 HMAM 的单元长度参数,以提高吸收性能。结论部分讨论了进一步的改进和扩展研究领域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Science China Technological Sciences
Science China Technological Sciences ENGINEERING, MULTIDISCIPLINARY-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
8.40
自引率
10.90%
发文量
4380
审稿时长
3.3 months
期刊介绍: Science China Technological Sciences, an academic journal cosponsored by the Chinese Academy of Sciences and the National Natural Science Foundation of China, and published by Science China Press, is committed to publishing high-quality, original results in both basic and applied research. Science China Technological Sciences is published in both print and electronic forms. It is indexed by Science Citation Index. Categories of articles: Reviews summarize representative results and achievements in a particular topic or an area, comment on the current state of research, and advise on the research directions. The author’s own opinion and related discussion is requested. Research papers report on important original results in all areas of technological sciences. Brief reports present short reports in a timely manner of the latest important results.
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