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.
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.
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