Original Article
  • A Study on the Design of Glass Fiber Fabric Reinforced Plastic Circuit Analog Radar Absorber Structure Using Machine Learning and Deep Learning Techniques
  • Jae Cheol Oh*, Seok Young Park*, Jin Bong Kim*† , Hong Kyu Jang*, Ji Hoon Kim*, Woo-Kyoung Lee*

  • * Composites Research Division, Korea Institute of Materials Science (KIMS), Korea

  • 머신러닝 및 딥러닝 기법을 활용한 유리섬유 직물 강화 복합재 적층판형 Circuit Analog 전파 흡수구조 설계에 대한 연구
  • 오재철* · 박석영* · 김진봉*† · 장홍규* · 김지훈* · 이우경*

  • This article is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


In this paper, a machine learning and deep learning model for the design of circuit analog (CA) radar absorbing structure with a cross-dipole pattern on a glass fiber fabric reinforced plastic is presented. The proposed model can directly calculate reflection loss in the Ku-band (12-18 GHz) without three-dimensional electromagnetic numerical analysis based on the geometry of the Cross-Dipole pattern. For this purpose, the optimal learning model was derived by applying various machine learning and deep learning techniques, and the results calculated by the learning model were compared with the electromagnetic wave absorption characteristics obtained by 3D electromagnetic wave numerical analysis to evaluate the comparative advantages of each model. Most of the implemented models showed similar calculated results to the numerical results, but it was found that the Fully-Connected model could provide the most similar calculated results

본 논문에서는 유리섬유 직물 강화 복합재 소재위에 Cross-Dipole 패턴이 배치된 정형적 Circuit Analog(CA) 전파 흡수 구조 설계를 위한 머신러닝 및 딥러닝 모델을 제시하였다. 제시된 모델은 Cross-Dipole 패턴의 형상에 따라서 Ku-band (12-18 GHz)에서의 전파흡수성능을 3차원 전자파 수치해석 없이 바로 계산할 수 있다. 이를 위하여 다양한 머신러닝 및 딥러닝 기술을 적용한 최적 학습 모델을 도출하고, 학습 모델이 계산한 결과를 3차원 전자파 수치해석결과로 얻은 전파흡수특성과 비교함으로써 각각의 모델 간의 성능의 비교우위를 평가하였다. 개발된 모델들은 대부분 수치해석결과와 유사한 계산결과를 보여주었지만, 그 중 Fully-Connected 모델이 가장 유사한 계산결과를 제공할 수 있음을 확인하였다

Keywords: 복합재료(Composite Materials), 머신러닝(Machine Learning), 딥러닝(Deep Learning), 전자파 흡수 구조(Radar Absorbing Structure), 유리섬유 직물 강화 복합재(Glass Fiber Reinforced Plastic)

This Article

Correspondence to

  • Jin Bong Kim
  • Composites Research Division, Korea Institute of Materials Science (KIMS), Korea

  • E-mail: jbkim@kims.re.kr