Journal of the Society of Naval Architects of Korea (대한조선학회논문집)

The Society of Naval Architects of Korea (대한조선학회)
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Study on the Reconstruction of Pressure Field in Sloshing Simulation Using Super-Resolution Convolutional Neural Network

심층학습 기반 초해상화 기법을 이용한 슬로싱 압력장 복원에 관한 연구

  • Kim, Hyo Ju (Department of Naval Architecture and Offshore Engineering, Dong-A University) ;
  • Yang, Donghun (Department of Intelligent Infrastructure Technology Research, KISTI) ;
  • Park, Jung Yoon (Department of Naval Architecture and Offshore Engineering, Dong-A University) ;
  • Hwang, Myunggwon (Department of Intelligent Infrastructure Technology Research, KISTI) ;
  • Lee, Sang Bong (Department of Naval Architecture and Offshore Engineering, Dong-A University)
  • 김효주 (동아대학교 조선해양플랜트공학과) ;
  • 양동헌 (한국과학기술정보연구원 지능형인프라기술연구단) ;
  • 박정윤 (동아대학교 조선해양플랜트공학과) ;
  • 황명권 (한국과학기술정보연구원 지능형인프라기술연구단) ;
  • 이상봉 (한국과학기술정보연구원 지능형인프라기술연구단)
  • Received : 2021.04.06
  • Accepted : 2022.01.11
  • Published : 2022.04.20
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Abstract

Deep-learning-based Super-Resolution (SR) methods were evaluated to reconstruct pressure fields with a high resolution from low-resolution images taken from a coarse grid simulation. In addition to a canonical SRCNN(super-resolution convolutional neural network) model, two modified models from SRCNN, adding an activation function (ReLU or Sigmoid function) to the output layer, were considered in the present study. High resolution images obtained by three models were more vivid and reliable qualitatively, compared with a conventional super-resolution method of bicubic interpolation. A quantitative comparison of statistical similarity showed that SRCNN model with Sigmoid function achieved best performance with less dependency on original resolution of input images.

Keywords

Acknowledgement

본 논문은 2021년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원(No. NRF-2019R1A2C1004682)을 받아 수행된 기초연구사업입니다.

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