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심층학습 기반 초해상화 기법을 이용한 슬로싱 압력장 복원에 관한 연구
김효주(Hyo Ju Kim),양동헌(Donghun Yang),박정윤(Jung Yoon Park),황명권(Myunggwon Hwang),이상봉(Sang Bong Lee) 대한조선학회 2022 大韓造船學會 論文集 Vol.59 No.1
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.