수리모형실험 영상의 딥러닝 분석을 통한 연안류 정량화
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Title
- 수리모형실험 영상의 딥러닝 분석을 통한 연안류 정량화
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Alternative Title
- Quantification of Nearshore Currents from Video Imagery of Wave Flume Experiments using Deep Learning Method
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Author(s)
- 김진아; 김재일; 신성원
- KIOST Author(s)
- Kim, Jinah(김진아)
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Alternative Author(s)
- 김진아
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Publication Year
- 2019-05-17
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Abstract
- In this study, we propose a deep learning method to quantify nearshore currents from video imagery acquired from wave flume experiments. A framework of convolutional neural network with skip connection is implemented for deep learning model and the model is trained with video imagery by deriving optimal vector fields for spatial transformation between two successive frames. By applying the model to video, the velocity for 10 minutes is computed with 1/3 seconds time interval along the wave flume.
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URI
- https://sciwatch.kiost.ac.kr/handle/2020.kiost/22615
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Bibliographic Citation
- 2019년도 한국해양과학기술협의회 공동학술대회, pp.1 - 3, 2019
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Publisher
- 한국해양과학기술협의회
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Type
- Conference
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Language
- Korean
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