수리모형실험 영상의 딥러닝 분석을 통한 연안류 정량화

Title
수리모형실험 영상의 딥러닝 분석을 통한 연안류 정량화
Alternative Title
Quantification of Nearshore Currents from Video Imagery of Wave Flume Experiments using Deep Learning Method
Author(s)
김진아; 김재일; 신성원
KIOST Author(s)
Kim, Jinah(김진아)
Publication Year
2019-05-17
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.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/22615
Bibliographic Citation
2019년도 한국해양과학기술협의회 공동학술대회, pp.1 - 3, 2019
Publisher
한국해양과학기술협의회
Type
Conference
Language
Korean
Publisher
한국해양과학기술협의회
Related Researcher
Research Interests

AI/Machine Learning,Climate Change,Marine Disaster,인공지능/기계학습,기후변화,해양기상재해

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