Convolutional LSTM을 이용한 유의 파고 및 파향의 실시간 추정 기법 연구Real-Time Significant Wave Height and Direction Estimation Using Convolutional Long Short-Term Memory
- Other Titles
- Real-Time Significant Wave Height and Direction Estimation Using Convolutional Long Short-Term Memory
- Authors
- 노영빈; 최희정; 이정호; 서승완; 강필성
- Issue Date
- 2020
- Publisher
- 대한산업공학회
- Keywords
- Wave Estimation; Convolutional Long Short-Term Memory; Image Processing
- Citation
- 대한산업공학회지, v.46, no.6, pp.683 - 693
- Indexed
- KCI
- Journal Title
- 대한산업공학회지
- Volume
- 46
- Number
- 6
- Start Page
- 683
- End Page
- 693
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/130956
- DOI
- 10.7232/JKIIE.2020.46.6.683
- ISSN
- 1225-0988
- Abstract
- Real-time estimation of wave condition is essential to improve sailing efficiency. However, existing methodologies are uneconomical due to the expensive radar and high computational complexity. To this end, we propose a neural network model capable of real-time estimation of significant wave height and direction by using raw ocean images collected from operating vessels. In the proposed method, multiple consecutive ocean images are concatenated as a single clip. Then, Convolutional Long Short-Term Memory (ConvLSTM), which combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), was trained on the clips. The final estimation is performed through regression or classification using the extracted spatiotemporal feature map. Based on the datasets collected from two different ships, our proposed method achieved the absolute error of 8cm and a relative error of 5% for significant wave height estimation. Besides, the proposed method yielded an absolute error of 6° for wave direction.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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