본문 바로가기
[학술저널]

  • 학술저널

손규빈(고려대학교) 최희정(고려대학교) 이정호(고려대학교) 노영빈(고려대학교) 강필성(고려대학교)

DOI : 10.7737/JKORMS.2020.45.1.011

UCI(KEPA) : I410-ECN-0101-2020-325-000411078

표지

북마크 0

리뷰 0

이용수 76

피인용수 0

초록

One of the most common measures implemented in the operation of large vessels is to find the route that takes the least fuel consumption based on marine conditions, such as wave height. The model that predicts wave height can roughly be categorized into two methods, namely, a numerical method that calculates by physical formula and a soft-computing method that collects weather information and learns the machine learning algorithm. These models are difficult to apply in the real world because of their high computational complexity and the use of expensive radar equipment. In this study, we propose to estimate the wave height in real time using the images of the ocean. We used the image data consisting of four consecutive images instead of a single image and applied the combination of convolutional LSTM and 3D CNN networks that can best handle the data structure as a regression model. In this way of prediction, existing methods are not only outperformed but are also more robust to outliers. We used data from the “Weather 1st” ship provided by Daewoo Shipbuilding & Marine Engineering and confirmed that the mean absolute error is 1.59 cm, and the mean absolute percentage error is as low as 1.61% based on the test set.

목차

Abstract
1. 서론
2. 관련 연구
3. 파고 추정 모형
4. 해상 이미지 데이터
5. 실험
6. 결론
참고문헌

참고문헌(0)

리뷰(0)

도움이 되었어요.0

도움이 안되었어요.0

첫 리뷰를 남겨주세요.
DBpia에서 서비스 중인 논문에 한하여 피인용 수가 반영됩니다.
인용된 논문이 DBpia에서 서비스 중이라면, 아래 [참고문헌 신청]을 통해서 등록해보세요.
Insert title here