해상 이미지를 활용한 3D 합성곱 신경망과합성곱 장단기 메모리 신경망 기반의 유의 파고 추정
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 손규빈 | - |
dc.contributor.author | 최희정 | - |
dc.contributor.author | 이정호 | - |
dc.contributor.author | 노영빈 | - |
dc.contributor.author | 강필성 | - |
dc.date.accessioned | 2021-08-31T17:23:36Z | - |
dc.date.available | 2021-08-31T17:23:36Z | - |
dc.date.created | 2021-06-17 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 1225-1119 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/59684 | - |
dc.description.abstract | 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. | - |
dc.language | Korean | - |
dc.language.iso | ko | - |
dc.publisher | 한국경영과학회 | - |
dc.title | 해상 이미지를 활용한 3D 합성곱 신경망과합성곱 장단기 메모리 신경망 기반의 유의 파고 추정 | - |
dc.title.alternative | Significant Wave Height Regression from a Raw Ocean Image with Convolutional LSTM and 3D Convolutional Networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 강필성 | - |
dc.identifier.bibliographicCitation | 한국경영과학회지, v.45, no.1, pp.11 - 24 | - |
dc.relation.isPartOf | 한국경영과학회지 | - |
dc.citation.title | 한국경영과학회지 | - |
dc.citation.volume | 45 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 11 | - |
dc.citation.endPage | 24 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002563623 | - |
dc.description.journalClass | 2 | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Significant Wave Height | - |
dc.subject.keywordAuthor | 3D Convolution | - |
dc.subject.keywordAuthor | Convolutional LSTM | - |
dc.subject.keywordAuthor | Image Processing | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.