| Link | https://onepetro.org/ISOPEIOPEC/proceedings-abstract/ISOPE23/All-ISOPE23/ISOPE-I-23-301/524672?redirectedFrom=PDF |
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| Author | Search for: He, Moqin1; Search for: Akinturk, Ayhan1; Search for: Zaman, Hasanat1; Search for: Mak, Lawrence1; Search for: Seo, Dong Cheol1ORCID identifier: https://orcid.org/0000-0002-5818-7475 |
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| Affiliation | - National Research Council of Canada. Ocean, Coastal and River Engineering
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| Format | Text, Article |
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| Conference | The 33rd International Ocean and Polar Engineering Conference June 19–23, 2023 Ottawa, Canada |
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| Subject | neural network; wave buoy; ship location; deep learning; machine learning; estimation; algorithm; artificial intelligence; ship motion |
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| Abstract | Accurate and timely wave prediction plays an important role in safe marine operations. Generally wave information are obtained from wave buoys, so there is a limitation in the spatial resolution considering that the buoys are mostly deployed inshore and sparsely. This paper presents a data analysis procedure using machine learning techniques to calculate the neighboring wave field parameters from motion measurements onboard a ship. Applications of this procedure and developed techniques are expected to overcome the shortage of wave information, hence they enable more advanced wave predictions and facilitate safer maritime operations. |
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| Publication date | 2023-06-19 |
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| Publisher | International Society of Offshore and Polar Engineers |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 1bb77b81-9a3f-408e-a602-af79fb3cbec6 |
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| Record created | 2025-06-09 |
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| Record modified | 2025-06-09 |
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