| DOI | Resolve DOI: https://doi.org/10.1109/AUV50043.2020.9267884 |
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| Author | Search for: Akinturk, Ayhan1; Search for: Zaman, Hasanat1; Search for: Seo, Dong1ORCID identifier: https://orcid.org/0000-0002-5818-7475; Search for: Mak, Lawrence1; Search for: He, Moqin1 |
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| Affiliation | - National Research Council Canada. Ocean, Coastal and River Engineering
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| Format | Text, Article |
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| Conference | 2020 IEEE/OES Autonomous Underwater Vehicles Symposium (AUV), September 30 - October 2, 2020, St Johns, Newfoundland, Canada |
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| Subject | wave field estimation; ship motion; machine learning and artificial neural networks |
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| Abstract | There can be many benefits of accessing real time accurate estimates of the surrounding wave field for ships and floating structures. This knowledge is important for operational risk mitigation as well as the safety of the vessel and its cargo. In the open literature, wave buoys, ship radars (including X-band and K-band radars) / satellite scanning and hydrodynamic modeling using ship motions seem to be highlighted among the methods to measure / estimate wave field. In the present study, an alternative approach is developed using ship motions and recent developments in artificial intelligence, namely artificial neural networks and machine learning type modelling. In this paper, machine learning approach is introduced briefly, followed by a description of the artificial neural network model used. The paper reports the results obtained for a near shore science vessel in head seas with various ship speeds and sea states. The preliminary results show very good performance in wave field estimation compared to the actual one using machine learning approach with ship motions. |
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| Publication date | 2020-11-30 |
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| Publisher | IEEE |
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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 | 161bb275-bb73-40f5-bcc0-8bac211c1e60 |
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| Record created | 2025-06-11 |
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| Record modified | 2025-06-11 |
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