| DOI | Resolve DOI: https://doi.org/10.1109/IEEECONF38699.2020.9389117 |
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| Author | Search for: Akinturk, Ayhan1; Search for: Zaman, Hasanat1; Search for: He, Moqin1; 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 Canada. Ocean, Coastal and River Engineering
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| Format | Text, Article |
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| Conference | Global Oceans 2020: Singapore - U.S. Gulf Coast, October 5-30, 2020, Biloxi, MS, USA |
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| Subject | wave field estimation; ship motions; machine learning; artificial neural networks; short time Fourier Transform |
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| Abstract | Real time estimation of actual and imminent wave fields in the vicinity of ships and floating structures are important for safe and efficient operations. Historically, three main approaches have been cited in the open literatures with varying degrees of accuracies: the use of wave buoys, ship radars (e.g. X-band and K-band radars) / satellite and ship motions. This paper presents two methods to predict approaching waves using ship motions by employing artificial neural networks and machine learning. Another method based on Short Time Fourier Transform (STFT) of the ship motion data was also used for cross comparison. Both methods were tested in head sea condition only with different ship speeds and sea states. The method based on machine learning estimates the wave elevations as time series, whereas STFT gives its estimates as spectrum. Both methods gave very good results. |
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| Publication date | 2020-10-05 |
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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 | 8dab6924-01b3-4bb1-bef5-948c7e164721 |
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| Record created | 2025-04-25 |
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| Record modified | 2025-04-28 |
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