| Lien | https://onepetro.org/ISOPEIOPEC/proceedings-abstract/ISOPE23/All-ISOPE23/ISOPE-I-23-301/524672?redirectedFrom=PDF |
|---|
| Auteur | Rechercher : He, Moqin1; Rechercher : Akinturk, Ayhan1; Rechercher : Zaman, Hasanat1; Rechercher : Mak, Lawrence1; Rechercher : Seo, Dong Cheol1Identifiant ORCID : https://orcid.org/0000-0002-5818-7475 |
|---|
| Affiliation | - Conseil national de recherches Canada. Génie océanique, côtier et fluvial
|
|---|
| Format | Texte, Article |
|---|
| Conférence | The 33rd International Ocean and Polar Engineering Conference June 19–23, 2023 Ottawa, Canada |
|---|
| Sujet | neural network; wave buoy; ship location; deep learning; machine learning; estimation; algorithm; artificial intelligence; ship motion |
|---|
| Résumé | 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. |
|---|
| Date de publication | 2023-06-19 |
|---|
| Maison d’édition | International Society of Offshore and Polar Engineers |
|---|
| Dans | |
|---|
| Langue | anglais |
|---|
| Publications évaluées par des pairs | Oui |
|---|
| Exporter la notice | Exporter en format RIS |
|---|
| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
|---|
| Identificateur de l’enregistrement | 1bb77b81-9a3f-408e-a602-af79fb3cbec6 |
|---|
| Enregistrement créé | 2025-06-09 |
|---|
| Enregistrement modifié | 2025-06-09 |
|---|
