Sea State Parameter Prediction Based on Residual Cross-Attention
The combination of onboard estimation and data-driven methods is widely applied for sea state parameter prediction. However, conventional data-driven approaches often exhibit limited adaptability to this task, resulting in suboptimal prediction performance. To enhance prediction accuracy, this study...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/12/2342 |
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| _version_ | 1850050030989213696 |
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| author | Lei Sun Jun Wang Zi-Hao Li Zi-Lu Jiao Yu-Xiang Ma |
| author_facet | Lei Sun Jun Wang Zi-Hao Li Zi-Lu Jiao Yu-Xiang Ma |
| author_sort | Lei Sun |
| collection | DOAJ |
| description | The combination of onboard estimation and data-driven methods is widely applied for sea state parameter prediction. However, conventional data-driven approaches often exhibit limited adaptability to this task, resulting in suboptimal prediction performance. To enhance prediction accuracy, this study introduces Cross-Attention mechanisms to optimize the task of real-time sea state parameters prediction for maritime operations, innovatively develops a Residual Cross-Attention mechanism, and integrates it into representative networks for sea state parameter prediction. Three benchmark networks were selected, each evaluated under three configurations, without attention, with Cross-Attention, and with Residual Cross-Attention, resulting in a total of nine experimental scenarios for error assessment. The results demonstrate that both Cross-Attention and Residual Cross-Attention reduce prediction error to varying degrees and improve model robustness. |
| format | Article |
| id | doaj-art-2c6a2feb281d4f32a5530699faeb43a4 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-2c6a2feb281d4f32a5530699faeb43a42025-08-20T02:53:34ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-12-011212234210.3390/jmse12122342Sea State Parameter Prediction Based on Residual Cross-AttentionLei Sun0Jun Wang1Zi-Hao Li2Zi-Lu Jiao3Yu-Xiang Ma4School of Naval Architecture, Dalian University of Technology, Dalian 116024, ChinaSchool of Naval Architecture, Dalian University of Technology, Dalian 116024, ChinaSchool of Naval Architecture, Dalian University of Technology, Dalian 116024, ChinaLianyungang Center of Taihu Laboratory of Deepsea Technological Science, Lianyungang 222000, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaThe combination of onboard estimation and data-driven methods is widely applied for sea state parameter prediction. However, conventional data-driven approaches often exhibit limited adaptability to this task, resulting in suboptimal prediction performance. To enhance prediction accuracy, this study introduces Cross-Attention mechanisms to optimize the task of real-time sea state parameters prediction for maritime operations, innovatively develops a Residual Cross-Attention mechanism, and integrates it into representative networks for sea state parameter prediction. Three benchmark networks were selected, each evaluated under three configurations, without attention, with Cross-Attention, and with Residual Cross-Attention, resulting in a total of nine experimental scenarios for error assessment. The results demonstrate that both Cross-Attention and Residual Cross-Attention reduce prediction error to varying degrees and improve model robustness.https://www.mdpi.com/2077-1312/12/12/2342sea state predictionshort-crested wavetime seriescross-attentionresidual connection |
| spellingShingle | Lei Sun Jun Wang Zi-Hao Li Zi-Lu Jiao Yu-Xiang Ma Sea State Parameter Prediction Based on Residual Cross-Attention Journal of Marine Science and Engineering sea state prediction short-crested wave time series cross-attention residual connection |
| title | Sea State Parameter Prediction Based on Residual Cross-Attention |
| title_full | Sea State Parameter Prediction Based on Residual Cross-Attention |
| title_fullStr | Sea State Parameter Prediction Based on Residual Cross-Attention |
| title_full_unstemmed | Sea State Parameter Prediction Based on Residual Cross-Attention |
| title_short | Sea State Parameter Prediction Based on Residual Cross-Attention |
| title_sort | sea state parameter prediction based on residual cross attention |
| topic | sea state prediction short-crested wave time series cross-attention residual connection |
| url | https://www.mdpi.com/2077-1312/12/12/2342 |
| work_keys_str_mv | AT leisun seastateparameterpredictionbasedonresidualcrossattention AT junwang seastateparameterpredictionbasedonresidualcrossattention AT zihaoli seastateparameterpredictionbasedonresidualcrossattention AT zilujiao seastateparameterpredictionbasedonresidualcrossattention AT yuxiangma seastateparameterpredictionbasedonresidualcrossattention |