Sea State Parameter Prediction Based on Residual Cross-Attention Article Swipe

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Lei Sun , Jun Wang , Zihao Li , Z. Jiao , Yuxiang Ma ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.3390/jmse12122342

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.

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Environmental Science

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Residual Environmental science State (computer science) Statistics Econometrics Computer science Mathematics Algorithm

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Type: article
Language: en
Landing Page: https://doi.org/10.3390/jmse12122342
PDF: https://www.mdpi.com/2077-1312/12/12/2342/pdf?version=1734698266
OA Status: gold
Cited By: 3
References: 30
Related Works: 10
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DOI: https://doi.org/10.3390/jmse12122342

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Title: Sea State Parameter Prediction Based on Residual Cross-Attention

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Type: article

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Language: en

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Publication year: 2024

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Publication date: 2024-12-20

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Authors: Lei Sun, Jun Wang, Zihao Li, Z. Jiao, Yuxiang Ma

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Landing page: https://doi.org/10.3390/jmse12122342

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Concepts: Residual, Environmental science, State (computer science), Statistics, Econometrics, Computer science, Mathematics, Algorithm

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Cited by: 3

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abstract_inverted_index.real-time	46
abstract_inverted_index.resulting	27, 89
abstract_inverted_index.scenarios	96
abstract_inverted_index.selected,	75
abstract_inverted_index.approaches	19
abstract_inverted_index.attention,	82
abstract_inverted_index.estimation	4
abstract_inverted_index.integrates	61
abstract_inverted_index.introduces	38
abstract_inverted_index.mechanism,	59
abstract_inverted_index.mechanisms	40
abstract_inverted_index.parameters	49
abstract_inverted_index.prediction	30, 34, 50, 110
abstract_inverted_index.suboptimal	29
abstract_inverted_index.assessment.	99
abstract_inverted_index.combination	1
abstract_inverted_index.data-driven	6, 18
abstract_inverted_index.demonstrate	102
abstract_inverted_index.operations,	53
abstract_inverted_index.prediction.	15, 70
abstract_inverted_index.robustness.	118
abstract_inverted_index.adaptability	23
abstract_inverted_index.conventional	17
abstract_inverted_index.experimental	95
abstract_inverted_index.innovatively	54
abstract_inverted_index.performance.	31
abstract_inverted_index.representative	64
abstract_inverted_index.Cross-Attention	39, 58, 105, 108
abstract_inverted_index.configurations,	80
abstract_inverted_index.Cross-Attention,	84, 88
cited_by_percentile_year.max	97
cited_by_percentile_year.min	96
corresponding_author_ids	https://openalex.org/A5076380771
countries_distinct_count	1
institutions_distinct_count	5
corresponding_institution_ids	https://openalex.org/I27357992
citation_normalized_percentile.value	0.86350824
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True