A Novel Class-Imbalanced Ship Motion Data-Based Cross-Scale Model for Sea State Estimation

Cheng, Xu; Wang, Kexin; Liu, Xiufeng; Yu, Qian; Shi, Fan; Ren, Zhengru; Chen, Shengyong

doi:10.1109/tits.2023.3315674

IEEE Trans. Intell. Transport. Syst.

2023

DOI: 10.1109/tits.2023.3315674

|View full text |Cite

A Novel Class-Imbalanced Ship Motion Data-Based Cross-Scale Model for Sea State Estimation

Xu Cheng,

Kexin Wang,

Xiufeng Liu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Prototype-Empowered Kernel-Varying Convolutional Model for Imbalanced Sea State Estimation in IoT-Enabled Autonomous Ship

Liu,

Cheng,

Shi

et al. 2024

IEEE Trans. Sustain. Comput.

View full text Add to dashboard Cite

A Prototype-Empowered Kernel-Varying Convolutional Model for Imbalanced Sea State Estimation in IoT-Enabled Autonomous Ship

Liu,

Cheng,

Shi

et al. 2024

IEEE Trans. Sustain. Comput.

View full text Add to dashboard Cite

Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review

Wang,

Guo,

She

et al. 2024

Information

View full text Add to dashboard Cite

The maritime industry is integral to global trade and heavily depends on precise forecasting to maintain efficiency, safety, and economic sustainability. Adopting deep learning for predictive analysis has markedly improved operational accuracy, cost efficiency, and decision-making. This technology facilitates advanced time series analysis, vital for optimizing maritime operations. This paper reviews deep learning applications in time series analysis within the maritime industry, focusing on three areas: ship operation-related, port operation-related, and shipping market-related topics. It provides a detailed overview of the existing literature on applications such as ship trajectory prediction, ship fuel consumption prediction, port throughput prediction, and shipping market prediction. The paper comprehensively examines the primary deep learning architectures used for time series forecasting in the maritime industry, categorizing them into four principal types. It systematically analyzes the advantages of deep learning architectures across different application scenarios and explores methodologies for selecting models based on specific requirements. Additionally, it analyzes data sources from the existing literature and suggests future research directions.

show abstract

Sea State Parameter Prediction Based on Residual Cross-Attention

Sun,

Wang,

et al. 2024

JMSE

View full text Add to dashboard Cite

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.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Novel Class-Imbalanced Ship Motion Data-Based Cross-Scale Model for Sea State Estimation

Cited by 3 publications

References 40 publications

A Prototype-Empowered Kernel-Varying Convolutional Model for Imbalanced Sea State Estimation in IoT-Enabled Autonomous Ship

A Prototype-Empowered Kernel-Varying Convolutional Model for Imbalanced Sea State Estimation in IoT-Enabled Autonomous Ship

Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review

Sea State Parameter Prediction Based on Residual Cross-Attention

Contact Info

Product

Resources

About