Deep learning–based framework for regional risk assessment in a multi–ship encounter situation based on the transformer network-Reference-Cited by-同舟云学术

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Deep learning–based framework for regional risk assessment in a multi–ship encounter situation based on the transformer network

Published:2024-01 Issue: Volume:241 Page:109636
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Gao Dawei^ORCID,Zhu Yongsheng^ORCID,Yan Ke,Soares C. Guedes^ORCID

Funder

China Scholarship Council

National Key Research and Development Program of China

Fundação para a Ciência e a Tecnologia

Centro de Engenharia e Tecnologia Naval e Oceânica, Instituto Superior Técnico

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference49 articles.

1. A two–stage black–spot identification model for inland waterway transportation;Zhang;Reliab Eng Syst Saf,2021

2. Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal;Silveira;J Navig,2013

3. An evidential reasoning–based CREAM to human reliability analysis in maritime accident process;Wu;Risk Anal,2017

4. A multicriteria outranking approach for ship collision risk assessment;Silveira;Reliab Eng Syst Saf,2021

5. A big data analytics method for the evaluation of ship-ship collision risk reflecting hydrometeorological conditions;Zhang;Reliab Eng Syst Saf,2021

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Interaction aware and multi-modal distribution for ship trajectory prediction with spatio-temporal crisscross hybrid network;Reliability Engineering & System Safety;2024-12

2. Cascading failure modelling in global container shipping network using mass vessel trajectory data;Reliability Engineering & System Safety;2024-09

3. A long sequence time-series forecasting model for ship motion attitude based on informer;Ocean Engineering;2024-08

4. A Multi-AttentionLSTM model for multi-ship encounter classification;2024 IEEE International Conference on Real-time Computing and Robotics (RCAR);2024-06-24

5. A pyramidal residual attention model of short‐term wind power forecasting for wind farm safety;Quality and Reliability Engineering International;2024-05-13

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