Deep Learning Methods to Mitigate Human-Factor-Related Accidents in Maritime Transport DOI Creative Commons
Genaro Cao-Feijóo, José M. Pérez-Canosa, Francisco Javier Pérez Castelo

и другие.

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(10), С. 1819 - 1819

Опубликована: Окт. 12, 2024

Artificial intelligence aims to be the solution multiple engineering problems by trying emulate human learning process. In this sense, maritime transport standards have clearly evolved, which are based on two principal pillars: International Convention for Safety of Life at Sea (SOLAS) and Prevention Pollution from Ships (MARPOL). Based a formal safety assessment research process, these pillars try solve most accidents, which, in their final steps, associated with factors. research, an original methodology employing deep process image recognition during mooring line operation, dangerous ships, is developed. The main results indicate that proposed method excellent tool advising ship officers watch and, consequently, provides new way prevent factors onboard causing future must considered international standards.

Язык: Английский

Attention-enhanced and integrated deep learning approach for fishing vessel classification based on multiple features DOI Creative Commons
Xin Cheng, Jintao Wang, Xinjun Chen

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 13, 2025

Effective fisheries management is the key to achieve sustainable globally, while accurate monitoring of fishing vessels essential improve effectiveness measures. Self-reported information on vessel types often limited and may not cover all operating vessels, causing incomplete in management. Therefore, a novel way objectively identify large quantity needed. In this study, we presented an innovative integrated deep learning model by using automatic identification system (AIS) data classify five including gillnetter, hook liner, trawler, fish carrier, stow net vessel, further improving performance classification. First, preprocessed removing erroneous information, dividing trajectories day obtain complete reliable dataset. Then, multidimensional feature vector was constructed combining geometric, static dynamic characteristics explain behavioral differences various more effectively. Finally, fed into ensemble two-dimensional bidirectional long short-term memory network convolutional neural with attention mechanism for training, prediction results were obtained through fully connected layer. The accuracy 91.90%, which higher than other single classifiers. experimental demonstrated that method remarkable could be adopted precision classification based AIS data.

Язык: Английский

Процитировано

1

Incorporating prior knowledge of collision risk into deep learning networks for ship trajectory prediction in the maritime Internet of Things industry DOI

Yu Zhang,

Ping Tu, Zhiyuan Zhao

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 146, С. 110311 - 110311

Опубликована: Фев. 20, 2025

Язык: Английский

Процитировано

0

A framework for ship semantic behavior representation and indexing DOI

Shunqiang Xu,

Liang Huang, Yamin Huang

и другие.

Ocean Engineering, Год журнала: 2025, Номер 329, С. 121023 - 121023

Опубликована: Апрель 7, 2025

Язык: Английский

Процитировано

0

Machine learning applications for risk assessment in maritime transport: Current status and future directions DOI
Y. Lin, Xue Li, Kum Fai Yuen

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 155, С. 110959 - 110959

Опубликована: Май 9, 2025

Язык: Английский

Процитировано

0

Deep Learning Methods to Mitigate Human-Factor-Related Accidents in Maritime Transport DOI Creative Commons
Genaro Cao-Feijóo, José M. Pérez-Canosa, Francisco Javier Pérez Castelo

и другие.

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(10), С. 1819 - 1819

Опубликована: Окт. 12, 2024

Artificial intelligence aims to be the solution multiple engineering problems by trying emulate human learning process. In this sense, maritime transport standards have clearly evolved, which are based on two principal pillars: International Convention for Safety of Life at Sea (SOLAS) and Prevention Pollution from Ships (MARPOL). Based a formal safety assessment research process, these pillars try solve most accidents, which, in their final steps, associated with factors. research, an original methodology employing deep process image recognition during mooring line operation, dangerous ships, is developed. The main results indicate that proposed method excellent tool advising ship officers watch and, consequently, provides new way prevent factors onboard causing future must considered international standards.

Язык: Английский

Процитировано

0