Knowledge-Based Systems, Год журнала: 2024, Номер unknown, С. 112869 - 112869
Опубликована: Дек. 1, 2024
Язык: Английский
Knowledge-Based Systems, Год журнала: 2024, Номер unknown, С. 112869 - 112869
Опубликована: Дек. 1, 2024
Язык: Английский
Applied Ocean Research, Год журнала: 2024, Номер 152, С. 104194 - 104194
Опубликована: Авг. 27, 2024
Язык: Английский
Процитировано
15Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108257 - 108257
Опубликована: Апрель 2, 2024
Язык: Английский
Процитировано
12Ocean Engineering, Год журнала: 2024, Номер 311, С. 118560 - 118560
Опубликована: Июль 31, 2024
A novel Maritime Multi-Object Tracking method is proposed, combining a deep learning-based object detector with target association algorithms to achieve robust sea-surface multi-object tracking. Specifically, the employs You Only Look Once version 7 for detection. In data part, module onboard camera motion compensation developed, maritime dynamic spatial information-based intersection-over-union presented as similarity metric, and progressive refinement cascade matching strategy designed enhance tracker's tracking capabilities. The Jari Dataset utilised validate effectiveness performance of proposed method. Experimental results demonstrate that compared earlier process, exhibits significant enhancement in multiple accuracy, an increase 27.8% achieved score 81.3. particular, it reduces number identifications switching missed targets, achieving holistically preferable performance. Meanwhile, speed fulfils engineering application requirements autonomous ship navigation system.
Язык: Английский
Процитировано
7Ocean Engineering, Год журнала: 2025, Номер 320, С. 120335 - 120335
Опубликована: Янв. 13, 2025
Язык: Английский
Процитировано
1Frontiers in Marine Science, Год журнала: 2024, Номер 11
Опубликована: Июль 9, 2024
Fishing vessels are important contributors to global emissions in terms of greenhouse gases and air pollutants. However, few studies have addressed the from fishing on grounds. In this study, a framework for estimating vessel emissions, using bottom-up dynamic method based big data Beidou VMS (vessel monitoring system) vessels, is proposed applied survey East China Sea. The results study established one-year emission inventory This was first use estimate area, will help support management their carbon emissions.
Язык: Английский
Процитировано
3Computers & Electrical Engineering, Год журнала: 2024, Номер 119, С. 109523 - 109523
Опубликована: Авг. 9, 2024
Язык: Английский
Процитировано
3Ocean Engineering, Год журнала: 2025, Номер 320, С. 120244 - 120244
Опубликована: Янв. 14, 2025
Язык: Английский
Процитировано
0Journal of Marine Science and Technology, Год журнала: 2025, Номер unknown
Опубликована: Янв. 15, 2025
Язык: Английский
Процитировано
0Journal of Computational Science, Год журнала: 2025, Номер unknown, С. 102572 - 102572
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Journal of Marine Science and Engineering, Год журнала: 2025, Номер 13(4), С. 746 - 746
Опубликована: Апрель 8, 2025
Autonomous vessels are becoming paramount to ocean transportation, while they also face complex risks in dynamic marine environments. Machine learning plays a crucial role enhancing maritime safety by leveraging its data analysis and predictive capabilities. However, there has been no review grounded bibliometric this field. To explore the research evolution knowledge frontier field of for autonomous shipping, was conducted using 719 publications from Web Science database, covering period 2000 up May 2024. This study utilized VOSviewer, alongside traditional literature methods, construct network map perform cluster analysis, thereby identifying hotspots, trends, emerging frontiers. The findings reveal robust cooperative among journals, researchers, institutions, countries or regions, underscoring interdisciplinary nature domain. Through review, we found that machine methods evolving toward systematic comprehensive direction, integration with AI human interaction may be next bellwether. Future will concentrate on three main areas: objectives towards proactive management coordination, developing advanced technologies, such as bio-inspired sensors, quantum learning, self-healing systems, decision-making algorithms generative adversarial networks (GANs), hierarchical reinforcement (HRL), federated learning. By visualizing collaborative networks, analyzing evolutionary lays groundwork pioneering advancements sets visionary angle future shipping. Moreover, it facilitates partnerships between industry academia, making concerted efforts domain USVs.
Язык: Английский
Процитировано
0