21   Artículos

 
en línea
Mingze Li, Bing Li, Zhigang Qi, Jiashuai Li and Jiawei Wu    
Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emer... ver más
Revista: ISPRS International Journal of Geo-Information    Formato: Electrónico

 
en línea
Ye Xiao, Yupeng Hu, Jizhao Liu, Yi Xiao and Qianzhen Liu    
Ship trajectory prediction is essential for ensuring safe route planning and to have advanced warning of the dangers at sea. With the development of deep learning, most of the current research has explored advanced prediction methods based on historical ... ver más
Revista: Journal of Marine Science and Engineering    Formato: Electrónico

 
en línea
Atefe Sedaghat, Homayoon Arbabkhah, Masood Jafari Kang and Maryam Hamidi    
This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to... ver más
Revista: Journal of Marine Science and Engineering    Formato: Electrónico

 
en línea
Xuhang Xu, Chunshan Liu, Jianghui Li, Yongchun Miao and Lou Zhao    
Vessel trajectory prediction is an important step in route planning, which could help improve the efficiency of maritime transportation. In this article, a high-accuracy long-term trajectory prediction algorithm is proposed for oil tankers. The proposed ... ver más
Revista: Journal of Marine Science and Engineering    Formato: Electrónico

 
en línea
Dapeng Jiang, Guoyou Shi, Na Li, Lin Ma, Weifeng Li and Jiahui Shi    
In the context of the rapid development of deep learning theory, predicting future motion states based on time series sequence data of ship trajectories can significantly improve the safety of the traffic environment. Considering the spatiotemporal corre... ver más
Revista: Journal of Marine Science and Engineering    Formato: Electrónico

 
en línea
Daping Xi, Yuhao Feng, Wenping Jiang, Nai Yang, Xini Hu and Chuyuan Wang    
The extraction of ship behavior patterns from Automatic Identification System (AIS) data and the subsequent prediction of travel routes play crucial roles in mitigating the risk of ship accidents. This study focuses on the Wuhan section of the dendritic ... ver más
Revista: ISPRS International Journal of Geo-Information    Formato: Electrónico

 
en línea
Wenbo Zhao, Dezhi Wang, Kai Gao, Jiani Wu and Xinghua Cheng    
Approximating the positions of vessels near underwater devices, such as unmanned underwater vehicles and autonomous underwater vehicles, is crucial for many underwater operations. However, long-term monitoring of vessel trajectories is challenging due to... ver más
Revista: Journal of Marine Science and Engineering    Formato: Electrónico

 
en línea
Xinyu Wang and Yingjie Xiao    
The rapid growth of ship traffic leads to traffic congestion, which causes maritime accidents. Accurate ship trajectory prediction can improve the efficiency of navigation and maritime traffic safety. Previous studies have focused on developing a ship tr... ver más
Revista: Information    Formato: Electrónico

 
en línea
Bakht Zaman, Dusica Marijan and Tetyana Kholodna    
The availability of automatic identification system (AIS) data for tracking vessels has paved the way for improvements in maritime safety and efficiency. However, one of the main challenges in using AIS data is often the low quality of the data. Practica... ver más
Revista: Journal of Marine Science and Engineering    Formato: Electrónico

 
en línea
Sizhe Luo, Weiming Zeng and Bowen Sun    
With the increasing popularity of automatic identification system AIS devices, mining latent vessel motion patterns from AIS data has become a hot topic in water transportation research. Trajectory similarity computation is a fundamental issue to many ma... ver más
Revista: Journal of Marine Science and Engineering    Formato: Electrónico

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