Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Yongfeng Suo"'
Autor:
Guoqiang Chen, Wenjin Wu, Yaqi Geng, Qiong Chen, Yuanzhen Ren, Sijia Li, Yongfeng Suo, Kai Wu, Huadong Guo
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTChanges in water content dynamics can serve as predictive indicators for catastrophic transitions in vegetation tipping elements. Vegetation water content is a rapidly changing parameter with sub-daily fluctuations, and current space-borne se
Externí odkaz:
https://doaj.org/article/d1bb2ec19bd44d169ca882fa4501326b
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 8, p 1321 (2024)
To address the complexity of ship trajectory prediction, this study explored the efficacy of the Mamba model, a relatively new deep-learning framework. In order to evaluate the performance of the Mamba model relative to traditional models, which ofte
Externí odkaz:
https://doaj.org/article/e83455e7c80d411bba977414efd115a3
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 8, p 1245 (2024)
There has been significant interest in the research field of ship automatic navigation, particularly in the area of autonomous berthing. To address the key challenges of path planning and control during ship berthing, we propose an enhanced Linear−
Externí odkaz:
https://doaj.org/article/124b91de5a9f4c5d8ef235a838588e37
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 7, p 1137 (2024)
Autonomous berthing technology is a crucial engineering control problem within the ship intelligence system, encompassing a series of complex operations and technologies. Firstly, this paper analyses the research on autonomous berthing technology fro
Externí odkaz:
https://doaj.org/article/5b502bfd5d1a4b0ea8657915e66d5bfb
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 5, p 731 (2024)
In recent years, the artificial potential field has garnered significant attention in ship route planning and traffic flow simulation. However, the traditional artificial potential field method faces challenges in accurately simulating a ship’s cus
Externí odkaz:
https://doaj.org/article/dfc0c42c4e304f98869722fda2053b18
Autor:
Xiaobin Tian, Yongfeng Suo
Publikováno v:
Journal of Marine Science and Engineering, Vol 11, Iss 9, p 1731 (2023)
This study proposes a solution to the problem of inaccurate and time-consuming ship trajectory prediction caused by frequent ship maneuvering in complex waterways. The proposed solution is a ship trajectory prediction model that uses a difference lon
Externí odkaz:
https://doaj.org/article/a68b396413234b2193684f4344585fb5
Publikováno v:
Journal of Marine Science and Engineering, Vol 11, Iss 7, p 1439 (2023)
The influence of the maritime environment such as water currents, water depth, and traffic separation rules should be considered when conducting ship path planning. Additionally, the maneuverability constraints of the ship play a crucial role in navi
Externí odkaz:
https://doaj.org/article/0443ccff7ea04ef7a3bfef464485bf44
Publikováno v:
Sensors, Vol 22, Iss 14, p 5281 (2022)
The successful emergence of real-time positioning systems in the maritime domain has favored the development of data infrastructures that provide valuable monitoring and decision-aided systems. However, there is still a need for the development of da
Externí odkaz:
https://doaj.org/article/937e1402a7ac4ee0b03e17887c272fce
Publikováno v:
Sensors, Vol 21, Iss 14, p 4741 (2021)
The successful implementation of Vessel Traffic Services (VTS) relies heavily on human decisions. With the increasing development of maritime traffic, there is an urgent need to provide a sound support for dynamic risk appraisals and decision support
Externí odkaz:
https://doaj.org/article/32fca8c885294eb4b727bfdd577dcf63
Publikováno v:
Sensors, Vol 20, Iss 18, p 5133 (2020)
Ship trajectory prediction is a key requisite for maritime navigation early warning and safety, but accuracy and computation efficiency are major issues still to be resolved. The research presented in this paper introduces a deep learning framework a
Externí odkaz:
https://doaj.org/article/880c10bca756402c9925a2290d06a293