Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Changshi Xiao"'
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 9, p 1560 (2024)
Simulation technology has been extensively utilized in the study of ship entry and exit from ports, as well as navigation through waterways. It effectively mirrors the stochastic dynamic changes and interrelationships among various elements within th
Externí odkaz:
https://doaj.org/article/3db7e5befd1a458bb2e1c6f81f569473
Publikováno v:
Journal of Marine Science and Engineering, Vol 12, Iss 9, p 1553 (2024)
Assessing carbon emission reduction potential is vital for achieving carbon peak and neutrality in the maritime sector. In this study, we proposed a universal framework for assessing the effectiveness of different measures on carbon emission reductio
Externí odkaz:
https://doaj.org/article/f820a003775d414e9b7a5bf42aca355e
Publikováno v:
Environmental Research Letters, Vol 19, Iss 4, p 044051 (2024)
In urban areas situated along busy waterways like the Yangtze River, the diesel engines of inland navigation ships emerge as significant contributors to air pollution. Among these vessels, certain high-emission ships exhibit considerably higher level
Externí odkaz:
https://doaj.org/article/60d83daeff9f461b8306606790df7b25
Publikováno v:
International Journal of Naval Architecture and Ocean Engineering, Vol 13, Iss , Pp 115-125 (2021)
Simulation-based hull form optimization is a typical HEB (high-dimensional, expensive computationally, black-box) problem. Conventional optimization algorithms easily fall into the “curse of dimensionality” when dealing with HEB problems. A recen
Externí odkaz:
https://doaj.org/article/e89ccea9e0e1455399747b4c33250697
Publikováno v:
Applied Sciences, Vol 13, Iss 5, p 3164 (2023)
Waterline usually plays as an important visual cue for the autonomous navigation of marine unmanned surface vehicles (USVs) in specific waters. However, the visual complexity of the inland waterline presents a significant challenge for the developmen
Externí odkaz:
https://doaj.org/article/8fdc2f5bd805426ab623f80534349edc
Publikováno v:
Journal of Marine Science and Engineering, Vol 11, Iss 4, p 745 (2023)
The swaying motion of ships can always be generated due to the influence of complex sea conditions. A novel offshore Self-Stabilized system based on motion prediction and compensation control was studied. Firstly, an autoregressive model of ship moti
Externí odkaz:
https://doaj.org/article/66e5715a60f249ccb174c8bab6286003
Publikováno v:
Drones, Vol 6, Iss 11, p 335 (2022)
Drones play an important role in the development of remote sensing and intelligent surveillance. Due to limited onboard computational resources, drone-based object detection still faces challenges in actual applications. By studying the balance betwe
Externí odkaz:
https://doaj.org/article/c82692f1d7e64a87b920a1a3606ce759
Publikováno v:
Journal of Marine Science and Engineering, Vol 10, Iss 6, p 809 (2022)
In the field of automatic detection of ship exhaust behavior, a deep learning-based multi-sensor hierarchical detection method for tracking inland river ship chimneys is proposed to locate the ship exhaust behavior detection area quickly and accurate
Externí odkaz:
https://doaj.org/article/29e1747bd2074b9da5d051206224b319
Publikováno v:
Journal of Marine Science and Engineering, Vol 10, Iss 5, p 639 (2022)
Due to the high error frequency of the existing methods in identifying a ship’s navigational intention, accidents frequently occur at intersections. Therefore, it is urgent to improve the ability to perceive ship intention at intersections. In this
Externí odkaz:
https://doaj.org/article/9b56249c690d4e0f9ee3cb83deed4fbc
Publikováno v:
Journal of Marine Science and Engineering, Vol 10, Iss 3, p 420 (2022)
Aiming at the problem that unmanned surface vehicle (USV) motion planning is disturbed by effects of wind and current, a USV motion planning method based on regularization-trajectory cells is proposed. First, a USV motion mathematical model is establ
Externí odkaz:
https://doaj.org/article/74c472c6c9b84481849f225b2cc661c6