Advances in Nautical Engineering and Maritime Transport

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 7062

Special Issue Editors

Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
Interests: ocean engineering; ais; nautical traffic model; navigational safety; ship maneuvering simulation
Hubei Key Laboratory of Inland Shipping Technology, Wuhan University of Technology, Wuhan 430063, China
Interests: autonomous collision avoidance of ships in order to reduce the incidence of maritime accidents; maritime; geographic information system; shipping; transportation; maritime security; transport management; transportation planning
Special Issues, Collections and Topics in MDPI journals
Navigation Department, Maritime University of Szczecin, Szczecin, Poland
Interests: sea navigation; risk; security; reliability; safety; marine operations; ocean engineering; process safety; LNG transport; gas handling; port design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Maritime Transport underpins global supply chain linkages and economic interdependency with shipping and ports estimated to handle over 80% of global merchandise trade by volume and more than 70% by value. The increasingly globalised world has witnessed the growing importance of the sector of nautical engineering and maritime transport, while the sector is facing great challenges in the coming decades, such as addressing the need for safer, more efficient and smarter shipping, pollution prevention and lower carbon emissions.

Technology is seen as an opportunity in the future, with the expectation of the expansion of autonomous shipping, increased digitization and further introduction of artificial intelligence systems. Looking into the subsystems of nautical engineering and maritime transport, the infrastructures, humans, ships and management are all moving towards intelligent development and jointly creating an intelligent, safe, green and efficient shipping environment. To address the advances in nautical engineering and maritime transport, the focus of this Special Issue includes, but is not limited to, the following topics:

  • Maritime risk model and assessment;
  • Human error and situation awareness;
  • Ship–infrastructure cooperation;
  • Autonomous ships and cooperative collision avoidance;
  • Innovations of ship design;
  • Green ship technology (energy)
  • Challenges in the maritime environment;
  • Coastal navigation route planning
  • Arctic navigation;
  • New schemes of search and rescue
  • Artificial Intelligence and big data in nautical engineering and maritime transport.

Prof. Dr. Junmin Mou
Dr. Mengxia Li
Prof. Dr. Maciej Gucma
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • maritime risk model and assessment
  • maritime transport
  • arctic navigation
  • autonomous ship and cooperative collision avoidance
  • green ship technology (energy)
  • coastal navigation route planning

Published Papers (6 papers)

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Research

23 pages, 6299 KiB  
Article
Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
Appl. Sci. 2023, 13(18), 10282; https://doi.org/10.3390/app131810282 - 13 Sep 2023
Viewed by 1215
Abstract
With the rapid development of artificial intelligence technology and unmanned surface vehicle (USV) technology, object detection and tracking have wide applications in marine monitoring and intelligent ships. However, object detection and tracking tasks on small sample datasets often face challenges due to insufficient [...] Read more.
With the rapid development of artificial intelligence technology and unmanned surface vehicle (USV) technology, object detection and tracking have wide applications in marine monitoring and intelligent ships. However, object detection and tracking tasks on small sample datasets often face challenges due to insufficient sample data. In this paper, we propose a ship detection and tracking model with high accuracy based on a few training samples with supervised information based on the few-shot learning framework. The transfer learning strategy is designed, innovatively using an open dataset of vehicles on highways to improve object detection accuracy for inland ships. The Shuffle Attention mechanism and smaller anchor boxes are introduced in the object detection network to improve the detection accuracy of different targets in different scenes. Compared with existing methods, the proposed method is characterized by fast training speed and high accuracy with small datasets, achieving 84.9% (mAP@0.5) with only 585 training images. Full article
(This article belongs to the Special Issue Advances in Nautical Engineering and Maritime Transport)
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19 pages, 3995 KiB  
Article
Navigation Safety on Shipping Routes during Construction
Appl. Sci. 2023, 13(15), 8593; https://doi.org/10.3390/app13158593 - 26 Jul 2023
Cited by 2 | Viewed by 646
Abstract
Construction work or other maintenance and repair activities in navigational channels are crucial to ensure and improve ships’ movement on the selected routes. However, during the performance of these works, the ships’ navigation along the construction area becomes more difficult due to the [...] Read more.
Construction work or other maintenance and repair activities in navigational channels are crucial to ensure and improve ships’ movement on the selected routes. However, during the performance of these works, the ships’ navigation along the construction area becomes more difficult due to the decreased parameters of passages for vessels and the operation of specific equipment on the route, e.g., dredgers and floating cranes. During construction work in navigational channels, it is impossible to stop navigation or limit ships’ parameters because there may not be other possibilities for vessels to reach their planned ports or other dedicated areas. The prior determination of ships’ sailing conditions and restrictions is essential to ensure maritime safety in such areas. The aim of this study is to develop a methodology that allows the precise determination of minimum passage parameters for the navigation of ships sailing through the areas in navigational channels where construction or development works are being carried out. The theoretical basis for the minimum passage parameter calculation is presented. The methodology for assessing the conditions and restrictions of navigation during construction work is proposed. The minimum width of the shipping passages in defined navigational, hydro-meteorological, and hydrological conditions and the possible minimum parameters sufficient to guarantee navigational safety are considered in a case study. The research results may be interesting for port authorities, shipping companies, and other entities involved in the organization of ships’ movement during construction work in navigational channels or other areas. Full article
(This article belongs to the Special Issue Advances in Nautical Engineering and Maritime Transport)
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22 pages, 6334 KiB  
Article
Maneuver Decision-Making Method for Ship Collision Avoidance in Chengshantou Traffic Separation Scheme Waters
Appl. Sci. 2023, 13(14), 8437; https://doi.org/10.3390/app13148437 - 21 Jul 2023
Viewed by 779
Abstract
A maneuvering decision-making model based on time series rolling and feedback compensation methods is proposed to solve the problem of high traffic risk in Chengshantou traffic separation scheme (TSS) waters. Firstly, a digital traffic environment model suitable for the TSS waters is proposed. [...] Read more.
A maneuvering decision-making model based on time series rolling and feedback compensation methods is proposed to solve the problem of high traffic risk in Chengshantou traffic separation scheme (TSS) waters. Firstly, a digital traffic environment model suitable for the TSS waters is proposed. Secondly, a navigation risk identification method in these waters is constructed based on the digitized traffic environment and situation identification model in the Chengshantou TSS waters. Thirdly, considering the requirements of the rules and good seamanship, minimum course altering is obtained by combining the collision avoidance mechanism. Lastly, a maneuvering decision-making model in the TSS waters based on time series rolling and feedback compensation methods is developed. The simulation results show that the ship can correctly identify the collision risk and appropriately obtain maneuvering decisions, and can resume the planned route under the premise of ensuring safety. When the target ships alter course or change speed, the ship can also make adaptive maneuvering decisions. In summary, the proposed method meets the requirement of safe navigation in Chengshantou waters and provides a theoretical basis for the realization of intelligent navigation in waters similar to TSS. Full article
(This article belongs to the Special Issue Advances in Nautical Engineering and Maritime Transport)
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12 pages, 2818 KiB  
Article
Machine Learning for Short-Term Prediction of Ship Motion Combined with Wave Input
Appl. Sci. 2023, 13(9), 5298; https://doi.org/10.3390/app13095298 - 24 Apr 2023
Viewed by 1423
Abstract
There is a response relationship between wave and ship motion. Based on the LSTM neural network, the mapping relationship between the wave elevation and ship roll motion is established. The wave elevation and ship motion duration data obtained by the CFD simulation are [...] Read more.
There is a response relationship between wave and ship motion. Based on the LSTM neural network, the mapping relationship between the wave elevation and ship roll motion is established. The wave elevation and ship motion duration data obtained by the CFD simulation are used to predict ship roll motion with different input data schemes. The results show that the prediction scheme considering the wave elevation input can predict ship roll motion. Compared with the direct prediction scheme based on the roll data input, the prediction scheme considering the wave elevation input factor can greatly improve the prediction accuracy and effective advance prediction time. Different wave elevation data inputs have different prediction effects. The advance prediction duration will increase with the increase in the input wave elevation position and the ship distance. The simultaneous input of multi-point wave elevation greatly increases the amount of data, allowing the trained model to utilize a greater data depth. This not only improves the advance prediction duration of the prediction model, but it also enhances the robustness of the model, making the prediction results more stable. Full article
(This article belongs to the Special Issue Advances in Nautical Engineering and Maritime Transport)
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19 pages, 6974 KiB  
Article
Statistical Modeling of Arctic Sea Ice Concentrations for Northern Sea Route Shipping
Appl. Sci. 2023, 13(7), 4374; https://doi.org/10.3390/app13074374 - 30 Mar 2023
Cited by 3 | Viewed by 1103
Abstract
The safe and efficient navigation of ships traversing the Northern Sea Route demands accurate information regarding sea ice concentration. However, the sea ice concentration forecasts employed to support such navigation are often flawed. To address this challenge, this study advances a statistical interpolation [...] Read more.
The safe and efficient navigation of ships traversing the Northern Sea Route demands accurate information regarding sea ice concentration. However, the sea ice concentration forecasts employed to support such navigation are often flawed. To address this challenge, this study advances a statistical interpolation method aimed at reducing errors arising from traditional interpolation approaches. Additionally, this study introduces an autoregressive integrated moving average model, derived from ERA5 reanalysis data, for short-term sea ice concentration forecasts along the Northern Sea Route. The validity of the model has been confirmed through comparison with ensemble experiments from the Coupling Model Intercomparison Project Phase 5, yielding reliable outcomes. The route availability is assessed on the basis of the sea ice concentration forecasts, indicating that the route will be available in the upcoming years. The proposed statistical models are also shown the capacity to facilitate effective management of Arctic shipping along the Northern Sea Route. Full article
(This article belongs to the Special Issue Advances in Nautical Engineering and Maritime Transport)
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18 pages, 4480 KiB  
Article
An Efficient Feature Augmentation and LSTM-Based Method to Predict Maritime Traffic Conditions
Appl. Sci. 2023, 13(4), 2556; https://doi.org/10.3390/app13042556 - 16 Feb 2023
Cited by 4 | Viewed by 1272
Abstract
The recent emergence of futuristic ships is the result of advances in information and communication technology, big data, and artificial intelligence. They are generally autonomous, which has the potential to significantly improve safety and drastically reduce operating costs. However, the commercialization of Maritime [...] Read more.
The recent emergence of futuristic ships is the result of advances in information and communication technology, big data, and artificial intelligence. They are generally autonomous, which has the potential to significantly improve safety and drastically reduce operating costs. However, the commercialization of Maritime Autonomous Surface Ships requires the development of appropriate technologies, including intelligent navigation systems, which involves the identification of the current maritime traffic conditions and the prediction of future maritime traffic conditions. This study aims to develop an algorithm that predicts future maritime traffic conditions using historical data, with the goal of enhancing the performance of autonomous ships. Using several datasets, we trained and validated an artificial intelligence model using long short-term memory and evaluated the performance by considering several features such as the maritime traffic volume, maritime traffic congestion fluctuation range, fluctuation rate, etc. The algorithm was able to identify features for predicting maritime traffic conditions. The obtained results indicated that the highest performance of the model with a valid loss of 0.0835 was observed under the scenario with all trends and predictions. The maximum values for 3, 6, 12, and 24 days and the congestion of the gate lines around the analysis point showed a significant effect on performance. The results of this study can be used to improve the performance of situation recognition systems in autonomous ships and can be applied to maritime traffic condition recognition technology for coastal ships that navigate more complex sea routes compared to ships navigating the ocean. Full article
(This article belongs to the Special Issue Advances in Nautical Engineering and Maritime Transport)
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