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JMSE
Special Issues
Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling
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Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 1 June 2024 | Viewed by 2726

Special Issue Editors

Dr. Xinqiang Chen

E-Mail Website
Guest Editor
Insitute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: video data-driven intelligent transportation environment perception and understanding; large-scale transportation data analysis (traffic flow data, AIS, etc.); smart ship/autonomous port
Special Issues, Collections and Topics in MDPI journals
Dr. Salvatore Antonio Biancardo

E-Mail Website
Guest Editor
Department of Civil, Construction and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
Interests: offshore civil engineering; coastal engineering; data mining; intelligent transportation system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It is reported that over 50% of people live in urban areas, a figure on track to increase to more than 65% in the future. More foods and varied living products are needed to meet the needs of peoples’ daily lives. Maritime transportation is considered to be a cost-effective manner used to transfer goods around the world. In that way, the maritime community has paid significant attention to enhancing maritime traffic efficiency as well as energy consumption. It is noted that approximately 3% of global carbon emissions come from the shipping industry; thus, there is a significant focus on reducing the carbon emissions of the shipping industry. Many countries encourage ships to sail with low-sulfur oil, whilst for ships sailing in inland waterways, it is mandatory to use low-sulfur oil to reduce carbon emissions.  

The focus of many researchers is on employing artificial intelligence (AI), big data, and computer-vision-related techniques to enhance maritime traffic efficiency. To help ships sail in a safer and faster manner, varied advanced techniques are integrated to determine ship maneuvering operations from multiple maritime data sources (e.g., radar, automatic identification system (AIS), maritime videos). It has been found that there are many challenges in the automatous shipping era, along with carbon peaking and carbon neutrality. For instance, it is not easy to automatically find an optimal ship trajectory with low economic cost and fuel consumption for a given voyage. In attempt to reach this aim, we welcome the submission of novel studies to promote cost-effective yet high-efficiency maritime traffic with feasible and transferable solutions. We welcome the submission of manuscripts which align with our Special Issue topic of “Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling Analysis”. We anticipate receiving submissions from a variety of research topics relevant to smart shipping and reductions in ship carbon emissions. The sample topics of interest include, but are not limited to, the following:

  • Ship carbon emissions and emission control area analysis;
  • Traffic knowledge mining from large-scale maritime data;
  • Maritime traffic situation awareness via varied sensory data (maritime surveillance video, AIS, radar, etc.);
  • Ship travelling path optimization under influence from varied uncertainty factors;
  • Energy consumption reduction analysis for the autonomous ship and port era;
  • Ship kinematic information exploitation and analysis via multiple maritime data sources.

Dr. Xinqiang Chen
Dr. Salvatore Antonio Biancardo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly 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 2600 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

  • smart shipping
  • maritime carbon emissions reduction
  • artificial intelligence
  • multiple maritime data source
  • maritime traffic safety
  • ship maneuvering operation
  • intelligent traffic situation awareness

Published Papers (4 papers)

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Research

16 pages, 3214 KiB  
Article
Research on the Deployment of Professional Rescue Ships for Maritime Traffic Safety under Limited Conditions
by Minghui Shao, Biao Wu, Yan Li and Xiaoli Jiang
J. Mar. Sci. Eng. 2024, 12(3), 497; https://doi.org/10.3390/jmse12030497 - 17 Mar 2024
Viewed by 531
Abstract
This paper focuses on optimizing the deployment plan for standby points of professional rescue vessels based on the data of maritime incidents in the Beihai area of China. The primary objective is to achieve multi-level and multiple coverage of the jurisdictional waters of [...] Read more.
This paper focuses on optimizing the deployment plan for standby points of professional rescue vessels based on the data of maritime incidents in the Beihai area of China. The primary objective is to achieve multi-level and multiple coverage of the jurisdictional waters of the Beihai Rescue Bureau. Models including the coverage quality of the jurisdictional waters, the coverage quality in high-risk areas, the maximum coverage of jurisdictional areas, and the maximum coverage of high-risk areas are constructed and solved using 0–1 integer programming. The optimal plan for eight standby points and their corresponding deployment plans for rescue vessels are obtained. A comparison with the current site selection plan of the Beihai Rescue Bureau validates the superiority of the proposed deployment plan for rescue vessel standby points in this paper. Full article
(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)
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13 pages, 7014 KiB  
Article
Ship Global Traveling Path Optimization via a Novel Non-Dominated Sorting Genetic Algorithm
by Shuling Zhao and Sishuo Zhao
J. Mar. Sci. Eng. 2024, 12(3), 485; https://doi.org/10.3390/jmse12030485 - 14 Mar 2024
Viewed by 587
Abstract
Due to the intensification of economic globalization and the impact of global warming, the development of methods to reduce shipping costs and reduce carbon emissions has become crucial. In this study, a multi-objective optimization algorithm was designed to plan the optimal ship route [...] Read more.
Due to the intensification of economic globalization and the impact of global warming, the development of methods to reduce shipping costs and reduce carbon emissions has become crucial. In this study, a multi-objective optimization algorithm was designed to plan the optimal ship route for safe cross-ocean navigation under complex sea conditions. Based on the traditional non-dominated sorting genetic algorithm, considering ship stability and complex marine environment interference, a non-dominated sorting genetic algorithm model considering energy consumption was designed with the energy consumption and navigation time of the ship as the optimization objectives. The experimental results show that although the proposed method is 101.23 nautical miles more than the large ring route, and the voyage is increased by 10.1 h, the fuel consumption is reduced by 92.24 tons, saving 6.94%. Compared with the traditional genetic algorithm, the voyage distance and time are reduced by 216.93 nautical miles and 7.5 h, and the fuel consumption is reduced by 58.82 tons, which is almost 4.54%. Through experimental verification, the proposed model can obtain punctual routes, avoid areas with bad sea conditions, reduce fuel consumption, and is of great significance for improving the safety and economy of ship routes. Full article
(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)
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16 pages, 9074 KiB  
Article
Multi-Parameter Fuzzy-Based Neural Network Sensorless PMSM Iterative Learning Control Algorithm for Vibration Suppression of Ship Rim-Driven Thruster
by Zhi Yang, Xinping Yan, Wu Ouyang, Hongfen Bai and Jinhua Xiao
J. Mar. Sci. Eng. 2024, 12(3), 396; https://doi.org/10.3390/jmse12030396 - 25 Feb 2024
Viewed by 630
Abstract
Aiming to reduce motor speed estimation and torque vibration present in the permanent magnet synchronous motors (PMSMs) of rim-driven thrusters (RDTs), a position-sensorless control algorithm using an adaptive second-order sliding mode observer (SMO) based on the super-twisting algorithm (STA) is proposed. In which [...] Read more.
Aiming to reduce motor speed estimation and torque vibration present in the permanent magnet synchronous motors (PMSMs) of rim-driven thrusters (RDTs), a position-sensorless control algorithm using an adaptive second-order sliding mode observer (SMO) based on the super-twisting algorithm (STA) is proposed. In which the sliding mode coefficients can be adaptively tuned. Similarly, an iterative learning control (ILC) algorithm is presented to enhance the robustness of the velocity adjustment loop. By continuously learning and adjusting the difference between the actual speed and given speed of RDT motor through ILC algorithm, online compensation for the q-axis given current of RDT motor is achieved, thereby suppressing periodic speed fluctuations during motor running. Fuzzy neural network (FNN) training can be used to optimize the STA-SMO and ILC parameters of RDT control system, while improving speed tracking accuracy. Finally, simulation and experimental verifications have been conducted on the vector control system based on the conventional PI-STA and modified ILC-STA. The results show that the modified algorithm can effectively suppress the estimated speed and torque ripple of RDT motor, which greatly improves the speed tracking accuracy. Full article
(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)
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16 pages, 859 KiB  
Article
An Attention-Averaging-Based Compression Algorithm for Real-Time Transmission of Ship Data via Beidou Navigation System
by Chunchang Zhang and Ji Zeng
J. Mar. Sci. Eng. 2024, 12(2), 300; https://doi.org/10.3390/jmse12020300 - 08 Feb 2024
Viewed by 631
Abstract
The real-time transmission of ship status data from vessels to shore is crucial for live status monitoring and guidance. Traditional reliance on expensive maritime satellite systems for this purpose is being reconsidered with the emergence of the global short message communication service offered [...] Read more.
The real-time transmission of ship status data from vessels to shore is crucial for live status monitoring and guidance. Traditional reliance on expensive maritime satellite systems for this purpose is being reconsidered with the emergence of the global short message communication service offered by the BeiDou-3 navigation satellite system. While this system presents a more cost-effective solution, its bandwidth is notably insufficient for handling real-time ship status data. This inadequacy necessitates the compression of such data. Therefore, this paper introduces an algorithm tailored for real-time compression of sequential ship status data. The algorithm is engineered to ensure both accuracy and the preservation of valid data range integrity. Our methodology integrates quantization, predictive coding employing an attention-averaging-based predictor, and arithmetic coding. This combined approach facilitates the transmission of succinct messages through the BeiDou Navigation System, enabling the live monitoring of ocean-going vessels. Experimental trials conducted with authentic data obtained from ship monitoring systems validate the efficiency of our approach. The achieved compression rates closely approximate theoretical minimum values. Consequently, this method exhibits substantial promise for the real-time transmission of parameters across various systems. Full article
(This article belongs to the Special Issue Smart and Low Carbon Emission-Oriented Maritime Traffic Management and Controlling)
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