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Intelligent Systems for Marine Transportation
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Intelligent Systems for Marine Transportation

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: 5 August 2025 | Viewed by 2094

Special Issue Editor

Prof. Dr. Hsuan-Shih Lee

E-Mail Website
Guest Editor
Department of Shipping and Transportation Management, National Taiwan Ocean University, Keelung 202301, Taiwan
Interests: shipping management; shipping performance evaluation; computer theory; artificial intelligence; big data; fuzzy theory; data-driven analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Marine transportation accounts for much of global logistics and serves as an important indicator of the health of the global economy. The best way to keep marine transportation effective, safe, and sustainable is by ensuring that environmental performance is critical to the shipping industry. New technologies play important roles in this respect. Unmanned surface vessels or maritime autonomous surface ships (MASSs) are also rapidly evolving technological developments. Combating climate change through emission reduction requires dedicated effort at every port and for even the smallest vessels throughout the world. Challenges related to the maritime technology spectrum range from wind turbines and kites, carbon capture, artificial intelligence and data sharing systems to hull coatings and fuel emulsifiers. To ensure that the shipping industry can operate fairly, effectively and safely, the IMO is developing new regulations such as the MASS Code for regulating new technologies used in marine transportation. Inflation may be caused by the cost of shipping and ocean freight soaring due to clogged ports, empty truck cabs, container shortages, Panama Canal congestion, etc.

We believe that intelligence systems provide promising answers to all the technological developments and problems in marine transportation. Therefore, this Special Issue will collect the latest research on intelligent systems for marine transportation. High-quality papers and novel techniques for marine transportation are encouraged. For publication, research topics may include, but are not limited to, the following:

  • Maritime autonomous surface ships (MASSs);
  • Sustainable marine transportation;
  • Marine transportation technology;
  • Digital twin bridge/engine (DTB/E) monitoring;
  • Intelligent port logistics;
  • Big data platform in marine transportation;
  • Intelligent logistics management;
  • Performance evaluation on marine transportation;
  • Environmental performance in marine transportation;
  • AI in marine transportation;
  • Data analytics in marine transportation.

Prof. Dr. Hsuan-Shih Lee
Guest Editor

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

  • maritime autonomous surface ships (MASSs)
  • sustainable marine transportation
  • marine transportation technology
  • digital twin bridge/engine (DTB/E) monitoring
  • intelligent port logistics
  • big data platform in marine transportation
  • intelligent logistics management
  • performance evaluation on marine transportation
  • environmental performance in marine transportation
  • AI in marine transportation
  • data analytics in marine transportation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

19 pages, 5537 KiB  
Article
Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy
by Junlang Yuan, Ke Yang, Taiwei Yang, Haoran Xu, Ting Xiong and Shidong Fan
J. Mar. Sci. Eng. 2025, 13(3), 598; https://doi.org/10.3390/jmse13030598 - 18 Mar 2025
Viewed by 295
Abstract
The suction-lifting system of cutter suction dredgers consumes a large amount of energy. Optimizing their performance is of great significance for enhancing the overall efficiency of dredgers. This study proposes the effective specific cutting energy, a new indicator suitable for evaluating the energy [...] Read more.
The suction-lifting system of cutter suction dredgers consumes a large amount of energy. Optimizing their performance is of great significance for enhancing the overall efficiency of dredgers. This study proposes the effective specific cutting energy, a new indicator suitable for evaluating the energy consumption of the cutting system of cutter suction dredgers. It reflects the cooperation state between the cutter system and the pump-pipe system and has important reference value for improving construction efficiency. The calculation method of the effective specific cutting energy is given, which is calculated by the cutter motor power, slurry concentration, and slurry flow rate. Based on the machine learning framework, a model framework for predicting the specific cutting energy according to the relevant parameters of the suction-lifting system is constructed. Real ship data from the cutter suction dredger “Changshi 12” are used for experiments. First, eigenvalue screening is carried out based on the dredging knowledge and mechanism, then outliers are removed, and finally data processing is performed using Spearman correlation coefficient and PCA dimensionality reduction techniques. Subsequently, five machine learning algorithms, such as RF and XGBoost, are used in combination with a grid search to find the optimal hyperparameters, and Lasso is used as the meta-learner to integrate the prediction results. The experimental results show that the Random Forest and Stacking models have high prediction accuracy for slurry concentration, cutter motor power, and slurry flow rate, verifying the feasibility of this method. Full article
(This article belongs to the Special Issue Intelligent Systems for Marine Transportation)
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22 pages, 6796 KiB  
Article
A Dynamic Cloud Center of Gravity Model for Real-Time System-Level Health Status Assessment of Intelligent Ship
by Lei Guo, Tianjian Wang, Xiao Dong, Peng Zhang, Hong Zeng and Jundong Zhang
J. Mar. Sci. Eng. 2025, 13(2), 384; https://doi.org/10.3390/jmse13020384 - 19 Feb 2025
Cited by 1 | Viewed by 385
Abstract
To enable early identification of failure risks in ship systems and equipment, a dynamic cloud center of gravity model is developed for real-time system-level health assessment. First, the Functional Analysis System Technique (FAST) was applied to decompose the operational functions and dependencies of [...] Read more.
To enable early identification of failure risks in ship systems and equipment, a dynamic cloud center of gravity model is developed for real-time system-level health assessment. First, the Functional Analysis System Technique (FAST) was applied to decompose the operational functions and dependencies of the intelligent machinery room system, enabling the structured establishment of a hierarchical evaluation index system. The comprehensive weight is derived through synergistic application of the fuzzy set (FS) theory and entropy weight. This process integrated expert-defined functional boundaries with measurable parameters critical to system performance. Then, an improved cloud center of gravity method based on the Gaussian cloud model and sliding time window method is used for the system’s adaptive health value calculation. The dynamic health model can achieve continuous online assessment and track the further evolution of the system. Finally, the proposed model is applied to the Fuel Oil Supply System (FOSS). The integration of system performance output and disassembly inspection results demonstrates that the method proposed in the article more accurately reflects the true health status changes in the system when mapping health values. Full article
(This article belongs to the Special Issue Intelligent Systems for Marine Transportation)
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30 pages, 2337 KiB  
Article
Analysis of Key Factors and Correlations Influencing the Adoption of Autonomous Ships by Shipping Companies—A Study Integrating Revised DEMATEL-AHP with BOCR
by Tien-Chun Ho and Hsuan-Shih Lee
J. Mar. Sci. Eng. 2024, 12(12), 2153; https://doi.org/10.3390/jmse12122153 - 25 Nov 2024
Viewed by 914
Abstract
In response to achieving the United Nations Sustainable Development Goals (SDGs) of Climate Action (#13) and Life Below Water (#14), the promotion of autonomous shipping technologies has advanced from the experimental stage to specific regional implementation, presenting the maritime industry with rapid and [...] Read more.
In response to achieving the United Nations Sustainable Development Goals (SDGs) of Climate Action (#13) and Life Below Water (#14), the promotion of autonomous shipping technologies has advanced from the experimental stage to specific regional implementation, presenting the maritime industry with rapid and significant changes and challenges. In the future era, where autonomous vessels dominate shipping, with automated operation systems taking the lead, how successfully shipping companies harness these new maritime transport modes will critically impact the safety, efficiency, and reliability of future vessel operations. With the emergence and development of autonomous vessels, it is crucial to effectively assess the importance and correlation of key factors influencing shipping companies’ adoption of autonomous ships. This study utilizes the Analytic Hierarchy Process (AHP) and Revised Decision Making and Trial Evaluation Laboratory (RDEMATEL) to survey senior managers in container and bulk shipping from Taiwan, China, Japan, and the European Union. Through a literature review on the benefits, opportunities, costs, and risks brought by autonomous shipping, this study aims to understand the critical factors important to shipping companies in adopting autonomous shipping, as well as the correlation between these influencing factors across different shipping sectors. The research findings indicate that “emergency response capability” is a critical factor influencing overall and bulk shipping in the adoption of autonomous vessels, while “incomplete regulations” are the primary factor influencing container shipping in the adoption of autonomous ships. Regarding the correlation of critical influencing factors, “vessel technology development” is the main influencing factor for overall, container, and bulk shipping; “operational performance enhancement” is the primary affected factor for overall and container shipping; and “enhancing personnel and vessel safety” is the main affected factor for bulk shipping. It is hoped that the results of this study can serve as a guide for shipping companies in understanding the benefits and opportunities to be emphasized when adopting autonomous shipping and assist in developing effective strategies to reduce costs and risks. Full article
(This article belongs to the Special Issue Intelligent Systems for Marine Transportation)
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