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State-of-the-Art in Maritime Safety and Smart Shipping
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State-of-the-Art in Maritime Safety and Smart Shipping

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: closed (10 February 2023) | Viewed by 16050

Special Issue Editors

Prof. Dr. Jian Wu

E-Mail Website
Guest Editor
School of Economics and Management, Shanghai Maritime University, Shanghai, China
Interests: decision sciences; shipping assessment; big data analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xianhua Wu

E-Mail Website
Guest Editor
School of Economics and Management, Shanghai Maritime University, Shanghai, China
Interests: maritime disaster risk analysis; big data analysis; applied statistics; economics of shipping industry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jihong Chen

E-Mail Website
Guest Editor
College of Management, Shenzhen University, Shenzhen, China
Interests: port and shipping management; ocean and maritime environmental governance; maritime safety management; maritime management and policy

Special Issue Information

Dear Colleagues,

Some new technologies play important roles in monitoring and management of maritime transport network, such as, blockchain, digital twin and edge computing. In recent years, the occurrence of uncertain events has an increasing impact on maritime network, which is the blood vessel of the global economy and trade. Thus, it paid more attention how to use new technologies to predict and manage possible risks and crises in maritime and shipping. This Special Issue on “State-of-the-Art in Maritime Safety and Smart Shipping” invites Authors to submit high-quality original empirical, quantitative, or conceptual research papers. Suggested topics of interest include, but are not limited to:

  • The optimization of maritime transport network
  • Monitoring and management of maritime public opinion
  • Intelligent assisted shipping decision supporting systems
  • Deep learning in Marine equipment fault diagnosis
  • Monitoring and early warning of maritime risks
  • Digitalization and decision analytics in maritime logistics
  • The assess waterway transportation safety of LNG 
  • Th Safety assessment of marine high-end equipment

Prof. Dr. Jian Wu
Dr. Xianhua Wu
Prof. Dr. Jihong Chen
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

  • maritime transport network optimization, maritime public opinion monitoring
  • risk identification and management
  • intelligent shipping decision supporting systems
  • digital and intelligent transformation in maritime logistics
  • safety of LNG
  • deep learning

Published Papers (7 papers)

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Research

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14 pages, 308 KiB  
Article
A Novel Variable Weight VIKOR Grade Assessment Method for Waterway Navigation Safe Routes Selection
by Gao-Feng Yu, Yu-Jin Lin and Xiao-Mei Luo
J. Mar. Sci. Eng. 2023, 11(2), 347; https://doi.org/10.3390/jmse11020347 - 04 Feb 2023
Cited by 1 | Viewed by 870
Abstract
According to the characteristics of waterway navigation safe routes selection, and considering the individual feelings and group benefits of information, as well as no-compensation information between indexes, this paper describes the safe rating of waterway navigation routes, and then puts forward an evaluation [...] Read more.
According to the characteristics of waterway navigation safe routes selection, and considering the individual feelings and group benefits of information, as well as no-compensation information between indexes, this paper describes the safe rating of waterway navigation routes, and then puts forward an evaluation model of and method for waterway navigation safe route selection based on variable weight VIKOR. First of all, from the concept and connotation of grade assessment, this paper describes the safe rating of waterway navigation routes, so as to avoid confusing the two essential different problems of safe rating and ranking. Then, the evaluation indexes and membership function of the appropriate grade of the safe rating of waterway navigation route are constructed, and the weights of an evaluation index based on entropy are proposed. Secondly, a variable weight VIKOR evaluation model and a binary semantic evaluation method for the safe grading of waterway navigation safe routes are proposed. Finally, through case study and comparative analysis, the rationality and feasibility of the model and method proposed in this paper are illustrated. This model can better reflect the connotation and characteristics of the appropriate grade assessment of waterway navigation safe routes, and provides new approaches and methods to support the development and management of waterway navigation safe route selection. Full article
(This article belongs to the Special Issue State-of-the-Art in Maritime Safety and Smart Shipping)
16 pages, 3079 KiB  
Article
The Construction and Application of Dual-Objective Optimal Speed Model of Liners in a Changing Climate: Taking Yang Ming Route as an Example
by Jinxing Lu, Xianhua Wu and You Wu
J. Mar. Sci. Eng. 2023, 11(1), 157; https://doi.org/10.3390/jmse11010157 - 09 Jan 2023
Cited by 20 | Viewed by 1394
Abstract
In a changing climate, ship speed optimization plays an important role in energy conservation and emission reduction. In order to establish a dual-objective optimization model of minimizing ship operating costs and reducing carbon emissions, fuel costs, berthing costs, emission costs and fixed cost [...] Read more.
In a changing climate, ship speed optimization plays an important role in energy conservation and emission reduction. In order to establish a dual-objective optimization model of minimizing ship operating costs and reducing carbon emissions, fuel costs, berthing costs, emission costs and fixed cost during sailing cycles, the emission reduction strategies of ships using MGO in emission control areas and the AMP in ports are taken into account. The PSO algorithm is adopted to find the Pareto solution set, and the TOPSIS algorithm is used to screen the optimal compromise solution, while Yang Ming, a trans-Pacific route, is selected to verify the applicability of the model. The result shows that the optimization model can effectively reduce the operating cost during sailing cycles and control carbon emissions, which can provide references for ship operation decision-making to achieve carbon peaking and carbon neutrality. Full article
(This article belongs to the Special Issue State-of-the-Art in Maritime Safety and Smart Shipping)
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21 pages, 1690 KiB  
Article
A New Leader–Follower Public-Opinion Evolution Model for Maritime Transport Incidents: A Case from Suez Canal Blockage
by Jian Wu, Yan Chen, Tiantian Gai, Yujia Liu, Yan Li and Mingshuo Cao
J. Mar. Sci. Eng. 2022, 10(12), 2006; https://doi.org/10.3390/jmse10122006 - 15 Dec 2022
Cited by 1 | Viewed by 1682
Abstract
The Suez Canal blockage (SCB) event, one of the world’s major transportation arteries, has attracted significant public attention. This article proposes a new leader–follower public-opinion evolution model on the SCB under online social media, which considers two aspects: (1) obtaining public opinion and [...] Read more.
The Suez Canal blockage (SCB) event, one of the world’s major transportation arteries, has attracted significant public attention. This article proposes a new leader–follower public-opinion evolution model on the SCB under online social media, which considers two aspects: (1) obtaining public opinion and attitudes about the SCB; and (2) grasping the evolutionary trend in public opinion on the SCB. To identify the sentiment tendency contained in the collected data, a hybrid sentiment analysis algorithm is presented to analyze Chinese and English data, which captures and analyzes public attitudes on the SCB. In addition, then, the opinion leader-identification mechanism algorithm is proposed, which divides leaders into three categories: positive, neutral and negative leaders. Moreover, the Hegselmann–Krause-based opinion leaders–followers opinion evolution model for the SCB event is established, which not only reflects the interaction of opinions among the online public, but also updates the opinions of the online public until it reaches a stable state. Finally, results and analysis for the SCB are discussed. Full article
(This article belongs to the Special Issue State-of-the-Art in Maritime Safety and Smart Shipping)
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18 pages, 1734 KiB  
Article
Factors Contributing to Fatality and Injury Outcomes of Maritime Accidents: A Comparative Study of Two Accident-Prone Areas
by Yang Zhang, Yujia Zhai, Jihong Chen, Qingjun Xu, Shanshan Fu and Huizhen Wang
J. Mar. Sci. Eng. 2022, 10(12), 1945; https://doi.org/10.3390/jmse10121945 - 08 Dec 2022
Cited by 6 | Viewed by 1798
Abstract
Shipping, as an important part of the global supply chain, has always been quite sensitive to maritime accidents. Fatality and injury are important metrics indicating an accident’s severity. Understanding the driving factors of fatality and injury outcomes of maritime accidents can help to [...] Read more.
Shipping, as an important part of the global supply chain, has always been quite sensitive to maritime accidents. Fatality and injury are important metrics indicating an accident’s severity. Understanding the driving factors of fatality and injury outcomes of maritime accidents can help to improve supply chain security. Based on maritime accident data obtained from the Lloyd’s List Intelligence, this paper identifies accident-prone sea areas through kernel density estimation (KDE) and selects two of the areas to conduct a comparative study on factors contributing to fatality and injury outcomes of maritime accidents through zero-inflated negative binomial (ZINB) and elastic analysis. The results show that collision and ship age significantly impact the number of fatalities and injuries. Specifically, collision and ship age have greater impacts on fatality and injury outcomes of accidents that occurred in the English Channel and North Sea. Whether the accident occurs in ports and whether the accident causes a total loss have more significant impacts on the fatality and injury outcomes of accidents in the Black Sea and the eastern Mediterranean Sea. The research results can potentially support the reduction of fatalities and injuries in maritime accident and help to manage maritime risk. Full article
(This article belongs to the Special Issue State-of-the-Art in Maritime Safety and Smart Shipping)
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25 pages, 3721 KiB  
Article
On the Sparse Gradient Denoising Optimization of Neural Network Models for Rolling Bearing Fault Diagnosis Illustrated by a Ship Propulsion System
by Shuangzhong Wang, Ying Zhang, Bin Zhang, Yuejun Fei, Yong He, Peng Li and Mingqiang Xu
J. Mar. Sci. Eng. 2022, 10(10), 1376; https://doi.org/10.3390/jmse10101376 - 26 Sep 2022
Cited by 4 | Viewed by 1344
Abstract
The drive rolling bearing is an important part of a ship’s system; the detection of the drive rolling bearing is an important component in ship-fault diagnosis, and machine learning methods are now widely used in the fault diagnosis of rolling bearings. However, training [...] Read more.
The drive rolling bearing is an important part of a ship’s system; the detection of the drive rolling bearing is an important component in ship-fault diagnosis, and machine learning methods are now widely used in the fault diagnosis of rolling bearings. However, training methods based on small batches have a disadvantage in that the samples which best represent the gradient descent direction can be disturbed by either other samples in the opposite direction or anomalies. Aiming at this problem, a sparse denoising gradient descent (SDGD) optimization algorithm, based on the impact values of network nodes, was proposed to improve the updating method of the batch gradient. First, the network is made sparse by using the node weight method based on the mean impact value. Second, the batch gradients are clustered via a distribution-density-based clustering method. Finally, the network parameters are updated using the gradient values after clustering. The experimental results show the efficiency and feasibility of the proposed method. The SDGD model can achieve up to a 2.35% improvement in diagnostic accuracy compared to the traditional network diagnosis model. The training convergence speed of the SDGD model improves by 2.16%, up to 17.68%. The SDGD model can effectively solve the problem of falling into the local optimum point while training a network. Full article
(This article belongs to the Special Issue State-of-the-Art in Maritime Safety and Smart Shipping)
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21 pages, 1490 KiB  
Article
CO2 Emission Efficiency Analysis of Rail-Water Intermodal Transport: A Novel Network DEA Model
by Weipan Zhang, Xianhua Wu and Ji Guo
J. Mar. Sci. Eng. 2022, 10(9), 1200; https://doi.org/10.3390/jmse10091200 - 27 Aug 2022
Cited by 8 | Viewed by 1728
Abstract
How to evaluate the carbon emission efficiency of multimodal transport is an important issue of public concern, and this article attempts to solve it with a network data envelopment analysis (DEA) model. DEA is a method to evaluate the efficiency of homogeneous decision-making [...] Read more.
How to evaluate the carbon emission efficiency of multimodal transport is an important issue of public concern, and this article attempts to solve it with a network data envelopment analysis (DEA) model. DEA is a method to evaluate the efficiency of homogeneous decision-making units (DMUs). First, this article studies the efficiency decomposition and efficiency aggregation of the general network structure for DEA model. In efficiency decomposition, the relationship between system efficiency and division efficiency is discussed; whereas in efficiency aggregation, the division tendency brought about by the definition of weights is analyzed. Then, a reasonable and single compromise solution to division efficiency scores is investigated while the system efficiency remains optimal. Finally, a two-stage network DEA model of rail-water intermodal transport is established with carbon dioxide (CO2) emissions as an undesirable output. Based on this model, the rail-water intermodal transport efficiencies of 14 ports in China in 2015 are evaluated by the methods of efficiency decomposition, efficiency aggregation, and non-cooperation. The results show that Rizhao Port, Tangshan Port, Nanjing Port, and Zhuhai Port have set an example to other ports. Qinhuangdao Port, Ningbo-Zhoushan Port, Guangzhou Port, and Beiliang Port need to improve the efficiency of railway transportation. Beibu Gulf port, Zhanjiang Port, Dalian Port, Lianyungang Port, Yantai Port, and Yichang Port should optimize their intermodal system. In addition, Yantai Port and Yichang Port urgently need to improve the port efficiency in low-carbon operation. The network DEA model constructed in this article can be further applied to the efficiency evaluation of multi-link supply chains, and the empirical results can provide a reference for the efficiency evaluation of ports in China. Full article
(This article belongs to the Special Issue State-of-the-Art in Maritime Safety and Smart Shipping)
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Review

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25 pages, 3692 KiB  
Review
Application of Artificial Intelligence in Marine Corrosion Prediction and Detection
by Md Mahadi Hasan Imran, Shahrizan Jamaludin, Ahmad Faisal Mohamad Ayob, Ahmad Ali Imran Mohd Ali, Sayyid Zainal Abidin Syed Ahmad, Mohd Faizal Ali Akhbar, Mohammed Ismail Russtam Suhrab, Nasharuddin Zainal, Syamimi Mohd Norzeli and Saiful Bahri Mohamed
J. Mar. Sci. Eng. 2023, 11(2), 256; https://doi.org/10.3390/jmse11020256 - 21 Jan 2023
Cited by 12 | Viewed by 5878
Abstract
One of the biggest problems the maritime industry is currently experiencing is corrosion, resulting in short and long-term damages. Early prediction and proper corrosion monitoring can reduce economic losses. Traditional approaches used in corrosion prediction and detection are time-consuming and challenging to execute [...] Read more.
One of the biggest problems the maritime industry is currently experiencing is corrosion, resulting in short and long-term damages. Early prediction and proper corrosion monitoring can reduce economic losses. Traditional approaches used in corrosion prediction and detection are time-consuming and challenging to execute in inaccessible areas. Due to these reasons, artificial intelligence-based algorithms have become the most popular tools for researchers. This study discusses state-of-the-art artificial intelligence (AI) methods for marine-related corrosion prediction and detection: (1) predictive maintenance approaches and (2) computer vision and image processing approaches. Furthermore, a brief description of AI is described. The outcomes of this review will bring forward new knowledge about AI and the development of prediction models which can avoid unexpected failures during corrosion detection and maintenance. Moreover, it will expand the understanding of computer vision and image processing approaches for accurately detecting corrosion in images and videos. Full article
(This article belongs to the Special Issue State-of-the-Art in Maritime Safety and Smart Shipping)
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