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Recent Developments and Knowledge in Intelligent and Safe Marine Navigation

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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 (20 August 2023) | Viewed by 17771

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Special Issue Editors

Dr. Mingyang Zhang

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Guest Editor

Department of Mechanical Engineering, Aalto University, Espoo, Finland
Interests: maritime safety; big data analytics; machine learning; emerging maritime technologies
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xinyu Zhang

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Guest Editor

Maritime Intelligent Transportation Research Team, Department of Navigation College, Dalian Maritime University, Dalian, China
Interests: autonomous ship; traffic management; intelligent maritime supervision; traffic simulation
Special Issues, Collections and Topics in MDPI journals

Dr. Shanshan Fu

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Guest Editor

College of Transport and Communications, Shanghai Maritime University, Shanghai, China
Interests: maritime safety; arctic shipping; cause analysis of maritime accidents; navigational risk assessment; risk and resilience of maritime transportation systems

Dr. Lei Dai

E-Mail Website
Guest Editor

State Key Laboratory of Ocean Engineering, Department of Transportation Engineering, Shanghai Jiao Tong University, Shanghai, China
Interests: green shipping; arctic shipping; shipping management; transport economics

Dr. Qing Yu

E-Mail Website
Guest Editor

School of Navigation, Jimei University, Xiamen, China
Interests: maritime risk assessment; intelligent decision-making for autonomous ships; ship traffic analysis and characteristic mining

Special Issue Information

Dear Colleagues,

Marine navigation is a critical backbone of international trade and the global economy, supporting more than 80% of global trade.

With the launch of intelligent shipping trails, emerging techniques have begun to play an important role in the maritime shipping domain. Intelligent and safe marine navigation can benefit from the development of artificial intelligence (AI), machine learning (ML), and big data analytic approaches in various aspects: intelligent shipping, traffic monitoring, early warning for critical scenarios, etc. Such cutting-edge techniques will enormously improve the intelligence and safety of ships now and in the future.

In light of these developments, the Journal of Marine Science and Engineering (JMSE) (https://www.mdpi.com/journal/jmse) is currently running a Special Issue (SI) entitled" Frontiers in Intelligent and Safe Marine Navigation".

This Special Issue seeks original contributions covering emerging and frontier technologies, especially the practical applications of the intelligence and safety of ongoing ships in real operational conditions. Topics of interest include, but are not limited to, the following:

Multi-source data integration methods for supporting intelligence and safety of ships;
Big data analytics methods for supporting the intelligence and safety of ships;
Maritime traffic modeling and simulation methods;
Maritime network extraction and route planning;
Intelligent traffic monitoring methods/tools;
Ship system identification methods;
Ship motion/trajectory prediction methods;
Early warning methods for operating ships (i.e., unsafe ship behaviors, illegal ship behaviors);
Proactive risk mitigation methods (i.e., collision and grounding);
Ship accident prevention methods/frameworks;
Seafarers’ unsafe acts identification and alert methods;
Decision-making techniques for critical scenario (i.e., collision and grounding) identification and prevention.

Dr. Mingyang Zhang
Prof. Dr. Xinyu Zhang
Dr. Shanshan Fu
Dr. Lei Dai
Dr. Qing Yu
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.

Published Papers (11 papers)

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Editorial

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4 pages, 181 KiB

Open AccessEditorial

Recent Developments and Knowledge in Intelligent and Safe Marine Navigation

by Mingyang Zhang, Xinyu Zhang, Shanshan Fu, Lei Dai and Qing Yu

J. Mar. Sci. Eng. 2023, 11(12), 2303; https://doi.org/10.3390/jmse11122303 - 05 Dec 2023

Viewed by 1012

Abstract

Marine navigation is the lifeblood of international trade and the global economy, facilitating over 80% of worldwide commerce [...] Full article

(This article belongs to the Special Issue Recent Developments and Knowledge in Intelligent and Safe Marine Navigation)

Research

Jump to: Editorial

22 pages, 6697 KiB

Open AccessArticle

A Semantic Network Method for the Identification of Ship’s Illegal Behaviors Using Knowledge Graphs: A Case Study on Fake Ship License Plates

by Hui Wan, Shanshan Fu, Mingyang Zhang and Yingjie Xiao

J. Mar. Sci. Eng. 2023, 11(10), 1906; https://doi.org/10.3390/jmse11101906 - 01 Oct 2023

Cited by 1 | Viewed by 1225

Abstract

With the advancement of intelligent shipping, current traffic management systems have become inadequate to meet the requirements of intelligent supervision. In particular, with regard to ship violations, on-site boarding is still necessary for inspection. This paper presents a novel approach for enhancing ships’ management and service capabilities through scientific knowledge graph technology to develop a ship knowledge graph. The proposed approach extracts key characteristics of ship violations from the ship knowledge graph, such as monitoring ships, expired ship certificates, multiple ship tracks, inconsistent ship tracks with port reports, and ships not reported to the port for a long time. Combining the characteristics of ship violations, the approach uses reasoning and identification techniques to detect specific instances of falsely licensed ships and other violations. The development of the ship knowledge graph analysis system enables the identification and verification of illegal ships using fake license plates, while also improving the effective utilization of maritime data and enhancing the ability to make informed decisions related to ship safety. By leveraging cognitive approaches and knowledge graphs, this study offers the potential to develop an intelligent decision-making system for maritime traffic management. Full article

(This article belongs to the Special Issue Recent Developments and Knowledge in Intelligent and Safe Marine Navigation)

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24 pages, 2867 KiB

Open AccessArticle

Leverage Bayesian Network and Fault Tree Method on Risk Assessment of LNG Maritime Transport Shipping Routes: Application to the China–Australia Route

by Zheng Chang, Xuzhuo He, Hanwen Fan, Wei Guan and Linsheng He

J. Mar. Sci. Eng. 2023, 11(9), 1722; https://doi.org/10.3390/jmse11091722 - 01 Sep 2023

Cited by 4 | Viewed by 1590

Abstract

The China–Australia Route, which serves as the southern economic corridor of the ‘21st Century Maritime Silk Road’, bears great importance in safeguarding maritime transportation operations. This route plays a crucial role in ensuring the security and efficiency of such activities. To pre-assess the risks of this route, this paper presents a two-stage analytical framework that combines fault tree analysis and Bayesian network for evaluating the occurrence likelihood of risk of transporting liquefied natural gas (LNG) on the China–Australia Route. In the first stage, our study involved the identification of 22 risk influencing factors drawn from a comprehensive review of pertinent literature and an in-depth analysis of accident reports. These identified factors were then utilized as basic events to construct a fault tree. Later, we applied an expert comprehensive evaluation method and fuzzy set theory, and by introducing voting mechanism into expert opinions, the prior probability of basic events was calculated. In the second stage, a fault tree was transformed into a Bayesian network, which overcame the deficiency that the structure and conditional probability table of the Bayesian network find difficult to determine. Consequently, the employment of the Bayesian network architecture was applied to forecast the likelihood of LNG maritime transport along the China–Australia shipping pathway. The probability importance and critical importance of each basic event was calculated through an importance analysis. The development of a risk matrix was achieved by considering the two primary dimensions of frequency and impact, which were subsequently utilized to categorize all relevant risk factors into high, moderate, or low risk categories. This allowed for effective risk mitigation and prevention strategies to be implemented. Finally, assuming that the final risk occurs, we calculated the posterior probability of the basic event to diagnose the risk. The research findings indicate that the primary reasons for the risk of transporting LNG on the China–Australia Route are the impact of natural forces and epidemics, piracy and terrorist attacks, and the risk of LNG explosions. In the final section, we provide suggestions and risk control measures based on the research results to reduce the occurrence of risks. Full article

(This article belongs to the Special Issue Recent Developments and Knowledge in Intelligent and Safe Marine Navigation)

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17 pages, 7432 KiB

Open AccessArticle

Online Estimation of Ship Dimensions by Combining Images with AIS Reports

by Zishuo Huang, Qinyou Hu, Lan Lu, Qiang Mei and Chun Yang

J. Mar. Sci. Eng. 2023, 11(9), 1700; https://doi.org/10.3390/jmse11091700 - 29 Aug 2023

Viewed by 831

Abstract

Ship dimensions are an important component of static AIS information, and are a key factor in identifying the risks of ship collisions. We describe a method of extracting and correcting ship contour information using inland waterway surveillance video combined with AIS information that does not depend on ship dimension data. A lightweight object detection model was used to determine the ship’s position in an image. Dynamic AIS information was included to produce multigroup control points, solve the optimal homography matrix, and create a transformation model to map image coordinates onto water surface coordinates. A semantic segmentation DeepLabV3+ model was used to determine ship contours from the images, and the actual dimensions of the ship contours were calculated using homography matrix transformation. The mAP of the proposed object detection model and the MIoU of the semantic segmentation model were 86.73% and 91.07%, respectively. The calculation error of the ship length and width were 5.8% and 7.4%, respectively. These statistics indicate that the proposed method rapidly and accurately detected target ships in images, and that the model estimated ship dimensions within a reasonable range. Full article

(This article belongs to the Special Issue Recent Developments and Knowledge in Intelligent and Safe Marine Navigation)

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17 pages, 2962 KiB

Open AccessArticle

Probabilistic Modeling of Maritime Accident Scenarios Leveraging Bayesian Network Techniques

by Shiguan Liao, Jinxian Weng, Zhaomin Zhang, Zhuang Li and Fang Li

J. Mar. Sci. Eng. 2023, 11(8), 1513; https://doi.org/10.3390/jmse11081513 - 29 Jul 2023

Cited by 1 | Viewed by 983

Abstract

This paper introduces a scenario evolution model for maritime accidents, wherein Bayesian networks (BNs) were employed to predict the most probable causes of distinct types of maritime incidents. The BN nodes encompass factors such as accident type, life loss contingency, accident severity, quarter and time period of the accident, and type and gross tonnage of the involved ships. An analysis of 5660 global maritime accidents spanning the years 2005 to 2020 was conducted. Using Netica software, a tree augmented network (TAN) model was constructed, thus accounting for interdependencies among risk-influencing factors. To confirm these results, a validation process involving sensitivity analysis and historical accident records was performed. Following this, both forward causal inference and reverse diagnostic inference were carried out on each node variable to scrutinize the accident development trend and evolution process under preset conditions. The findings suggest that the model was competent in effectively predicting the likelihood of various accident scenarios under specific conditions, as well as extrapolating accident consequences. Forward causal reasoning unveiled that general cargo ships with a gross tonnage of 1–18,500 t were most prone to experiencing collision and stranding/grounding accidents in the first quarter. Reverse diagnostic reasoning indicated that, in the early morning hours, container ships, general cargo ships, and chemical ships with a tonnage of 1–18,500 t were less likely to involve life loss in the event of collision accidents. Full article

(This article belongs to the Special Issue Recent Developments and Knowledge in Intelligent and Safe Marine Navigation)

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16 pages, 4290 KiB

Open AccessArticle

A COLREGs-Compliant Ship Collision Avoidance Decision-Making Support Scheme Based on Improved APF and NMPC

by Haibin Li, Xin Wang, Tianhao Wu and Shengke Ni

J. Mar. Sci. Eng. 2023, 11(7), 1408; https://doi.org/10.3390/jmse11071408 - 13 Jul 2023

Viewed by 922

Abstract

In this paper, combined with the improved artificial potential field (IAPF) method and the nonlinear model predictive control (NMPC) algorithm, a collision avoidance decision-making support scheme considering ship maneuverability and the International Regulations for Preventing Collisions at Sea (COLREGs) is proposed. First, to comply with the requirements of COLREGs, an improved repulsive potential field is presented for different encounter scenarios when the ship detects the risk of collision, and the coordinated ship domain is applied to provide safety criteria for collision avoidance. Then, by transforming the MMG model to a discrete-time nonlinear system, the NMPC is utilized to predict the future state of the ship according to the current state, and the IAPF method is incorporated to calculate the potential field in each future state as the objective function. Following this approach, the action taken to avoid collision is more effective, the ship motion in avoiding collision is more accurate, and the collision avoidance decision making is more reasonable. Finally, two simulation examples of multi-ship encounter scenarios are applied to illustrate the merits and effectiveness of the proposed collision avoidance decision-making support scheme. Full article

(This article belongs to the Special Issue Recent Developments and Knowledge in Intelligent and Safe Marine Navigation)

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22 pages, 4833 KiB

Open AccessArticle

A Deep Learning Method for Ship Detection and Traffic Monitoring in an Offshore Wind Farm Area

by Xintong Liu, Yutian Hu, Huiting Ji, Mingyang Zhang and Qing Yu

J. Mar. Sci. Eng. 2023, 11(7), 1259; https://doi.org/10.3390/jmse11071259 - 21 Jun 2023

Cited by 1 | Viewed by 1915

Abstract

Newly built offshore wind farms (OWFs) create a collision risk between ships and installations. The paper proposes a real-time traffic monitoring method based on machine vision and deep learning technology to improve the efficiency and accuracy of the traffic monitoring system in the vicinity of offshore wind farms. Specifically, the method employs real automatic identification system (AIS) data to train a machine vision model, which is then used to identify passing ships in OWF waters. Furthermore, the system utilizes stereo vision techniques to track and locate the positions of passing ships. The method was tested in offshore waters in China to validate its reliability. The results prove that the system sensitively detects the dynamic information of the passing ships, such as the distance between ships and OWFs, and ship speed and course. Overall, this study provides a novel approach to enhancing the safety of OWFs, which is increasingly important as the number of such installations continues to grow. By employing advanced machine vision and deep learning techniques, the proposed monitoring system offers an effective means of improving the accuracy and efficiency of ship monitoring in challenging offshore environments. Full article

(This article belongs to the Special Issue Recent Developments and Knowledge in Intelligent and Safe Marine Navigation)

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25 pages, 4293 KiB

Open AccessArticle

A Ship Route Planning Method under the Sailing Time Constraint

by Yuankui Li, Jinlong Cui, Xinyu Zhang and Xuefeng Yang

J. Mar. Sci. Eng. 2023, 11(6), 1242; https://doi.org/10.3390/jmse11061242 - 17 Jun 2023

Cited by 1 | Viewed by 1622

Abstract

This paper realizes the simultaneous optimization of a vessel’s course and speed for a whole voyage within the estimated time of arrival (ETA), which can ensure the voyage is safe and energy-saving through proper planning of the route and speed. Firstly, a dynamic sea area model with meteorological and oceanographic data sets is established to delineate the navigable and prohibited areas; secondly, some data are extracted from the records of previous voyages, to train two artificial neural network models to predict fuel consumption rate and revolutions per minute (RPM), which are the keys to route optimization. After that, speed configuration is introduced to the optimization process, and a simultaneous optimization model for the ship’s course and speed is proposed. Then, based on a customized version of the A* algorithm, the optimization is solved in simulation. Two simulations of a ship crossing the North Pacific show that the proposed methods can make navigation decisions in advance that ensure the voyage’s safety, and compared with a naive route, the optimized navigation program can reduce fuel consumption while retaining an approximately constant time to destination and adapting to variations in oceanic conditions. Full article

(This article belongs to the Special Issue Recent Developments and Knowledge in Intelligent and Safe Marine Navigation)

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18 pages, 2626 KiB

Open AccessArticle

Parameter Prediction of the Non-Linear Nomoto Model for Different Ship Loading Conditions Using Support Vector Regression

by Jiafen Lan, Mao Zheng, Xiumin Chu and Shigan Ding

J. Mar. Sci. Eng. 2023, 11(5), 903; https://doi.org/10.3390/jmse11050903 - 23 Apr 2023

Cited by 4 | Viewed by 1629

Abstract

Significant changes in the load of cargo ships make it difficult to simulate and control their motion. In this work, a parameter prediction method for a ship maneuvering motion model is developed based on parameter identification and support vector regression (SVR). First, the effects of least-squares (LS) and multi-innovation least-squares (MILS) parameter identification methods for the non-linear Nomoto model are investigated. The MILS method is then used to identify the parameters of the non-linear Nomoto model under various load conditions, and model training datasets are established. On this basis, SVR is used to predict the parameters of the non-linear Nomoto model. The results reveal that the MILS method converges faster than the LS method. The SVR method achieves lower accuracy than the MILS method, but exhibits reasonable prediction accuracy for zigzag motions, and the maneuvering motion model can be predicted as navigation conditions change. Full article

(This article belongs to the Special Issue Recent Developments and Knowledge in Intelligent and Safe Marine Navigation)

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27 pages, 11902 KiB

Open AccessArticle

An Intelligent Algorithm for USVs Collision Avoidance Based on Deep Reinforcement Learning Approach with Navigation Characteristics

by Zhe Sun, Yunsheng Fan and Guofeng Wang

J. Mar. Sci. Eng. 2023, 11(4), 812; https://doi.org/10.3390/jmse11040812 - 11 Apr 2023

Cited by 4 | Viewed by 1549

Abstract

Many achievements toward unmanned surface vehicles have been made using artificial intelligence theory to assist the decisions of the navigator. In particular, there has been rapid development in autonomous collision avoidance techniques that employ the intelligent algorithm of deep reinforcement learning. A novel USV collision avoidance algorithm based on deep reinforcement learning theory for real-time maneuvering is proposed. Many improvements toward the autonomous learning framework are carried out to improve the performance of USV collision avoidance, including prioritized experience replay, noisy network, double learning, and dueling architecture, which can significantly enhance the training effect. Additionally, considering the characteristics of the USV collision avoidance problem, two effective methods to enhance training efficiency are proposed. For better training, considering the international regulations for preventing collisions at sea and USV maneuverability, a complete and reliable USV collision avoidance training system is established, demonstrating an efficient learning process in complex encounter situations. A reward signal system in line with the USV characteristics is designed. Based on the Unity maritime virtual simulation platform, an abundant simulation environment for training and testing is designed. Through detailed analysis, verification, and comparison, the improved algorithm outperforms the pre-improved algorithm in terms of stability, average reward, rules learning, and collision avoidance effect, reducing 26.60% more accumulated course deviation and saving 1.13% more time. Full article

(This article belongs to the Special Issue Recent Developments and Knowledge in Intelligent and Safe Marine Navigation)

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20 pages, 2793 KiB

Open AccessArticle

Neural Network, Nonlinear-Fitting, Sliding Mode, Event-Triggered Control under Abnormal Input for Port Artificial Intelligence Transportation Robots

by Yaping Zhu, Qiang Zhang, Yang Liu, Yancai Hu and Sihang Zhang

J. Mar. Sci. Eng. 2023, 11(3), 659; https://doi.org/10.3390/jmse11030659 - 21 Mar 2023

Cited by 1 | Viewed by 1152

Abstract

A new control algorithm was designed to solve the problems of actuator physical failure, remote network attack, and sudden change in trajectory curvature when a port’s artificial intelligence-based transportation robots track transportation in a freight yard. First of all, the nonlinear, redundant, saturated sliding surface was designed based on the redundant information of sliding mode control caused by the finite nature of control performance; the dynamic acceleration characteristic of super-twisted sliding mode reaching law was considered to optimize the control high frequency change caused by trajectory mutation; and an improved super-twist reaching law was designed. Then, a nonlinear factor was designed to construct a nonlinear, fault-tolerant filtering mechanism to compensate for the abnormal part of the unknown input that cannot be executed by adaptive neural network reconstruction. On this basis, the finite-time technology and parameter-event-triggered mechanism were combined to reduce the dependence on communication resources. As a result, the design underwent simulation verification to verify its effectiveness and superiority. In the comparative simulation, under a consistent probability of a network attack, the tracking accuracy of the algorithm proposed in this paper was

22.65 %

12.69 %

and

11.48 %

higher those that of the traditional algorithms. Full article

(This article belongs to the Special Issue Recent Developments and Knowledge in Intelligent and Safe Marine Navigation)

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