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Special Issue "Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships—Volume II"

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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: 10 June 2023 | Viewed by 2037

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Special Issue Editors

Prof. Dr. Yuanqiao Wen

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Guest Editor

National Engineering Research Center Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China
Interests: nautical traffic safety and simulation; artificial intelligence and its applications in maritime; maritime autonomous surface ships
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Prof. Dr. Axel Hahn

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OFFIS - Institut für Informatik, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
Interests: simulation; maritime systems; transportation; navigation
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Dr. Osiris Valdez Banda

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Research Group on Safe and Efficient Marine Systems, Marine Technology, Department of Mechanical Engineering, Aalto University, 02150 Espoo, Finland
Interests: safety and systems engineering; risk analysis; maritime safety; winter navigation; autonomous ships
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Prof. Dr. Yamin Huang

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Guest Editor

National Engineering Research Center Water Transport Safety, Wuhan University of Technology, Wuhan 430063, China
Interests: human–machine cooperation; autonomous ships; ship collision avoidance; maritime traffic management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding, modeling, and predicting ship behaviors are fundamental and essential issues for planning, controlling, and operating different levels of Maritime Autonomous Surface Ships (MASS). The maritime traffic data (e.g., radar data, AIS data, CCTV data, etc.) provide designers, officers on watch, and traffic operators with extensive information about the states of ships at present and in history, which are the treasure for behavior analysis. Additionally, navigation rules and regulations (i.e., knowledge) offer valuable prior knowledge about ship manners at sea. Combining multi-source heterogeneous big data and artificial intelligence techniques inspire innovative and important means for the development of MASS, leading to smart, safe, green, and efficient shipping.

With the successful Special Issue ”Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships” in 2021, this special issue is continuing to provide an excellent medium to present the latest developments on methods and tools suitable for relevant issues, including but not limited to:

Data-driven behavior modeling and simulation
Knowledge-driven behavior modeling and reasoning
Multi-source heterogeneous traffic data fusion
Semantic analysis of ship behaviors
Quantifying COLREGs and seamanship for machine
Inference engine and ontology reasoning for rule-compliant MASS
Intention Inference based on behavior observations
Maritime traffic situational awareness
Multi-agent simulation
Risk analysis and management of MASS
Safety and Cyber Security of MASS

Prof. Dr. Yuanqiao Wen
Prof. Dr. Axel Hahn
Dr. Osiris Valdez Banda
Prof. Dr. Yamin Huang
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 2200 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 (MASS)
data-driven modeling
knowledge-driven modeling
behavior modeling
knowledge graph
navigation simulation
multi-source heterogeneous data analysis
nautical safety
quantifying rules and regulations

Published Papers (3 papers)

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Research

Open AccessArticle

An Offshore Self-Stabilized System Based on Motion Prediction and Compensation Control

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J. Mar. Sci. Eng. 2023, 11(4), 745; https://doi.org/10.3390/jmse11040745 - 29 Mar 2023

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Abstract

The swaying motion of ships can always be generated due to the influence of complex sea conditions. A novel offshore Self-Stabilized system based on motion prediction and compensation control was studied. Firstly, an autoregressive model of ship motion exposed to various sea conditions was established, and the parameters of the model were initialized and updated by offline and online learning historical data. Using the autoregressive model with the acquired parameters, the prediction of the ship’s motion was achieved. Then, a Self-Stabilized system platform composed of six electric cylinders in parallel was designed, and the corresponding inverse kinematics were established. The corresponding controller using the result of motion prediction as the input was also proposed to counteract the extra motion variables of the ship. Various experiments, by simulating different sea conditions, can be carried out. The results show that the average error of the motion prediction was less than 1%. The maximum error of the self-stabilizing control was 1.6°, and the average error was stable within 0.7°. The Self-Stabilized system was able to effectively compensate for the rocking motion of ships affected by waves, which was of great significance for improving the maritime safety guarantee and the intelligent level of shipborne equipment. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships—Volume II)

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Open AccessArticle

Spatiotemporal Companion Pattern (STCP) Mining of Ships Based on Trajectory Features

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J. Mar. Sci. Eng. 2023, 11(3), 528; https://doi.org/10.3390/jmse11030528 - 28 Feb 2023

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Abstract

Spatiotemporal companion pattern (STCP) mining is one of the means to identify and detect group behavioral activities. To detect the spatiotemporal traveling pattern of ships from massive spatiotemporal trajectory data and to understand the movement law of group ships, this article proposes a feature-driven approach for STCP mining that consists of (1) generating the grid index via the rasterizing of geospace and characterizing trajectory points via the spatiotemporal trajectory grid sequences (STTGSs) of ships; (2) designing filtering rules with the constraints of range, time and distance to construct a candidate set for ship STCP mining; and (3) measuring the STTGS similarity of the associated ships and setting the confidence threshold to realize spatiotemporal companion mining. The effectiveness of the proposed method is practically validated on a real trajectory dataset which is collected from the Taiwan Strait waters. The experimental results are as follows: 825 pairs of associated ships and 225 pairs of accompanying ships are mined when the grid size is 0.05° and the confidence is 0.5. Larger grid sizes can increase the inclusiveness of the associated ship trajectory similarity measurement, which can result in an increase in confidence of pattern. A large number of pseudo-accompaniment ships are extracted to the result set, resulting in a more dispersed distribution of pattern confidence. By verifying the proposed method, accompanying behavioral activities such as ship cooperative operation, companion navigation method, and so on, can be detected. These results can provide a reference for the research of ship group behavior identification and have an important application value for water transportation management. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships—Volume II)

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Open AccessArticle

Research on Black-Box Modeling Prediction of USV Maneuvering Based on SSA-WLS-SVM

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J. Mar. Sci. Eng. 2023, 11(2), 324; https://doi.org/10.3390/jmse11020324 - 02 Feb 2023

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Abstract

Unmanned surface vessels (USVs) are required to perform motion prediction during a task. This is essential for USVs, especially when conducting motion control, and this work has been proven to be complicated. In this paper, an off-line black box modeling method for USV maneuvering, the Sparrow search algorithm-based weighted-least-squares support vector machine (SSA-WLS-SVM) was proposed to recognize the motion model of a USV. Firstly, the construction of the USV test platform and the processing process of the experimental data were introduced, the correctness of the MMG model was verified using a comparison of the test data and the simulation results, and then the MMG model was used to produce sample data later. To improve the stability and robustness of LS-SVM, weighted least squares and SSA were introduced to perform the optimization of the parameters of the algorithm and its kernel function. Then, the random maneuvering dataset was obtained using simulation on the MMG model, which was then preprocessed and used for training the black-box model. To verify the generalization ability of the identified model, the black-box model was used for comparison analysis between motion prediction with the proposed model and maneuvering test on the USV platform in a scenario different from the training data. Full article

(This article belongs to the Special Issue Data/Knowledge-Driven Behaviour Analysis for Maritime Autonomous Surface Ships—Volume II)

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