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Marine Information Sensing and Energy Systems
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Marine Information Sensing and Energy Systems

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 13462

Special Issue Editors

Dr. Peng Peng

E-Mail Website
Guest Editor
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: geographical information science; spatial and temporal information modelling; complex network analysis; knowledge graph
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tianzhen Wang

E-Mail Website
Guest Editor
Department of Electrical Engineering, Shanghai Maritime University, Shanghai 201306, China
Interests: fault diagnosis; fault tolerance fault detection; control systems; control theory; tidal and wave power
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cyril Ray

E-Mail Website
Guest Editor
Naval Academy Research Institute & Arts et Métiers Institute of Technology, 29240 Brest, France
Interests: location-based services; moving objects; data modelling and analysis
Prof. Dr. Aldo Napoli

E-Mail Website
Guest Editor
Mines ParisTech, CRC, PSL Research University, 75272 Paris, France
Interests: risk modeling; geomatics sciences; systems engineering

Special Issue Information

Dear Colleagues,

A wide range of opportunities have recently emerged in the maritime domain, from the development of monitoring systems to renewable energy systems. This entails the need for sound and accurate real-information sensing systems to observe and monitor maritime environments, from the real-time observation of the physical ocean to the tracking and analysis of human activities at sea. Key elements for the sound observation of a maritime environment covers a wide range of sensing systems from satellite and radio-based localization systems to physical sensors to wind, wave, tidal, and marine current energy resources. Reporting on recent progress in the performance of both high-tech sensors, as well as low-cost sensors, this Special Issue will highlight advances in the development, testing, and modeling of maritime information sensing and energy systems. Submissions covering, but not limited to, the following areas are particularly welcome:

  • Large-scale maritime navigation multi-sensor tracking (radar, lidar, AIS, satellite, electromagnetic sensing, etc.), monitoring, data fusion, analysis, and forecasting the maritime environment (maritime GIS, remote sensing, complex networks, etc.)
  • Multi-sensor floating systems and smart grids for marine energy
  • Innovative underwater energy sensor networks (tidal turbines, offshore wind farms)
  • Innovative maritime sensing systems (autonomous systems, UAVs, ROVs, etc.)
  • Big maritime data applications (security, disaster prevention, protecting the environment, energy, etc.)

This Special Issue will cover an often-neglected domain while offering many opportunities for the development of novel sensing systems and architectures. As the coverage includes the observation and sensing of both the physical ocean and human activities it might offer an integrated view of all sensing systems that can be deployed in the maritime environment.

Dr. Peng Peng
Prof. Dr. Tianzhen Wang
Prof. Dr. Cyril Ray
Prof. Dr. Aldo Napoli
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. Sensors is an international peer-reviewed open access semimonthly 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.

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Published Papers (4 papers)

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Research

16 pages, 5370 KiB  
Article
A Hierarchical Spatial-Temporal Embedding Method Based on Enhanced Trajectory Features for Ship Type Classification
by Tao Sun, Yongjun Xu, Zhao Zhang, Lin Wu and Fei Wang
Sensors 2022, 22(3), 711; https://doi.org/10.3390/s22030711 - 18 Jan 2022
Cited by 2 | Viewed by 2511
Abstract
Ship type classification is an essential task in maritime navigation domains, contributing to shipping monitoring, analysis, and forecasting. Presently, with the development of ship positioning and monitoring systems, many ship trajectory acquisitions make it possible to classify ships according to their movement pattern. [...] Read more.
Ship type classification is an essential task in maritime navigation domains, contributing to shipping monitoring, analysis, and forecasting. Presently, with the development of ship positioning and monitoring systems, many ship trajectory acquisitions make it possible to classify ships according to their movement pattern. Existing methods of ship classification based on trajectory include classical sequence analysis and deep learning methods. However, the real ship trajectories are unevenly distributed in geographical space, which leads to many problems in inferring the ship movement mode on the original ship trajectory. This paper proposes a hierarchical spatial-temporal embedding method based on enhanced trajectory features for ship type classification. We first preprocess the trajectory and combine the port information to transform the original ship trajectory into the moored records of ships, removing the unevenly distributed points in the trajectory data and enhancing key points’ semantic information. Then, we propose a Hierarchical Spatial-Temporal Embedding Method (Hi-STEM) for ship classification. Hi-STEM maps moored records in the original geographical space into the feature space and can efficiently find the classification plane in the feature space. Experiments are conducted on real-world datasets and compared with several existing methods. The result shows that our approach has high accuracy in ship classification on ship moored records. We make the source code and datasets publicly available. Full article
(This article belongs to the Special Issue Marine Information Sensing and Energy Systems)
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17 pages, 5123 KiB  
Article
Optimal Staffing for Vessel Traffic Service Operators: A Case Study of Yeosu VTS
by Sang-Lok Yoo and Kwang-Il Kim
Sensors 2021, 21(23), 8004; https://doi.org/10.3390/s21238004 - 30 Nov 2021
Cited by 7 | Viewed by 3661
Abstract
Vessel traffic volume and vessel traffic service (VTS) operator workloads are increasing with the expansion of global maritime trade, contributing to marine accidents by causing difficulties in providing timely services. Therefore, it is essential to have sufficient VTS operators considering the vessel traffic [...] Read more.
Vessel traffic volume and vessel traffic service (VTS) operator workloads are increasing with the expansion of global maritime trade, contributing to marine accidents by causing difficulties in providing timely services. Therefore, it is essential to have sufficient VTS operators considering the vessel traffic volume and near-miss cases. However, no quantitative method for determining the optimal number of workstations, which is necessary for calculating the VTS operator staffing level, has yet been proposed. This paper proposes a new, microscopic approach for calculating the number of workstations from vessel trajectories and voice recording communication data between VTS operators and navigators. The vessel trajectory data are preprocessed to interpolate different intervals. The proposed method consists of three modules: Information services, navigational assistance services, and traffic organization service. The developed model was applied to the Yeosu VTS in Korea. Another workstation should be added to the current workstation based on the proposed method. The results showed that even without annual statistical data, a reasonable VTS operator staffing level could be calculated. The proposed approach helps prevent vessel accidents by providing timely services even if the vessel traffic is congested if VTS operators are deployed to a sufficient number of workstations. Full article
(This article belongs to the Special Issue Marine Information Sensing and Energy Systems)
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25 pages, 6189 KiB  
Article
Long-Term Ship Position Prediction Using Automatic Identification System (AIS) Data and End-to-End Deep Learning
by Ibadurrahman, Kunihiro Hamada, Yujiro Wada, Jota Nanao, Daisuke Watanabe and Takahiro Majima
Sensors 2021, 21(21), 7169; https://doi.org/10.3390/s21217169 - 28 Oct 2021
Cited by 9 | Viewed by 3774
Abstract
The establishment of maritime safety and security is an important concern. Ship position prediction for maritime situational awareness (MSA), as a critical aspect of maritime safety and security, requires a longer time interval than collision avoidance and maritime traffic monitoring. However, previous studies [...] Read more.
The establishment of maritime safety and security is an important concern. Ship position prediction for maritime situational awareness (MSA), as a critical aspect of maritime safety and security, requires a longer time interval than collision avoidance and maritime traffic monitoring. However, previous studies focused mainly on shorter time-interval predictions ranging from 30 min to 10 h. A longer time-interval ship position prediction is required not only for MSA, but also for efficient allocation of ships by shipping companies in accordance with global freight demand. This study used an end-to-end tracking method that inputs the previous position of a vessel to a trained deep learning model to predict its next position with an average 24-h interval. An AIS dataset with a long-time-interval distribution in a nine-year timespan for capesize bulk carriers worldwide was used. In the first experiment, a deep learning model of the Indian Ocean was examined. Subsequently, the model performance was compared for six different oceans and six primary maritime chokepoints to investigate the influence of each area. In the third experiment, a sample location within the Malacca Strait area was selected, and the number of ships was counted daily. The results indicate that the ship position can be predicted accurately with an average time interval of 24 h using deep learning systems with AIS data. Full article
(This article belongs to the Special Issue Marine Information Sensing and Energy Systems)
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18 pages, 21793 KiB  
Article
A Dynamic Risk Appraisal Model and Its Application in VTS Based on a Cellular Automata Simulation Prediction
by Yongfeng Suo, Zhihong Sun, Christophe Claramunt, Shenhua Yang and Zhibing Zhang
Sensors 2021, 21(14), 4741; https://doi.org/10.3390/s21144741 - 11 Jul 2021
Cited by 7 | Viewed by 2380
Abstract
The successful implementation of Vessel Traffic Services (VTS) relies heavily on human decisions. With the increasing development of maritime traffic, there is an urgent need to provide a sound support for dynamic risk appraisals and decision support. This research introduces a cellular automata [...] Read more.
The successful implementation of Vessel Traffic Services (VTS) relies heavily on human decisions. With the increasing development of maritime traffic, there is an urgent need to provide a sound support for dynamic risk appraisals and decision support. This research introduces a cellular automata (CA) simulation-based modelling approach the objective of which is to analyze and evaluate real-time maritime traffic risks in port environments. The first component is the design of a CA model to monitor ships’ behavior and maritime fairway traffic. The second component is the refinement of the modelling approach by combining a cloud model with expert knowledge. The third component establishes a risk assessment model based on a fuzzy comprehensive evaluation. A typical scenario was experimentally implemented to validate the model’s efficiency and operationality. Full article
(This article belongs to the Special Issue Marine Information Sensing and Energy Systems)
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