Safety Of Maritime Navigation Research Articles

Verification of the AIS service availability model based on dynamic data streams recorded from three receiving stations in the Polish coastal area

The reliability aspects of the operation of radio navigation systems constitute a crucial element for the safety of maritime navigation.. Technological progress in ship traffic monitoring is achieved through the design of ship systems and shore infrastructure equipped with Automatic Identification System (AIS) devices. One of the issues with AIS operation is the limited availability of the service in the form of data streams with an extended data age recorded on the receiving side. Another problem is the emission and reception by ships of incomplete positional reports without navigational parameters. Such situations render the system operationally unfit in terms of processed information. Therefore, it is essential to investigate the operational characteristics of radio navigation systems and develop tools to monitor the AIS service status on the receiving side. This article presents the development of a model for the availability of an AIS for vessels based on the determined mean time of the occurrence of incomplete navigation parameter values in AIS messages and the results of research in the domain of time and frequency using a mathematical method of the Fast Fourier Transform (FFT). The study results refer to six basic navigation parameters and show a varying service availability factor for the navigation parameters under study, i.e. the latitude (LAT), longitude (LON), speed over ground (SOG), course over ground (COG), heading (HDT), and the rate of turn (ROT). The data recorded by three receiving AIS stations on the Polish coast, i.e. PLKOL, PLSZZ, and PLSWI, were used as a key source of practical knowledge on the limitations of the AIS service availability. The experiment observed interruptions in the regular transmission of data from navigation equipment in the AIS service operational zone. As a result, the functional relationship was described based on the spectral analysis of the frequency of occurrence of times between the service repair (Time To Repair, TTR), and the model was proposed to be applied to the study of other variables. The presented model is a tool that allows for improving the monitoring of vessel traffic in terms of reliability, which directly affects the improvement of maritime traffic safety.

A two-stage approach for ship detection in restricted visibility based on dehazing and SE-YOLO algorithms

ABSTRACT The rapid and automatic detection of ships in restricted visibility is crucial for protecting the safety of maritime navigation. The frequent presence of fog in maritime environments causes restricted visibility, contributing to a higher occurrence of maritime accidents. Aiming to address ship target detection's low accuracy in restricted visibility weather, this paper proposes an improved dehazing model based on a two-stage ship detection algorithm. First, during the dehazing stage, a specialized model for maritime environments addresses the problem of unclear ship features in foggy images. The model is trained by a combination of synthetic images training and real images fine-tuning. Then, during the detection stage, a modified YOLO network for small ship detection called SE-YOLO is introduced to solve the issue of small ship targets' low detection accuracy. On the one hand, the modified Spatial Pyramid Pooling – Fast(SPPF) module is designed to reduce information loss during feature extraction; on the other hand, the attention mechanisms are integrated to enhance the network's sensitivity to the details of small ship targets and improve the model's overall detection performance for ships. Moreover, to simulate the real sea scene and test the effectiveness of our method, a maritime-haze dataset containing different concentrations of fog and various brightness is made for this research. Finally, The experimental results indicate that, compared to the traditional YOLOv5 method, our method performs better on detecting ships in restricted visibility environments, with the mean average precision (mAP) increased from 62.64% to 77.23%.

Ship Bridge OOW Activity Status Detection Using Wi-Fi Beamforming Feedback Information

Officers on Watch (OOWs) of the ship’s bridge play a vital role in maritime navigation safety, monitoring the ship’s navigational status, and ensuring maritime safety. The status of inactive watch officers, such as fatigued driving and negligence on lookout, is one of the main causes of accidents. Intelligent technology for real-time perception and state evaluation of ship OOWs significantly reduces accidents caused by human factors. The traditional computer vision method is difficult to adapt to the complex environment of a ship bridge, and carries strong privacy risks. With the development of Internet of Things technology, sensing technology based on ubiquitous Wi-Fi devices provides a new way to accurately monitor the status of ship OOWs. In this paper, we use commercial off-the-shelf (COTS) Wi-Fi devices to propose a ship driving activity state detection method based on beamforming feedback information (BFI). Using wireless sensing data to sense the number of OOWs and their driving behavior realizes low-cost and high-precision detection of the behavioral status of the ship’s bridge watchkeeper. Experiments were conducted in a ship-driving simulation laboratory and on a real-world Yangtze River cruise ship. The experimental results demonstrate that our proposed method achieves 92.4% and 98.1% accuracy for tracking active status and estimating the number of OOWs, respectively.

Identification of multi-ship maritime traffic situation based on ship traffic complexity measurement model

Maritime Traffic Situation (MTS) is crucial for enhancing the efficiency and safety of maritime navigation, with the complexity of ship traffic serving as a key aspect of identifying and managing maritime risks. This paper proposes a ship traffic complexity measurement model for MTS. The proposed model quantifies the complexity between ships by evaluating factors such as density, proximity factor, and mitigation index, and then integrates these evaluations into multiple ships interaction scenarios to enable a comprehensive assessment of ship traffic complexity. The results of case study have fully demonstrated the model's capability in identifying potential collision risks during the interaction processes of multiple ships situation within a navigational sea. Research on the ship traffic complexity measurement model can provide strong theoretical and practical support for maritime traffic management and navigational safety.

YOLOv7-Ship: A Lightweight Algorithm for Ship Object Detection in Complex Marine Environments

Accurate ship object detection ensures navigation safety and effective maritime traffic management. Existing ship target detection models often have the problem of missed detection in complex marine environments, and it is hard to achieve high accuracy and real-time performance simultaneously. To address these issues, this paper proposes a lightweight ship object detection model called YOLOv7-Ship to perform end-to-end ship detection in complex marine environments. At first, we insert the improved “coordinate attention mechanism” (CA-M) in the backbone of the YOLOv7-Tiny model at the appropriate location. Then, the feature extraction capability of the convolution module is enhanced by embedding omnidimensional dynamic convolution (ODconv) into the efficient layer aggregation network (ELAN). Furthermore, content-aware feature reorganization (CARAFE) and SIoU are introduced into the model to improve its convergence speed and detection precision for small targets. Finally, to handle the scarcity of ship data in complex marine environments, we build the ship dataset, which contains 5100 real ship images. Experimental results show that, compared with the baseline YOLOv7-Tiny model, YOLOv7-Ship improves the mean average precision (mAP) by 2.2% on the self-built dataset. The model also has a lightweight feature with a detection speed of 75 frames per second, which can meet the need for real-time detection in complex marine environments to a certain extent, highlighting its advantages for the safety of maritime navigation.

Yemen's Houthis (Ansar Allah): impact of the Iranian proxy force on security in the region (in light of the crisis in the Red Sea)

The article is devoted to the analysis of the activities of the Yemeni pro-iranian rebel group “Ansar Allah” (also called the Houthis) in the context of the maritime navigation crisis through the Red Sea that emerged at the end of 2023, which continues to this day. The relevance of the study lies in the fact that the activities of the Houthi group to effectively block most of the maritime traffic through the Red Sea have already caused significant economic damage, and the very safety of maritime navigation along the shortest route from Asia to Europe has actually been called into question. The object of the study is the Yemeni movement “Ansar Allah” (Yemen Houthis movement). The subject of the study is the activities and influence of the Houthi group on the international economy and the safety of maritime navigation through the Red Sea. The research methodology is based on general scientific research methods (content analysis, classification), as well as on special historical methods (narrative, historical-genetic). The scientific novelty of the article lies in a detailed analysis of the events of the crisis in the Red Sea, which began at the end of 2023 and is currently ongoing. The authors' particular contribution to the study is description of the activities of the Houthi movement in the Red Sea in the period from December 2023 to January 2024. Separately, the authors analyze the degree of influence of the crisis on Russia and possible ways to overcome it. In conclusion, the authors conclude that Iran, with the help of its proxy forces, was actually able to block an important sea route for international trade, as well as cause serious economic damage to Western countries and Israel. And the consequences of such damage could ultimately affect the rest of the world.

Seabed Sediment Classification using Multifrequency Backscatter

Seabed sediment serves as an important metric for assessing marine ecosystems. Different sectors, including the development and utilization of marine resources, marine engineering, environmental research, marine fisheries, and maritime navigation safety, all depend on the classification and identification of different forms of seabed sediment. Hydrographic surveys employing Multibeam Echosounders provide information that can be used to categorize the types of seabed sediment. The data acquired during the hydrographic surveys using Multibeam Echosounders include bathymetric data and backscatter data. The backscatter data is obtained from the use of Multibeam Echosounder equipment for bathymetric data acquisition. The backscatter data from Multibeam Echosounders can be utilized for determining the classification of seabed sediment as well as for identifying seabed features. This backscatter data represents the types of sediment on the seafloor and can be used for seafloor profiling and acoustic backscatter analysis to determine the several kinds of seafloor sediment. This study uses multi-frequency mosaic backscatter data and an Artificial Neural Network (ANN) approach to classify the different types of seabed sediment in the area of Benoa Port in Bali Province. The research aims to produce a distribution map of sediment types in the study area. A training accuracy of 55% and a testing accuracy of 66% were obtained from the DNN modelling. Three out of six sediment types were identified based on these accuracy results: silty sand, gravelly sand, and sandy silt with gravel corals. According to their distribution, gravely sand had the smallest distribution (5%), while silty sand had the biggest (77%).

The model of vessel trajectory abnormal behavior detection based on graph attention prediction and reconstruction network

The identification of atypical vessel behavior is a crucial factor in ensuring the safety of maritime navigation and enhancing the effectiveness of waterway oversight. The current approach to detecting vessel abnormal behavior lacks consideration for the inter-dependency among different time series and various characteristics associated with abnormal vessel behavior. Additionally, the subjective setting of anomaly detection threshold adversely affects the real-time performance of anomaly detection, resulting in poor detection accuracy. In order to tackle these issues, this paper explores the monitoring and detection mechanism of vessel abnormal behavior based on Graph Attention Prediction and Reconstruction Network. The proposed mechanism employs a sliding window to generate fixed data input. It effectively captures the inter-dependencies among vessel behavior characteristics. Additionally, it dynamically adjusts the threshold for identifying anomalies based on variations in the marine environment, utilizing the Peak Over Threshold (POT) method. In order to achieve real-time detection of abnormal vessel behavior in multivariate, multi-dimensional time series data, it is necessary to complete the task. The results of the paper are derived from an analysis of the AIS dataset encompassing vessel movements between Dalian and Yantai. The temporal scope of the dataset spans from November 1, 2021, to December 31, 2021. The detection accuracy, detection precision and recall rate are 98.29%, 92.44%, 87.09% respectively. The results indicate that there is an enhancement in the accuracy of detecting abnormal behavior in vessels, leading to a significant reduction in the blind area of VTS (Vessel traffic services) system monitoring.

M-STCP: an online ship trajectory cleaning and prediction algorithm using matrix neural networks

Accurate prediction of ship trajectories is crucial to guarantee the safety of maritime navigation. In this paper, a matrix neural network-based online ship track cleaning and prediction algorithm called M-STCP is suggested to forecast ship tracks. Firstly, the GPS-provided historical ship trajectory data is cleaned, and the data cleaning process is finished using the anomaly point algorithm. Secondly, the trajectory is input into the matrix neural network for training and prediction, and the algorithm is improved by using Kalman filtering, which reduces the influence of noise on the prediction results and improves the prediction accuracy. In the end, the effectiveness of the method is verified using real GPS trajectory data, and compared with the GRU model and long-short-term memory networks. The M-STCP method can improve the prediction accuracy of ship trajectory to 89.44%, which is 5.17% higher than LSTM and 1.82% higher than GRU, effectively improving the prediction accuracy and time efficiency.

A novel method for ship trajectory prediction in complex scenarios based on spatio-temporal features extraction of AIS data

Ship trajectory prediction plays a key role in the early warning and safety of maritime navigation. Ship pilots must have a complete grasp of the future trajectories of ships within a certain period of time when moving the ship to effectively avoid collisions. However, the accuracy of ship trajectory prediction is a significant issue that needs to be resolved at present. In this paper, we propose a ship trajectory prediction model covering spatio-temporal awareness graph attention network (GAT) based on long short-term memory network (LSTM) to predict the future trajectories of ships in complex scenarios, named STPGL model, which adopts the design of encoder–decoder structure. The historical encoder uses an LSTM to extract the kinematic sequence features of each ship from historical trajectories in the temporal dimension. The interactive encoder summarizes the interaction features between different ships through GAT in the spatial dimension. Then, the two features are fused and fed into the decoder module to infer the future trajectories of ships. The experimental results demonstrate that STPGL model can effectively improve the prediction accuracy of short-term, medium-term and long-term ship trajectory. It has excellent performance and has a certain reference value for the advancement of unmanned ship collision avoidance.

Unsupervised vessel trajectory reconstruction

A trajectory is a sequence of observations in time and space, for examples, the path formed by maritime vessels, orbital debris, or aircraft. It is important to track and reconstruct vessel trajectories using the Automated Identification System (AIS) data in real-world applications for maritime navigation safety. In this project, we use the National Science Foundation (NSF)'s Algorithms for Threat Detection program (ATD) 2019 Challenge AIS data to develop novel trajectory reconstruction method. Given a sequence of N unlabeled timestamped observations Χ={x1,x2,...,xN}, the goal is to track trajectories by clustering the AIS points with predicted positions using the information from the true trajectories Χ. It is a natural way to connect the observed point xî with the closest point that is estimated by using the location, time, speed, and angle information from a set of the points under consideration xi ∀ i ∈ {1, 2, …, N}. The introduced method is an unsupervised clustering-based method that does not train a supervised model which may incur a significant computational cost, so it leads to a real-time, reliable, and accurate trajectory reconstruction method. Our experimental results show that the proposed method successfully clusters vessel trajectories.

The Problem of Ensuring Maritime Security in the Asian Region

INTRODUCTION. Modern maritime law is replete with a variety of legal acts of the interstate level, but the problems of ensuring security in this area remain relevant. At the international level, it is very difficult to establish a clear connection between the rules of law and actual legal relations. In order to strive to realize this, it is necessary to constantly analyze the real situation, trying to improve it or put it in order with the help of legal mechanisms, which is what this study is aimed at. This work is devoted to the analysis of relations in the field of merchant shipping from the point of view of ensuring security and stability in order to understand the possibility of improving international legal norms. As a territorial scope, interstate relations of Asian countries, including Russia, were considered as the most promising and interesting region from a scientific point of view, including in the context of increased measures of negative economic pressure on Russia from a number of states in North America and Europe. In the current political and economic conditions, not only the doctrine turns its attention to this direction, positioning the vector of domestic development as Eurasian, and sometimes completely Asian. MATERIALS AND METHODS. To prepare this work, an analysis of international legal acts, law enforcement practice was carried out, and expert assessments were monitored on various issues of ensuring the safety of navigation in the Asian region. The results obtained are based on the consideration of economic, political science, management and other literature in addition to the works of legal scholars. The comparative legal method was used, as well as deduction and synthesis. RESEARCH RESULTS. As a result, it is proposed to consider the possibility of taking additional measures aimed at expanding the functionality of the captain of the vessel to ensure the safety of maritime navigation, including countering illegal actions on board. At the level of the domestic legislation of the Russian Federation and the national legislations of other states of the region, establish clear rules for determining the law applicable on board a ship and extending the jurisdiction of the relevant state to relations arising on a ship. At the same time, the provisions of the domestic legislation of each state must comply with the rules of the United Nations Convention on the Law of the Sea (UNCLOS) of December 10, 1982. Grant broader powers to captains of ships in terms of implementing measures to prevent crimes on board the ship and transfer the person who committed the crime to representatives of foreign law enforcement agencies. DISCUSSION AND CONCLUSIONS. International merchant shipping is a very complex area of public relations with a large number of entities that have different legal status and, accordingly, are related to each other in the most diverse ways. The main threats to the implementation of these activities are piracy, offenses on board a ship in isolation from the mainland, and illegal extraction of biological resources. Fishing by residents of some states in the territorial waters of others is a topical issue. This work is devoted to the study of the main trends in the development of the system for ensuring the safety of international navigation from different points of view. As an object of research, legal relations in the Asian region are taken as the most diverse from social, economic, political and other points of view. Also, the countries of Asia, including Russia, are among the most rapidly developing, which demands new approaches to the relationship between subjects that require their own legal regulation. As a result, separate proposals have been developed, subject to the adoption of which it will be possible to optimize law enforcement practice on ensuring the safety of navigation.

Evaluating the effect of the incidence angle of ALOS-2 PALSAR-2 on detecting aquaculture facilities for sustainable use of coastal space and resources.

Driven by the growing world population, aquaculture plays a key role in meeting the increasing demand for food. However, aquaculture facilities in Japan are widely installed in coastal waters where natural disasters, such as typhoons and tsunamis, might wash these facilities away, thereby interfering with maritime navigation safety. Therefore, it is imperative to efficiently monitor the state of aquaculture facilities daily, particularly after a disaster in real time. To this end, several new space-borne L-band synthetic aperture radars (SARs) continue to be launched now and in the future, whose utilizations are expected to increase nationally and internationally. An example is the Japan Aerospace Exploration Agency, currently operating a SAR that can be operated day and night, and even under cloudy conditions, called ALOS-2 PALSAR-2. Based on the above facts, this study evaluated the effect of the incidence angle of ALOS-2 PALSAR-2 HH single-polarization data, using 3 m spatial resolution, on aquaculture raft detection. As the study site, we selected Ago Bay, located on the Pacific coast of Mie Prefecture in central Japan since the Nankai Trough Megathrust Earthquake and tsunamis have been predicted to occur in the future around this area. Then, we analyzed the sigma zero (backscattering coefficient) of aquaculture rafts and their surrounding sea surfaces, including the relationships between satellite orbits and aquaculture raft directions. Investigations revealed that the optimum incidence angle for detecting aquaculture rafts in this study was 33.8°-45.1°. Differences in the sigma zero values existed between the ascending and descending orbits. However, the incidence angles differed on the orbits. Then, differences in the median sigma zero values across a range of incidence angles were evaluated under the descending orbit. In addition, when the directions of the aquaculture rafts were closely perpendicular to the satellite orbit, aquaculture rafts tended to show the highest values of sigma zero due to Bragg resonance scattering. Hence, this knowledge may allow for the rapid detection of aquaculture rafts during an emergency without going on-site.

A method for detecting abnormal behavior of ships based on multi-dimensional density distance and an abnormal isolation mechanism.

Abnormal ship behavior detection is essential for maritime navigation safety. Most existing abnormal ship behavior detection methods only build A ship trajectory position outlier detection model; however, the construction of a ship speed outlier detection model is also significant for maritime navigation safety. In addition, in most existing methods for detecting a ship's abnormal behavior based on abnormal thresholds, one unsuitable threshold leads to the risk of the ship not being minimized as much as possible. In this paper, we proposed an abnormal ship behavior detection method based on distance measurement and an isolation mechanism. First, to address the problem of traditional trajectory compression methods and density clustering methods only using ship position information, the minimum description length principle based on acceleration (AMDL) algorithm and Multi-Dimensional Density Clustering (MDDBSCAN) algorithm is used in this study. These algorithms not only considered the position information of the ship, but also the speed information. Second, regarding the issue of the difficulty in determining the anomaly threshold, one method for determining the anomaly threshold based on the relationship between the velocity weights and noise points of the MDDBSCAN algorithm has been introduced. Finally, due to the randomness issue of the selected segmentation value in iForest, a strategy of selectively constructing isolated trees was proposed, thus further improving the efficiency of abnormal ship behavior detection. The experimental results on the historical automatic identification system data set of Xiamen port prove the practicality and effectiveness of our proposed method. Our experiment results show that the proposed method achieves an improvement of about 10% over the trajectory outlier detection based on the local outlier fraction method, about 14% over the isolation-based online anomalous trajectory method in terms of the accuracy of ship position information anomaly detection, and about 3% over the feature fusion method in terms of the accuracy of ship speed anomaly detection. This method improves algorithm efficiency by about 5% compared to the traditional isolation forest anomaly detection algorithm.

Prediction Method for Ocean Wave Height Based on Stacking Ensemble Learning Model

Wave heights are important factors affecting the safety of maritime navigation. This study proposed a stacking ensemble learning method to improve the prediction accuracy of wave heights. We analyzed the correlation between wave heights and other oceanic hydrological features, according to eleven features, such as measurement time, horizontal velocity, temperature, and pressure, as the model inputs. A fusion model consisting of two layers was established according to the principle of stacking ensemble learning. The first layer used the extreme gradient boosting algorithm, a light gradient boosting machine, random forest, and adaptive boosting to determine the deep relations between the wave heights and the input features. The second layer used a linear regression model to fit the relation between the first layer outputs and the actual wave heights, using the data from the four models of the first layer. The fusion model was trained based on the 5-fold cross-verification algorithm. This paper used real data to test the performances of the proposed fusion model, and the results showed that the mean absolute error and the mean squared error of the fusion model were at least 35.79% and 50.52% better than those of the four models.

Activities of the International Maritime Organization in the field of maritime safety

The relevance of the study is due to the active activity of IMO in the field of ensuring the safety of navigation. The purpose of the study is to analyze this activity. Accordingly, the author analyzes the structure and objectives of the Organization, which are enshrined in the IMO Convention and considers the powers of the Maritime Safety Committee. The article also discusses current international legal acts adopted by IMO and regulating the range of issues related to the safety of maritime navigation. The subject of the study is the legal basis for the functioning of IMO in the field of ensuring the safety of maritime navigation. The object of the study is interstate relations in the field of international legal regulation of maritime safety. The methodological foundations of the research include general scientific and private scientific research methods, including: formal legal and comparative legal methods. The novelty of the research lies in the conclusions made by the author in the work. One of the main conclusions is the statement of the need to amend Article 15 of the IMO Convention in order to create for the Assembly the right to reject documents submitted by the Council. The author points to the need to expand the composition of the Council, through the adoption by States of amendments approved by Resolution A.1152 (32) of January 8, 2021. The peculiarity of some standards on the safety of maritime navigation adopted by IMO is the obligation of their implementation by all States. The author explains this fact for three reasons. Firstly, all IMO member States participate in the creation and amendment of international legal acts that enshrine standards of this kind, since they are part of the Maritime Safety Committee. Secondly, this approach excludes unequal conditions between States. Thirdly, it is aimed at reducing the number of States unwilling to adhere to the requirements established by maritime safety standards.

A Critical Appraisal of The Anti-Piracy Law of Nigeria

Nigeria’s June 2019 law on piracy and other maritime offences is an important step in securing the country’s coastline and seas. The Ant-Piracy law on Suppression of Piracy and Other Maritime Offences Act, [1] 2019 (POMO Act) aims to ‘prevent and suppress piracy, armed robbery and any other unlawful act against a ship, aircraft and any other maritime craft, including fixed and floating platforms.’ It also observed that this law gives effect to the United Nations Convention on the Law of the Sea, 1982 (UNCLOS) and the Convention for the Suppression of Unlawful Acts against the Safety of Maritime Navigation, 1988 (SUA). Despite the enactment of this huge law and policies targeted at ameliorating the numerous problems at Nigeria’s coastline and seas, the situation in Nigeria seems degenerating owing to the fact that there are no adequate compliance of the laws as well as the ineffective implementation or enforcement of the law. This paper seeks to critically evaluate the impacts of the Act and also its weaknesses. It also discusses Nigeria’s capacity to implement its new law and detect and prosecute crimes. Analysis includes an evaluation of the steps taken by the governments of the region and the effectiveness of the implemented strategies to counter the threat posed by piracy in the region. In identifying the barriers and challenges to combat piracy, a comprehensive arrangement based effective cooperation is proposed in the paper. Keywords : PIRACY LAW, NIGERIA DOI: 10.7176/JLPG/113-06 Publication date: September 30 th 2021 * PhD & LLM (Coventry), BL (Nigeria), LLB (London Met), Lecturer, Department of Public Law, Faculty of Law, Rivers State University. Email: victor.enebeli@yahoo.co.uk , Phone: 09020176657 ** LL.M (RSU) in view; BL (Abuja); LL.B (UK); Dip-in-Law (UK). Senior Associate Tuduru Ede, SAN & Co. Port Harcourt, Rivers State, Nigeria. Email: david.chibuike22@yahoo.com/ikemefula20@yahoo.com . 08065357894, 08052339985 [1] https://lawnigeria.com/2019/12/suppression-of-piracy-and-other-maritime-offences-act-2019/ accessed 04/08/2021

Ship Classification Method for Massive AIS Trajectories Based on GNN

Since criminals and maritime terrorism may tamper with AIS data and make the track suspicious, it is urgent to classify ships accurately and improve maritime navigation safety. Ship classification based on trajectory data can make up for the deficiency of traditional radar identification and optical identification which has important academic significance and practical value. The target recognition technology based on the traditional neural network can only process conventional Euclidean structure data, while the emerging graph neural network shows great advantages in processing non-Euclidean structure data. The ship trajectory data has the characteristics of the time and space domain and shows a non-Euclidean structure; therefore this paper proposes a classification and recognition method based on the graph neural network to process ship AIS data. First of all, the ship trajectory data is preprocessed and converted into graph data with vertices and edges. Then we use GNN to classify 4 types of ships including fishing vessels, passenger ships, oil tankers, and container ships. Finally, we compare the results with the SVM method. And it shows that this method is valid and proves that it is an effective method of ship classification.

Encroachments against the Safety of Maritime Navigation in Russian and International Criminal Law

Introduction. The necessity of criminalising acts against the safety of maritime navigation in na- tional legislation is based on the requirement to comply with the international obligations. The relevance of the research is that the implementation of such criminalisation is not always carried out in a timely and adequate manner. Тhe author’s versions of the criminal law can be used by the legislator to further improve the Criminal Code. Theoretical Basis. Methods. The methodological basis of the study was a set of both general scientific and private scientific methods of cognition. In particular these included the analysis, synthesis, comparative, formal and the legal. Special attention was paid to the international legal standards, and the regulation of criminal liability for encroachments against security sea shipping. Results. The article analyses the regulations at the national level governing the crime of acts which infringe upon the safety of navigation at sea and in the river space. This is Art. 211 “Hijacking of an aircraft or water transport or railway rolling stock” and Art. 227 “Piracy” as provided for in the Criminal Code of the Russian Federation. The article considers the provisions corresponding to these from international treaties, and investigates the problems of compliance in implementing norms of the Russian criminal law with the basic contractual provisions. At the same time, international acts are analysed in their latest and current version, taking into account all the changes and additions made to them. Discussion and Conclusion. As a result of the study, the authors come to the conclusion that it is necessary to make alterations to the national criminal legislation to bring it in line with the current international standards due to the absence of provisions in it regarding liability for crimes committed against sea vessels, as well as on board or against fixed platforms located on the continental shelf. At the same time, the authors propose specific additions to the Criminal Code of the Russian Federation. In particular, these are on the inclusion of certain signs of corpus delicti relating to the number of qualifying items, and which also indicate the need to formulate specific criminal law norms providing for liability for crimes against sea vessels, as well as on board or against fixed platforms located on the continental shelf.

Research and development of liquid hydrogen (LH2) temperature monitoring system for marine applications

Monitoring the temperature in liquid hydrogen (LH2) storage tanks on ships is important for the safety of maritime navigation. In addition, accurate temperature measurement is also required for commercial transactions. Temperature and pressure define the density of liquid hydrogen, which is directly linked to trading interests. In this study, we developed and tested a liquid hydrogen temperature monitoring system that uses platinum resistance sensors with a nominal electrical resistance of approximately 1000 Ω at room temperature, PT-1000, for marine applications. The temperature measurements were carried out using a newly developed temperature monitoring system under different pressure conditions. The measured values are compared with a calibrated reference PT-1000 resistance thermometer. We confirm a measurement accuracy of ±50 mK in a pressure range of 0.1 MPa–0.5 MPa.