Behavior Of Vessels Research Articles

A Novel & Efficient LR LSTM AIS Route Data Prediction for Longer Range

The growth of technology has enabled different industries to generate an excessive amount of data- one such industry being the maritime sector. Sophisticated sensory systems installed on various vessels generate information at a very large scale which can further be used in optimizing operational efficiency, improving safety standards, and aiding in the decision-making process. Researchers have henceforth identified statistical learning methods and machine learning techniques as potent tools for excavating useful insights from this copious amount of data available. This research evaluates how these algorithms work by focusing exclusively on the analysis of sensory data collected from vessels within the maritime domain. A comparison study has been conducted between statistical learning methods (which includes regression analysis, and time series analysis) vis-a-vis machine learning approaches. The major objective of this study was to determine the most effective method for detecting anomalies while simplifying marine operations and optimizing vessel behavior. The scope of the conducted analysis is restricted to the prediction of the next trajectory points. Accurate prediction of vessel positions plays a crucial role in maritime operations, enabling efficient route planning, collision avoidance, and maritime traffic management. In this article, the authors propose a combination model that combines the benefits of Linear Regression (LR) and Long Short-Term Memory (LSTM) techniques to anticipate vessel positions based on Automatic Identification System (AIS) data. The proposed model takes advantage of the interpretability of LR and the temporal dependencies collected by LSTM to capture temporal dependencies, which improve prediction accuracy and reveal the underlying links between vessel features and future positions.

Exploration of COLREG-relevant parameters from historical AIS-data

Abstract Reliable anti-collision control algorithms conforming with the rules regulating traffic at sea, the International Regulations for Preventing Collisions at Sea (COLREG), are essential for the deployment of autonomous vessels in waters shared with other ships. The development of such methods is an active field of research. However, little attention has been given to how these rules are interpreted by experienced mariners, and how such information can be parametrised for use in automatic control systems and autonomous ships. This paper presents a method for exploiting historical automatic identification system (AIS) data to characterise parameters indicating the prevalent practices at sea in encounters with high collision risk. The method has been tested on data gathered in areas off the Norwegian coast over several years. Statistics on relevant parameters from the resulting dataset and the relation between them is presented. The results indicate that the strongest influence on vessel behaviour is the type of situation, and the amount of land and grounding hazards in the vessel's proximity.

VesNet: A Vessel Network for Jointly Learning Route Pattern and Future Trajectory

Vessel trajectory prediction is the key to maritime applications such as traffic surveillance, collision avoidance, anomaly detection, and so on. Making predictions more precisely requires a better understanding of the moving trend for a particular vessel since the movement is affected by multiple factors like marine environment, vessel type, and vessel behavior. In this paper, we propose a model named VesNet, based on the attentional seq2seq framework, to predict vessel future movement sequence by observing the current trajectory. Firstly, we extract the route patterns from the raw AIS data during preprocessing. Then, we design a multi-task learning structure to learn how to implement route pattern classification and vessel trajectory prediction simultaneously. By comparing with representative baseline models, we find that our VesNet has the best performance in terms of long-term prediction precision. Additionally, VesNet can recognize the route pattern by capturing the implicit moving characteristics. The experimental results prove that the proposed multi-task learning assists the vessel trajectory prediction mission.

Reconstruction of the Physiological Behavior of Real and Synthetic Vessels in Controlled Conditions

The objective of this study is to assess the ability of an Artificial Circulatory Phantom (ACP) to verify its accuracy in simulating the movement of artificial vessels vs. real vessels under changing cardiovascular parameters such as heartbeat, ejection fraction, and total peripheral resistance. The experiments were conducted with blood-like fluid that flows through two types of vessels: iliac arteries and different types of ePTFE vascular prostheses. Parameters such as diameter and tortuosity were measured and analyzed. The flow characteristics included a pulsating pattern with a frequency of 60–120 min−1 and ejection volumes ranging from 70 to 115 mL. The results showed a predominantly positive correlation between wall displacement (Wd) and tortuosity index (Ti) for the iliac artery (R2 = 0.981), as well as between Wd and mean tortuosity index (MTi) (R2 = 0.994). Similarly, positive correlations between Wd and Ti (R2 = 0.942) and Wd and MTi (R2 = 0.922) were computed for the ePTFE vascular prosthesis. The ACP introduced in this study is a valuable tool for evaluating various vessel types and the spatial configurations of vascular prostheses under diverse hemodynamic conditions. These findings are promising for the advancement of novel approaches to the testing and design of vascular grafts, ultimately enhancing their patency rates in future applications.

Mechanobiological tortuosity of blood vessels with stress-modulated growth and remodeling

The stability of blood vessels is essential for maintaining their functions, while severe blood vessel tortuosity leads to various clinical complications. The growth and remodeling of blood vessels, which are regulated by mechanical and biochemical cues, cause residual stresses that affect vessel stability. In this paper, we combine theory and simulations to study the mechanobiological behavior of blood vessels with stress-modulated growth and remodeling. Effects of the volumetric growth of the matrix and the turnover and reorientation of collagen fibers are accounted for. Linear stability analysis is first carried out to investigate the mechanobiological stability of blood vessels. By developing a finite element method that incorporates stress-modulated growth and remodeling, we validate the theoretical solution of the critical state and further capture the postbuckling evolution of blood vessels. Our results show that an increased internal pressure can lead to the thickening of the vessel wall, which stabilizes the blood vessels mechanically, whereas the effect of internal pressure on the mechanobiological buckling is nonmonotonic. Compared to the mechanical instability, the mechanobiological buckling results in a larger mode number and a shorter wavelength, as usually observed in varicose veins. Vessels during postbuckling may exhibit non-uniform wall thicknesses, in consistency with the experimental observation on tortuous aortas. These findings highlight the crucial role of mechanical remodeling in tissue morphogenesis and could deepen the understanding of mechanobiological mechanisms underlying the formation and development of blood vessel tortuosity.

Mooring Design and Fatigue Damage on Variant Fairlead Positions During FPSO Operation – Case Study

This comprehensive case study delves into the intricate dynamics of deepwater mooring systems, specifically focusing on Floating Production Storage and Offloading (FPSO) operations. Our investigation centers on the Anoa Natuna FPSO, situated in the Arafura Sea. We examine how varying fairlead positions, employing a 4x3 chain configuration, influence structural integrity, operational stability, and fatigue damage. The research meticulously analyzes the response of the FPSO to environmental forces, providing a detailed understanding of the vessel's behavior under different fairlead positions. The results showcase the vessel's stability, elucidate variations in its motion response, and highlight the effects of fairlead positioning on structural fatigue, offering valuable insights for safe and efficient deepwater FPSO operations.

Optimizing Multi-Vessel Collision Avoidance Decision Making for Autonomous Surface Vessels: A COLREGs-Compliant Deep Reinforcement Learning Approach

Automatic collision avoidance decision making for vessels is a critical challenge in the development of autonomous ships and has become a central point of research in the maritime safety domain. Effective and systematic collision avoidance strategies significantly reduce the risk of vessel collisions, ensuring safe navigation. This study develops a multi-vessel automatic collision avoidance decision-making method based on deep reinforcement learning (DRL) and establishes a vessel behavior decision model. When designing the reward function for continuous action spaces, the criteria of the “Convention on the International Regulations for Preventing Collisions at Sea” (COLREGs) were adhered to, taking into account the vessel’s collision risk under various encounter situations, real-world navigation practices, and navigational complexities. Furthermore, to enable the algorithm to precisely differentiate between collision avoidance and the navigation resumption phase in varied vessel encounter situations, this paper incorporated “collision avoidance decision making” and “course recovery decision making” as state parameters in the state set design, from which the respective objective functions were defined. To further enhance the algorithm’s performance, techniques such as behavior cloning, residual networks, and CPU-GPU dual-core parallel processing modules were integrated. Through simulation experiments in the enhanced Imazu training environment, the practicality of the method, taking into account the effects of wind and ocean currents, was corroborated. The results demonstrate that the proposed algorithm can perform effective collision avoidance decision making in a range of vessel encounter situations, indicating its efficiency and robust generalization capabilities.

Status Identification in Support of Fishing Effort Estimation for Tuna Longliners in Waters near the Marshall Islands Based on AIS Data

Visualising the fishing behaviour of vessels and quantifying the spatial distribution of fishing effort is the scientific basis for assessing and managing fisheries resources. The information on the dynamics of fishing vessel voyages provided by the automatic identification system (AIS) of vessels serves as high-precision fishery data and provides a means of quantifying fishing effort with high spatial and temporal resolution in the tuna longline fishery. Based on the AIS data of five tuna longliners operating in the waters near the Marshall Islands from 2020 to 2021, this study used three methods, namely the threshold screening method, the construction of a BP neural network and the support vector machine (SVM) to identify the fishing and non-fishing status of the tuna longliners, respectively. This study investigates the status identification and fishing effort estimation of the tuna longliner (VESSEL A) in 2021 based on the constructed optimal model, and spatial correlation analyses are performed between the fishing effort estimated in hours based on AIS data and in hooks based on fishing logbook data, by month. The results showed (1) the recognition accuracy of the threshold screening method is 89.9%, the recognition accuracy of the BP neural network classification model is 95.11%, the kappa coefficient is 0.51, the recognition accuracy of the SVM classification model is 95.74% and the kappa coefficient is 0.52; (2) in comparison, the SVM classification model performs better than the other two status identification methods for tuna longliners; and (3) the correlation coefficients between the two types of effort of VESSEL A were greater than 0.79 on all fishing months, indicating that there was no significant difference in the spatial and temporal distribution between the two types of effort. This study suggests that the SVM model can be used to identify the status and estimate the fishing effort of longliners.

CFD analysis of the full-scale resistance of an oil tanker in presence of a mud–water interface

The presence of mud layers on the bottom of ports and waterways can have negative effects on the hydrodynamic behaviour of marine vessels. This numerical study investigates the effect of muddy seabeds on the full-scale resistance of an oil tanker sailing straight ahead. The objective is to determine the influence of factors such as the densimetric Froude number, UKC and mud rheology at speeds between 3 and 9 knots. The numerical study is conducted using a finite-volume Reynolds-Averaged Navier–Stokes (RANS) flow solver combined with the Volume-Of-Fluid (VOF) method to capture the mud–water interface. At certain critical speeds, the presence of mud increased the ship’s total resistance by up to 15 times compared to the case with solid bottoms. The non-Newtonian rheology of mud was found to influence the ship’s resistance mainly at low speeds and when sailing through the mud layer. This article also shows that, when sailing through mud, the computed resistance at high speeds may be underestimated because of two effects, namely ‘water lubrication’ and ‘numerical ventilation’.

Retinal vascular dynamics: A window for observing an irregular heartbeat. A case report.

We aimed to characterize several aspects of retinal vascular dynamics in a patient with arrythmia in order to elicit additional diagnostic information on microvascular dysfunction. A 68-year-old male patient with arrythmia and an age- and gender-matched control subject underwent ocular examination including dynamic retinal vessel assessment with flicker light provocation. Retinal vessel diameters were measured continuously following a standard protocol (IMEDOS Systems, Jena, Germany). The data were evaluated using methods of signal analysis. Retinal vessel response following flicker provocation as well as local structural and functional behavior of retinal vessels were comparable between both individuals. The arrhythmia case demonstrated irregular arterial and venous heart rate (HR) pulsation with an average frequency of 1Hz. Moreover, the case showed a higher magnitude and larger periods of low-frequency retinal vessel oscillations as well as lower periodicity of both HR pulsations and low-frequency vasomotions. Besides numerical examination of irregular HR pulsations in case of arrhythmia, from the direct noninvasive assessment of retinal vessel dynamics one can derive more detailed information on microvascular function including the whole spectrum of retinal arterial and venous pulsations and vasomotions. This may have implications for health screening not limited to atrial fibrillation.

Introduction to dynamic retinal vessel analyses

Retinal vessels possess the same structure and function as cerebral vessels and reflect the status of the central microcirculation. In the pre‐, post‐ and capillary areas, the behaviour of retinal vessels does influence blood flow and flow‐related metabolism through changes in arterial and venous diameter. In dynamic retinal vessel analysis, the function and structure of retinal vessels are subjected to a non‐invasive functional and structural examination over a period before, during and after a specific vascular stimulation. The use of several methodological modalities together with their relevant quantitative biomarkers represents a promising approach for the evaluation of ocular and systemic diseases of vascular origin as well as cerebro‐ or cardiovascular morbidity and mortality. Dynamic retinal vessel analysis has been employed in cross‐sectional and interventional clinical studies in ophthalmology and interdisciplinary in other branches of the medicine including cardiology, neurology, neurosurgery, nephrology, diabetology, hypertensiology, gynaecology and others. The paper aims to give a brief overview in the technology and the methodology of dynamic retinal vessel analysis.

Predictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation

Predictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation

Melt electrowritten scaffold architectures to mimic tissue mechanics and guide neo-tissue orientation

All human tissues present with unique mechanical properties critical to their function. This is achieved in part through the specific architecture of the extracellular matrix (ECM) fibres within each tissue. An example of this is seen in the walls of the vasculature where each layer presents with a unique ECM orientation critical to its functions. Current adopted vascular grafts to bypass a stenosed/damaged vessel fail to recapitulate this unique mechanical behaviour, particularly in the case of small diameter vessels (<6 mm), leading to failure. Therefore, in this study, melt-electrowriting (MEW) was adopted to produce a range of fibrous scaffolds to mimic the extracellular matrix (ECM) architecture of the tunica media of the vasculature, in an attempt to match the mechanical and biological behaviour of the native porcine tissue. Initially, the range of collagen architectures within the native vessel was determined, and subsequently replicated using MEW (winding angles (WA) 45°, 26.5°, 18.4°, 11.3°). These scaffolds recapitulated the anisotropic, non-linear mechanical behaviour of native carotid blood vessels. Moreover, these grafts facilitated human mesenchymal stem cell (hMSC) infiltration, differentiation, and ECM deposition that was independent of WA. The bioinspired MEW fibre architecture promoted cell alignment and preferential neo-tissue orientation in a manner similar to that seen in native tissue, particularly for WA 18.4° and 11.3°, which is a mandatory requirement for long-term survival of the regenerated tissue post-scaffold degradation. Lastly, the WA 18.4° was translated to a tubular graft and was shown to mirror the mechanical behaviour of small diameter vessels within physiological strain. Taken together, this study demonstrates the capacity to use MEW to fabricate bioinspired scaffolds to mimic the tunica media of vessels and recapitulate vascular mechanics which could act as a framework for small diameter graft development to guide tissue regeneration and orientation.

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.

Investigating the effects of Coating systems on the degradation behavior of 3D-Printed pressure vessels

This study investigates the long-term performance of 3D-printed pressure vessels subjected to accelerated aging through water mass infusion. Thermoplastic composites were used to construct the pressure vessels using Material extrusion 3D printing, and subsequent submersion in an ocean water solution at elevated temperatures was employed to simulate accelerated aging. The effectiveness of various coating systems in minimizing the long-term degradation of these 3D-printed pressure vessels was also examined. Implosion experiments were conducted on both aged specimens with and without coating systems using a high-pressure tank facility. The results demonstrate that the application of polyurethane coating systems significantly slows the degradation process of 3D-printed vessels, with aged specimens exhibiting a reduced critical operational depth by 10.3 % compared to those without coatings. The pressure differential between unweathered specimens with and without coatings was 3.61 %, increasing to 23.3 % for weathered specimens. Coatings proved effective in reducing mass absorption and increasing critical collapse pressure. The analytical collapse pressure prediction closely aligns with experimental results, enabling performance generalization for other coating systems. This research contributes insights into the use of coating systems to enhance the longevity and performance of 3D-printed pressure vessels, highlighting their potential for mitigating degradation and improving structural integrity in long-term underwater applications.

The Big Picture: An Improved Method for Mapping Shipping Activities

Density maps support a bird’s eye view of vessel traffic, through providing an overview of vessel behavior, either at a regional or global scale in a given timeframe. However, any inaccuracies in the underlying data, due to sensor noise or other factors, evidently lead to erroneous interpretations and misleading visualizations. In this work, we propose a novel algorithmic framework for generating highly accurate density maps of shipping activities, from incomplete data collected by the Automatic Identification System (AIS). The complete framework involves a number of computational steps for (1) cleaning and filtering AIS data, (2) improving the quality of the input dataset (through trajectory reconstruction and satellite image analysis) and (3) computing and visualizing the subsequent vessel traffic as density maps. The framework describes an end-to-end implementation pipeline for a real world system, capable of addressing several of the underlying issues of AIS datasets. Real-world data are used to demonstrate the effectiveness of our framework. These experiments show that our trajectory reconstruction method results in significant improvements up to 15% and 26% for temporal gaps of 3–6 and 6–24 h, respectively, in comparison to the baseline methodology. Additionally, a use case in European waters highlights our capability of detecting “dark vessels”, i.e., vessel positions not present in the AIS data.

Safety and COLREG evaluation for marine collision avoidance algorithms

Verification is a necessary step in system development, so too for autonomous surface vehicles (ASVs). However, formal and analytical verification methods are not well suited for such highly complex systems. Simulation-based testing has therefore been proposed as a viable approach. This would require numerous simulations to be performed and evaluated, raising the need for automatic evaluation.An important assessment measure with regards to ASV safety, is the degree of International Regulations for Preventing Collisions at Sea (COLREG) compliance. One important aspect of these rules is vessel behavior in situations where a risk of collision with other vessels is present.This paper presents a comprehensive method for automatic evaluation of collision avoidance maneuvers in terms of safety and compliance with a subset of the COLREG steering and sailing rules. The rules are formulated as mathematical expressions, and insight is given into the authors’ interpretation of the rules through the choice of values for parameters and weights. Results from simulated encounters and from encounters between vessels in normal operation show the method’s capability to correctly identify situations and detect and penalize undesired behaviors. They also evoke the need for more research into how to interpret the COLREG in terms of angles, velocities, and distance between vessels.

Application of Artificial Intelligence in the Study of Fishing Vessel Behavior

Monitoring and understanding the behavior of fishing vessels are important in facilitating effective management, preventing illegal fishing, informing fishing grounds and evaluating effects of harvests on fishery resources. In recent decades, a large quantity of real-time data of fishing vessels have become available with the development of vessel-tracking systems, making it possible to study the behavior of fishing vessels in high spatial and temporal resolutions. To effectively and efficiently deal with the large amount of data, algorithms from artificial intelligence (AI) are increasingly applied in the study of fishing vessel behavior. In this paper, we first introduce the various data sources for studying fishing vessel behavior and compare their pros and cons. Secondly, we review the AI methods that have been used to monitor and extract the behavior of fishing vessels from big data. Then, studies on the physical, ecological and social mechanisms affecting the behavior of fishing vessels were synthesized. Lastly, we review the applications of fishing vessel behavior in fishery science and management.

Thermal Effects on Dynamic Behavior of CFETR Vacuum Vessel

The vacuum vessel (VV) of the China Fusion Engineering Test Reactor (CFETR) was designed to withstand the variable structural loads resulting from dynamic excitation, such as seismic and plasma disruption. Modal analysis, a powerful tool, was used to evaluate the structural dynamic characteristics, such as frequency and mode shape. In addition, the CFETR VV has three different temperature conditions: room temperature (20°C), normal operation temperature (100°C), and baking temperature (200°C). In this paper, in order to investigate the influence of such different temperature conditions to the dynamic behavior of the VV, three independent finite element analysis with the same modal analysis method were performed. According to analysis results, there are obvious thermal effects on the dynamic behavior, such as nature frequency and mode shape, among the different temperature cases for the CFETR VV. Moreover, the results show that the natural frequency for each order decreases as the temperature increases, and the mode shape of the VV also changed with temperature.

Investigation into the long-term stress relaxation behaviors of white oak (Quercus alba L.) based on the time–temperature–moisture superposition principle

ABSTRACT Curving woodworks exhibit wide applications in wooden furniture manufacturing due to the conformable visual and tactile experience, are welcome among consumers. However, the corresponding long-term shape stability facing the stress relaxation is an attention-worthy issue with the fundamental insights about the mesoscopic behaviors of vessels have not been provided. Here, we proposed the investigation into the effect of vessels on stress relaxation behavior of white oak (Quercus alba L.). Results demonstrated that stress relaxation modulus of specimen decreased with the elevation in MC and temperature, and vessel-containing specimens displayed more evident stress relaxation. Providing more physical connotation, Zener model is more suitable for the numerically investigation into the stress relaxation than logarithmic model. The time-temperature-moisture superposition principle (TTMSP) allows us to predict the~10 d and ~1 d stress relaxation behavior of white oak facing different temperatures and MCs. Additionally, stress relaxation of white oak with vessels is more evident, which can be attributed to the stress concentration and resultant deformation around the vessel. We believe that this work is beneficial for obtaining a mesoscopic understanding of vessels on stress relaxation behaviors of wood. It thus paves a way to exploit the methodologies to improve the shape-stability of curving woodworks.