1,721,008 research outputs found
Dynamic evolution of maritime accidents: comparative analysis through data-driven Bayesian networks
No full textMaritime accident research has primarily focused on characteristics and risk analysis, which often overlooks the evolution of the associated risk patterns over time. This study aims to investigate the dynamic changes in maritime accidents from 2012 to 2021 by employing a data-driven Bayesian Network (BN) model and conducting a systematic dynamic pattern comparison. It presents two-stage models for two databases and five models against different timeframes to capture the evolving characteristics of global maritime accidents. Furthermore, within the context of the accident investigation, this study pioneers the analysis of the effectiveness of two network structures, namely a layered BN model and a Tree-Augmented Naive Bayesian (TAN) network, in terms of the accuracy of predicting the accident severity. The key findings regarding the changes in maritime accidents in the past decade include: (1) a significant rise in maritime risks linked to large ships (30.8%), port areas (11.67%), anchoring (11.82%), and manoeuvering operations (3.8%); (2) a connection between poor anchoring practices on fishing boats and ‘overboard’ accidents, and between inadequate equipment on tankers or chemical ships and ‘fire/explosion’ accidents; (3) the TAN model's superior performance in forecasting accident severity compared to the layered BN model; and (4) the probability of ‘very serious’ accidents in terms of ship-related factors is 74.7%, which is for the layered BN network, significantly lower than the TAN network's 99.4%. This study reveals shifts in accident patterns over time and underscores the importance of continuous monitoring and analysis for effective safety and risk management
Incorporation of a global perspective into data-driven analysis of maritime collision accident risk
No full textShip collision accidents are one of the most frequent accident types in global maritime transportation. Nevertheless, conducting an in-depth analysis of collision prevention poses a formidable challenge due to the constraints of limited Risk Influential Factors (RIFs) and available datasets. This paper aims to incorporate a global perspective into a new data-driven risk model, scrutinize the root causes of collision accidents, and advance measures for their mitigation. Additionally, it seeks to analyze the spatial distribution and conduct a comprehensive comparative study on collision characteristics for both pre- and post-COVID-19, utilizing the real accident dataset collected from two reputable organizations: Global Integrated Shipping Information System (GISIS) and Lloyd's Register Fairplay (LRF). The research findings and implications encompass several crucial aspects: 1) the constructed model demonstrates its reliability and accuracy in predicting collision accidents, as evident from its prediction performance and various scenario analysis; 2) the most hazardous voyage segment for collision accidents is identified to provide valuable guidance to different stakeholders; and 3) the hierarchical significance of various ship types in the context of collision accident is highlighted regarding the most probable scenario for collision occurrences; 4) During the pandemic, the rise in collision probabilities, particularly involving older vessels and bulk carriers, implies heightened operational challenges or maintenance issues for these ship types; (5) The prominence of favorable and adverse sea conditions in collision reports underscores the significant influence of weather on accidents during the pandemic. These findings and implications help enhance safety protocols, ultimately reducing the frequency of collision accidents in the global maritime domain
Integrating geometric and causation probability approaches into Dynamic Bayesian Networks for real-time collision risk prediction
No full textMaritime transportation is vital for international trade, yet collision accidents continue to pose serious risks to navigational safety and global economic stability. This study develops a novel collision risk prediction model based on Dynamic Bayesian Networks (DBN), incorporating both geometric and causation probability approaches to realise real-time ship collision risk prediction and probabilistic risk assessment. Leveraging raw Automatic Identification System (AIS) data, the proposed model dynamically updates the probabilities of influential factors using Markov-chain-based transition analyses, mitigating uncertainties caused by noisy or incomplete data. In contrast to traditional deterministic models, the DBN captures mutual dependencies among dynamic risk factors, including variations in speed ratio, relative bearing, and temporal-spatial parameters such as Distance to Closest Point of Approach (DCPA), Time to Closest Point of Approach (TCPA) and relative distance. The model categorises collision risk into five discrete levels, ranging from very low to very high, providing decision-makers with actionable insights for real-time navigational safety. A key innovation lies in modelling these interdependencies among influential factors, which enables a holistic understanding of collision dynamics. Simulation results demonstrate that the DBN model outperforms traditional Collision Risk Index (CRI) approaches, particularly in accurately predicting complex collision scenarios and reflecting aggressive manoeuvres. This study presents a robust framework for maritime collision risk prediction, offering a foundation for enhancing navigational safety in increasingly congested and mixed-traffic environments involving the coexistence of manned and unmanned vessels.</p
COLERGs-constrained safe reinforcement learning for realising MASS's risk-informed collision avoidance decision making
Full text linkMaritime autonomous surface ship (MASS) represents a significant advancement in maritime technology, offering the potential for increased efficiency, reduced operational costs, and enhanced maritime traffic safety. However, MASS navigation in complex maritime traffic and congested water areas presents challenges, especially in Collision Avoidance Decision Making (CADM) during multi-ship encounter scenarios. Through a robust risk assessment design for time-sequential and joint-target ships (TSs) encounter scenarios, a novel risk and reliability critic-enhanced safe hierarchical reinforcement learning (RA-SHRL), constrained by the International Regulations for Preventing Collisions at Sea (COLREGs), is proposed to realize the autonomous navigation and CADM of MASS. Finally, experimental simulations are conducted against a time-sequenced obstacle avoidance scenario and a swarm obstacle avoidance scenario. The experimental results demonstrate that RA-SHRL generates safe, efficient, and reliable collision avoidance strategies in both time-sequential dynamic obstacles and mixed joint-TSs environments. Additionally, the RA-SHRL is capable of assessing risk and avoiding multiple joint-TSs. Compared with Deep Q-network (DQN) and Constrained Policy Optimization (CPO), the search efficiency of the algorithm proposed in this paper is improved by 40% and 12%, respectively. Moreover, it achieved a 91.3% success rate of collision avoidance during training. The methodology could also benefit other autonomous systems in dynamic environments
Optimizing anti-collision strategy for MASS: a safe reinforcement learning approach to improve maritime traffic safety
No full textMaritime autonomous surface ships (MASS) promise enhanced efficiency, reduced human errors, and to improve maritime traffic safety. However, MASS navigation in complex maritime traffic presents challenges, especially in collision avoidance strategy optimization (CASO). This paper proposes a novel risk-based CASO approach based on safe reinforcement learning (SRL) with a reliability and risk hierarchical critic network (SRL-R2HCN) approach. Key steps in developing the approach start with the formulation of collision risk assessment. This is followed by the construction of a hierarchical network structure, supplemented by the supporting reward function, multi-objective function, and reliability measurement to realize the SRL-R2HCN. Finally, simulation experiments are conducted in mixed obstacle scenarios, and the results are compared with traditional algorithms to showcase the advancement and fidelity of the new SRL-R2HCN method. The results demonstrate that the proposed method can accurately assess collision risks in mixed obstacle scenarios and generate safe, efficient, and reliable collision avoidance strategies. The outcomes of this research provide a sound theoretical basis for the future development of MASS navigation safety and significant potential to improve the safe and efficient operations of MASS. Furthermore, the methodology could also benefit maritime transportation and shipping management.<br/
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Simulations of Dynamic Interaction Between a Bluff Body and Installation Vessel During Launch and Recovery in Rough Seas
No full textSimulation of marine operations for launch and recovery of bluff bodies such as autonomous underwater vehicles (AUV), remotely operated vehicles (ROV) or subsea templates is traditionally performed in calm to moderate sea conditions. The reason for doing so is partly due to the interaction between the complex dynamic response of an installation vessel, a moving bluff body and the wave kinematics of the rough sea condition. This is in addition to the need for accurate hydrodynamic coefficients that would enable proper simulation and modelling of the launch and recovery process. The key objective of the current methodology is to minimize risks of damage to the vessel and total loss of assets during the deployment and recovery process for marine operations in rough sea conditions.
The aim of this paper is to present the results of experimental and numerical investigation on the prediction of dynamic response of a bluff body during launch and recovery from a surface vessel in rough sea condition. Experimental measurements of hydrodynamic coefficients and responses of a large scale bluff body using a scaled model were completed. Further studies using a time-domain numerical tool have been undertaken to measure the response characteristic of bluff bodies in rough seas. The study also predicted the contributions of vessel motion in rough seas to the dynamic response of the bluff bodies. The results obtained have shown that simulation of launch and recovery operations in rough seas can be carried out efficiently if their hydrodynamic coefficients through the wave active regions of the rough seas are predicted and then adequately implemented in the simulations.</jats:p
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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