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This study develops a novel non-parametric vibration-based deep learning technique to detect and diagnose damage to coastal structures, whereas wharves and jetties are the selected objects. To overcome the challenges of a large number of sensor points, we connect the sensor values at each location into a map containing ridges first, then we combine the Fast Marching Algorithm (FMA) with a Single-input Convolutional Neural Network (SCNN) to propose an FMA-SCNN method. Therefore, the created ridge images are processed with the SCNN to determine the parameters of deviation from the alarm state. During the testing phase, the optimization function’s Multiple Damage Guarantee Criteria (MDGC) is maximized by the set of damage variables offered for assessment, based on the dissimilarity coefficient Dw between the maps and the acceleration of the ridge tops. Also, the damage level DH(χ) allows us to accurately evaluate the structure’s current state, from healthy to serious levels. In comparison to previous CNN applications, the proposed algorithms demonstrated outstanding efficiency and achieved the highest accuracy of 98.26 % on embedded Python 3.6 testing. After applying the FMA-SCNN, the optimization function of MDGC enables early damage prediction, helping evaluate the overall structure effectiveness. [ABSTRACT FROM AUTHOR]

Based on multi-station spatial diversity capability, GEO-UAV distributed radar could achieve high-precision aerial target localization with the single-transmitting and multiple-receiving configuration. However, the actual observation area can hardly be covered by several receiving stations simultaneously. Thus, it is necessary to explore a novel target localization method under a single receiving station condition. In this manuscript, an aerial target localization method with GEO-UAV bistatic configuration is presented, where ТOА and AOA measurements are employed. Firstly, measurement models, including bistatic range-delay, pitching AOA, and azimuth AOA, are established using the spatial geometric relationship between the bistatic radar and the target. Then, the receiving range can be estimated using digital beamforming technology based on the receiving array antenna, where the antenna beam coverage information and the prior target altitude information are combined. Finally, the three-dimensional target localization is skillfully derived according to the bistatic configuration, and thus to avoid the parameter unrecognizable problem caused by insufficient degrees of freedom. The proposed algorithm fully exploits the intrinsic correlation characteristics between the measurement information and the bistatic configuration, which provides an effective way for aerial target localization. Simulation results verify the effectiveness of the proposed algorithm.

Detecting oil spills in marine environments is crucial for avoiding environmental damage and facilitating rapid response efforts. In this study, we propose a robust method for oil spill detection leveraging state-of-the-art (SOTA) deep learning techniques. We constructed an extensive dataset comprising images and frames extracted from video sourced from Google, significantly augmenting the dataset through frame extraction techniques. Each image is meticulously labeled to ensure high-quality training data. Utilizing the Yolov8 segmentation model, we trained our oil spill detection model to accurately identify and segment oil spills in ocean environments. K-means and Truncated Linear Stretching algorithms are combined with trained model weight to increase model detection accuracy. The model demonstrated exceptional performance, yielding high detection accuracy and precise segmentation capabilities. Our results indicate that this approach is highly effective for real-time oil spill detection, offering a promising tool for environmental monitoring and disaster management. In training metrics, the model reached over 97% accuracy in 100 epochs. In evaluation, model achieved its best detection rates by 94% accuracy in F1, 93.9% accuracy in Precision, and 95.5% mAP@0.5 accuracy in Recall curves. [ABSTRACT FROM AUTHOR]

Offshore wind energy production has seen a significant expansion in recent years. With technologies rapidly improving and prices dropping, it is now one of the key instruments in the green energy transition. The implications of offshore wind farm expansion for maritime security and ocean governance have, so far, received sparse attention in the literature. This article offers one of the first thorough analyses of the security of offshore wind farms and related installations, such as underwater electricity cables, energy islands, and hydrogen plants. The technical vulnerabilities of wind farm systems is reviewed and threats from terrorism, crime and State hostilities, including physical and cyber risk scenarios, are discussed. The expansion of green offshore energy production must keep pace with the changing threat landscape that follows from it. Prospective solutions for the protection of wind farms systems, including surveillance, patrols and self-protection are discussed. The current repertoire of maritime security solutions is in many ways capable of dealing with the threats and risks effectively if adjusted accordingly. The analysis builds important new bridges between debates in energy security and maritime security, as well as the implications of climate change adaption and mitigation for security at sea. [ABSTRACT FROM AUTHOR]

Detecting oil spills in marine environments is crucial for avoiding environmental damage and facilitating rapid response efforts. In this study, we propose a robust method for oil spill detection leveraging state-of-the-art (SOTA) deep learning techniques. We constructed an extensive dataset comprising images and frames extracted from video sourced from Google, significantly augmenting the dataset through frame extraction techniques. Each image is meticulously labeled to ensure high-quality training data. Utilizing the Yolov8 segmentation model, we trained our oil spill detection model to accurately identify and segment oil spills in ocean environments. K-means and Truncated Linear Stretching algorithms are combined with trained model weight to increase model detection accuracy. The model demonstrated exceptional performance, yielding high detection accuracy and precise segmentation capabilities. Our results indicate that this approach is highly effective for real-time oil spill detection, offering a promising tool for environmental monitoring and disaster management. In training metrics, the model reached over 97% accuracy in 100 epochs. In evaluation, model achieved its best detection rates by 94% accuracy in F1, 93.9% accuracy in Precision, and 95.5% mAP@0.5 accuracy in Recall curves.

This paper presents the improvement of the wave maker control system. The wave maker is a facility widely used in hydromechanics laboratories to generate waves in towing tanks. It is equipped with an electrohydraulic drive and an actuator submerged into water. The waves are generated to model the environmental conditions for physical experiments, performed on reduced-scale models of maritime objects. The physical experiments allow to predict the behaviour of full-scale objects and prove the results of numerical analyses. The overriding goal of the fluid dynamics experiments is to improve the human safety and survivability of constructions. The reported investigation and application works were vital for the improvement of physical model tests. The optimization of the proportional controllers was performed in terms of the Integral of Squared Error (ISE). The final evaluation was performed in terms of the frequency response characteristic. The results of the proportional control optimization were evaluated versus the previously applied control. The experimental research was conducted in the real towing tank located at the Maritime Advanced Research Centre. The investigation has shown the advantage of the ISE optimized conventional proportional control. It has particularly proven the affordability and swiftness of the optimization process. It also proved a more efficient frequency response of the wave maker obtained within a required and reasonable lead time. The performed investigation has greatly contributed to the development of a new method of physical experiments in white noise waves. The results related to this new more efficient method are presented as well.

Rip currents are like rivers of fast-moving water that can quickly carry the unwary out to sea. They are not easy to recognize, especially to the untrained observer. Other than in-situ current measurements, there exists a number of methods that analyzes images and videos to detect rip currents. Most of these techniques base their detection on the appearance of rip currents such as foamy pattern, discoloration of water, and locations of breaking waves. Leading methods use either image processing or machine learning of images and/or video input. In this paper, we analyze the behavior of water movement rather than simply its appearance to detect rip currents. Specifically, we investigated several flow visualization methods and tune them to detect rip currents. Based on our study, we recommend two methods that allowed us to detect rip currents where other methods have failed. And because the methods originated as visualization techniques, any presence of rip currents are automatically highlighted. We also evaluated these two methods against previously annotated results by rip current experts, and found that our detections were sufficiently sensitive that some expert annotations were relabeled.

Maneuvers of human-operated and autonomous marine vessels in the safety zone of drilling rigs, wind farms and other installations present a risk of collision. This article proposes an algorithmic toolkit that ensures maneuver safety, taking into account the restrictions imposed by ship dynamics. The algorithms can be used for anomaly detection, decision making by a human operator or an unmanned vehicle guidance system. We also consider a response to failures in the vessel’s control systems and emergency escape maneuvers. Data used by the algorithms come from the vessel’s dynamic positioning control system and positional survey charts of the marine installations. [ABSTRACT FROM AUTHOR]

The diversity and non-uniformity of the positioning systems available in maritime navigation systems often impede the watchkeeping officer in the selection of the appropriate positioning system, in particular, in restricted basins. Thus, it is necessary to introduce a mathematical apparatus to suggest, in an automated manner, which of the available systems should be used at the given moment of a sea trip. Proper selection of the positioning system is particularly important in integrated navigation systems, in which the excess of navigation information may impede the final determinations. In this article, the authors propose the use of the decision-robustness function to assist in the process of selecting the appropriate positioning system and reduce the impact of navigation observations encumbered with large errors in self-positioning accuracy. The authors present a mathematical apparatus describing the decision function (a priori object), with the determination of decision-assistance criteria, and the robustness function (a posteriori object), with different types of attenuation function. In addition, the authors present a computer application integrating both objects in the decision-robustness function. The study was concluded by a test showing the practical application of the decision-robustness function proposed in the title. [ABSTRACT FROM AUTHOR]

The normal functioning of the respiratory system is a leading factor, both for the survival of people caught in an aquatic environment in a marine casualty and for the effectiveness of the assistance activities. In view of this, in 2020 a team of researchers from Varna Medical University, the Naval Academy Varna and the Bulgarian Red Cross set out to study some physiological respiratory parameters and related anthropometric data of 51 individuals, 41 men and 10 women aged between 17 and 48 years of age. The subjects were divided into two groups: 1) trainees in the water rescue courses conducted at BRC-Varna and 2) 1st - year students of the Naval Academy. All participants in the study are non-smokers. The examination of the respiratory indicators is performed according to a standardized methodology with the same spirometer, which is able to measure and register over 30 indicators. Of these, three were analyzed in the present study: forced expiratory volume for 1 s (FEV1), forced vital capacity (FVC), and their FEV1/FVC percentage (Tiffeneau index). The results show that, according to anthropometric data, FVC and FEV1 are higher in water rescue trainees than in Naval Academy cadets. The Tiffeneau index is higher than the reference values ​​of the participants in both study groups, but the results are better in the group of naval cadets. As far as the functional capabilities of the respiratory system are essential for the activity of marine professionals, it can be assumed that the high results obtained from the spirometric examination of the participants from both groups are a prerequisite for higher efficiency of rescue operations and improvement of their personal safety. when working in a marine environment. The prospect is that after collecting a statistically significant amount of data combined with results obtained from other types of functional tests, to create better safety practices for people who are professionally engaged in maritime activities. As far as the functional capabilities of the respiratory system are essential for the activity of marine professionals, it can be assumed that the high results of the spirometric study in all study participants are a prerequisite for improving both their personal safety when working in the marine environment and for higher efficiency and success of rescue operations. The perspective is, after collecting a statistically significant volume of data and in combination with other functional studies, to create good practices to improve the safety systems of people in contact with the marine environment .

Joint aeronautical and maritime search and rescue is the most effective way of performing rescues at sea. The value and effectiveness of a search and rescue (SAR) are far greater when using a coordinated air-maritime search than when using only vessels or aircraft. However, the harmonization of aeronautical and maritime SAR is complex and potentially life-threatening. When the location of the target in distress is unknown, the search process must be carried out. As the sole way to locate and rescue survivors, the search process is the most costly, hazardous, and complicated part of the whole SAR operation. This article focuses on the key problem of the optimal selection of search facilities, that is often encountered in large-area maritime search practice and urgently needs to be solved in joint aeronautical and maritime search operations. The problem may be abstracted into an optimization model with vessel and aircraft quantitative constraints that fully considers the area of the sea region to be searched, maximum speeds, search capabilities, initial distances of vessels and aircraft from the search area, and maximum endurance of aircraft. By introducing 0-1 decision variables, the search facility selection can be judged and optimized directly and effectively. By analyzing the results with different vessel and aircraft quantities, and taking the relationship between search coverage time and the number of search facilities (cost) into account, the optimal (most economic and feasible) search facility selection scheme can be produced.

This paper aims to present an approach to investigate cyber risk perception with use of recognized psychological models, and to give an overview of state-of-the-art research within the field of cyber risk perception in general and in the context of the maritime domain. The focus will be on determinative dimensions within the psychometric paradigm and cognitive biases, and to give recommendations on further research within these fields. Okoli and Schabram’s eight-step guide to plan, select, extract, and execute a systematic literature review is used as guidance. The search process resulted in 25 relevant articles which describes 24 dimensions of cyber risk perception in different online environments. Research within the area of maritime cyber security is increasing, however, no studies relevant for our literature review were found within the maritime domain. The nine dimensions in the psychometric model, perceived benefit and the optimistic bias is presented and discussed in a maritime context. Cyber risk perception is a complex research-area where both determinative factors and other cognitive processes can be influenced by each other. This can indicate that the dimensions differ across populations and professions, creating grounds for why context-specific studies are important. Further research may benefit from more multidisciplinary, descriptive, and inductive approaches, and contextual studies within maritime cyber risk perception can contribute to develop targeted tools for risk mitigation to enhance safety at sea.

The development of coastal infrastructure and related maritime transport necessitatesthe intensification of vessel traffic monitoring. Navigation systems used in this research are traditionally based on the information transmitted by radio waves. Marine traffic safety requires constant supervision carried out by dedicated systems, the operation of which may be limitedby difficult environmental conditions. The possibilities of supporting navigation systems with underwater observation systems are explored here. The research was carried out using an underwater measurement system. Local disturbances of the hydroacoustic and hydrodynamic field from the moving vessels were analysed. The potential for identifying a moving vessel, for example for offshore infrastructure security purposes, is demonstrated.

In order to efficiently cover maritime areas at over the horizon (OTH) distances and thus increase marine safety in a nation's exclusive economic zone (EEZ), a network of maritime sensors built around High Frequency Surface Wave Radars (HFSWR) can be an excellent choice. The critical parameter for success of the deployed sensor network is a real time tracking of all detected vessels. During the tracking process, data association (DA) is the first step and it defines the complexity and thus the speed of the whole tracking process. This paper presents a density based clustering DA procedure where the cluster complexity determines the applied DA procedure within the cluster itself. It is demonstrated that the great majority of clusters (over 98 % of all clusters in the worst case) may be processed in a timely manner with an optimal DA procedure, or more precisely, a Joint Probability Data Association (JPDA). However, a small number of unusually large clusters (less than 2 % of all clusters) requires the application of a sub-optimal DA procedure, more accurately, the Roecker's suboptimal JPDA algorithm, in order to maintain real time performance of the whole tracking process. Moreover, unlike standard JPDA procedure which tends to be inapplicable for real time tracking in heavily cluttered environment, the density based clustering DA procedure presented here provides real time performances in the very same environment. The whole analysis is done on real HFSWR data obtained from two HFSWR, located in the Gulf of Guinea. The data set used for the experiments includes data obtained during a month and a half of constant HFSWR operation.

In this paper, an area decomposition algorithm is presented that is suitable for multiple vessels and multiple aircraft participating in maritime searches over a large region. The algorithm can decompose the entire sea region to be searched (deemed as polygonal area) into nonoverlapping subpolygons (search subareas) according to the sizes of the areas covered by various search facilities while considering their search capabilities as well as their corresponding commence search points. The algorithm draws on the concept of a polygon division algorithm in computational geometry. The main novelty in this study is the optimization of the classic polygon division algorithm by introducing a “maximizing-minimum-angle” strategy, which can effectively compensate for the deficiency of the traditional algorithm, as reflected in the area decomposition result. This improved algorithm can produce rectangle-like subareas, especially for a rectangular search region, which is commonly used in maritime search operations. For nonrectangular search regions, a right-angle division can be achieved so that the shapes of the search subareas are more conducive to planning specific search routes for search facilities. Fast maritime search coverage over a large region can be achieved. The effectiveness of the algorithm is validated by comparing decomposition results before and after the improvement.

Coral reefs are the sub-aqueous calcium carbonate structures collected by the invertebrates known as corals. The charm and beauty of coral reefs attract tourists, and they play a vital role in preserving biodiversity, ceasing coastal erosion, and promoting business trade. However, they are declining because of over-exploitation, damaging fishery, marine pollution, and global climate changes. Also, coral reefs help treat human immune-deficiency virus (HIV), heart disease, and coastal erosion. The corals of Australia’s great barrier reef have started bleaching due to the ocean acidification, and global warming, which is an alarming threat to the earth’s ecosystem. Many techniques have been developed to address such issues. However, each method has a limitation due to the low resolution of images, diverse weather conditions, etc. In this paper, we propose a bag of features (BoF) based approach that can detect and localize the bleached corals before the safety measures are applied. The dataset contains images of bleached and unbleached corals, and various kernels are used to support the vector machine so that extracted features can be classified. The accuracy of handcrafted descriptors and deep convolutional neural networks is analyzed and provided in detail with comparison to the current method. Various handcrafted descriptors like local binary pattern, a histogram of an oriented gradient, locally encoded transform feature histogram, gray level co-occurrence matrix, and completed joint scale local binary pattern are used for feature extraction. Specific deep convolutional neural networks such as AlexNet, GoogLeNet, VGG-19, ResNet-50, Inception v3, and CoralNet are being used for feature extraction. From experimental analysis and results, the proposed technique outperforms in comparison to the current state-of-the-art methods. The proposed technique achieves 99.08% accuracy with a classification error of 0.92%. A novel bleached coral positioning algorithm is also proposed to locate bleached corals in the coral reef images. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a customized diving computer that is interlocked with a textile type sensor attached to a suit to enhance the safety factor of a diver and a monitoring system to help diver safety management. The development of this diving computer is largely divided into three stages. The first is to develop a function to check the status of the diver in conjunction with the textile type wearable sensor attached to the suit. Second, the development of a diving computer is based on Android OS based on the existing closed OS. The third is the development of actual diving computer hardware focusing on waterproof function. This course aims to focus on safety factors of the diver, to collect biometric information through a wearable sensor attached to the diver suit, to analyze it in the diving computer, and to provide a customized diving guide. The performance evaluation of the result was conducted through the usability evaluation of the diving computer, and the safety and efficiency were verified through the overall satisfaction and the functional evaluation of 92.7%. Future research will focus on the safety factors to ensure differentiation from the existing products, and we will carry out ongoing research.

The diversity and non-uniformity of the positioning systems available in maritime navigation systems often impede the watchkeeping officer in the selection of the appropriate positioning system, in particular, in restricted basins. Thus, it is necessary to introduce a mathematical apparatus to suggest, in an automated manner, which of the available systems should be used at the given moment of a sea trip. Proper selection of the positioning system is particularly important in integrated navigation systems, in which the excess of navigation information may impede the final determinations. In this article, the authors propose the use of the decision-robustness function to assist in the process of selecting the appropriate positioning system and reduce the impact of navigation observations encumbered with large errors in self-positioning accuracy. The authors present a mathematical apparatus describing the decision function (a priori object), with the determination of decision-assistance criteria, and the robustness function (a posteriori object), with different types of attenuation function. In addition, the authors present a computer application integrating both objects in the decision-robustness function. The study was concluded by a test showing the practical application of the decision-robustness function proposed in the title.

Maritime transportation which mediates the transmission of major part of the world's trading volume is a type of transportation with high probability of encountering dangerous situations due to the instability of its conditions. In the past years, it became clear that the even smallest negativity caused perilous results. Thus, accurate implementation and evaluation of sea accident analysis is important to establish a policy for developing preventive measures and increasing awareness by determining the reason of accident. Analysis of sea accidents forms the fundamental subject of the study with data mining method which allow us to make estimated and definitive interpretations with computer programs by accessing significant information within large data stacks. In this study sea accidents occurring in Turkish territorial waters have been reviewed. In this context, the purpose of the study consists of determining which factors conduct together in sea accidents by association rule which is one of analysis methods of data mining. As a result of analysis, it has been determined that ship's loading condition, existence of maritime pilot in the ship, conditions of equipments such as bow thruster and quarterdeck thruster, ship flag and type and meteorological elements have been effective factors on the subject. [ABSTRACT FROM AUTHOR]

The Drake Passage is an ocean area between South America and Antarctica with very extreme wind and wave climates. The quality of forecasts of surface winds and significant wave heights is analyzed in the present paper, in order to study the error and distribution of model uncertainties as a function of forecast range, severity, location, and numerical model. The operational metocean forecasts selected are run twice a day by the Brazilian Navy and the forecast horizon analyzed is five days. A period of 1 year (2017) is considered for the assessment. Observations consist of four satellite missions: JASON2, JASON3, CRYOSAT, and SARAL. The atmospheric models with 10-m winds are Global Forecast System (GFS) and Icosahedral Nonhydrostatic Model (icon). They are used as inputs to force the wave model WAVEWATCH III with a mosaic of two grids. Results show that under calm to moderate conditions, within the first 2 days of forecast, the wind and wave model skill is very high. However, above the 90th percentile and beyond the third forecast day, the predictability drops significantly. It highlights specific contours of forecast range versus percentiles where the wind and wave modelers should focus, in order to anticipate and to improve the predictability of extreme events at the Drake Passage. [ABSTRACT FROM AUTHOR]

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Coral reefs are the sub-aqueous calcium carbonate structures collected by the invertebrates known as corals. The charm and beauty of coral reefs attract tourists, and they play a vital role in preserving biodiversity, ceasing coastal erosion, and promoting business trade. However, they are declining because of over-exploitation, damaging fishery, marine pollution, and global climate changes. Also, coral reefs help treat human immune-deficiency virus (HIV), heart disease, and coastal erosion. The corals of Australia’s great barrier reef have started bleaching due to the ocean acidification, and global warming, which is an alarming threat to the earth’s ecosystem. Many techniques have been developed to address such issues. However, each method has a limitation due to the low resolution of images, diverse weather conditions, etc. In this paper, we propose a bag of features (BoF) based approach that can detect and localize the bleached corals before the safety measures are applied. The dataset contains images of bleached and unbleached corals, and various kernels are used to support the vector machine so that extracted features can be classified. The accuracy of handcrafted descriptors and deep convolutional neural networks is analyzed and provided in detail with comparison to the current method. Various handcrafted descriptors like local binary pattern, a histogram of an oriented gradient, locally encoded transform feature histogram, gray level co-occurrence matrix, and completed joint scale local binary pattern are used for feature extraction. Specific deep convolutional neural networks such as AlexNet, GoogLeNet, VGG-19, ResNet-50, Inception v3, and CoralNet are being used for feature extraction. From experimental analysis and results, the proposed technique outperforms in comparison to the current state-of-the-art methods. The proposed technique achieves 99.08% accuracy with a classification error of 0.92%. A novel bleached coral positioning algorithm is also proposed to locate bleached corals in the coral reef images.

To enhance the immediacy and accuracy of search and rescue missions conducted by Taiwan Coast Guard Administration (TCGA), this study examines the characteristics of ocean currents and estimates the drifting trajectory of objects for two cases of marine incidents. Two methods are used to predict object's drift tracks: the first one is simply calculating the advection driven by currents and the second method adopts a Monte Carlo based software, SARMAP. Ocean currents data from a high-frequency radar system comprised of 18 radar units surrounding Taiwan supplemented by bottom-mounted Acoustic Doppler Current Profilers (ADCP) data and wind data are used as the primary oceanic environment data. The estimated ending points of drifting for both cases of missing persons in water by using both methods are consistent with the actual recovery locations. Our analyses demonstrate that ocean currents data, if properly used, can be very useful for rapid response in marine search and rescue. [ABSTRACT FROM AUTHOR]

Vessel risk analysis is critical for safe ship navigation and maritime safety management. Near-miss collisions by ships comprise an significant risk, which may be complicated by factors, such as the ship conditions, waterway environment, and driving behavior of any ships encountered. Previous studies have rarely considered how to automatically and adaptively estimate the risk of near-miss collisions for different situations, particularly in port areas. In this paper, we propose an automatic identification system-based approach for adaptively calibrating near-miss collision risk model and assessing a ship’s near-miss collision risk by using the vessel’s speed and course patterns to obtain a robust estimate of the collision risk. Six measures are employed to determine the hierarchical geographical distribution of the near-miss collision risk for ships in port areas and to identify the high-risk areas. Some predicted high-risk areas were validated as official precautionary areas in the Xiamen Port area. All predicted areas may help the port administration to plan monitoring areas to ensure safe traffic flow in the port. [ABSTRACT FROM AUTHOR]

In this study, numerical simulations are conducted in order to analyze the capsizing of roll-on/roll-off passenger ship, Sewol, which occurred on 16 April 2014. Since little is known about the reason of ship capsize, numerical simulation aims to the finding of possible scenarios which can cause this tragic accident. To this end, 4-degree-of-freedom (surge, sway, yaw, and roll motions) maneuvering equations are solved. The hydrodynamic coefficients obtained from a similar ship are applied and the WISH-Maneuver program of Seoul National University is used to perform the numerical simulation. The present analysis utilizes the probabilistic approach, which considers various physical variables and involves the simulation of a large number of cases using different combinations of the variables. The total number of simulation cases is over 70 millions, and the simulation results that are close to the conditions of the actual accident situation are selected. Through this process, the effects of the simulation variables are investigated and the potential causes of the accident are identified. The results show the hydrostatic instability of the ship in the operational condition owing to cargo overload and insufficient ballast water, which raised the vertical center of gravity. The reduced stability combined with large starboard steering angle and cargo movement during large heel may have been the decisive reason of capsizing. [ABSTRACT FROM AUTHOR]

This study investigated the factors influencing the implementation of a port state control (PSC) system. The analytical hierarchy process (AHP) was employed to develop a framework including 4 dimensions and 14 factors for assessing the priority of the major factors and subfactors. An expert questionnaire was conducted in Taiwan. The priority levels of the factors in influencing PSC operation are presented herein. In addition, conclusions were derived from an empirical case study of Taiwan. Some challenges are also discussed to reveal the common problems and special difficulties faced by independent PSC regimes that remain excluded from regional memoranda of understanding.

A very significant number of marine accidents occur because of human errors. This study aimed to prevent ship collisions by identifying types of navigators’ errors. Based on Reason’s classification theory, the possible human errors are classified into skill-based slips (SBSs) (errors caused by the lack of skills), rule-based mistakes (RBMs) (errors caused by the misapplication of rules), and knowledge-based mistakes (KBMs) (errors caused by the lack of navigator’s knowledge). For this study, a scenario-based experiment using a ship-handling simulator was conducted with 50 recruited student navigators. The results revealed two primary human errors of accidents, namely lack of knowledge and misapplication of rules. The results suggest that a collision can be minimized when a navigator has sufficient knowledge of an appropriate course of action and a deep understanding of safety rules. Accidents cannot be prevented by identifying errors, but steps can be taken to narrow the knowledge gap. Based on the results, we proposed a simulation training on navigator error in an unfamiliar situation. The results are expected to reduce errors in the maritime sector using a human-centric work system.

The reliability of marine diesel engines is extremely important for a safe navigation. For the condition monitoring of diesels engines, the independent component analysis (ICA) has been proven to be effective in separating useful vibration sources from the engine vibration signals. However, ICA still needs expert knowledge to identify the source of interest. To avoid human factors in ICA, an automatic vibration-source extraction and feature visualization method is proposed in this paper for fault detection of marine diesel engines. In this method, the Stockwell transform was used to construct a time-frequency reference signal to guide the separation process of the kernel ICA. Only the fault-related source was separated by this improved time-frequency supervised kernel ICA (TFSKICA). Then the t-distributed stochastic neighbor embedding (t-SNE) was employed to extract and visualize the fault features. Lastly, the extreme learning machine (ELM) based classifier was built to identify the engine faults in an intelligent manner. Experimental data acquired from a commercial diesel engine was used to evaluate the performance of the proposed method. The analysis results demonstrate that the TFSKICA is able to separate the vibration source of interest for distinct fault feature extraction by the t-SNE in a visualization manner. The fault recognition rate of the proposed method is also better than that of some existing approaches. [ABSTRACT FROM AUTHOR]

Seafarers are required to make quick decisions to avoid accidents in case of emergencies. However, officers with anxiety generally have a high probability of making wrong decisions that threaten safety and security during the voyage. With the help of a shiphandling simulator, this study aims to investigate the emotional changes of seafarers under simulated scenarios of emergencies. The State-Trait Anxiety Inventory (S-TAI) scale and electrocardiograph (ECG) signal are adopted to evaluate the emotions of the participant seafarers. To classify the anxiety state of the participants, a support vector machine-based method is applied to establish an anxiety recognition model. Classification results reveal that this proposed model can effectively identify different emotions of participants based on ECG features (cross-validation accuracy: 86.0%; test accuracy: 92.3%). The experimental results show that poor visibility could cause the greatest impact on the anxiety of seafarers. In addition, navigational officers and marine pilots react differently in case of emergencies. Seafarers tend to experience more anxiety when dealing with emergency situations, while marine pilots experience more anxiety during multi-ship encounter periods. Consequently, the findings of this study aid to effectively identify the scenarios that cause anxiety emotion of different professional seafarers, providing the corresponding reference for the training of seafarers. This could help prevent catastrophic accidents that pose a threat to oceans and coasts caused by human error. [ABSTRACT FROM AUTHOR]

This study reveals the most important role of effective marine accident investigation in the improvement of maritime safety through prevention similar casualties from occurring in the future, explores the legal obligations of all parties under different international maritime conventions, and codes. As most of marine accidents resulted from shortage in applying international regulations related to maritime safety, so this research determines, the shortage of application of the international regulations adopted by IMO related to maritime safety, and the shortage of legal measures to support safety at sea and protection or reduce marine pollution. This research also aims to improve special measures for procedures of marine accident investigation, with case study are highlighted, and the importance of identifying the liability. Finally the shortage of civil liability of damage resulting from marine accident under International and National Jurisdiction are discussed with some illustrative cases.