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Managing Maritime Safety
\"Shipping is a pillar of global trade, with 90% of the world's trade in goods and raw materials carried by ship. Despite the economic benefits this delivers, maritime operations can be dangerous, and when accidents occur the consequences are serious. Consequential outcomes from hazards at sea include loss of cargo, destruction of the marine environment, serious injuries, deaths and material damage. Managing Maritime Safety will give you a thorough understanding of contemporary maritime safety and its management. It provides varying viewpoints on traditional safety topics in conjunction with critical discussions of the international safety management code and its application. The book also offers new perspectives on maritime safety such as ship and equipment design for safety and the relevance of safety management systems, in particular the application of the International Safety Management code to remote controlled or autonomous ships. The authors all work in the maritime industry, as practitioners, in education, research, government and classification. The combination of wide-ranging and extensive experience provides an unprecedented span of views with a strong connection to the real issues in the maritime domain. This book sets out to provide much needed consolidated knowledge for university level students on maritime safety management, incorporating theoretical, historical, research, operational and design perspectives.\"--Provided by publisher.
Book
A lightweight ship target detection model based on improved YOLOv5s algorithm
Real-time and accurate detection of ships plays a vital role in ensuring navigation safety and ship supervision. Aiming at the problems of large parameters, large computation quantity, poor real-time performance, and high requirements for memory and computing power of the current ship detection model, this paper proposes a ship target detection algorithm MC-YOLOv5s based on YOLOv5s. First, the MobileNetV3-Small lightweight network is used to replace the original feature extraction backbone network of YOLOv5s to improve the detection speed of the algorithm. And then, a more efficient CNeB is designed based on the ConvNeXt-Block module of the ConvNeXt network to replace the original feature fusion module of YOLOv5s, which improves the spatial interaction ability of feature information and further reduces the complexity of the model. The experimental results obtained from the training and verification of the MC-YOLOv5s algorithm show that, compared with the original YOLOv5s algorithm, MC-YOLOv5s reduces the number of parameters by 6.98 MB and increases the mAP by about 3.4%. Even compared with other lightweight detection models, the improved model proposed in this paper still has better detection performance. The MC-YOLOv5s has been verified in the ship visual inspection and has great application potential. The code and models are publicly available at https://github.com/sakura994479727/datas .
Journal Article
A navigational risk evaluation of ferry transport: Continuous risk management matrix based on fuzzy Best-Worst Method
Ferry transport has witnessed numerous fatal accidents due to unsafe navigation; thus, it is of paramount importance to mitigate risks and enhance safety measures in ferry navigation. This paper aims to evaluate the navigational risk of ferry transport by a continuous risk management matrix (CRMM) based on the fuzzy Best-Worst Method (BMW). Its originalities include developing CRMM to figure out the risk level of risk factors (RFs) for ferry transport and adopting fuzzy BWM to estimate the probability and severity weights vector of RFs. Empirical results show that twenty RFs for ferry navigation are divided into four zones corresponding to their risk values, including extreme-risk, high-risk, medium-risk, and low-risk areas. Particularly, results identify three extreme-risk RFs: inadequate evacuation and emergency response features, marine traffic congestion, and insufficient training on navigational regulations. The proposed research model can provide a methodological reference to the pertinent studies regarding risk management and multiple-criteria decision analysis (MCDA).
Journal Article
Accuracy evaluation of smartphone-based GNSS position and speed tracking for ski-slope and safety management
Smartphones with integrated global navigation satellite system (GNSS) functionality are increasingly used in various apps beyond communication, including positioning, navigation, and tracking. This study explores the potential of smartphone GNSS data to improve ski slope safety through motion data analysis. Apps such as iSKI, Skitude, Slopes, and Strava measure speeds, distances, and altitude differences, generating valuable data on skiers’ movements. These data help ski resorts in planning and accident prevention by identifying high-risk areas based on movement patterns. We compared the accuracy of position and speed data from four apps across four smartphone models (two Android and two iOS) against a differential GNSS (dGNSS) reference system. Data were collected at two ski resorts during the winter of 2022/23, with smartphones recording at 1 Hz and dGNSS at 50 Hz. Analysis focused on downhill runs, excluding initial recording phases and vertical position data. Accuracy was assessed by calculating the Euclidean distance between the time-synchronized smartphone data and dGNSS reference data. High-end smartphones provided more accurate position data, with an average error of approximately 4 m, compared to 6 m for low-end models. Speed data were reliable across all devices, with an average error <1.9 km/h. However, accuracy diminished with increasing speeds and varied based on location-specific environmental factors. Thus, although smartphone position data can evaluate non-exact position-dependent parameters, such as slope utilization and user density, more precise systems, such as dGNSS, are necessary for exact position-dependent evaluations. Speed data derived from cleaned position data are reliable for estimating skier speeds, and data from different apps can be combined if consistent calculation methods are used. Future advances in smartphone technology are expected to enhance data accuracy. Recommendations include using smartphone data in open terrain for better accuracy and exercising caution when interpreting absolute position data for accident prevention or other context-specific analyses.
Journal Article
A multi-criteria decision-making framework for managing the safety of marine recreational powered platforms: Integration with the SHELL model
The rise of marine recreational activities has led to a growing use of marine recreational powered platforms, raising safety concerns related to navigation. In Taiwan, the current regulatory system for such platforms remains fragmented and under debate. This study aims to support policy development by identifying key safety management priorities. This study utilized the four core components of the SHELL model, which include Software, Hardware, Environment, and Liveware, as the analytical foundation and identified 20 preliminary safety criteria through an extensive review of relevant literature. A Modified Delphi Method and DEMATEL analysis were applied to gather expert insights and prioritize 10 representative indicators. The resulting Influence Network Relation Map revealed that “Comprehensive Management Regulations” had the highest causal influence across all dimensions. Additionally, “Basic Navigation Concepts” and “Emergency Response and Safety Knowledge” were found to be the most central elements. Based on these findings, the study recommends targeted measures including enhanced regulation, improved training, radar monitoring, and spatial planning to reduce navigation risks and promote safer marine recreation. Building on the above findings, this study confirms the effectiveness of an innovative integration of the SHELL model and the DEMATEL method, which provides a structured and adaptive framework capable of systematically identifying systemic navigational risks in marine recreational activities.
Journal Article
A Critical AI View on Autonomous Vehicle Navigation: The Growing Danger
Autonomous vehicles (AVs) represent a transformative advancement in transportation technology, promising to enhance travel efficiency, reduce traffic accidents, and revolutionize our road systems. Central to the operation of AVs is the integration of artificial intelligence (AI), which enables these vehicles to navigate complex environments with minimal human intervention. This review critically examines the potential dangers associated with the increasing reliance on AI in AV navigation. It explores the current state of AI technologies, highlighting key techniques such as machine learning and neural networks, and identifies significant challenges including technical limitations, safety risks, and ethical and legal concerns. Real-world incidents, such as Uber’s fatal accident and Tesla’s crash, underscore the potential risks and the need for robust safety measures. Future threats, such as sophisticated cyber-attacks, are also considered. The review emphasizes the importance of improving AI systems, implementing comprehensive regulatory frameworks, and enhancing public awareness to mitigate these risks. By addressing these challenges, we can pave the way for the safe and reliable deployment of autonomous vehicles, ensuring their benefits can be fully realized.
Journal Article
Successful early warning and emergency response of a disastrous rockslide in Guizhou province, China
Early warning of landslides is crucial for risk management and reduction, attracting a lot of attention from both scientists and stakeholders. However, it is challenging due to the complex nature of landslide behaviors and failure mechanisms. Here, we present a recent case of successful early warning and timely evacuation in advance of a large rockslide that occurred on 17 February 2019, in Guizhou Province, China. The rockslide was initially triggered in 2014 due to the excavation of slope toe for road expansion. Since then, the rockslide had become a potential threat to the local residents, pedestrians, and traffic. To ensure their safety, a wireless monitoring network combining on-site sensors and the geodetic method by Global Navigation Satellite System (GNSS) was installed to continuously monitor the surface displacement of the rockslide. Field monitoring data measured by crack gauges, rain gauges, and tiltmeter were transmitted to a real-time early warning system developed using the new artificial intelligence by the authors’ institute. Since the deformation of the rock mass was found increasing, nearby residents were evacuated immediately. Using predefined early warning thresholds for rockslides in the system, the rockslide was successfully forecasted 53 min in advance. Prompt action taken by scientists and local authorities averted human and economic losses completely. In this study, we introduce the real-time early warning system, its concept, the method for determining warning threshold, and performance, followed by the emergency mitigation measures performed for this particular rockslide. It is the 5th time our early warning system successfully forecasted a landslide since its implementation in 2017, and hence we discuss the key characteristics of the system in order to make it applicable for other cases globally.
Journal Article
Experimental Assessment of OSNMA-Enabled GNSS Positioning in Interference-Affected RF Environments
This article investigates the performance of the Galileo Open Service Navigation Message Authentication (OSNMA) system in real-life environments prone to RF interference (RFI), jamming, and/or spoofing attacks. Considering the existing data that indicate a relatively high number of RFI- and spoofing-related incidents reported in Eastern Europe, this study details a data-collection campaign along various roads through urban, suburban, and rural settings, mostly in three border counties in East and South-East of Romania, and presents the results based on the data analysis. The key performance indicators are determined from the perspective of an end user relying only on Galileo OSNMA authenticated signals. The Galileo OSNMA signals were captured using one of the few commercially available GNSS receivers that can perform this OSNMA authentication algorithm incorporating the satellite signals. This work includes a presentation of the receiver’s operation and of the authentication results obtained during test runs that experienced an unusually high number of RFI-related incidents, followed by a detailed analysis of instances when such RFI impaired or fully prevented obtaining an authenticated position, velocity, and time (PVT) solution. The results indicate that Galileo OSNMA demonstrates significant robustness against interference in real-life RF-degraded environments, dealing with both accidental and intentional interference.
Journal Article