Search Results (134)

Maritime piracy poses an ongoing operational threat to commercial shipping in high-risk regions, where fast-approach attack scenarios leave vessel crews with critically limited reaction time. Automated threat detection technologies—including radar, electro-optical, and thermal imaging sensors—are increasingly integrated into maritime security architectures; however, their operational effectiveness has rarely been evaluated through quantitative engineering frameworks. This study presents a technical analysis of mechatronic detection systems, focusing on detection range, reaction time constraints, and classification reliability under representative piracy conditions. A kinematic time-to-contact model is introduced to quantify how detection distance directly governs the available defensive response window: extending reliable detection from 1 NM to 3 NM expands the reaction margin from approximately 171 s to over 440 s, a difference that may determine whether protective measures can be executed in time. Classification performance is assessed using standard metrics, with recall identified as the operationally critical indicator in asymmetric threat environments. Model-based simulations indicate that, under the assumed scenario parameters, automated detection systems can reduce operational risk by up to 45%, illustrating the sensitivity of survivability outcomes to early detection capability. The findings translate directly into design thresholds for sensor range, algorithmic sensitivity, and processing latency, providing actionable engineering recommendations for practitioners responsible for maritime security system design and vessel protection planning. Full article

Collision avoidance in maritime unmanned aerial vehicle (UAV) operations must satisfy two competing objectives: ensuring reliable safety separation and minimizing unnecessary maneuver commands that increase operator burden and communication overhead. While deep reinforcement learning (DRL) has shown promise in handling high-dimensional encounter states, standard DRL approaches often prioritize safety at the cost of operational suitability, leading to frequent, oscillatory, or unnecessary avoidance commands that erode remote operator trust and consume limited communication bandwidth. To address this challenge, this paper proposes Resource-Aware Intrinsic Surprise Exploration (RAISE), a unified framework that balances collision avoidance performance with command economy. We conceptualize the issuance of avoidance maneuvers as a consumable “virtual resource”, compelling the agent to optimize its intervention budget. RAISE integrates this mechanism into the Soft Actor–Critic (SAC) architecture, augmented by a surprise-based intrinsic reward derived from the ensemble forward dynamics prediction error. This allows the agent to efficiently explore complex encounter scenarios driven by curiosity, while a resource-aware coefficient adaptively suppresses redundant actions when the communication or operational budget is constrained. Furthermore, an adaptive exponential moving average (EMA) scaling mechanism is introduced to stabilize the interplay between intrinsic and extrinsic rewards. Extensive simulations under diverse resource constraints and encounter geometries demonstrate that RAISE outperforms state-of-the-art baselines. It significantly reduces maneuver reversal rates and strengthens command stability without compromising safety margins. Specifically, under resource-constrained settings, RAISE suppresses excessive and unstable advisory behavior by reducing strengthening and reversal commands while maintaining effective collision avoidance; under resource-rich settings, it flexibly enhances safety buffers, demonstrating superior adaptability and operational realism for autonomous maritime UAV systems. Robustness evaluation confirms that RAISE maintains stable performance under sensor noise and wind disturbances. Full article

This conceptual integrative review and theoretical proposal investigates how the functional architecture of Sternberg’s Triarchic Theory of Intelligence is reconfigured when the framework is translocated from low-risk academic settings, in which analytical intelligence predominates, to high-stakes professional environments characterised by extreme cognitive load, temporal compression, irreversible consequences, and multicultural team dynamics. To construct a mechanistic account of this translocation, we integrate the triarchic framework with three complementary cognitive–ecological traditions—Cognitive Load Theory, the three-level model of Situational Awareness, and the distributed-cognition tradition—and we use the maritime industry as a paradigmatic case where communication failures are directly implicated in catastrophic outcomes. On this basis we propose a Context-Dependent Reweighting Model of Successful Intelligence which maps how, under high-stakes conditions, practical intelligence shifts from a supporting role to a central, integrative function that operates in part through distributed cognitive systems, while creative intelligence assumes elevated weight for adaptive problem-solving when standardised procedures fail. We treat this reweighting as a theoretical proposition supported by convergent but heterogeneous secondary evidence, and we frame the cross-domain extension to aviation, emergency medicine, military operations, and other safety-critical sectors as theoretically plausible parallels and hypotheses for future empirical testing rather than as established empirical claims. The review concludes by articulating implications for intelligence research, proposing a pedagogical framework operationalised through a Triarchic Maritime ESP curriculum, and explicitly delimiting the boundary conditions and limitations of the present contribution. Full article

The rapid development of Maritime Autonomous Surface Ships (MASS) requires advanced data-driven approaches for navigation safety monitoring and human-factor-aware risk analysis. This research proposes a fractal–episodic framework for assessing ship-control microvariability from normalized AIS/ECDIS trajectories in risk-oriented navigation monitoring, with particular relevance to MASS. The framework converts local micro-motion irregularities into passage-level indicators through sliding-window analysis of XTE-derived signals; computation of Higuchi, DFA, and Katz fractal measures; formation of a nine-component track signature; min–max normalization; and weighted aggregation into a chaos score complemented by a confidence index. The proposed framework can support intelligent monitoring and decision-support systems in autonomous maritime operations by providing interpretable behavioral indicators derived from AIS/ECDIS data. Full article

Vessel traffic service systems in remote or newly established maritime regions face significant operational limitations due to the scarcity of historical AIS data, which undermines the reliability of trajectory-based situational awareness and collision risk assessment. Existing deep learning models, predominantly validated on data-rich major shipping corridors, suffer severe performance degradation under cross-domain deployment, rendering them impractical for vessel traffic management in underserved waters. To bridge this operational gap, this study proposes a Boundary-Aware Distillation and LoRA-Based Transfer (BD-LT) framework that enables reliable trajectory prediction with as few as 132 target-domain trajectories. The framework integrates HDBSCAN-based geographic-semantic domain partitioning, a Time-Aware Transformer with Time2Vec encoding for irregular AIS sampling, hybrid knowledge distillation with error-boundary gating for selective cross-domain transfer, and LoRA-based parameter-efficient adaptation to mitigate overfitting. Validated on NOAA full-scale AIS measurements, the framework reduces the 60 min Final Displacement Error by 35.2% relative to the no-framework baseline, consistently outperforming state-of-the-art models across all prediction horizons, with statistical reliability confirmed via bootstrap resampling. These results demonstrate the practical feasibility of deploying data-driven trajectory prediction in maritime regions where conventional approaches have historically been inapplicable, with direct implications for collision avoidance decision support and port approach traffic management. Full article

This perspective paper develops a system-level characterization of the transportation metaverse as a persistent, policy-aware digital environment integrating digital twins, real-time data, advanced analytics, and human–machine interaction into a unified operational framework. The study presents a cross-modal review of metaverse applications in road, rail, maritime, and aviation systems, identifying common opportunities, limitations, and research challenges. It further proposes a structured metaverse-based framework for smart roads as a reference case. The framework demonstrates how persistent virtualization, parallel future scenarios, embedded governance constraints, and human-in-the-loop decision support can improve uncertainty-aware planning, management, and operations. The paper positions the metaverse not as a deployable technology, but as an emerging paradigm for transportation governance. The study provides an architectural vision and research agenda for developing more resilient, transparent, and adaptive transportation systems. Potential applications include smart road management, multimodal traffic coordination, real-time operational control, infrastructure resilience planning, and decision support for policymakers under uncertain conditions. Full article

Maritime emergency response requires broadband and reliable communications in sea areas where shore coverage is limited or emergency connectivity is temporarily unavailable, making rapid on-demand aerial networking essential. Unmanned aerial vehicles (UAVs) acting as aerial base stations can be rapidly deployed to provide on-demand coverage; however, ship mobility, heterogeneous emergency priorities, and UAV endurance limitations make the joint optimization of user association and multi-UAV deployment a challenging mixed-integer, long-horizon decision problem. This paper considers a multi-UAV maritime emergency communication system where ships are categorized into multiple priority classes and served links must satisfy a minimum signal-to-noise ratio (SNR) constraint. We formulate a long-term system-utility maximization problem that jointly determines (i) per-slot association between UAVs and ships under capacity, priority, and SNR constraints, and (ii) dynamic UAV deployment under mobility, geofencing, and battery constraints. To obtain tractable and high-quality solutions, we decompose the problem into two coupled subproblems. For user association, we propose a Priority-Aware Branch-and-Cut (PA-BAC) algorithm that integrates linear programming relaxation, cutting-plane tightening, and priority-guided branching, with a priority-greedy feasible initialization to accelerate incumbent improvement. For dynamic deployment, we develop an Enhanced Multi-Agent Proximal Policy Optimization (E-MAPPO) method featuring a global value network, entropy regularization, and sequential actor updates to enhance learning stability and exploration. Importantly, the PA-BAC association is embedded into the learning loop to provide reliable, constraint-satisfying per-slot rewards and reduce the burden of end-to-end learning over hybrid-action spaces. Simulation results demonstrate that PA-BAC consistently improves normalized priority-weighted throughput over heuristic association baselines. Moreover, by mathematically enforcing priority and QoS feasibility at every slot and delegating only continuous mobility to MARL, the integrated E-MAPPO-PA-BAC framework achieves higher long-term system utility, improved energy efficiency, and strong robustness across varying ship densities—properties that are vital for time-sensitive maritime emergency communications. Additional runtime, sensitivity, and AIS-driven trace evaluations further verify the computational practicality of PA-BAC and the applicability of the proposed framework under realistic ship mobility patterns. Full article

Geopolitical volatility and conflict are increasingly altering the operating conditions of maritime transport by affecting route feasibility, service reliability, port operations, regulatory compliance, and energy-related decisions. However, the relevant literature remains fragmented across smart port studies, shipping optimization research, cybersecurity analysis, and resilience-oriented discussions. This review addresses that fragmentation by examining smart port and shipping optimization as interdependent components of maritime resilience rather than as separate efficiency-oriented domains. Methodologically, the paper adopts a structured, semi-systematic review design combining bibliometric mapping and thematic synthesis to identify the evolution, thematic structure, and major research gaps of the field. The review shows that smart port research highlights the resilience value of real-time visibility, interoperable data exchange, dynamic terminal control, digital twins, and cyber-secure infrastructure, while shipping-optimization research emphasizes conflict-aware routing, schedule recovery, network redesign, capacity reallocation, and fuel-related decision support. At the same time, the literature provides only limited integration across the port–shipping interface, where resilience is actually produced through coordination between nodes, networks, and governance arrangements. Based on this synthesis, the paper argues that future research should move beyond isolated technical solutions and develop more integrated approaches that jointly address digitalization, operational adaptation, security, and decarbonization under geopolitical stress. The review contributes by clarifying the intellectual structure of this emerging field and by proposing a more system-oriented perspective on maritime resilience. Full article

This study develops a physics-informed digital twin framework for quasi-real-time assessment and forecasting of marine plate heat exchanger performance under variable environmental and operational conditions. Unlike conventional steady-state or purely data-driven approaches, the proposed framework integrates first-principles thermohydraulic modeling, an iterative successive-approximation solver, and continuous synchronization with operational ship data, enabling adaptive state estimation and degradation tracking. The methodology explicitly accounts for coupled thermal, hydraulic, and fouling processes, and incorporates uncertainty-aware validation under real ship operating conditions. A case study based on a central cooling system of a cargo vessel demonstrates that seawater temperature variations of 3–4 K can induce nonlinear system responses, including up to a fourfold increase in coolant demand, a 10–15% reduction in heat-transfer efficiency, and a 15–25% rise in hydraulic losses. A threshold operating regime is identified, characterized by rapid degradation and fouling amplification. Comparative analysis against a static baseline model shows that the digital twin improves predictive accuracy and enables early detection of performance deterioration. Energy-efficiency assessment indicates that adaptive cooling control supported by the digital twin can reduce auxiliary power demand and contribute to fuel savings. The proposed framework provides a scalable foundation for predictive maintenance and intelligent thermal management in maritime systems. Full article

Wide-area maritime surveillance is an increasingly important focus for countries with large Exclusive Economic Zones (EEZ), such as Portugal, which are responsible for monitoring and protecting these zones and their resources. Passive acoustic autonomous buoy networks equipped with hydrophones are a promising approach for wide-area maritime surveillance. However, achieving a discrete, low-cost system introduces many technical challenges. This work describes a practical, low-power, two-state architecture that separates continuous ship detection from detailed vessel class classification. First, an always-on microcontroller performs continuous binary ship presence detection and triggers the higher-power classifier only when a vessel is detected. The high-accuracy acoustic classifier was tested across embedded controllers to identify the minimum platform capable of sustaining its intended 1 Hz classification rate. A Raspberry Pi 5 achieved the 1 s target with a measured continuous consumption of 4 W; however, adding sensing, storage, and communications is expected to raise the always-on consumption to around 5 W. If this node was used by itself, a week-long autonomy requirement, therefore, would imply 840 Wh of usable energy storage, and recovering this deficit rapidly under limited insolation would require several hundred watts of photovoltaic capacity, driving both buoy volume and cost up. To address this, an always-on edge node based on an ESP32-S3 microcontroller was implemented, running a lightweight binary detection of a vessel presence model trained in Edge Impulse using a subset of Ocean Networks Canada recordings. The edge node consumes $0.69$ W continuously and is intended to trigger a wake-up line to power the higher-performance node only when a ship is detected, reducing average energy demand while maintaining the ability to run a richer classifier on demand. The presented architecture, profiling workflow, and energy calculations provide a path to power-aware passive acoustic monitoring systems suitable for extended maritime deployments. Full article

Automatic Identification System (AIS) trajectories provide valuable spatiotemporal information for maritime route structure mining, but robust extraction of key route nodes remains difficult because raw data are noisy, turning behaviors are easily masked by local fluctuations, and conventional Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is sensitive to fixed parameters and ignores heading differences. To address these issues, this study proposes a key route node extraction framework based on multi-constraint turning-point identification and heading-aware adaptive DBSCAN (HA-DBSCAN). Raw AIS data are first cleaned, segmented, and compressed using a heading-aware Douglas–Peucker strategy to reduce redundancy while preserving geometric and directional characteristics. Valid turning points are then identified by jointly considering heading change rate, geometric curvature, and temporal stability. Finally, HA-DBSCAN integrates a heading-aware distance metric, adaptive neighborhood estimation, and density-aware MinPts optimization to cluster turning points and extract representative route nodes. Experiments using AIS data from the Ningbo–Zhoushan Port area retained 287,614 valid records and 754 continuous trajectory segments, from which 1710 turning points were identified. The proposed method generated 45 stable clusters with a noise ratio of 0.0450 and route coverage of 95.5%. These results indicate that, within the current study setting, the framework can distinguish crossing routes, adapt to heterogeneous traffic densities, and provide an interpretable intermediate layer for subsequent maritime route-structure modeling. Supplementary validation on the same AIS dataset further showed that, compared with DBSCAN, Ordering Points To Identify the Clustering Structure (OPTICS), and HDBSCAN baselines as well as several pipeline ablations, the full framework achieved a more balanced performance in terms of coverage, noise suppression, and avoidance of cluster over-fragmentation. Full article

This paper presents a novel Multi-Scale Temporal Uncertainty-aware Hierarchical Adaptive Ensemble (MSTU-HAE) algorithm for intelligent ship emission monitoring and prediction in maritime environmental compliance applications. The maritime shipping industry contributes approximately 3% of global CO₂ emissions and significant amounts of nitrogen oxides and sulfur oxides, necessitating advanced predictive monitoring systems. The proposed MSTU-HAE algorithm integrates three key innovations: multi-scale temporal feature extraction using causal convolutions at short-term (5 samples), medium-term (20 samples), and long-term (60 samples) windows; gas-specific attention mechanisms that automatically weight temporal scales based on individual emission gas characteristics; and three-level hierarchical uncertainty quantification encompassing individual model uncertainty, ensemble disagreement, and regulatory compliance risk assessment. Experimental validation was conducted using emission data collected from a fishing vessel over 3 operational days (1732 original samples), augmented to 17,320 samples via controlled replication with noise injection to support model training. Rigorous temporal data splitting with 70%/15%/15% train/validation/test partitioning ensures no data leakage. Comparative analysis against six baseline methods (XGBoost, LSBoost, AdaBoost, Ridge Regression, Random Forest, and K-Nearest Neighbors) demonstrates that MSTU-HAE achieves superior average performance, with R² = 0.9670 and NSE = 0.9670 across all emission gases. This research contributes a robust, interpretable, and scalable prediction framework that advances the state of the art in maritime environmental monitoring through novel algorithmic innovations in temporal feature learning and uncertainty quantification. Full article

This study addresses the question of how augmented reality (AR) technologies can be designed and integrated into maritime navigation systems to meet the needs of young navigators within contemporary socio-technical bridge environments. The article is based on a qualitative, literature-based research methodology involving a structured analysis and synthesis of peer-reviewed journal articles and conference proceedings related to AR interfaces, human performance, decision support, and maritime training. The reviewed studies indicate that AR can enhance perceptual and situational awareness by overlaying navigational information directly into the navigator’s field of view, thereby reducing head-down time, improving spatial alignment of information, and supporting performance in low-visibility and high-traffic conditions. The literature also shows that AR-enabled visualizations and shared displays can support individual and team-based decision-making by facilitating real-time, context-aware information exchange on the ship’s bridge. Safety-related benefits are identified as indirect outcomes of improved perception and cognitive support rather than as isolated technological effects. Simultaneously, the findings highlight that these benefits depend strongly on human-centered interface design and appropriate training. The study concludes that AR has significant potential to enhance maritime navigation for young navigators when integrated as part of a balanced socio-technical system combining technology, human factors, and structured education. Full article

Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and modeling published between 2022 and 2024 using a structured literature search and screening process informed by PRISMA principles. The review presents a five-stage workflow, spanning data processing, data analysis, knowledge extraction, modeling, and runtime applications with emphasis on how these stages contribute to perception, prediction, and decision support in automated navigation. Four dimensions are considered in data analysis, including statistical analysis, safety indicators, situational awareness, and anomaly detection. The modeling approaches are categorized into classification, regression, and optimization, highlighting current limitations such as data quality, algorithmic transparency, and real-time performance, while also assessing runtime feasibility for onboard or edge deployment. Three runtime application directions are identified: autonomous vessel functions, remote monitoring and control operations, and onboard decision-support tools, with numerous studies focusing on constrained waterways and port-approach scenarios. Future directions suggest integrating multi-source data and advancing machine learning models to improve robustness in complex traffic and harbor environments. By linking theoretical insights with practical onboard needs, this study provides guidance for developing intelligent, adaptive, and safety-enhancing maritime systems. Full article

To systematically review the research progress, methodological frameworks, and application characteristics of energy management technologies for All-Electric Ships (AES), this review provides a comprehensive and critical survey of studies published over the past two decades, following the technical trajectory of multi-energy coupling–multi-objective optimization–engineering-oriented operation. Based on a structured analysis of representative literature, the review first elucidates the overall architecture and operational characteristics of AES energy systems from a system-level perspective, highlighting their core advantages as “mobile microgrids” in terms of multi-energy coordination and dispatch flexibility. On this basis, a structured classification framework for energy management strategies is established, and the theoretical foundations, applicable scenarios, and engineering feasibility of rule-based, optimization-based, uncertainty-aware, and intelligent/data-driven approaches are comparatively reviewed and discussed. Furthermore, focusing on key research themes—including multi-energy system optimization, ship–port–microgrid coordinated operation, battery safety and lifetime-oriented management, and real-time energy management strategies—the review synthesizes the main findings and engineering validation progress reported in recent studies. The analysis indicates that, with the integration of fuel cells, renewable energy sources, and Hybrid Energy Storage Systems (HESS), energy management for AES has evolved from a single power allocation problem into a system-level optimization challenge involving multiple time scales, multiple objectives, and diverse sources of uncertainty. Optimization-based and Model Predictive Control (MPC) methods have shown promising performance in many simulation and pilot-scale studies for improving energy efficiency and emission performance, while robust optimization and data-driven approaches offer useful support for enhancing operational resilience, prediction capability, and decision quality under complex and uncertain conditions. These advances collectively contribute to the environmental, economic, and operational sustainability of maritime transport by reducing greenhouse gas emissions, extending equipment lifetime, and enabling efficient integration of renewable energy sources. At the same time, the current literature still reveals important limitations related to model fidelity, data availability, validation maturity, and the gap between methodological sophistication and practical deployment. Overall, an increasingly structured but still evolving research framework has emerged in this field. Future research should further strengthen ship–port–microgrid coordinated energy management frameworks, develop system-level optimization methods that integrate safety constraints and uncertainty, and advance intelligent Energy Management Systems (EMS) oriented toward sustainable zero-carbon shipping objectives. Full article