Search Results (231)

The crashworthiness of stiffened hull plates is essential for improving ship safety under collision and grounding loads. In this study, the impact resistance and energy-absorption mechanism of a U-shaped stiffened hull plate subjected to a wedge-shaped impact are investigated through drop-weight tests, nonlinear finite-element simulations, and analytical derivations. The experimental results show that the specimen experiences local indentation of the face plate, folding of the U-shaped stiffener webs, and crack propagation along the stiffener direction. The maximum residual deformation reaches 112 mm, and the failure mode is governed by the combined effect of face-plate stretching, web folding, and tearing near the contact or welded region. A finite-element model is established in ABAQUS and validated against the experimental deformation mode and force–indentation response. Furthermore, an analytical model based on the plastic upper-bound theorem is proposed to predict the instantaneous structural resistance. The total resistance is decomposed into contributions from the face plate, inclined webs, cap plate, and the tearing correction term. The analytical prediction agrees reasonably with the experimental and numerical results, with a peak collision force of approximately 620 kN at an indentation depth of about 124.5 mm. The proposed method provides a practical reference for rapid resistance prediction and crashworthy design of U-shaped stiffened hull plates. Full article

Accurate significant wave height prediction is essential for fuel-efficient ship operation and weather routing, as wave-induced resistance directly affects propulsion demand and fuel consumption. This study proposes a Residual U-Net-based deep-learning correction model to improve long-range SWH forecasts from WAVEWATCH III (WW3). WW3 global forecast fields were corrected using the proposed model, with CMEMS reanalysis data used as the ground-truth reference. The corrected outputs, denoted as WW3_UNET, were evaluated against 10 min resolution main engine fuel oil consumption (ME1_FOC) records and onboard wave observations from a commercial vessel traversing the South Atlantic in 2025. WW3_UNET showed markedly improved agreement with ship observations compared with the raw WW3 forecast across all lead times from 0 to 288 h. When a 24 h moving average was applied, WW3_UNET achieved a correlation of 0.720 with ME1_FOC at the 168–180 h lead time, closely approaching the 0.736 obtained from onboard wave measurements. These results indicate that AI-corrected forecasts can provide observation-consistent wave information up to 7–8 days in advance. The proposed approach can support fuel-aware weather routing and voyage planning, thereby contributing to improved maritime energy efficiency and decarbonization. Full article

Ship self-sinking accidents threaten maritime safety, human life, property, and the marine environment, and understanding their causal-factor associations is essential for developing effective preventive measures. This study aims to identify the multi-level factors, recurrent association patterns, and critical structural nodes involved in ship self-sinking accidents. A hybrid framework integrating grounded theory, the Human Factors Analysis and Classification System (HFACS), FP-growth association rule mining, and complex network analysis was applied to 150 accident investigation reports released by the China Maritime Safety Administration between 2014 and 2024. Findings suggest that adverse weather and sea conditions, inadequate ship safety management, and crew incompetence are the most frequent factors. Thirty causal factors were identified and classified into four HFACS levels, and 229 association rules were generated to construct a directed weighted causal-factor association network with 19 nodes and 229 edges. Network results indicate that inadequate ship safety management, crew incompetence, ship unseaworthiness, insufficient maintenance of hull weathertight integrity, and improper or untimely emergency measures occupy critical positions in the association structure. This research offers insight into ship self-sinking accidents and identifies priority intervention points for more targeted maritime supervision, safety management and accident prevention. Full article

To address the demand for high-precision, high-efficiency, and standardized hydrographic information in intelligent shipping, this study systematically investigates key technologies for high-resolution hydrographic data parsing and intelligent information services. Focusing on the East China Sea, a space–air–ground integrated monitoring data access system is established. A hybrid data assimilation method combining four-dimensional variational (4D-Var) and ensemble Kalman filter is adopted to realize quality control, deep fusion, and optimal state estimation of multi-source heterogeneous hydrographic observations. A hybrid tidal harmonic response model is further developed to improve the refined forecasting accuracy of tide levels and ocean currents. A hierarchically decoupled system architecture is designed, and modules for data production, sharing, exchange, and visualization are developed in compliance with the international S-100 standard. By integrating hybrid spatiotemporal indexing, multi-level caching, and intelligent query optimization, the system achieves low-latency and high-concurrency service capabilities. Experimental results show that, compared with conventional models, the proposed framework reduces tidal forecast RMSE by approximately 15.8% under extreme weather, raises the continuity index of current vectors to 0.93, and cuts the S-100 product generation latency to less than 30 s. This research establishes a full-chain technical system from data parsing and product generation to intelligent services, providing a reliable technical support platform for ship intelligent navigation, dynamic route planning, and maritime safety assurance. Full article

Background/Objectives. Automated lateral ventricle volumetry is increasingly used in population-based neuroimaging, but correlation between methods does not establish agreement of absolute volumes. We quantified agreement and calibration between FreeSurfer and a visually quality-controlled FSL/FAST–ALVIN lateral ventricle workflow within the Study of Health in Pomerania (SHIP). Methods. This cross-sectional agreement-and-calibration study included 2988 SHIP participants with visually accepted FSL/FAST–ALVIN total lateral ventricle volumes; paired FreeSurfer data were available for 1913 participants. FSL/FAST–ALVIN was treated as the study reference scale rather than biological ground truth. Agreement was assessed using Pearson and Spearman correlations, Bland–Altman analysis, log-ratio agreement, Lin’s concordance correlation coefficient, and a two-way mixed-effects single-measure absolute agreement intraclass correlation coefficient. Directional calibration models predicted FSL/FAST–ALVIN volume from FreeSurfer volume and were internally validated using 2000 bootstrap resamples. Results. In the paired sample, volumes were almost perfectly associated (Pearson r = 0.9978; Spearman ρ = 0.9974), but FreeSurfer yielded systematically lower values (mean FreeSurfer-minus-FSL bias, −3.02 mL; 95% limits of agreement, −4.52 to −1.53 mL; geometric mean FreeSurfer/FSL ratio, 0.844). Lin’s concordance coefficient and the absolute agreement ICC were both 0.9598. Calibration was strong but workflow-specific: FSL/FAST–ALVIN volume = 2.611 + 1.0210 × FreeSurfer volume (R2 = 0.9955; optimism-corrected RMSE = 0.732 mL). Conclusions. FreeSurfer and visually quality-controlled FSL/FAST–ALVIN preserved participant ranking extremely well but were not directly interchangeable as absolute measurements. Cross-workflow comparisons require explicit method reporting, formal agreement analysis, and calibration to the intended measurement scale; the equation should not be used as a universal conversion formula outside comparable acquisition, segmentation, QC and software settings. Full article

This study sets and evaluates a practical framework for predicting ship resistance under real operational conditions along a global shipping route. Despite extensive research, the literature lacks straightforward studies that separately assess wind, wave, and current resistance using real-world performance data for ships in varying conditions. To address this gap, a methodology is established using recommended semi-empirical approaches combined with full-scale onboard operational measurements and Copernicus Marine Service environmental data in a unified assessment procedure. Calm water resistance is scaled from reference values under near-calm conditions, wind resistance is calculated using established regression models, wave-induced resistance is estimated using state-of-the-art semi-empirical formulations and spectral calculations, and current effects are modelled through a dynamic correction based on speed-over-ground measurements. The aim is to assess the reliability and applicability of added resistance calculation methods recommended by recent regulatory standards. Validation is performed by comparing the predicted resistance components, converted to equivalent shaft power, against full-scale onboard shaft-power measurements. In addition, a comparison between onboard measurements of wind and current and Copernicus data is presented. Predicted resistance components are validated against full-scale power measurements, showing agreement with an average error of approximately 9%. The resulting framework provides a practical tool for assessing energy losses due to environmental factors along specific routes using readily available ship data. Full article

Shipboard helicopter landing in the near-deck region requires stable attitude regulation and high-precision deck-relative motion control under substantial model uncertainty and environmental disturbances, conditions under which conventional model-based controllers may lose performance or become overly conservative. This paper proposes a task-oriented, learning-enhanced control algorithm for ship-relative near-deck station keeping and landing by integrating a model-based baseline controller with residual reinforcement learning in a deck-relative closed-loop framework. The algorithmic contribution is the deck-relative baseline–residual control architecture: a split-channel incremental nonlinear dynamic inversion (INDI) outer loop and a reduced-order dynamic inversion (DI) inner loop provide the nominal baseline pathway, while a bounded residual Proximal Policy Optimization (PPO) policy supplies compensation in the same physical outer-loop command channels to suppress unmodeled nonlinearities and time-varying disturbances. Simulation results show that Residual PPO improves hover robustness and landing performance relative to the baseline controller and Pure PPO. With approximately 20–30% residual authority, it achieved 90.0% Desired landing rates in both tested descent-and-landing scenes. Full article

In the context of the Global Gateway Green Shipping Corridors, an ambitious initiative to facilitate the transition to renewable and low carbon fuels for maritime transport in Africa, Latin America, and Asia, we explore potential areas where production costs can be competitive. We propose a methodology to rank 250 locations worldwide in terms of production costs of hydrogen and derived synthetic fuels as an initial screening indicator for potential production costs. The methodology uses the PyPSA software package, explicitly accounting for renewable energy capacity and technology costs, setting a common ground to benchmark locations in terms of their estimated levelized cost of fuel (LCOX). Results across the 250 locations determined ranges of 212–404.6 EUR/MWh for hydrogen, 258.3–414.3 EUR/MWh for ammonia, 308 to 478.4 EUR/MWh for methanol, and 298–466 EUR/MWh for methane. The optimal system designs tend to overbuild solar photovoltaic capacity, allowing for a higher utilization rate of the electrolyzer, the most expensive component of the system. This leads to lower production costs even though electricity production may need to be curtailed. Full article

Accurate ship trajectory prediction is vital for intelligent maritime traffic management, yet conventional hybrid models often fail to balance local feature extraction, long-term dependency capture, and flexible feature weighting when processing AIS data. This paper proposes an MSC-BiLSTM-ATTENTION model that integrates trajectory clustering and an adaptive attention mechanism into a unified framework. Its fundamental advance over existing incremental hybrid architectures is twofold. First, a K-means clustering step groups trajectories with similar motion patterns before model training, effectively reducing the impact of data heterogeneity on prediction accuracy. Second, the deep learning backbone synergizes multi-scale convolution (MSC)—which captures local features at multiple temporal granularities via parallel kernels—with a bidirectional LSTM (BiLSTM) for forward–backward dependency learning, and an adaptive self-attention mechanism that dynamically optimizes feature weights to amplify critical navigation information. Extensive experiments on AIS data from the Gulf of Mexico and the U.S. Atlantic Coast, covering four seasons, benchmark the model against attention-enhanced architectures including Transformer, CNN-BiLSTM-ATTENTION, and DenseNet-BiGRU-ATTENTION across two distinct regions. The proposed model achieves significant improvements in predicting longitude, latitude, speed over ground, and course over ground, reducing MAE by over 76.9% and RMSE by over 65.3% compared with the strongest baseline. Ablation studies confirm that the synergy of all three modules is essential. The results demonstrate the model’s effectiveness and its practical value for intelligent maritime supervision, navigation risk warning, and waterborne traffic management. Full article

Ship trajectory prediction from Automatic Identification System (AIS) data has been predominantly approached as a time-series forecasting problem, where sequential models operate on coordinate sequences to predict future positions. This paradigm, while effective, neglects a key observation: the spatial layout of multiple vessel trajectories on a chart-like plane carries rich interaction information that is difficult to capture through sequential processing alone. To address this, Mapex (Map Exploitation) is proposed as a vision-based framework that rasterizes multi-vessel AIS trajectories into chart-like multi-channel images and processes them with a visual encoder, treating trajectory prediction as a map-reading task. Each vessel contributes three image channels encoding its trajectory heatmap, speed field, and heading field, converting raw coordinates into a spatial representation where physical movement patterns become visually apparent. A parallel coordinate branch supplies the course-over-ground information that the raster does not encode explicitly, and a fusion module combines both streams for autoregressive five-channel trajectory generation. Unlike coordinate-domain models that process position sequences numerically, Mapex understands vessel motion through its spatial layout, capturing relative positions, trajectory shapes, and kinematic patterns as visual features rather than abstract number sequences. Experiments on the Piraeus AIS dataset demonstrate that Mapex reduces the average displacement error (ADE) by approximately $68\%$ compared to the best coordinate-domain baseline and the mean squared error (MSE) by over $80\%$ compared to the strongest prior method, while requiring significantly fewer parameters than recent LLM-based approaches. These results suggest that spatial visualization of trajectories provides a fundamentally richer representation than coordinate sequences for multi-vessel trajectory prediction. Full article

Ship target detection is a prerequisite for achieving automated monitoring in ship detection systems. To address the challenge of accurately detecting ship targets in complex water environments, this study proposes a ship target detection method based on an improved YOLOv11 framework. To enhance the model’s ability to perceive and fuse features across multiple scales and in complex backgrounds, an Iterative Attention Feature Fusion (iAFF) module and a Biformer module are integrated at the end of the backbone network. The iAFF module iteratively optimizes multi-scale features through a two-stage attention mechanism, effectively focusing on key target regions, thereby improving the model’s detection capability for small, medium-sized, and occluded ships. The Biformer module leverages its innovative Bi-level Routing Attention (BRA) mechanism to enhance the modeling of global semantic information while reducing computational complexity, mitigating false detections caused by occlusions among ship targets, and consequently improving detection precision. This study employs the Minimum Point Distance Intersection over Union (MPDIoU) loss function, which more comprehensively measures the similarity between predicted and ground-truth bounding boxes by optimizing the distances of their key geometric points, effectively enhancing the accuracy of bounding box regression. Experimental results show that the proposed model achieved 93.96% mAP, 92.93% recall, and 94.97% precision on a self-built ship dataset, surpassing mainstream detection algorithms including YOLOv11 in multiple metrics. The model has only 2.90 M parameters, achieving a good balance between accuracy and efficiency. This provides an accurate and efficient solution for intelligent ship supervision. Full article

Federated learning enables privacy-preserving distributed intelligence but faces challenges in balancing computation, communication, and privacy in heterogeneous networks. To address these issues, this paper proposes a privacy-preserving U-shaped split federated learning (USFL) framework for space–air–ground–sea integrated networks. The proposed architecture combines split learning and federated learning in a U-shaped structure, ensuring that both raw data and labels remain localized at client devices. In addition, a differential privacy mechanism is introduced to perturb intermediate features during transmission, enhancing resistance to inference attacks. A mathematical framework is established to model the learning process under resource constraints, and the convergence behavior and privacy loss are theoretically analyzed. Experimental results on the SeaShips dataset demonstrate that the proposed method achieves competitive accuracy compared with centralized and existing distributed approaches, while reducing communication overhead and improving privacy protection. These results validate the effectiveness of the proposed framework for secure and efficient distributed learning in complex network environments. Full article

To enhance fleet replenishment efficiency and ensure navigational safety, this paper investigates the Underway Replenishment Routing Problem (URRP), focusing on the dynamic characteristics of receiving ships. Mathematical models for replenishment ship travel time and formation vessel speed adjustment are formulated, explicitly considering navigational state transitions and formation stability (risk control). Consequently, a dynamic route optimization model is constructed to provide intelligent decision support for fleet commanders. An intelligent optimization algorithm, the Hybrid Genetic Algorithm with Adaptive Variable Neighborhood Search (HGA-AVNS), is proposed to solve this model. Computational results demonstrate that the proposed approach outperforms the traditional empirical replenishment strategy, validating its effectiveness in enhancing maritime safety and operational efficiency. Extensive sensitivity analyses further reveal that under the strict premise of maintaining formation stability, appropriately reducing the cruise speed can offset the increase in overall speed over ground (SOG) induced by following ocean currents, thereby preventing systematic time loss. Additionally, fine-tuning the execution timing of sudden tactical turning based on the replenishment ship’s real-time operational status can further maximize overall replenishment efficiency without compromising navigational safety. Full article

As the maritime industry transitions towards green shipping, operational sustainability and energy efficiency are increasingly crucial for long-endurance Unmanned Surface Vehicle (USV) missions. To this end, proactively adjusting driving strategies based on the prediction of other USVs’ motion is essential. This proactive approach directly minimizes carbon emissions and reduces high-energy driving behaviors resulting from passive sudden braking or sharp turns in unexpected situations. However, existing trajectory prediction methods are trained based on low-frequency automatic identification system data of large merchant vessels, which cannot be directly used on the highly dynamic USV data. To address this limitation, this study constructs a large-scale simulated USV scenario dataset grounded in nonlinear ship hydrodynamics, which contains complicated interactive scenarios with multiple USV agents. To effectively model the interaction among agents for accurate prediction, we further propose USV-Former, a hierarchical encoder-decoder architecture designed for proactive navigation. The framework integrates a symmetric encoding structure with a dual-stage pipeline: a Local Attention Module captures high-frequency dynamics, while a Global Graph Attention Module enforces COLREGs-compliant topological constraints. Experimental results demonstrate that the proposed model outperforms established baselines in prediction accuracy. Qualitative analysis further reveals that by accurately anticipating target intentions, the model minimizes unnecessary avoidance maneuvers, enabling more stable and momentum-conserving velocity profiles. Ultimately, this architecture exhibits high computational efficiency, reduces operational energy waste, and provides a robust, measurable algorithmic foundation for green autonomous shipping and marine environmental protection. Full article

Decarbonizing ocean-going shipping requires decision-grade environmental evidence for propulsion transitions, yet conventional LCA relies on static inventories that inadequately represent dynamic operations and route-dependent renewable generation. This study evaluates well-to-wake (WtW) Global Warming Potential (GWP) for two large container ships operated by a Korean company under four scenarios: conventional diesel main engine, diesel–electric with onboard generator, full battery-electric supplied by shore electricity from the Republic of Korea grid, and battery-electric with a route-resolved solar PV system. A Live-LCA (LLCA) framework couples LCI data with MATLAB/Simulink power and propulsion modeling driven by actual operating profiles and route environmental conditions to generate operational inventories for impact calculation. Diesel–electric operation increases annual WtW GWP by over 26% for both ships versus the baseline of a conventional diesel main engine, whereas shore-electric battery operation is able to reduce WtW GWP by around 40% versus diesel–electric. With limited PV installation, additional reductions are marginal. Depending on electricity profile, it can increase battery-electric GHG emissions by approximately 27%, highlighting sensitivity to electricity evolution. Overall, electric propulsion delivers climate benefits only when paired with low-carbon electricity, and LLCA enables operationally and route-grounded LCA for large container ships. Full article