Search Results (395)

Ship exhaust emissions have become an increasingly prominent global atmospheric environmental issue, triggering a series of ecological disturbances and adverse public health consequences. However, comprehensive analyses of the research progress and evolution trends in this field remain scarce. This study systematically retrieved 1346 scholarly publications in the ship exhaust emissions field for the period 2011–2025 from the Web of Science Core Collection and carried out a bibliometric analysis encompassing publication outputs, contributing countries/regions, and keyword characteristics. The findings reveal a sustained and robust growth trajectory in global research output, with annual publications increasing nearly fivefold over the 15-year study period. Notably, academic interest in this field has increased significantly since 2020 due to the implementation of the global sulfur cap regulation. Core thematic clusters (mean silhouette S = 0.7205) in this field include source apportionment, numerical modeling analysis, atmospheric criteria pollutants, and technological emission reduction strategies. The geographical distribution of research output shows a significant positive correlation with the importance of regional maritime economies. China, the United States, and Germany are the leading contributors in terms of publication outputs, while frequent research collaborations have been observed among European countries. Since 2021, the emergence of Automatic Identification System data as a keyword with high burst strength (intensity = 3.60) marks a paradigm shift toward a “big data-enabled refined management” framework. Concurrently, the sustained burst activity of keywords including nitrogen oxides, volatile organic compounds, and traffic-related emissions from 2023 to 2025 indicates rapidly growing scholarly attention to secondary aerosol precursors from shipping, and the critical need for coordinated multi-pollutant control strategies. Future research directions for ship exhaust emissions are expected to transition from fundamental characterization research to big data-driven monitoring and estimation methods, as well as advanced emission reduction technologies. The bibliometric insights derived from this study provide a valuable reference framework for subsequent in-depth studies on ship exhaust emissions. Full article

To overcome the inherent limitations of conventional offline programming in adapting to dimensional deviations and assembly-induced errors during robotic welding of ship structures, this paper proposes a point-cloud-enhanced visual scanning paradigm that enables automatic weld seam identification and collision-free trajectory planning. A dedicated monochromatic vision system is rigidly integrated onto a six-axis industrial robot, enabling high-fidelity feature extraction and geometric contour reconstruction for the precise localization of multi-configuration weld seams. The proposed approach substantially reduces manual teaching operations, enhances environmental adaptability in unstructured shipbuilding workshops, and improves global positioning accuracy. The core technical contributions are threefold: (1) systematic design and precision calibration of the integrated robotic vision system, including a hand–eye calibration procedure; (2) development of a hybrid 2D image-3D point cloud processing pipeline that combines SURF and FLANN for image stitching with RANSAC-based plane segmentation and PCA-driven contour reconstruction; and (3) extensive experimental validation across five distinct workpiece configurations. These results confirm the system’s strong applicability for intelligent and efficient shipbuilding welding, significantly outperforming conventional offline programming, which exhibits deviations exceeding 5 mm under identical conditions. Quantitative error analysis demonstrates that the online recognition method achieves a weld localization root mean square error (RMSE)of 0.82 mm, a standard deviation of 0.45 mm, and a verified maximum absolute deviation of 1.5 mm. Full article

Detecting small ship targets in sea–sky background environments is challenging due to interference from clouds, islands, sea clutter, and the limited spatial information in long-range infrared imagery. To address these issues, this paper proposes a robust detection framework that integrates multi-scale spatial feature enhancement with temporal trajectory analysis. First, a candidate target extraction method based on a multi-scale differential histogram of oriented gradients is introduced. By exploiting gradient distribution differences between targets and surrounding backgrounds, our method effectively enhances target responses while suppressing structured background edges. This response is further fused with a log-spectrum-based saliency map to improve target contrast and reduce clutter. Next, a candidate trajectory extraction algorithm based on inverse optical flow matching is developed to utilize temporal consistency. Optical flow-based grayscale compensation predicts target intensity changes between frames, while Kalman filtering estimates motion states and performs trajectory association. Finally, a multi-feature trajectory filtering strategy is designed, combining motion entropy stability, peak signal-to-noise ratio, and trajectory lifecycle to distinguish true targets from false alarms. Experimental results on eight infrared maritime sequences demonstrate superior performance. The proposed method achieves an average Background Suppression Factor (BSF) of 45.2 and an average Signal-to-Clutter Ratio Gain (SCRG) of 22.3 × 10³, representing a substantial improvement over all baseline algorithms. Receiver Operating Characteristic analysis further confirms a mean detection rate exceeding 90% at a false-alarm rate of 10⁻³ across all sequences, confirming improved detection performance and robustness in complex maritime environments. Full article

This paper investigates trajectory tracking and collision-avoidance problems for unmanned surface vehicles (USVs) in maritime sports support scenarios. These tasks require accurate tracking, disturbance rejection, safe motion around static and moving obstacles, and predictable transient performance within task-level time constraints. To address these requirements, an adaptive predefined-time sliding mode control (APTSMC) strategy is formulated for the considered CyberShip II-based USV tracking error system. A predefined-time sliding surface and reaching law are used to provide an explicit convergence-time design parameter for the nominal tracking subsystem, while an adaptive compensation mechanism estimates the unknown bound of lumped disturbances without requiring prior knowledge. To support collision avoidance, a velocity-modulated artificial potential field correction is incorporated as a reactive avoidance layer. The modulation term strengthens repulsion when the USV approaches an obstacle and reduces unnecessary deviation when the relative motion is safe. Numerical results in a constructed maritime sports boundary-tracking simulation scenario with multiple static and moving obstacles further demonstrate the potential effectiveness of the integrated framework in balancing tracking accuracy and collision avoidance safety. Full article

A comprehensive understanding of the spatial dynamics and operational characteristics of fishing activities in the Arabian Sea is critical for effective marine management and regional resource conservation. Based on VIIRS/DNB nighttime light imagery from 2017 to 2022 and the YOLOv11 model, this study presents an applied observational pipeline for the spatial extraction of fishing vessel positions. Spatial statistical methods were employed to analyze the operational patterns of light-fishing fleets, and habitat niches were identified by integrating marine environmental data. The results indicate that: (1) The YOLOv11 model achieved a precision (P) of 0.966, a recall (R) of 0.954, and a mean average precision (mAP) of 0.969. Under clear-sky and thin-cloud conditions, it demonstrated superior detection accuracy compared to existing VBD (VIIRS Boat Detection) products. (2) Through Kernel Density Hotspot Analysis (KDHSA), the primary spatial distribution of the light-fishing fleet was delineated. Fishing Operation Areas (FOAs) exhibited a pronounced seasonal “clustering–diffusion–re-clustering” pattern. The Center of Effort (CoE) generally followed a counter-clockwise migration trajectory, though a clockwise shift was observed during the 2019–2020 fishing season. (3) Random Forest analysis identified dissolved oxygen at 200 m (DO200), sea surface height (SSH), and temperature at 200 m (T200) as the primary predictive environmental features associated with vessel distribution. The core spatial ranges associated with high vessel density were 9.5–14.9 mmol⋅m⁻³ for DO200, 0.24–0.36 m for SSH, and 17.3–18.0 °C for T200. Notably, the statistical contribution of subsurface factors significantly exceeded that of sea surface temperature (SST). Future research should integrate ship position data with fishery biological data to further explore the drivers of FOA variations. This study provides a scientific basis for the sustainable management and rational development of marine resources in the Northwest Indian Ocean. 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

Ship–bridge collisions in cross-sea bridge waterways are rare but potentially catastrophic events, making conventional accident-based risk assessment difficult to implement effectively. Existing AIS-based indicators capture vessel behaviors but insufficiently quantify bridge-passage safety margins, especially dynamic aspects such as crossing posture and readiness prior to bridge transit. To address this limitation, this study proposes a data-driven framework for detecting unsafe ship–bridge passages using two bridge-passage-oriented surrogate safety measures (SSMs) and extreme value theory (EVT). The Bridge-passage Lateral Clearance Margin (BLCM) quantifies the effective lateral safety margin retained during the realized bridge-crossing stage, while the Bridge-passage Readiness Lead Time (BRLT) measures how early a vessel becomes stably prepared for bridge passage before crossing. The Peaks Over Threshold (POT) model is first used to characterize the marginal extremes of the two indicators, and a bivariate threshold exceedance model (BTE) is then established to examine their joint risk behavior. Case studies of the Jintang Bridge and Zhoudai Bridge waterways demonstrate that the proposed framework can effectively screen and identify trajectories with unsafe or margin-deficient bridge-passage characteristics. The results show that unsafe passages are typically associated with both reduced lateral clearance and insufficient preparation time, and that joint modeling of the two indicators improves risk identification performance. The findings suggest that ship–bridge risk is better interpreted from the perspective of passage quality deficiency rather than simple geometric proximity. The proposed framework provides an interpretable tool for retrospective unsafe passage screening, traffic monitoring support, and post-event safety analysis in complex bridge waterways. Full article

The paper concerns motion modeling for full-scale ships under the frame of system identification (SI) principles. Several groups of full-scale ship maneuvering experiments have been implemented to collect research data. On structure identification, as an innovation, a nonlinear integrating ship motion model is identified and established. The concerned model includes 21 parameters. Under the premise of error criterion, a batch least-squares (BLS)-based parameter estimation process is used to estimate the 21 parameters. The strategy is verified for feasibility and availability by using a pragmatic case study. The accuracy of the estimated parameter values is checked by comparing the track in simulation with the trial trajectory. Research indicates that the technical process proposed in the paper from the perspective of SI principles can be applied to the modeling of ship maneuvering motion. 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

The Automatic Identification System (AIS) generates massive volumes of real-world ship trajectory data, providing a critical foundation for maritime ship-type classification. However, existing methods often struggle to simultaneously capture long-range temporal dependencies, maintain computational efficiency, and ensure model interpretability, making accurate multi-class classification challenging in real-world maritime environments. To address these limitations, this study proposes a robust and efficient hybrid framework that integrates a Feature Transformer module for deep temporal feature extraction with a LightGBM model for ensemble classification. The multi-head self-attention within the Feature Transformer captures long-range dependencies in preprocessed AIS sequences to generate compact 64-dimensional trajectory fingerprints. These deep representations are concatenated with 103 carefully designed kinematic, geometric, statistical, frequency-domain, and segment-level features and fed into a LightGBM classifier for final ship-type identification. We evaluate the framework on a real-world AIS dataset of 2196 trajectories collected between 2019 and 2023, covering 14 ship types under a natural long-tail distribution. Across five random seeds, the proposed hybrid model achieves 78.06% ± 1.15% accuracy (95% CI) and 74.09% ± 1.82% Macro-F1 (95% CI), significantly outperforming Transformer-only (65.09% accuracy) and LightGBM-only (66.85%) baselines, with paired statistical tests confirming the improvement (McNemar χ² = 172.07, p < 10⁻³⁹ vs. Transformer; χ² = 92.24, p < 10⁻²¹ vs. LightGBM). The hybrid model offers ultra-fast inference at 0.051 ms per trajectory on GPU at batch size 128 (≈19,500 samples/s), and provides instance-level interpretability via SHapley Additive exPlanations (SHAP) analysis. These properties make the framework practical for near-real-time maritime traffic monitoring and decision support. Full article

Ship collision risk is a critical issue in maritime traffic safety regulation, as it directly affects the safety, efficiency, and sustainability of maritime transportation. It depends not only on the current encounter geometry among ships, but is also closely related to the ship trajectory distribution structure and the traffic state. Existing studies have mostly identified collision risk based on collision avoidance parameters. Although such methods can characterize explicit collision risks, they remain insufficient in identifying the additional risks induced by trajectory densification, uncovering the potential risks reflected by frequent trajectory intersection and change, and representing the structural collision risks of regional traffic. To address these limitations, this study proposes a trajectory analysis-based regional collision risk model within the framework of the radial distribution function. First, the mapping relationships between collision risk and three aspects, namely trajectory density, trajectory conflict, and trajectory abruptness, are established, which are respectively characterized by trajectory density and aggregation, trajectory intersections and time differences, and trajectory alterations and fluctuations. Then, the ship traffic system is transformed into a particle system, and two-dimensional radial distribution feature planes for the above three aspects are constructed to identify the risk level of a region from different dimensions. Finally, a three-dimensional fusion space is further developed to achieve a comprehensive quantification of collision risk in a specified water area. Experiments were conducted using one week of daytime and nighttime Automatic Identification System (AIS) data from the Bohai Strait. The proposed model showed a strong temporal correlation with AIS record-based high-risk patterns (R = 0.866, p = 0.01), and, compared with a regional collision risk model based on traditional collision avoidance parameters, exhibited 20–50% higher sensitivity in identifying additional and potential risks caused by dense, intersecting, and abrupt trajectory patterns. The proposed model can provide methodological support for maritime authorities in collision risk monitoring of key waters, precise allocation of regulatory resources, and proactive safety regulation, thereby contributing to safer and more sustainable maritime transportation. 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

Under strict spatial constraints and environmental interference, autonomous navigation of vessels in inland bridge-restricted waterways demands precise coordination between collision avoidance and trajectory tracking. To meet these operational demands, an integrated framework that directly combines spatiotemporal risk assessment with dynamic control execution is developed. Based on a digital traffic model integrating bridge piers and channel boundaries, collision risks are evaluated by combining trajectory-predicted time to safe distance with the velocity obstacle interval. Such a formulation quantifies the actual spatial difficulty of evasion rather than relying solely on temporal urgency. Driven by this continuous assessment, a time-series rolling strategy calculates feasible maneuvering intervals, generating trajectories that comply strictly with inland navigation rules and physical vessel limits. Subsequently, an adaptive model predictive control algorithm executes these commands, implicitly compensating for the localized hydrodynamic disturbances typical of bridge areas. The effectiveness of the architecture is validated through comprehensive simulations covering rule-based encounters and complex multi-vessel scenarios. Quantitative results indicate that under wind and current disturbances, the maximum route tracking deviation is constrained below 53 m, while the minimum encounter distance with target ships is consistently maintained above 51 m. These performance metrics confirm the capacity to execute safe, rule-compliant maneuvers while preserving high navigational precision in confined inland environments. Full article

Cruise ship manufacturing is a high-tech, complex industry where development depends on coordination across stages and organizations. We advance the coordination literature by treating the supply chain, industry chain, and value chain as a complex system, and by linking cross-chain coordination to high-quality development in a way that is comparable to theoretical debates on capability building and productivity-oriented development. Empirically, we collect city-level panel data for ten Chinese cruise port cities from 2008 to 2023 and combine a coupling–coordination framework with a panel data qualitative comparative analysis (PD-QCA) to capture both coordination dynamics and configurational causality. Our results show substantial heterogeneity in coordination trajectories, which can be grouped into decline–recovery, high-level stability, and persistent decline/high-variability patterns. We also show that high coupling does not guarantee high-quality outcomes, which are jointly shaped by industrial foundations, high-end value creation, and innovation capacity. Moreover, we identify two main pathways: an anchoring pathway that depends on output capacity and resource inputs, and an optimizing pathway that mainly relies on investment intensity, demand-side output, and value efficiency, with cross-chain coordination acting as an enabling condition that helps improve cross-chain matching. Full article