Search Results (602)

Predicting the roll damping coefficient of a ship is a crucial factor in determining the dynamic stability of the vessel. However, a nonlinear analysis that considers the viscosity of the fluid is required to accurately estimate the roll damping coefficient. This study numerically analyzed the hydrodynamic coefficients related to the roll motion of ships, focusing on the eddy-making damping coefficient. A series of forced vibration tests were conducted on a two-dimensional triangular cylinder floating on the water surface. The overset method and the volume-of-fluid method were applied, and the governing equations were solved using the open-source software OpenFOAM v2106. Uncertainties in the grid size and time intervals were identified through the International Towing Tank Conference (ITTC) procedure, and the obtained hydrodynamic coefficients were compared with available experimental data and potential flow results. Additionally, eddy-making damping was extracted from the shed vortex for various excitation frequencies and amplitudes. The study found that the uncertainty in the roll damping coefficient was less than 8%, with eddy-making damping being the dominant factor influencing the results. Numerical results showed a good agreement with experimental data, with an average deviation of 4.4%, highlighting the importance of considering nonlinear effects at higher excitation amplitudes. Comparison with experimental data and empirical formulas revealed that the nonlinearity due to the excitation amplitude must be considered in empirical formulations. Full article

Inverse Synthetic Aperture Radar (ISAR) images are extensively used in Radar Automatic Target Recognition (RATR) for non-cooperative targets. However, acquiring training samples for all target categories is challenging. Recognizing target classes without training samples is called Zero-Shot Learning (ZSL). When ZSL involves multiple modalities, it becomes Multi-modal Zero-Shot Learning (MZSL). To achieve MZSL, a framework is proposed for generating ISAR images with optical image aiding. The process begins by extracting edges from optical images to capture the structure of ship targets. These extracted edges are used to estimate the potential locations of the target’s scattering centers. Using the Geometric Theory of Diffraction (GTD)-based scattering center model, the edges’ ISAR images are generated from the scattering centers. Next, a mapping is established between the edges’ ISAR images and the actual ISAR images. Neighbor-Adapted Local Linear Embedding (NALLE) generates pseudo-ISAR images for the unseen classes by combining the edges’ ISAR images with the actual ISAR images from the seen classes. Finally, these pseudo-ISAR images serve as training samples, enabling the recognition of test samples. In contrast to the network-based approaches, this method requires only a limited number of training samples. Experiments based on simulated and measured data validate the effectiveness. Full article

Icebergs pose significant risks to shipping, offshore oil exploration, and underwater pipelines. Detecting and monitoring icebergs in the North Atlantic Ocean, where darkness and cloud cover are frequent, is particularly challenging. Synthetic aperture radar (SAR) serves as a powerful tool to overcome these difficulties. In this paper, we propose a method for automatically detecting and classifying icebergs in various sea conditions using C-band dual-polarimetric images from the RADARSAT Constellation Mission (RCM) collected throughout 2022 and 2023 across different seasons from the east coast of Canada. This method classifies SAR imagery into four distinct classes: open water (OW), which represents areas of water free of icebergs; open water with target (OWT), where icebergs are present within open water; sea ice (SI), consisting of ice-covered regions without any icebergs; and sea ice with target (SIT), where icebergs are embedded within sea ice. Our approach integrates statistical features capturing subtle patterns in RCM imagery with high-dimensional features extracted using a pre-trained Vision Transformer (ViT), further augmented by climate parameters. These features are classified using XGBoost to achieve precise differentiation between these classes. The proposed method achieves a low false positive rate of 1% for each class and a missed detection rate ranging from 0.02% for OWT to 0.04% for SI and SIT, along with an overall accuracy of 96.5% and an area under curve (AUC) value close to 1. Additionally, when the classes were merged for target detection (combining SI with OW and SIT with OWT), the model demonstrated an even higher accuracy of 98.9%. These results highlight the robustness and reliability of our method for large-scale iceberg detection along the east coast of Canada. Full article

With the ever-increasing volume of maritime traffic, the risks of ship navigation are becoming more significant, making the use of advanced multi-source perception strategies and AI technologies indispensable for obtaining information about ship navigation status. In this paper, first, the ship tracking system was optimized using the Bi-YOLO network based on the C2f_BiFormer module and the OC-SORT algorithms. Second, to extract the visual trajectory of the target ship without a reference object, an absolute position estimation method based on binocular stereo vision attitude information was proposed. Then, a perception data fusion framework based on ship spatio-temporal trajectory features (ST-TF) was proposed to match GPS-based ship information with corresponding visual target information. Finally, AR technology was integrated to fuse multi-source perceptual information into the real-world navigation view. Experimental results demonstrate that the proposed method achieves a mAP0.5:0.95 of 79.6% under challenging scenarios such as low resolution, noise interference, and low-light conditions. Moreover, in the presence of the nonlinear motion of the own ship, the average relative position error of target ship visual measurements is maintained below 8%, achieving accurate absolute position estimation without reference objects. Compared to existing navigation assistance, the AR-based navigation assistance system, which utilizes ship ST-TF-based perception data fusion mechanism, enhances ship traffic situational awareness and provides reliable decision-making support to further ensure the safety of ship navigation. Full article

The quality of ISAR (Inverse Synthetic Aperture Radar) images has a significant impact on the detection and recognition of targets. Therefore, ISAR image quality assessment is a fundamental prerequisite and primary link in the utilization of ISAR images. Previous ISAR image quality assessment methods typically extract hand-crafted features or use simple multi-layer networks to extract local features. Hand-crafted features and local features from networks usually lack the global information of ISAR images. Furthermore, most deep neural networks obtain feature representations by abridging the prediction quality score and the ground truth, neglecting to explore the strong correlations between features and quality scores in the stage of feature extraction. This study proposes a Gramin Transformer to explore the similarity and diversity of features extracted from different images, thus obtaining features containing quality-related information. The Gramin matrix of features is computed to obtain the score token through the self-attention layer. It prompts the network to learn more discriminative features, which are closely associated with quality scores. Despite the Transformer architecture’s ability to extract global information, the Channel Attention Block (CAB) can capture complementary information from different channels in an image, aggregating and mining information from these channels to provide a more comprehensive evaluation of ISAR images. ISAR images are formed from target scattering points with a background containing substantial silent noise, and the Inter-Region Attention Block (IRAB) is utilized to extract local scattering point features, which decide the clarity of target. In addition, extensive experiments are conducted on the ISAR image dataset (including space stations, ships, aircraft, etc.). The evaluation results of our method on the dataset are significantly superior to those of traditional feature extraction methods and existing image quality assessment methods. Full article

In smart ports, accurately perceiving the motion state of a ship during berthing and unberthing is essential for the safety and efficiency of the ship and port. However, in actual scenarios, the obtained data are not always complete, which impacts the accuracy of the ship’s motion state. This paper proposes a spatial visualization method to analyze a ship’s motion state in the incomplete data by introducing the GIS spatial theory. First, for the complete part under incomplete data, this method proposes a new technique named LGFCT to extract the key points of this part. Then, for the missing part under the incomplete data, this method applies the key point prediction technique based on the line features to extract the key points of this part. Note that the key points will be used to calculate the key parameters. Finally, spatial visualization and spatial-temporal tracking techniques are employed to spatially analyze the ship’s motion state. In summary, the proposed method not only spatially identifies a ship’s motion state for the incomplete data but also provides an intuitive visualization of a ship’s spatial motion state. The accuracy and effectiveness of the proposed method are verified through experimental data collected from a ship in Dalian Port, China. Full article

In synthetic aperture radar (SAR) images, pixel-level Ground Truth (GT) is a scarce resource compared to Bounding Box (BBox) annotations. Therefore, exploring the use of unsupervised instance segmentation methods to convert BBox-level annotations into pixel-level GT holds great significance in the SAR field. However, previous unsupervised segmentation methods fail to perform well on SAR images due to the presence of speckle noise, low imaging accuracy, and gradual pixel transitions at the boundaries between targets and background, resulting in unclear edges. In this paper, we propose a Multi-threshold Adaptive Decision Network (MtAD-Net), which is capable of segmenting SAR ship images under unsupervised conditions and demonstrates good performance. Specifically, we design a Multiple CFAR Threshold-extraction Module (MCTM) to obtain a threshold vector by a false alarm rate vector. A Local U-shape Feature Extractor (LUFE) is designed to project each pixel of SAR images into a high-dimensional feature space, and a Global Vision Transformer Encoder (GVTE) is designed to obtain global features, and then, we use the global features to obtain a probability vector, which is the probability of each CFAR threshold. We further propose a PLC-Loss to adaptively reduce the feature distance of pixels of the same category and increase the feature distance of pixels of different categories. Moreover, we designed a label smoothing module to denoise the result of MtAD-Net. Experimental results on the dataset show that our MtAD-Net outperforms traditional and existing deep learning-based unsupervised segmentation methods in terms of pixel accuracy, kappa coefficient, mean intersection over union, frequency weighted intersection over union, and F1-Score. Full article

Polarimetric decomposition methods are widely used in polarimetric Synthetic Aperture Radar (SAR) data processing for extracting scattering characteristics of targets. However, polarization SAR methods for ship detection still face challenges. The traditional constant false alarm rate (CFAR) detectors face sea clutter modeling and parameter estimation problems in ship detection, which is difficult to adapt to the complex background. In addition, neural network-based detection methods mostly rely on single polarimetric-channel scattering information and fail to fully explore the polarization properties and physical scattering laws of ships. To address these issues, this study constructed two novel characteristics: a helix-scattering enhanced (HSE) local component and a multi-scattering intensity difference (MSID) edge component, which are specifically designed to describe ship scattering characteristics. Based on the characteristic differences of different scattering components in ships, this paper designs a context aggregation network enhanced by local and edge component characteristics to fully utilize the scattering information of polarized SAR data. With the powerful feature extraction capability of a convolutional neural network, the proposed method can significantly enhance the distinction between ships and the sea. Further analysis shows that HSE is able to capture structural information about the target, MSID can increase ship–sea separation capability, and an HV channel retains more detailed information. Compared with other decomposition models, the proposed characteristic combination model performs well in complex backgrounds and can distinguish ship from sea more effectively. The experimental results show that the proposed method achieves a detection precision of 93.6% and a recall rate of 91.5% on a fully polarized SAR dataset, which are better than other popular network algorithms, verifying the reasonableness and superiority of the method. Full article

Sea transportation plays a vital role in global trade, and studying the impact of emergencies on global sea transportation is essential to ensure the stability of trade. At present, the conflict between Palestine and Israel has attracted extensive attention worldwide. However, there is a lack of specific research on the impact of conflict on shipping, particularly on global shipping costs. By using the global vessel trajectory data of tankers from the Automatic Identification System (AIS) and taking the global sea transportation of large tankers as an example, this paper quantifies and visualizes the changes in global sea transportation before and after conflicts from a data-driven perspective. Firstly, the complete vessel trajectory, as well as the port of departure and the port of destination are extracted. Then, from the perspective of shipping cost and vessel traffic flow, we evaluate the vessel traffic flow changes caused by the conflict by using the route distance to replace the shipping costs and quantify the cost increase for the relevant countries caused by the vessel detour based on the shipping cost increment index. The research results show that after the outbreak of the conflict, the number of vessels passing through the Red Sea area has decreased significantly. About 3.1% of global vessels were affected, with global sea transportation costs of large tankers increasing by about 0.0825%. This study takes the Israeli–Palestinian conflict as an example and analyzes the impact of emergencies on the global sea transportation situation of tankers based on AIS data. The research results reveal the characteristics of international shipping to a certain extent and provide guidance for global sea transportation route planning. Full article

With the widespread application of fuel cell technology in the fields of transportation and energy, Battery Management Systems (BMSs) have become one of the key technologies for ensuring system stability and extending battery lifespan. As an auxiliary power source in fuel cell systems, the prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for enhancing the reliability and efficiency of fuel cell ships. However, due to the complex degradation mechanisms of lithium batteries and the actual noisy operating conditions, particularly capacity regeneration noise, accurate RUL prediction remains a challenge. To address this issue, this paper proposes a lithium battery RUL prediction method based on an Adaptive Modal Enhancement Network (RIME-VMD-SEInformer). By incorporating an improved Variational Mode Decomposition (VMD) technique, the RIME algorithm is used to optimize decomposition parameters for the adaptive extraction of key modes from the signal. The Squeeze-and-Excitation Networks (SEAttention) module is employed to enhance the accuracy of feature extraction, and the sparse attention mechanism of Informer is utilized to efficiently model long-term dependencies in time series. This results in a comprehensive prediction framework that spans signal decomposition, feature enhancement, and time-series modeling. The method is validated on several public datasets, and the results demonstrate that each component of the RIME-VMD-SEInformer framework is both necessary and justifiable, leading to improved performance. The model outperforms the state-of-the-art models, with a MAPE of only 0.00837 on the B0005 dataset, representing a 59.96% reduction compared to other algorithms, showcasing outstanding prediction performance. Full article

An accelerated sea-level rise (SLR) may threaten the future livability of the Netherlands. Three perspectives to anticipate this SLR are elaborated here regarding technical, physical, and spatial aspects: Protect, Advance, and Accommodate. The overall objective was to explore the tools and measures that are available for adaptation, assess their spatial impacts, and identify dos and don’ts in current spatial issues like housing, climate adaptation, infrastructure, and the energy transition. Each elaboration was performed by a consortium consisting of representatives from private parties (engineering consultancy, project contractors, (landscape) architects, economists), knowledge institutes (including universities), and government, using an iterative process of model computations and design workshops. The elaborations made clear that a realistic and livable future perspective for the Dutch Delta continues to exist, even with a maximum analyzed SLR of 5 m, and will consist of a combination of elements from all three perspectives. This will require large investments and space for new and upgraded water infrastructure and will have large impacts on land use, water availability, agriculture, nature, residential buildings, shipping, and regional water systems. There is still a significant degree of uncertainty regarding future SLR; therefore, it is not advisable to make major investment decisions at this time. Nevertheless, some no-regret measures are already clear: continuation of the protection of the Randstad agglomeration (Amsterdam, The Hague, Rotterdam, and Utrecht) and its economic earning potential for future generations, adaptation of agriculture to more brackish and saline conditions, designation of space for additional future flood protection, extra storage capacity (for river discharge and increased precipitation), river discharge, and sand extraction (for future coastal maintenance). The research identified concrete actions for today’s decision-making processes, even though the time horizon of the analysis captures centuries. Including the perspectives in long term, policy planning is already necessary because the transition processes will take decades, if not more than a century, to be implemented. Full article

With the tightening of marine carbon emission reduction policies, the sustainable development of the shipping industry has attracted much attention, and it is of great significance to use Automatic Identification System (AIS) big data to study the carbon emissions of marine ships. Taking ships around Bohai Bay as the research object, this paper constructs a calculation method of ship carbon emissions driven by the ship AIS trajectory. The AIS information of ships is extracted, and the sailing status is determined. The carbon emission calculation model is built based on the AIS data, the carbon emission in 2023 is empirically measured, and the characteristics are analyzed. At the same time, a speed simulation model was built to evaluate the impact of speed reduction on carbon emissions and put forward emission reduction measures. The results show that the carbon emission of ships around Bohai Bay in 2023 was 8.8072 million tons, with cargo ships contributing the most, and the carbon emissions of the cruise state was significant. A 10% reduction in speed would reduce annual carbon emissions by about 6%. This study provides a reference for understanding the impact of speed on carbon emissions and formulating emission reduction measures, which can be used to compare historical and future data to support the emission reduction in ports and shipping enterprises. Full article

Maritime behavior detection is vital for maritime surveillance and management, ensuring safe ship navigation, normal port operations, marine environmental protection, and the prevention of illegal activities on water. Current methods for detecting anomalous vessel behaviors primarily rely on single time series data or feature point analysis, which struggle to capture the relationships between vessel behaviors, limiting anomaly identification accuracy. To address this challenge, we proposed a novel vessel anomaly detection framework, which is called the BPEF-TSD framework. It integrates a ship behavior pattern recognition algorithm, Smith–Waterman, and text similarity measurement methods. Specifically, we first introduced the BPEF mining framework to extract vessel behavior events from AIS data, then generated complete vessel behavior sequence chains through temporal combinations. Simultaneously, we employed the Smith–Waterman algorithm to achieve local alignment between the test vessel and known anomalous vessel behavior sequences. Finally, we evaluated the overall similarity between behavior chains based on the text similarity measure strategy, with vessels exceeding a predefined threshold being flagged as anomalous. The results demonstrate that the BPEF-TSD framework achieves over 90% accuracy in detecting abnormal trajectories in the waters of Xiamen Port, outperforming alternative methods such as LSTM, iForest, and HDBSCAN. This study contributes valuable insights for enhancing maritime safety and advancing intelligent supervision while introducing a novel research perspective on detecting anomalous vessel behavior through maritime big data mining. Full article

Infrared ship images have low resolution and limited recognizable features, especially for small targets, leading to low accuracy and poor generalization of traditional detection methods. To address this, we design a prior knowledge auxiliary loss for leveraging the unique brightness distribution of infrared ship images, we construct a joint feature extraction module that sufficiently captures context awareness, channel differentiation, and global information, and then we propose a prior-knowledge- and joint-feature-extraction-based YOLO (PJ-YOLO) for use in detecting infrared ships. Additionally, a residual deformable attention module is designed to integrate multi-scale information, enhancing detail capture. Experimental results on the SFISD and InfiRray Ships datasets demonstrate that the proposed PJ-YOLO achieves state-of-the-art detection performance for infrared ship targets. In particular, PJ-YOLO achieves improvements of 1.6%, 5.0%, and 2.8% in mAP₅₀, mAP₇₅, and mAP_50:95 on the SFISD dataset, respectively. Full article

Synthetic Aperture Radar (SAR) is a crucial remote sensing technology with significant advantages. Ship detection in SAR imagery has garnered significant attention. However, existing ship detection methods often overlook feature extraction, and the unique imaging mechanisms of SAR images hinder the direct application of conventional natural image feature extraction techniques. Moreover, oriented bounding box-based detection methods often prioritize accuracy excessively, leading to increased parameters and computational costs, which in turn elevate computational load and model complexity. To address these issues, we propose a novel two-stage detector, Burgs-rooted vertex offset encoding scheme (BurgsVO), for detecting rotated ships in SAR images. BurgsVO consists of two key modules: the Burgs equation heuristics module, which facilitates feature extraction, and the average diagonal vertex offset (ADVO) encoding scheme, which significantly reduces computational costs. Specifically, the Burgs equation module integrates temporal information with spatial data for effective feature aggregation, establishing a strong foundation for subsequent object detection. The ADVO encoding scheme reduces parameters through anchor transformation, leveraging geometric similarities between quadrilaterals and triangles to further reduce computational costs. Experimental results on the RSSDD and RSDD benchmarks demonstrate that the proposed BurgsVO outperforms the state-of-the-art detectors in both accuracy and efficiency. Full article