Search Results (2,795)

Radar is one of the primary means of monitoring maritime targets. Compared to electro-optical systems, radar offers the advantage of all-weather, day-and-night operation. However, existing radar target detection algorithms predominantly achieve binary detection (i.e., determining the presence or absence of a target) and are unable to accurately classify target types. This limitation is particularly significant for coastal-deployed maritime surveillance radars, which must contend with not only maritime vessels but also various land-based and island targets within their monitoring range. This paper aims to enhance the informational breadth of existing binary detection methods by proposing a land–sea classification method of radar targets based on dynamic dense connections. The core idea behind this method is to merge the interlayer output features of the network and to augment and weigh them through dynamic convolutional combinations to improve the feature extraction capability of the network. The experimental results demonstrate that the proposed attribute recognition method outperforms current deep network architectures. Full article

The growing need for high-efficiency and reliable propulsion systems in naval applications, particularly within the evolving landscape of submarine warfare, has led to an increased interest in multiphase Permanent Magnet AC motors. This study presents a modelling and simulation approach for a 3MW, seventeen-phase Permanent Magnet AC motor designed for submarine propulsion, integrating an AI-based drive control system. Despite the advantages of multiphase motors, such as higher power density and enhanced fault tolerance, significant challenges remain in achieving precise torque and variable speed, especially for externally mounted motors operating under deep-sea conditions. Existing control strategies often struggle with the inherent nonlinearities, unmodelled dynamics, and extreme environmental variations (e.g., pressure, temperature affecting oil viscosity and motor parameters) characteristic of such demanding deep-sea applications, leading to suboptimal performance and compromised reliability. Addressing this gap, this research investigates advanced control methodologies to enhance the performance of such motors. A MATLAB/Simulink framework was developed to model the motor, whose drive system leverages an AI-optimised dual fuzzy-PID controller refined using the Harmony Search Algorithm. Additionally, a combination of Indirect Field-Oriented Control (IFOC) and Space Vector PWM strategies are implemented to optimise inverter switching sequences for precise output modulation. Simulation results demonstrate significant improvements in torque response and control accuracy, validating the efficacy of the proposed system. The results highlight the role of AI-based propulsion systems in revolutionising submarine manoeuvrability and energy efficiency. In particular, during a test case involving a speed transition from 75 RPM to 900 RPM, the proposed AI-based controller achieves a near-zero overshoot compared to an initial control scheme that exhibits 75.89% overshoot. Full article

Background: Increasing regulatory pressure for maritime decarbonization (e.g., IMO CII, FuelEU) drives adoption of low-carbon fuels and prompts reassessment of regional ports’ competitiveness. This study aims to evaluate the economic and environmental viability of rerouting deep-sea container services to regional ports in a decarbonizing world. Methods: A scenario-based analysis is used to evaluate total costs and CO₂ emissions across the entire container shipping supply chain, incorporating deep-sea shipping, port operations, feeder services, and inland rail/road transport. The Port of Liverpool serves as the primary case study for rerouting Asia–Europe services from major ports. Results: Analysis indicates Liverpool’s competitiveness improves with shipping lines’ slow steaming, growth in hinterland shipment volume, reductions in the emission factors of alternative low-carbon fuels, and an increased modal shift to rail matching that of competitor ports (e.g., Southampton). A dual-port strategy, rerouting services to call at both Liverpool and Southampton, shows potential for both economic and environmental benefits. Conclusions: The study concludes that rerouting deep-sea services to regional ports can offer cost and emission advantages under specific operational and market conditions. Findings on factors and conditions influencing competitiveness and the dual-port strategy provide insights for shippers, ports, shipping lines, logistics agents, and policymakers navigating maritime decarbonization. Full article

In this research, the effect of ship navigation on the mooring system of a deep-sea floating tidal energy platform is experimentally investigated. Hydrodynamic experiments were conducted on a figure-of-eight mooring system with a KCS ship (KRISO Container Ship) as the sailing ship model and a catamaran as the carrier model of the tidal current energy generator under the combined effect of waves and ocean currents. The experimental results show that the increase in ship speed increases the amplitude of the carrier motion re-response. When the ship speed increases from 1.2 m/s to 1.478 m/s, the roll amplitude increases by 220%. At the same time, a decrease in the distance and draft of the navigating vessel also increases the amplitude of the motion response. Then, the actual sea conditions are simulated by the combined effect of ship waves and regular waves. As the wave period decreases and the height increases, the platform motion response is gradually reduced by the ship-generated waves. These findings provide important insights for optimizing the mooring system design in wave-dominated marine environments. Full article

Screw propulsors have attracted increasing attention for their potential applications in amphibious vehicles and benthic robots, owing to their ability to perform both terrestrial and underwater locomotion. To investigate their hydrodynamic characteristics, a two-stage numerical analysis was carried out. In the first stage, steady-state simulations under various advance coefficients were conducted to evaluate the influence of key geometric parameters on propulsion performance. Based on these results, a representative configuration was then selected for transient analysis to capture unsteady flow features. In the second stage, a Detached Eddy Simulation approach was employed to capture unsteady flow features under three rotational speeds. The flow field information was analyzed, and the mechanisms of vortex generation, instability, and dissipation were comprehensively studied. The results reveal that the propulsion process is dominated by the formation and evolution of tip vortices, root vortices, and cylindrical wake vortices. As rotation speed increases, vortex structures exhibit a transition from ordered spiral wakes to chaotic turbulence, primarily driven by centrifugal instability and nonlinear vortex interactions. Vortex breakdown and energy dissipation are intensified downstream, especially under high-speed conditions, where vortex integrity is rapidly lost due to strong shear and radial expansion. This hydrodynamic behavior highlights the fundamental difference from conventional propellers, and these findings provide theoretical insight into the flow mechanisms of screw propulsion. Full article

Given the harsh working conditions of underwater pan-tilt systems, their energy consumption management is particularly crucial. This study proposes an underwater pan-tilt operation method with an automatic wake-up mechanism, which activates only upon target detection, replacing conventional timer-based triggering. Furthermore, departing from fixed-duration observation strategies, we introduce a Q-learning algorithm to optimize operational modes. The algorithm dynamically adjusts working modes based on surrounding biological activity frequency: employing a low-power mode (reduced energy consumption with lower monitoring intensity) during periods of sparse biological presence and switching to a high-performance mode (extended observation duration, higher energy consumption, and enhanced monitoring intensity) during frequent biological activity. Simulation results demonstrate that compared to fixed-duration observation schemes, the proposed optimization strategy achieves a 11.11% improvement in monitoring effectiveness while achieving 16.21% energy savings. Full article

Sea ice parameters are crucial for polar ship design. During China’s 39th Antarctic Scientific Expedition, ice condition from the entire navigation process of the research vessel Xuelong 2 was recorded using shipborne cameras. To obtain sea ice parameters, two deep learning models, Ice-Deeplab and U-Net, were employed to automatically obtain sea ice concentration (SIC) and sea ice thickness (SIT), providing high-frequency data at 5-min intervals. During the observation period, ice navigation accounted for 32 days, constituting less than 20% of the total 163 voyage days. Notably, 63% of the navigation was in ice fields with less than 10% concentration, while only 18.9% occurred in packed ice (concentration > 90%) or level ice regions. SIT ranges from 100 cm to 234 cm and follows a normal distribution. The results demonstrate that, to achieve enhanced navigation efficiency and fulfill expedition objectives, the research vessel substantially reduced duration in high-concentration ice areas. Additionally, the results of SIC extracted from shipborne camera images were compared with the data from the Copernicus Marine Environment Monitoring Service (CMEMS) satellite remote sensing. In summary, the sea ice parameter data obtained from shipborne camera images offer high spatial and temporal resolution, making them more suitable for engineering applications in establishing sea ice environmental parameters. Full article

Deep learning with unmanned aerial vehicles (UAVs) is transforming maritime search and rescue (SAR) by enabling rapid object identification in challenging marine environments. This study benchmarks the performance of YOLO models for maritime SAR under diverse weather conditions using the SeaDronesSee and AFO datasets. The results show that while YOLOv7 achieved the highest mAP@50, it struggled with detecting small objects. In contrast, YOLOv10 and YOLOv11 deliver faster inference speeds but compromise slightly on precision. The key challenges discussed include environmental variability, sensor limitations, and scarce annotated data, which can be addressed by such techniques as attention modules and multimodal data fusion. Overall, the research results provide practical guidance for deploying efficient deep learning models in SAR, emphasizing specialized datasets and lightweight architectures for edge devices. Full article

The growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain. Full article

The escalating environmental noise pollution along urban rail transit corridors, exacerbated by rapid urbanization, necessitates innovative and efficient noise control measures. A comprehensive investigation was conducted that utilized field measurements of train passing-by noise to establish a statistical energy analysis model for evaluating the acoustic performance of both vertical (VB) and fully enclosed (FB) barrier configurations. The study incorporated Maa’s theory of micro-perforated plate (MPP) parameter optimization and developed a neural network surrogate model focused on insertion loss maximization for barrier geometric design. Key findings revealed significant barrier-induced near-track noise amplification, with peak effects observed at the point located 1 m from the barrier and 2 m above the rail. Frequency-dependent analysis demonstrated a characteristic rise-and-fall reflection pattern, showing maximum amplifications of 1.47 dB for VB and 4.13 dB for FB within the 400–2000 Hz range. The implementation of optimized MPPs was found to effectively eliminate the near-field noise amplification effects, achieving sound pressure level reductions of 4–8 dB at acoustically sensitive locations. Furthermore, the high-precision surrogate model (R² = 0.9094, MSE = 0.8711) facilitated optimal geometric design solutions. The synergistic combination of MPP absorption characteristics and geometric optimization resulted in substantially enhanced barrier performance, offering practical solutions for urban rail noise mitigation strategies. Full article

Permanent magnetic couplings provide critical advantages for deep-sea systems through static-sealed, contactless power transmission. However, conventional metallic isolation sleeves incur significant eddy current losses, limiting efficiency and high-speed operation. Limited torque capacities fail to meet the operational demands of harsh marine environments. This study presents a novel permanent magnet coupling featuring a ceramic isolation sleeve engineered for deep-sea cryogenic ammonia submersible pumps. The ceramic sleeve eliminates eddy current losses and provides exceptional corrosion resistance in acidic/alkaline environments. To withstand 3.5 MPa hydrostatic pressure, a 6-mm-thick sleeve necessitates a 10 mm operational air gap, challenging magnetic circuit efficiency. To address this limitation, an improved 3D magnetic equivalent circuit (MEC) model was developed that explicitly accounts for flux leakage and axial end-effects, enabling the accurate characterization of large air gap fields. Leveraging this model, a Taguchi method-based optimization framework was implemented by balancing key parameters to maximize the torque density. This co-design strategy achieved a 21% increase in torque density, enabling higher torque transfer per unit volume. Experimental validation demonstrated a maximum torque of 920 Nm, with stable performance under simulated deep-sea conditions. This design establishes a new paradigm for high-power leak-free transmission in corrosive, high-pressure marine environments, advancing applications from deep-sea propulsion to offshore energy systems. Full article

Traps are central to petroleum exploration, where hydrocarbons accumulate during migration. Reservoirs are likewise an essential petroleum system element and serve as the primary medium for hydrocarbon storage. The buried hill is a geological formation highly favorable for reservoir development. However, the factors influencing hydrocarbon accumulation in buried hill reservoirs are highly diverse, especially in areas with complex, active fault systems. Fault systems play a dual role, both in the formation of reservoirs and in the migration of hydrocarbons. Therefore, understanding the impact of complex fault systems helps enhance the exploration success rate of buried hill traps and guide drilling deployment. In the Beibuwan Basin in the South China Sea, buried hill traps are key targets for deep-buried hydrocarbon exploration in this faulted basin. The low level of exploration and research in buried hills globally limits the understanding of hydrocarbon accumulation conditions, thereby hindering large-scale hydrocarbon exploration. By using drilling data, logging data, and seismic data, stress fields and tectonic faults were restored. There are two types of buried hills developed in the Beibuwan Basin, which were formed during the Late Ordovician-Silurian period and Permian-Triassic period, respectively. The tectonic genesis of the Late Ordovician-Silurian period buried hills belongs to magma diapirism activity, while the tectonic genesis of the Permian-Triassic period buried hills belongs to reverse thrust activity. The fault systems formed by two periods of tectonic activity were respectively altered into basement buried hills and limestone buried hills. The negative structural inversion controls the distribution and interior stratigraphic framework of the deformed Carboniferous strata in the limestone buried hill. The faults and derived fractures of the Late Ordovician-Silurian period and Permian-Triassic period promoted the diagenesis and erosion of these buried hills. The faults formed after the Permian-Triassic period are not conducive to calcite cementation, thus facilitating the preservation of the reservoir space formed earlier. The control of hydrocarbon accumulation by the fault system is reflected in two aspects: on the one hand, the early to mid-Eocene extensional faulting activity directly controlled the depositional process of lacustrine source rocks; on the other hand, the Late Eocene-Oligocene, which is closest to the hydrocarbon expulsion period, is the most effective fault activity period for connecting Eocene source rocks and buried hill reservoirs. This study contributes to understanding of the role of complex fault activity in the formation of buried hill traps within hydrocarbon-bearing basins. Full article

Rare earth elements and yttrium (REY) are critical metals underpinning high-technology industries. Marine deposits have attracted growing interest due to their abundant REY reserves and high grades. This review synthesizes current knowledge on sources, distribution, and enrichment mechanisms of marine REY, with a particular focus on the role of microorganisms in REY phase transitions, fractionation, and enrichment. We highlight the largely untapped potential of marine-specific microbial strains and critically assess their influence on REY cycling. Key research challenges are proposed, followed by actionable directions to advance understanding of microbial–REY interactions. This review aims to deepen insights into marine REY cycling and support the sustainable development of deep-sea REY resources, emphasizing the need to integrate molecular-scale microbial processes into marine REY biogeochemical models. Full article

The aperture of the towed horizontal line array is limited and the received signal is unstable in a complex ocean environment, making it difficult to distinguish the location of the sound source. To address this challenge, this paper presents a MoELocNet (Mixture of Experts Localization Network) for deep-sea sound source localization, leveraging interference structures in range-frequency domain signals from a towed horizontal line array. Unlike traditional correlation-based methods constrained by time-varying ocean environments and low signal-to-noise ratios, the model employs multi-expert and multi-task learning to extract interference periods from single-frame data, enabling robust estimation of source range and depth. Simulation results demonstrate its superior performance in the deep-sea shadow zone, achieving a range localization error of 0.029 km and a depth error of 0.072 m. The method exhibits strong noise robustness and delivers satisfactory results across diverse deep-sea zones, with optimal performance in shadow zones and secondary effectiveness in the direct arrival zone. Full article

Underwater sensor networks (UWSNs) play a crucial role in subsea operations like marine exploration and environmental monitoring. A major challenge for UWSNs is achieving effective and energy-efficient data collection, particularly in deep-sea mining, where energy limitations and long-term deployment are key concerns. This study introduces a Channel-Aware AUV-Aided Data Collection Scheme (CADC) that utilizes deep reinforcement learning (DRL) to improve data collection efficiency. It features an innovative underwater node traversal algorithm that accounts for unique underwater signal propagation characteristics, along with a DRL-based path planning approach to mitigate propagation losses and enhance data energy efficiency. CADC achieves a 71.2% increase in energy efficiency compared to existing clustering methods and shows a 0.08% improvement over the Deep Deterministic Policy Gradient (DDPG), with a 2.3% faster convergence than the Twin Delayed DDPG (TD3), and reduces energy cost to only 22.2% of that required by the TSP-based baseline. By combining a channel-aware traversal with adaptive DRL navigation, CADC effectively optimizes data collection and energy consumption in underwater environments. Full article