Reliable near-coastal localization is difficult because LiDAR and scanning radar fail in complementary regimes: LiDAR provides precise local geometry but loses support over open water, whereas radar preserves long-range observations but suffers from clutter, multipath, and low spatial resolution. We present RACIF-SLAM, a reliability-aware framework that unifies continuous localization and hierarchical occupancy mapping. RACIF-SLAM transforms sensor-native motion increments into a common vessel frame, infers temporally filtered reliability from registration evidence, and applies an innovation gate to suppress the less credible modality under cross-sensor conflict before fusion on SE(2). When LiDAR tracking collapses, a radar bridge sustains motion estimation and re-anchors the recovered LiDAR segment without trajectory discontinuity. For mapping, LiDAR retains local occupied/free-space authority, while spatially broadened radar evidence extends support beyond the effective LiDAR range. In the four evaluated near-coastal segments, every reported pose was supported by at least one accepted sensor increment; the dead-reckoning fallback was not invoked. Against synchronized dual-antenna GNSS, RACIF-SLAM achieves a mean ATE of 0.700 m and a 10-frame translational RPE of 0.183 m, reductions of 66.3% and 65.1%, respectively, over the next-best evaluated method. These segment-specific results indicate that evidence-dependent authority transfer can improve localization continuity while retaining fine local map structure.
This study employs a Contracted and Loaded Tip (CLT) propeller to replace the conventional pump-jet rotor, and a comparative analysis is conducted using the Detached Eddy Simulation (DES) method. The reliability of the numerical method is first validated through open-water tests. The CLT pump-jet propulsors are systematically investigated in two series: the varying endplate width (CW) and varying endplate length (CL). The results indicate that varying the endplate width has a significant impact on the hydrodynamic performance. A smaller width leads to a more pronounced decrease in the thrust coefficient, torque coefficient, and efficiency. In contrast, varying the endplate length primarily affects the efficiency at high advance coefficients. Regarding the vortical characteristics, the gap region is dominated by the tip leakage vortex (TLV) and tip separation vortex (TSV), whose intensity and evolution are influenced by the endplate structure. A reduced endplate width expands the low-velocity region within the gap, while an increased length contributes to a more stable vortex development. In terms of the noise performance, the varying-width designs effectively suppress pressure fluctuations and achieve noise reduction. Specifically, the CW3 model exhibits maximum reductions in the overall sound source level of 5.7 dB and 4.0 dB at distances of 1 m and 20 m, respectively. Conversely, the varying-length designs intensify the pressure fluctuations and show no significant noise reduction effect.
Spectral wave models such as SWAN provide reliable harbor wave fields but require repeated, computationally expensive runs during preliminary design. This study develops a domain-knowledge-guided, U-Net-based surrogate model that rapidly approximates two-dimensional wave fields from SWAN simulations. The model takes water depth, incident significant wave height, incident wave direction, and a signed distance field (SDF) encoding structure geometry as inputs, and it combines a dynamic-resolution preprocessing scheme that preserves the terrain aspect ratio with a shadow-weighted loss function that emphasizes the sheltered zone behind breakwaters. Trained on 240 SWAN scenarios that combine 8 idealized geometries with 5 incident directions and 6 wave heights (about 15.7 million grid points), the model reproduced the SWAN significant wave height with a coefficient of determination of R2 = 0.8807 and a mean absolute error of 0.07 m over the whole domain under an untrained but within-range (interpolated) wave height condition, with comparable accuracy in the region of interest behind breakwaters (ROI R2 = 0.85). In a real sea application to Sokcho Harbor benchmarked against SWAN rather than field data, the model reproduced the offshore to harbor wave height pattern; along entrance and propagation-axis transects, it followed the SWAN profiles with high correlation (R ≈ 0.94–0.95), while overestimating the innermost calm zone waves by a non-negligible margin. The model is thus suited to rapid wave-field screening in preliminary harbor design.
Multi-module DC–DC converters are well suited to shipboard DC power systems with stringent requirements for high power density, operational safety, and continuous power supply. By distributing the system voltage, current, and power among multiple submodules (SMs), the modular architecture reduces device stresses and facilitates capacity expansion, maintenance, and redundant operation. Among the available modular configurations, the input-series output-parallel (ISOP) structure is particularly suitable for interfacing high-voltage DC buses with low-voltage, high-current loads. However, conventional voltage- or current-sharing strategies generally neglect efficiency differences among SMs. Under equal power sharing, low-efficiency SMs generate greater losses and experience higher thermal stress, resulting in thermal imbalance and accelerated aging. To address this issue, an efficiency-consensus-based power-distribution strategy is proposed for ISOP LLC-DAB hybrid converters. A distributed efficiency observer based on multi-agent consensus theory dynamically regulates the power references according to the relative efficiencies of the SMs, allowing high-efficiency modules to process more power while reducing the loading of low-efficiency modules. Experimental results obtained from a three-module prototype include comparative efficiency measurements and temperature-distribution tests. The results demonstrate that the proposed strategy improves the efficiency consistency among the three SMs, redistributes power according to their relative efficiency states, and reduces the temperature difference among the modules, thereby mitigating localized loss concentration and thermal imbalance. The proposed method provides a feasible solution for improving the electrothermal operating conditions of modular DC–DC converters. The achieved reduction in thermal imbalance may contribute to enhanced long-term reliability by alleviating uneven electrothermal stress.
A residual multilayer perceptron (ResNetMLP) surrogate framework is presented to predict the broadband radiated sound power spectra of submerged circular cylindrical shells. The surrogate maps the shell mean radius, wall thickness, and longitudinal excitation position to the unit-force sound power spectra generated by a high-fidelity frequency-domain solver. The trained model is deployed within a diagonal power superposition scheme using equivalent nodal forces derived from an unsteady computational fluid dynamics surface pressure field. Comparing surrogate predictions with direct diagonal vibroacoustic calculations confirms the high predictive accuracy within the diagonal approximation. Crucially, a key limitation of sound power-based surrogates is highlighted: because acoustic power is a quadratic scalar quantity, it cannot capture phase-coherent load interaction, providing a clear rationale for future pressure-based surrogate formulations.
Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) archives usually provide only one delay–Doppler map (DDM) for each recorded observation condition, limiting the representation of residual DDM variability. This study proposes a Position-Guided Conditional Normalizing Flow (PGCFlow) for observation-grounded probabilistic expansion of ocean bistatic radar cross section (BRCS) DDMs. PGCFlow uses four invertible affine coupling blocks to map a 17 × 11 DDM to an equal-dimensional Gaussian latent space. Wind–Auxiliary Condition Modulation incorporates a seven-dimensional condition vector into affine-parameter prediction, while Position-Guided Cross-Partition Aggregation (PGCA) uses deterministic grid descriptors to retain explicit cell locations and facilitate spatial-dependence modeling. Experiments used 5,819,042 quality-controlled CYGNSS observations from 2024. PGCFlow was compared with a conditional variational autoencoder and a generic conditional invertible neural network on 8000 held-out recorded conditions drawn from the same empirical observation domain, with 16 generated DDMs per condition. Although the cVAE achieved the highest balanced-aggregate structural similarity (SSIM) of 0.9396, PGCFlow obtained the lowest Fair Energy Score (FES) and Variogram Score (VS) of 0.2242 and 0.0641 and the closest relative local-neighborhood dispersion to unity at 1.0501. It also achieved the lowest frozen-estimator response RMSE and response MAE of 1.1900 and 0.9129 m/s, respectively. Ablation results indicated individual contributions from both proposed modules. Overall, PGCFlow achieved a favorable trade-off among the evaluated fidelity, dependence, dispersion, and response-consistency measures.
Inland-vessel speed reductions targeted at priority segments can reallocate a normalized burden proxy across a fixed corridor. We develop a route-integrity speed-planning model that accepts a plan only when it attains the priority target, preserves route-wide improvement, and caps the largest non-priority-segment increase. The kinematics distinguish speed over ground, along-route current, and speed through water; without voyage-matched current or speed-through-water observations, the cases use zero-current analytical references. We evaluate two AIS-derived cases: the 27-point Case A as a sparse stress test and Case B as the primary spatial case after WGS84 and ESA WorldCover alignment checks. Under the equal-time constraint, an unprotected 2% target induces local deterioration. With a 0.5% non-priority cap, Case B supports a certified maximum target of 0.999%; a 2% late-arrival allowance raises the certified maximum target to 11.912%. Direct search over 0.1-kn commands finds a feasible plan for Case B; however, none of 1000 sampled ±0.2-kn execution-error profiles remains feasible. The framework identifies model-internal proxy transfer rather than measured energy, emissions, or exposure effects.
The integrated fusion and inversion of seismic data and marine controlled-source electromagnetic (MCSEM) data can identify the gas hydrate distributions. However, due to differences in observation systems and scales between seismic and MCSEM data, current fusion methods have failed to effectively address the critical issue of physical property variation within gas hydrate reservoirs. This research seeks to consolidate the two datasets into a cohesive observational framework. By transforming the MCSEM data into low-frequency constraints applicable to seismic impedance inversion, it is possible to realize an effective integrative interpretation that combines both seismic and MCSEM data. Using a 3 km-long seismic dataset and MCSEM data from the Shenhu Sea area in the South China Sea as a case study, we apply the Poisson blending algorithm to integrate seismic and MCSEM data, enabling precise identification and characterization of gas hydrate reservoirs and underlying gas-bearing fluids. The gas hydrate saturation results, derived from seismic inversion constrained by MCSEM data, demonstrate strong consistency with well logging and geological interpretation. This concordance validates the efficacy of the integrated fusion and inversion methodology and highlights its advantages in accurately predicting the spatial distribution of gas hydrate enrichment. The developmental positions of deep gas-bearing fluid pathways, coupled with the fault locations within the free gas zone and gas hydrate-bearing layer, play significant roles in the heterogeneous enrichment of gas hydrates. This research provides important technical and theoretical support for the precise and efficient prediction of gas hydrate reservoirs.
This study investigates the hydrodynamic behavior of an amphibious rescue and operation platform equipped with detachable floating boxes and a front bucket system, and it further develops a control-oriented turning optimization framework for a two-bucket steering configuration. Calm-water resistance, free-surface evolution, running attitude, and roll decay were analyzed using a Reynolds-averaged Navier–Stokes/volume-of-fluid solver with overset grids and dynamic fluid–body interaction. Straight-ahead non-rotating cases were computed using a symmetry-based half-domain model, whereas roll- and turning-related cases were simulated in the full domain. The numerical method was validated against towing-tank data for a benchmark amphibious vehicle, and the predicted resistance showed an overall deviation of 2.11%. The results show that the detachable floating boxes slightly increase resistance at 2 km/h, but reduce resistance by approximately 11.4% at 8 km/h owing to favorable wave interference. They also reduce trim and heave over the investigated speed range and markedly improve transverse stability, with the roll motion decaying to nearly zero within about 20 s. By contrast, the installation of the bucket substantially increases hydrodynamic resistance; at the design cruising speed of 8 km/h, the resistance increase reaches about 74.4%, while a bucket-induced bow-down moment modifies the running attitude and suppresses heave. At cruising speed, the bucket swing-arm angle has a non-monotonic influence: the resistance reaches a local peak near 6°, the minimum resistance is obtained at 20°, and the smallest trim is achieved at 4°. Based on these findings, a symmetry-preserving hydrodynamic surrogate and a constrained optimization strategy were established for bucket-controlled turning-radius allocation. The results indicate that differential bucket motion is the primary steering mechanism, whereas the bucket-arm angle provides secondary steering amplification at the cost of additional drag. The present study provides an integrated hydrodynamic basis for the design, operation, and steering-oriented control allocation of amphibious rescue platforms.
This study develops a SCHISM-based hydrodynamic model and an offline Lagrangian virtual-particle workflow for the Zadar Channel, a geometrically complex island–mainland passage in the eastern Adriatic. Independent hourly observations from the MP Zadar tide gauge operated by the Hydrographic Institute of the Republic of Croatia (HHI) were used to evaluate the modelled free-surface response. After exclusion of the first 24 h ramping period, 192 matched hourly pairs gave a Pearson correlation of 0.913, a mean bias of 0.012 m, a mean absolute error of 0.051 m, and a root-mean-square error of 0.070 m; cross-correlation was maximized at zero lag. The model reproduced the timing of the observed oscillations but underestimated their amplitude, with simulated and observed standard deviations of 0.117 and 0.157 m, respectively. The adopted unstructured mesh contains 16,962 triangular elements and 9081 nodes. In four 24 h particle-sensitivity tests, maximum reach ranges from 8.4 to 14.2 km; a 15-fold change in horizontal diffusivity affects reach less than sampling a lower model layer, which reduces reach by 32.8%. In the June 2025 event calculation, cumulative numerical shoreline contact increases from zero to all 1000 particles. The approximately 4 km Copernicus regional product masks the narrow interior passages and is therefore used only to assess spatial representativeness, not to validate channel currents. The tide-gauge comparison supports the modelled sea-level response and its timing at one station, but does not constitute direct validation of local current velocities. The reported trajectories are current-driven passive-particle diagnostics; wave–current coupling, Stokes drift, and material-specific fate processes are not represented.
Battery-powered propulsion offers a pathway for reducing inland shipping emissions. However, low battery energy density limits sailing range and may require energy replenishment during a voyage, thereby complicating energy and voyage planning for inland electric vessels. Time-of-use (TOU) pricing creates cost-saving opportunities but further complicates planning because energy replenishment and sailing speed are closely coupled. This study develops a joint optimization framework for an inland electric vessel under TOU pricing that determines replenishment ports, technologies, amounts, and leg-specific sailing speeds to minimize total replenishment costs. The problem is formulated as a mixed-integer nonlinear programming model and reformulated as a mixed-integer linear programming approximation through equivalent linearization and speed discretization. A Yangtze River case study with five operating conditions evaluates the proposed framework. The results show that, under the proposed framework, TOU pricing reduces total replenishment costs by 42.4–43.4% compared to fixed pricing. Relative to a sailing-speed optimization benchmark, joint optimization under TOU pricing reduces replenishment costs by 9.8–12.5% and energy consumption by 4.2–5.6%. The cost-saving potential also varies with the voyage time limit, voyage start time, relative charging and battery swapping rates, and the availability of opportunity charging.
Direct-drive wave power generation systems based on permanent magnet linear generators (PMLGs) produce fluctuating electromagnetic power under irregular wave excitation, which may affect DC-bus voltage stability and load-side power quality. To smooth the fluctuating output power, this paper develops an empirical mode decomposition (EMD)-based power allocation strategy for a battery–supercapacitor hybrid energy storage system (HESS). In the proposed strategy, EMD is used to decompose the fluctuating electromagnetic power into low-frequency and high-frequency components according to their time-scale characteristics. The low-frequency component is assigned to the battery for energy buffering, while the high-frequency component is assigned to the supercapacitor for transient power compensation. Finite-control-set model predictive current control (FCS-MPCC) is adopted on the generator side to improve the current response of the PMLG, and an MPC-based HESS controller is designed to track the assigned power commands and regulate the DC-bus voltage. Simulation results show a battery power-tracking error of 3.93 W and a DC-bus voltage standard deviation of 0.108 V; compared with LPF, EMD reduced the load-step voltage deviation by 11.94%. Experiments confirm that the PMLG back-EMF follows the translator velocity, the storage currents track their references, and the DC-bus voltage remains within ±2 V of its reference.
Winter ice-cover periods pose substantial risks to Apostichopus japonicus aquaculture in northern China. We combined field monitoring of three representative aquaculture ponds between 2014 and 2017 with controlled single-factor laboratory exposures to low temperature, hypoxia, and hyposalinity in adult and juvenile sea cucumbers. Field observations revealed pronounced temporal, vertical, spatial, and interannual variability in water temperature, salinity, and dissolved oxygen (DO) during freezing and ice-melting periods. All three stressors caused clear deterioration in physiological condition, with juveniles generally exhibiting greater short-term sensitivity than adults under low-temperature and hypoxic exposure, whereas severe hyposalinity caused pronounced deterioration in both life stages. Lactate, malondialdehyde (MDA), and glutathione (GSH) showed distinct treatment- and time-dependent responses, consistent with stress-associated changes in anaerobic metabolism, lipid peroxidation, and glutathione-associated antioxidant status. Integration of field observations with laboratory responses indicated that water temperatures approaching 0 °C and salinities approaching approximately 20‰ represent environmentally relevant conditions associated with elevated overwintering risk. DO concentrations approaching approximately 3 mg/L may warrant intensified monitoring and management intervention, whereas 2 mg/L represents a more severe experimental hypoxia condition. The more extreme treatments of −2 °C and 15‰ should similarly be regarded as severe experimental scenarios rather than commonly occurring field conditions. These environmental values should therefore be interpreted as preliminary management-oriented risk references rather than definitive physiological thresholds. Overall, the study provides an empirical field-to-laboratory basis for biologically informed winter environmental monitoring and risk-based overwintering management of A. japonicus aquaculture.
As marine development extends into deep and far-offshore waters, fixed breakwaters become increasingly costly and difficult to construct and may have greater environmental impacts. Floating breakwaters (FBs) offer flexible deployment across a wide range of water depths and have therefore received growing attention. Yet the dominant wave attenuation mechanisms and key hydrodynamic responses differ among FB configurations, making it difficult to compare their performance and select suitable analysis methods. This review examines advances in FB configurations, analysis methods, and hydrodynamic characteristics. It summarizes structural types and shows that FB design has evolved from reliance on principal dimensions and wave reflection toward the combined use of multiple attenuation mechanisms. The applicability and limitations of empirical methods, potential flow methods, computational fluid dynamics, and experimental methods are compared. Structural optimization, mooring system design, wave attenuation mechanisms, and fluid–structure interaction are also reviewed, with attention paid to motions, loads, and local flows. Key unresolved challenges include broadband attenuation of long-period waves, safety under extreme sea states, performance trade-offs in multifunctional systems, and scale effects and prototype validation. By linking FB configuration characteristics with dominant wave attenuation mechanisms, key hydrodynamic responses, and the applicability of different analysis methods, this review provides an integrated comparative framework for evaluating FB performance, selecting appropriate analysis methods, and supporting engineering assessment in deep and far-offshore waters.
With the rapid advancement of artificial intelligence (AI), maritime autonomous surface ships (MASSs)—also referred to as autonomous vessels—have emerged as an important avenue of research in the maritime industry [...]
Unmanned Underwater Vehicle (UUV) swarms operating in complex marine environments must accurately assess threats from surrounding targets to ensure mission success and navigational safety. However, existing threat assessment methods face three fundamental bottlenecks when applied to underwater swarms: the inability to track temporally evolving target intentions, reliance on subjective indicator weighting for multi-target ranking, and vulnerability to spatially heterogeneous sonar noise. This paper proposes a hierarchical threat assessment framework that addresses these bottlenecks through three integrated modules. First, a Dynamic Bayesian Network with a specially designed heading factor tracks target intention over time, propagating threat probabilities across sequential observations and enabling early warning before the closest point of approach. Second, a Vieta’s theorem-based algebraic ranking algorithm constructs comprehensive threat vectors via elementary symmetric polynomials of six indicator utilities, avoiding explicit expert-defined weighting coefficients in the multi-attribute ranking stage while capturing both independent and synergistic indicator interactions. Third, a distance-weighted swarm aggregation strategy suppresses individual sonar noise by assigning higher fusion weights to geographically closer nodes, exploiting the spatial diversity inherent in swarm configurations. Simulation experiments under representative target-motion scenarios validate the framework across four complementary experimental studies. Results demonstrate that the DBN reduces output variance by over 56% compared to static Bayesian networks and responds to abrupt intention changes within 15 s. The algebraic ranking algorithm achieves identical prioritization to TOPSIS without requiring any manual or data-dependent weights. The distance-weighted aggregation reduces root mean square error by 63.2% and improves signal-to-noise ratio by 8.7 dB over equal-weight averaging. The proposed framework provides a principled and interpretable solution for simulation-based autonomous threat perception in representative underwater swarm scenarios.
As environmental pressures intensify, sustaining ecologically and economically important marine species such as the American lobster has become an increasing challenge. Climate-driven stressors are expected to disproportionately affect early life stages of the American lobster (Homarus americanus), as sea surface temperatures are warming more rapidly than benthic habitats. In addition, environmental pollutants, including micro- and nanoplastics, trace metals, persistent organic pollutants, and per- and polyfluoroalkyl substances, can interact with climate change to exacerbate physiological stress. Previous studies have demonstrated that changes in temperature, salinity, and pH significantly influence development, moulting, cardiac function, survival, and disease susceptibility in H. americanus. In this context, aquaculture-based approaches are being explored as complementary tools to support stock resilience and reduce reliance on wild populations. Recirculating aquaculture systems (RAS) offer a controlled framework to investigate the combined effects of environmental stressors on H. americanus physiology while assessing the feasibility of closed-system rearing. Overall, this review synthesizes current knowledge on the physiological responses of H. americanus to multiple environmental stressors, with particular emphasis on identifying optimal rearing conditions in RAS. By integrating these findings, the review aims to inform strategies for improving the sustainability and long-term viability of American lobster production under changing environmental conditions.
An empirical ridge statistic that correlates weekly level ice thickness with keel depth and weekly number of ridges was investigated using upward looking sonars (ULS) data from 2006 to 2020 in the Fram Strait. The applied methodology was similar to the previous studies for the Beaufort Sea. The data were divided into weekly segments. For each of them, level ice thickness (hi), the number of ridges (N) and the average (hk) and deepest (hk_deep) keel depth were calculated based on the Rayleigh criterion with a threshold of 2.5 m and a minimum keel draft of 5 m. The broader idea was to compare regions with different ice regimes, in order to see how the ridge statistics change and how well the model can handle these differences. In this paper, we compare the Beaufort Sea and the Fram Strait. Both have a positive correlation between level ice thickness and keel depth. Linear regressions hk=5.91+ 0.60hi and hk_deep=8.67+ 5.91hi with Pearson correlation coefficients r=0.48 and r=0.45, respectively, are established in this study, which are both lower than the Beaufort Sea results.
Infrared and visible images provide complementary cues for maritime ship detection, but practical dual-sensor systems often produce image pairs that are not strictly registered. Directly fusing such unregistered pairs may introduce ghosting artifacts, blurred target boundaries, and feature conflicts, especially over weak-texture sea surfaces where reliable correspondence cues are sparse. To address this problem, we propose UA-FusionDet, an unregistered-aware infrared-visible fusion framework with cross-modal feature alignment for maritime ship detection. The proposed framework extracts visible and infrared features with a dual-branch encoder, aligns the visible feature to the infrared reference through cross-modal deformable feature alignment, and suppresses unstable background offsets using sea-surface saliency guidance. Wavelet-guided complementary fusion then decomposes the aligned features into low- and high-frequency sub-bands, enabling frequency-aware fusion of infrared thermal saliency and visible structural details before feeding the fused representation to both a lightweight reconstruction decoder and a ship detection head. The reconstruction decoder provides auxiliary image-level regularization, while the detection branch supervises the task-oriented fused representation with ship bounding-box annotations. UA-FusionDet does not require registration ground truth or fused-image ground truth during training, making it suitable for realistic maritime monitoring scenarios with imperfectly aligned visible and infrared sensors. Experiments on 3132 unregistered visible–LWIR maritime image pairs show that UA-FusionDet achieves a precision of 0.904, a recall of 0.866, an mAP50 of 0.912, and an mAP50–95 of 0.566, exceeding the strongest competing method by 2.5 and 2.4 percentage points on the two mAP metrics, respectively, while maintaining an inference speed of 52.6 FPS. These results demonstrate that the proposed alignment and fusion framework improves detection accuracy under cross-modal misregistration while retaining practical inference efficiency.
In long-distance subsea pipelines, pipeline inspection gauge (PIG) speed fluctuations under complex operating conditions can cause a mismatch between the sampling frequency and the operating state. This mismatch may compromise the integrity of defect detection signals. To address this problem, this study proposes an adaptive sampling method for in-line inspection of pipeline data based on multi-sensor fusion and a spatial sampling interval model predictive controller (SSI-MPC). The impact of PIG velocity fluctuations on defect detection signal integrity was analyzed, and a multi-sensor velocity fusion estimation model based on Kalman filtering was established to fuse inertial measurement unit (IMU) acceleration data with cumulative displacement information from the odometer. The sampling frequency adjustment was formulated as a spatial sampling interval tracking problem, and an SSI-MPC controller was developed. The effectiveness of the proposed method was verified through comparative simulations with fixed-time sampling and odometer-based equidistant sampling. The results indicate that SSI-MPC adaptive sampling more effectively restores the key quantitative features of defect detection signals under velocity-jump and high-speed conditions. The RSE and RAE of the defect detection signals are controlled within 3.44% and 2.10%, respectively. Compared with fixed-time sampling, the proposed method reduces the detection signal data volume by 39.37%.
