Search Results (291)

Computational fluid dynamics (CFD)-driven optimization of engineering systems is often constrained by high computational cost, particularly when surrogate models must be constructed from limited simulation samples. Although surrogate-assisted multi-objective optimization can substantially reduce CFD evaluations, local prediction errors in decision-sensitive Pareto regions may alter feasibility classification and engineering recommendations near active constraints. To address this issue, this study proposes a Pareto-active-region-guided sequential surrogate modeling framework (PAR-SSM) for multi-objective optimization of liquid-cooled battery thermal management systems. Starting from 15 face-centered central composite design (FCCD) samples, the framework selectively introduces additional high-fidelity CFD evaluations into Pareto-active and constraint-sensitive regions, yielding a 21-sample refined surrogate model. Rather than uniformly improving global prediction accuracy, PAR-SSM directs the limited CFD budget toward regions where surrogate errors can directly influence engineering decisions. After model freezing, three independent Fluent cases were used exclusively for validation, yielding mean absolute deviations of 0.098 °C for maximum temperature and 0.341 °C for temperature difference, while also revealing residual feasibility risk near active constraint boundaries. Application to an autonomous underwater vehicle (AUV) battery module showed that the N = 3 configuration dominated the nominally constrained Pareto set and provided a favorable thermal–hydraulic trade-off under low auxiliary energy consumption. Overall, PAR-SSM provides a decision-oriented strategy for balancing computational cost and optimization credibility in CFD-intensive, constrained multi-objective design. Full article

Gravity-matching navigation is one of the important means for the covert navigation of underwater vehicles. The production and application of gravity reference maps as core key technologies have a very significant impact on the accuracy of underwater navigation. Firstly, this paper adopts the factor analysis method to obtain the comprehensive results of nine characteristic parameters such as the standard deviation and roughness of the gravity reference map by setting a range sliding window. Secondly, the TERCOM algorithm is introduced to conduct simulation verification calculations within the sliding window. After comparing and verifying with the comprehensive results of the factor analysis characteristic parameters, the limitations of statistical methods in the evaluation of the adaptability of gravity reference maps are analyzed. Thirdly, intelligent processing methods such as the learning vector quantization neural network algorithm and the extreme learning machine are proposed. The characteristic parameters of some sliding window gravity reference maps and the simulation verification results of the TERCOM algorithm are used as training samples to predict the adaptability evaluation effect of underwater gravity navigation for other sliding windows. The results show that the prediction results are generally in good agreement with the simulation verification results of the TERCOM algorithm. Compared with the learning vector quantization neural network algorithm, the extreme learning machine algorithm exhibits superior performance in terms of classification accuracy and computational efficiency. Full article

Incremental object detection (IOD) is critical for autonomous underwater perception, where detectors deployed on long-duration underwater platforms must continuously adapt to evolving marine environments while retaining previously learned recognition and localization capabilities. However, underwater IOD is particularly challenging because visual degradation, small-object ambiguity, background dominance, rare-class dilution, and non-stationary data distributions jointly cause structure instability in incremental representations. Existing response-based distillation methods, such as elastic response distillation (ERD), mainly preserve output-level responses and often rely on rigid backbone updating and static optimization strategies, making them insufficient for maintaining hierarchical representation stability under degraded and imbalanced underwater observations. To address these limitations, we propose a structure-stable underwater IOD framework that jointly regulates representation update, replay topology, and optimization dynamics within a unified stability–plasticity formulation. Specifically, Structure-Adaptive Residual Routing (SARR) replaces binary layer freezing with adaptive residual paths, task-aware gradient routing, and semantic-sensitive update gates, enabling parameter-efficient incremental representation routing. Topology-Aware Semantic Replay (TASR) maintains class prototypes, teacher-guided relation matrices, and decision-boundary anchor samples to preserve the neighborhood topology of rare old classes in the feature manifold. Uncertainty-Aware Plasticity–Stability Feedback (UPSF) dynamically adjusts classification and localization distillation strengths according to old-class forgetting risk, new-class learning difficulty, and localization uncertainty. Extensive experiments on UTDAC2020 and DUO demonstrate that the proposed method outperforms representative distillation- and transformer-based IOD baselines in most incremental settings. In particular, our method achieves 30.7% AP on UTDAC2020 under the 2 + 2 setting and narrows the gap to full-data training, validating the effectiveness of structure-stable representation evolution for robust incremental underwater object detection. Full article

Tight oil reservoir quality and development effectiveness are highly dependent on microscopic pore structure characteristics and spatial heterogeneity. In this study, tight sandstones from the Chang 3, Chang 6, Chang 7, and Chang 8 members of the Triassic Yanchang Formation in the Xunyi exploration area, southern Ordos Basin, were selected as research objects. By integrating X-ray diffraction (XRD), cast thin sections, scanning electron microscopy (SEM), high-pressure mercury injection (HPMI) experiments, and multifractal theory, the multi-scale heterogeneity characteristics of pore structures in different layers were quantitatively characterized. The response relationships between multifractal parameters, macroscopic physical properties, and pore size distributions were discussed, and the geological control mechanisms of sedimentation and diagenesis on heterogeneity were revealed. The results indicate that the sedimentary environment plays a fundamental role in controlling reservoir physical properties. The Chang 3 and Chang 8 members, deposited in underwater distributary channels, are dominated by primary and dissolution pores, with physical properties significantly superior to the gravity flow-deposited Chang 6 and Chang 7 members. Multifractal analysis shows that the Chang 3 member has the largest singularity spectrum width (Δα =1.943 ± 0.56) and heterogeneity index (R_d = 1.782 ± 0.99), reflecting its broadest pore size distribution, strongest heterogeneity, and significant intra-layer differences; while the pore structures from Chang 6 to Chang 8 are relatively stable, with the Chang 8 member exhibiting high spatial connectivity. This study demonstrates that the quantitative evaluation method based on multifractal theory can effectively identify microscopic structural differences in tight sandstones, providing a critical supporting basis for reservoir classification characterization and favorable layer selection in the Yanchang Formation of the Ordos Basin. Full article

Fouling on ship hulls increases hydrodynamic drag, fuel consumption, and emissions. This, in turn, necessitates the development of efficient methods for side cleaning and inspection. This work focuses on the application of image-based classification to assess the cleanliness of the surface of the hull in robotic cleaning systems, with respect to the ISO 8501-4 standard. Due to limited data availability, transfer learning techniques using pre-trained convolutional neural networks (ResNet50, EfficientNetB0 and MobileNetV2) were used. Both end-to-end models and hybrid approaches that combine deep feature extraction with XGBoost (version 3.2.0) classification were evaluated. Experiments were carried out on binary classification (cleaned vs. uncleaned surfaces) and multi-class classification of cleanliness levels (WA1, WA2, WA2.5). The results show that transfer learning enables effective recognition of cleaning status, achieving high performance for binary classification despite a small dataset. However, multi-class classification remains challenging due to subtle differences between classes and data limitations. The proposed approach supports automated visual inspection of underwater robotic platforms and represents a step toward objective standards-based assessment of hull cleaning processes. Full article

High-precision identification of acoustic convergence zones (CZs) and acoustic shadow zones (SZs) is a core prerequisite for deep-sea sonar performance prediction and long-range underwater target detection. However, in data-scarce marine environments, traditional acoustic identification methods suffer from high environmental sensitivity and significant computational costs, while pure data-driven deep learning methods face dilemmas such as a lack of physical consistency and poor generalization on small samples. To address these issues, a three-level cascaded recognition framework based on physics-prior-augmented deep learning is proposed in this paper, enabling accurate segmentation of CZs and intelligent classification of sound field types under data-scarce scenarios. In this framework, physical acoustic principles are incorporated exclusively as priors through a training dataset generated by a Gaussian beam acoustic propagation code (Bellhop) and through hand-crafted geometric features derived post hoc from the initial segmentation outputs. Taking a typical deep-sea area in the Northwest Pacific Ocean as the research object, a hybrid dataset comprising 5000 simulated transmission loss images and 500 simulated images from a geographically distinct sea area is constructed. The sound field is categorized into four types: strong convergence, usable convergence, weak convergence, and shadow zone. In the first stage, the ResNet-34 backbone is improved by integrating deformable convolution and a global statistical feature module, which, combined with a joint loss function, achieves high-precision pixel-level segmentation of CZs and SZs, with the regional gray contrast reaching 86.9%. In the second stage, a customized dual-channel VGG16 architecture is designed to fuse the extracted geometric priors and visual features, achieving a sound field classification accuracy of 89.91%. In the third stage, a hybrid data augmentation technique combining Mixup and convolutional autoencoder is adopted alongside a transfer learning strategy to mitigate the data scarcity under cross-domain conditions, boosting the small-sample classification accuracy to 84.45%. The experimental results demonstrate that the models in each stage of the proposed framework significantly outperform traditional methods and baseline networks. This study provides a novel methodology and technical support for intelligent sound field identification in data-scarce marine environments. Finally, the core contributions and current limitations are summarized, and future research directions, such as constructing a dynamic hydrological parameter feedback mechanism and identifying three-dimensional complex sound fields, are prospected. Full article

Underwater image analysis is affected by light scattering, wavelength-dependent attenuation, low contrast, and suspended particles, which reduce the discriminative visual features. Current multi-scale Vision Transformers are not well-suited to these degradations because they cannot effectively fuse features across scales to achieve accurate classification. Although Vision Transformers (ViTs) can model long-range interactions, single-scale patch tokenization remains suboptimal for underwater images, where both fine-grained textures and global structures are important. This study proposes a Multi-Scale Vision Transformer (MS-ViT) with Cross-Scale Biased Attention Fusion (CSBAF) for underwater image classification. Before transformer encoding, the CSBAF introduces a learnable source–target scale-pair bias and an input-dependent scale-reliability gate. This differs from standard multi-scale fusion and cross-attention methods, which mainly concatenate features or exchange information between scale branches. The proposed design enables the model to emphasize reliable scales while suppressing degraded-scale responses. A hybrid dataset containing 14,000 images from the Roboflow Aquarium and RUIE datasets across five classes was used for evaluation. MS-ViT with CSBAF achieved 88.9% accuracy and an 88.8% F1-score, outperforming the CNN baseline by 7.6% and state-of-the-art transformer models, including UWFormer, DP-ViT, and CvT, by 2.3–4.2%. Ablation studies showed a 1.7% accuracy improvement over simple multi-scale concatenation, whereas cross-dataset testing achieved 84.4% accuracy, indicating reasonable cross-dataset robustness. These results demonstrate that explicit scale–aware fusion can improve transformer-based underwater visual understanding. Full article

Coral reefs are among the most important and vulnerable marine ecosystems worldwide. AI-powered underwater visual monitoring has become essential for effective reef conservation, yet current methods still face severe limitations: spectral ambiguity caused by underwater turbidity, fine-grained confusion in early coral health assessment, and discrete forecasting models that cannot represent continuous ecological degradation dynamics. To address these issues, we propose Spectral-YOLOv13, a dual-domain vision-Mamba sensing framework for high-precision coral health evaluation and continuous ecological forecasting. The framework incorporates three novel components: a Wavelet-Integrated Omni-Neck (WIO-Neck) to perform multi-scale spectral filtering and suppress turbidity-induced noise; a Contrastive Prototype Head (CP-Head) to enhance discriminability between visually similar health states; and a Bio-Mamba Predictor based on state-space models to capture long-term continuous health trajectories. Extensive experiments on the CR-Mix++ dataset demonstrate that Spectral-YOLOv13 achieves 53.8% mAP with strong robustness in turbid underwater environments. It reduces four-week forecasting error by 26.8% and maintains real-time inference speed at 112 FPS. This work provides a reliable and high-performance vision framework for practical underwater coral reef monitoring and proactive conservation management. Full article

The oceans and other marine ecosystems are indispensable to life, so the understanding and knowledge of their biodiversity is crucial to the use of their resources and exploration. These environments are complex and difficult to access, so different types of remote sensing technologies are used to study them. These intelligent sensors can collect a massive amount of data, which, once reviewed and analyzed, can help to draw conclusions and increase knowledge of these underwater environments. Manually reviewing and organizing through this large amount of information is both time-consuming and costly. Therefore, it is advisable to employ automated techniques from machine learning and deep learning fields. In recent years, these methods have proven to be efficient and have obtained very good results in solving different problems applied to the marine world: image enhancement, image classification, segmentation and object detection. This paper presents a systematic review, conducted in accordance with the PRISMA 2020 guidelines, aimed at summarizing the methods used to address underwater problems and their reported results. Full article

Cross-domain semantic classification of 3D point clouds remains challenging due to strong domain shifts between heterogeneous sensing modalities. Most existing classification frameworks are domain-specific, limiting their use in integrated land–water mapping applications. This study evaluates the transferability of classical geometric machine learning classifiers between terrestrial and underwater point cloud domains without target-domain retraining. Experiments were conducted using terrestrial data acquired with a Leica BLK360 terrestrial laser scanner (TLS) and underwater point clouds collected with a Blueview BV5000 mechanical scanning sonar (MSS). Two dimensionality-based frameworks, CANUPO–Support Vector Machine (SVM) and 3DMASC–Random Forest (RF), were implemented in CloudCompare and assessed under intra-domain and cross-domain configurations. Strong intra-domain performance was achieved, with terrestrial–terrestrial accuracies of 0.99 for CANUPO–SVM and 0.97 for 3DMASC. In underwater evaluation, CANUPO maintained high accuracy (0.97), whereas 3DMASC decreased to 0.86 due to increased variability in the submerged data. Under cross-domain transfer, CANUPO achieved 0.93 accuracy for terrestrial-to-underwater and 0.89 for underwater-to-terrestrial classification, while 3DMASC demonstrated stable generalisation with 0.95 accuracy in both directions. Overall, dimensionality-based geometric descriptors capture stable structural cues across sensing environments, providing an interpretable and efficient pathway for applications such as hydrographic surveying, coastal monitoring, and underwater search-and-rescue detection. Future work will extend validation to larger datasets and explore domain adaptation strategies to further reduce cross-modality domain shift. Full article

In complex marine environments, underwater magnetic anomaly detection is challenging because target magnetic anomaly signals are typically weak and easily overwhelmed by background magnetic noise. Although deep learning-based methods have significantly improved detection capability, most existing approaches still rely on abundant labeled target data, which is difficult to obtain in practical applications. To address this challenge, this paper proposes an unsupervised underwater magnetic anomaly detection method based on Gaussian granular ball one-class classification (GBOC). A density-guided hierarchical partitioning strategy is introduced to divide the latent space into multiple compact high-density regions and construct corresponding Gaussian granular ball representations. This strategy enables more effective modeling of complex background magnetic noise and improves anomaly detection under low signal-to-noise ratio (SNR) conditions. Experimental results show that the proposed method achieves robust performance across different SNR levels in the unsupervised setting. Compared with other methods, it yields a higher detection rate and more stable results under a fixed false alarm rate. Furthermore, a semi-supervised magnetic anomaly detection method is developed by introducing a small amount of prior information on magnetic anomalies. Experimental results demonstrate that the proposed semi-supervised method can further improve detection accuracy while maintaining good robustness and stability. Full article

This study focuses on how mesoscale eddies at the Kuroshio boundary in the East China Sea modulate underwater acoustic propagation. Using high-resolution reanalysis data from the Hybrid Coordinate Ocean Model (HYCOM) and validated acoustic ray-tracing simulations, the OW + SLA method is employed for eddy identification and classification. Statistical analysis of 120 eddy events from 2015 to 2020 clarifies their seasonal variation characteristics. Warm eddies shift the convergence zone 15–30 km away from the sound source and broaden it by 20–40%, while cold eddies shift it 10–25 km toward the source and narrow it by 15–35%. A linear relationship exists between eddy amplitude and acoustic transmission loss (TL = 72.4 + 0.42 h, R² = 0.61), where TL is the transmission loss in decibels (dB) and h is the eddy amplitude in meters (m), and there are depth-dependent transmission loss modulation effects. These results provide practical guidance not only for sonar system design and acoustic communication optimization but also for error correction in underwater acoustic navigation systems operating in eddy-prone environments. Full article

Underwater dynamic target detection, classification, localization, and tracking (DCLT) is central to maritime surveillance and monitoring and increasingly relies on distributed AUV-based robotic sonar networks operating in passive listening and, when required, cooperative multistatic modes. Achieving a robust performance in realistic oceans remains challenging, because sensor placement must adapt to time-varying acoustic conditions and target priors while preserving acoustic communication connectivity, and because frequent reconfiguration under dynamic currents makes classical large-scale planning computationally expensive. This paper presents an integrated deep reinforcement learning (DRL)-based framework for passive-stage sonar placement and dynamic reconfiguration in distributed AUV networks. First, we cast placement as a constructive finite-horizon Markov decision process (MDP) and train a Proximal Policy Optimization (PPO) agent to sequentially build a collision-free layout on a discretized surveillance grid. The terminal reward is formulated to jointly optimize the environment-aware detection performance, computed from BELLHOP-based transmission loss models, and global network connectivity, quantified using algebraic connectivity. Second, to enable time-critical reconfiguration, we estimate flow-aware motion costs for all AUV–destination pairs using a PPO with a Long Short-Term Memory (LSTM) trajectory policy trained for partial observability. The learned policy can be deployed onboard, allowing each AUV to refine its path online using locally sensed currents, improving robustness to ocean-model uncertainty. The resulting cost matrix is solved via an efficient zero-element assignment method to obtain the optimal one-to-one reassignment. In the reported simulation studies, the proposed Sequential PPO placement method achieves a final reward 16–21% higher than Particle Swarm Optimization (PSO) and 2–3.7% higher than the Genetic Algorithm (GA), while the proposed PPO + LSTM planner reduces average travel time by 30.44% compared with A*. The proposed closed-loop architecture supports frequent re-optimization, scalable fleet operation, and a seamless transition to communication-supported cooperative multistatic tracking after detection, enabling efficient, adaptive DCLT in dynamic marine environments. Full article

Coral reefs are among the most biodiverse marine ecosystems, playing irreplaceable roles in maintaining marine ecological balance and coastal services. Under dual pressures of global climate change and human activities, coral bleaching and degradation have become increasingly frequent, creating an urgent need for large-scale, long-term, and highly automated monitoring technologies. In recent years, advances in underwater imaging and deep learning have made visual recognition a core approach for coral classification and health assessment. However, most studies only focus on isolated model accuracy optimization, lacking systematic full-chain analysis integrating datasets, model evolution, cross-domain generalization, engineering constraints, and ecological adaptation, which severely hinders large-scale cross-regional and long-term application. This paper systematically reviews coral visual recognition technologies. It summarizes underwater image acquisition, public dataset characteristics, and annotation system evolution, then compares traditional feature engineering and deep learning in key tasks, highlighting their differences in feature representation and generalization. Four core challenges are identified: class imbalance, poor underwater image quality, weak cross-device/region generalization, and mismatched algorithm metrics with ecological needs. Finally, feasible solutions based on self-supervised pre-training, domain adaptation, and multimodal fusion are discussed to enhance model robustness and ecological interpretability, providing methodological support for intelligent coral reef monitoring systems. Full article

The increasing deployment of underwater vehicles demands accurate and energy-efficient target tracking in sensor networks. However, existing approaches have largely addressed tracking accuracy and energy efficiency in isolation, and a system-level framework that jointly optimizes both remains lacking. To address this gap, this paper proposes a joint optimization framework with two main contributions. First, to improve tracking accuracy under complex maneuvering conditions, we develop an Interactive Multi-Model using Long Short-Term Memory Classification (IMM-LSTM-C) algorithm, which integrates multi-step model likelihoods into an LSTM network for precise motion classification, achieving a 7.1% accuracy improvement over IMM-BP. Second, to reduce network energy consumption while maintaining tracking performance, we introduce an Improved Binary Prairie Dog Optimization (IBPDO) algorithm for node selection, enhanced with Cauchy mutation and opposition-based learning. Simulation results show that IBPDO achieves 6.1–8.2% higher accuracy than BWOA and reduces energy consumption by 12% compared to LNS. Furthermore, the complete joint framework demonstrates synergistic effects, reducing tracking error by 19.3% and energy consumption by 15.4% over the IMM + LNS baseline. The proposed framework provides an effective balance between tracking accuracy and energy efficiency in underwater acoustic sensor networks. Full article