Search Results (59,123)

To autonomously dock LNG unloading arms under adverse sea states, this study formulates a dynamic docking process as a trajectory forecasting task. By integrating visual-perception-based spatial localization with trajectory acquisition and forecasting, a comprehensive operational pipeline is established. To predict the dynamic trajectory of the vessel flange, an improved KSE-GRU model is proposed. By extracting implicit kinematic features, the model effectively enhances trajectory characterization under extreme sea states, thereby significantly improving forecasting accuracy and worst-case error constraints. To ensure the operational feasibility of autonomous docking, a robust control strategy is introduced to complement the trajectory predictions. The experimental results demonstrate that the proposed model outperforms traditional time-series forecasting models across all evaluation metrics. Compared with the baseline neural network models, the Mean-3D error is reduced by 19.14%, and the Max-3D error is capped at 348.77 mm, representing an 8.8% improvement over the baseline. Furthermore, the model demonstrates clear advantages in maintaining trajectory consistency and forecasting reliability. In summary, in this study, a robust trajectory forecasting model is developed for vessel target flanges integrated with a comprehensive control framework, thereby offering a practical approach to autonomous docking under dynamic oceanic conditions. Full article

Against the backdrop of global energy transition and the widespread adoption of Hydrogen-Blended Natural Gas (HBNG), the safety of urban gas pipeline networks faces severe challenges. This paper systematically reviews the research progress of numerical simulation in the field of natural gas pipeline safety, focusing on its core supporting roles throughout the “Leakage–Dispersion–Evolution–Consequence” disaster chain. First, it analyzes the kinetic modeling of high-pressure leakage holes and property corrections based on real gas equations of state, elaborating on the numerical characterization of HBNG multi-component transport. Second, it compares the dispersion mechanisms and environmental coupling modeling methods in typical scenarios such as buried porous media, confined spaces in utility tunnels, underwater environments, and urban building clusters. Third, it reviews leakage monitoring technologies based on physical field simulation and data-driven approaches (e.g., Convolutional Neural Network, Long Short-Term Memory), emphasizing the value of numerical simulation in constructing digital twin training sets. Furthermore, it explores the dynamic evolution of explosion flame–shock wave interactions and the evaluation models for secondary disaster consequences. Finally, the current research status of grid-based risk pre-warning and emergency response strategies is summarized. In conclusion, numerical simulation is not only a robust method for precisely quantifying and characterizing complex physical mechanisms but also a critical technological foundation for building smart and resilient energy cities. Future research should focus on the deep coupling of multi-physics fields, physics-informed learning, and the development of system-level integrated defense systems. Full article

Aircraft skin defect recognition is a safety-critical visual inspection task in which lightweight models must maintain high diagnostic accuracy while suppressing false alarms caused by complex surface textures, illumination variations, and weak defect patterns. This study proposes HQCA-Net, a simulation-based hybrid quantum-classical channel attention network for reliable aircraft skin defect recognition. The core component, termed Residual Quantum Channel Attention (RQCA), embeds a 10-qubit variational quantum circuit into a classical ResNet-18 backbone to perform compact and structured nonlinear feature recalibration, introducing only 30 trainable quantum-gate parameters. The quantum circuit is evaluated using state-vector simulation, and this study focuses on model-level feature recalibration, reliability, and robustness within the evaluated dataset rather than implementation on physical quantum hardware. Experiments on a six-class aircraft skin defect dataset show that HQCA-Net achieves 97.93% classification accuracy and a global false positive rate of 0.49%, outperforming ResNet-18 and classical lightweight attention mechanisms including SE, ECA, and SimAM. Additional analyses using confidence calibration, Grad-CAM visualization, Gaussian noise perturbation, few-shot training, and circuit-depth ablation further indicate that the proposed RQCA module improves feature discrimination and false-alarm suppression under compact parameter constraints. These results suggest that the hybrid quantum-classical attention module can serve as a parameter-efficient nonlinear feature recalibration strategy for reliable visual defect inspection under the tested experimental conditions. Full article

Current landslide displacement prediction models often suffer from insufficient integration between physical mechanisms and data-driven approaches, weak model generalizability, and limited operational applicability. To address these issues, this study develops a physics-informed online learning framework for landslide displacement prediction. The core of this framework is a Physics-informed Long Short-Term Memory network (Phys-LSTM). By embedding discretized forms of the stress balance, creep constitutive, and kinematic equations as hard constraints into the LSTM’s gating mechanisms and loss function, the model ensures physically consistent predictions and enhanced interpretability throughout the learning process. Leveraging real-time data streams from the Sichuan Provincial Geological Hazard Monitoring and Warning Platform, we developed an online processing pipeline for real-time multi-source data ingestion, automated quality control, spatiotemporal alignment, and physics-informed feature engineering. A progressive three-stage learning algorithm was designed to support model cold-start, incremental training, and rolling prediction. Validation across 45 model-development landslide sites and one independent application case demonstrated the framework’s significant superiority over traditional models in displacement prediction accuracy (RMSE ≤ 1.78 mm, R² ≥ 0.96), cross-site generalization stability, and its capability to capture accelerated deformation phases. This research indicates that deeply integrating geomechanical prior knowledge into an online learning framework can effectively improve the reliability, interpretability, and operational applicability of landslide displacement prediction models, thereby providing methodological support for subsequent landslide early warning applications. Full article

Soluble solids content (SSC) is one of the most important indicators of sweetness, ripeness, and market quality in sweet cherries. However, conventional SSC determination is destructive, labor-intensive, and unsuitable for rapid or large-scale quality assessment. Therefore, there is a need for fast, non-destructive, and data-driven sensing approaches that can estimate internal fruit quality without damaging the sample. This study aimed to develop a non-destructive approach for SSC prediction in sweet cherries by combining open-ended coaxial probe dielectric spectroscopy with deep learning models. An open-ended coaxial probe measurement system was designed and developed to determine the dielectric properties of sweet cherries and was coupled with an Agilent E4991A impedance analyzer operating over a frequency range of 5–3005 MHz. A total of 10,080 dielectric measurements and 2100 reference SSC measurements were collected over 26 experimental days. The dielectric constant (ε′), loss factor (ε″), and loss tangent (tan δ) were extracted and used to construct separate ε′, ε″, tan δ, and integrated combined datasets. Six deep learning architectures, namely convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), CNN-LSTM, and convolutional long short-term memory (ConvLSTM), were trained and optimized using Bayesian optimization and early stopping. CNN achieved the best performance on the tan δ dataset (test R² = 0.9099, RMSE = 0.8354 °Brix, MAE = 0.6599 °Brix), whereas GRU yielded the highest accuracy on the integrated combined dataset (test R² = 0.8622, RMSE = 1.0331 °Brix, MAE = 0.7958 °Brix). ConvLSTM provided the most consistent performance across all four datasets (test R² = 0.8081–0.8651), demonstrating strong predictive capability and practical computational efficiency. These findings confirm the potential of reduced-range dielectric spectroscopy combined with deep learning for rapid, non-destructive SSC assessment in sweet cherries. Full article

Image dehazing is a fundamental visual restoration task for improving visual perception under low-visibility weather conditions, especially in UAV-based remote sensing, traffic monitoring, and surveillance scenarios. Existing convolutional neural networks are effective in local feature extraction but remain limited in long-range dependency modeling, while Transformer-based methods improve global modeling at the cost of high computational complexity. To address these issues, this paper proposes an efficient image-dehazing framework termed FSSM, which integrates frequency-enhanced State Space Modeling with a hierarchical encoder–decoder architecture. Specifically, an FFT-based State Space Block (FFTSSB) is designed to reformulate state propagation as frequency-domain two-sided non-causal convolution, enabling efficient bidirectional global dependency modeling without explicit recursive scanning. Furthermore, a Frequency-Aware Discriminative Enhancement Block (FDEB) is introduced to enhance local textures, edges, and structural details through spatial gating and lightweight block-wise frequency modulation. Based on these two components, a Frequency-Aware State Interaction (FASI) block is constructed to progressively couple global state propagation and local frequency-aware enhancement. Experimental results on the HazyDet dataset demonstrate that FSSM achieves favorable restoration accuracy, structural consistency, and perceptual quality compared with representative dehazing methods. Ablation studies further validate the effectiveness of the proposed two-sided FFT-based state modeling, frequency-aware enhancement, and hierarchical multi-scale design. Full article

The processing of two-dimensional (2D) spectral images constitutes a critical and multifaceted discipline in contemporary astronomical data analysis. As spectroscopic instruments evolve towards higher multiplexing, resolution, and sensitivity, the raw 2D data captured by detectors present increasingly complex challenges that transcend simple one-dimensional extraction. This review provides a systematic and comprehensive examination of the methodological evolution in this field over the past two decades. It gathered relevant studies by searching mainstream academic repositories and general search engines with the core keyword ‘2D Spectral Image’, and selected qualified references according to accessibility and research relevance. We categorize the landscape into three major paradigms: (1) physics-based modeling and algorithmic correction techniques for geometric distortion, scattered light, and sky background; (2) data-driven machine learning and deep learning approaches for image correction, spectral classification, and faint signal detection; and (3) the development of open-source software pipelines that democratize advanced processing. A central contribution of this review is a detailed comparative analysis of the performance metrics, underlying assumptions, and practical limitations of prominent algorithms. We highlight the transformative impact of convolutional neural networks (CNNs) and vision transformers (ViTs) on tasks such as celestial object classification and exoplanet detection, while also acknowledging the enduring importance of robust physical models for calibration and uncertainty quantification. The discussion culminates in an assessment of persistent challenges—including computational scalability, model generalizability, and interpretability—and outlines promising future directions at the intersection of AI, statistical inference, and large-scale survey science. Full article

Monitoring inland surface water quality is essential for water security, ecosystem conservation, public health, and sustainable water resource management. Although in situ measurements remain indispensable, they are often limited by high costs, restricted spatial coverage, low temporal frequency, and discontinuous monitoring networks. This study presents a systematic literature review, guided by the PRISMA 2020 framework, of empirical studies published between 2021 and 2025 on the integration of artificial intelligence (AI) and remote sensing (RS) for inland surface water quality monitoring. Searches were conducted in the Web of Science database, resulting in a final corpus of 367 peer-reviewed articles. Preliminary bibliometric characterization and qualitative content analysis were performed to identify sensors, platforms, AI paradigms, algorithms, estimated parameters, validation strategies, limitations, challenges, trends, and research gaps. The results show rapid growth in the field, with Sentinel-2 and Landsat-8 as the most recurrent sensors and multispectral data as the dominant spectral source. Machine learning approaches, especially Random Forest, Artificial Neural Networks, XGBoost, and Support Vector Machine, predominated, while deep learning, multi-source integration, hybrid models, and Explainable AI emerged as relevant trends. AI–RS integration shows strong potential to complement conventional monitoring, but persistent challenges remain regarding in situ data dependence, limited external and temporal validation, model transferability, generalization, uncertainty reporting, validation robustness, and interpretability. Full article

Electroluminescence (EL) imaging is widely used for photovoltaic (PV) defect inspection, yet fair comparison of deep learning backbones remains difficult because datasets, labels, and protocols vary across studies. This work presents a controlled image-level benchmark of six architectures (ConvNeXt-T, ViT-B/16, DeiT-B/16, Swin-T, DenseNet121, and MobileNetV3-Large) across five hierarchical tasks for monocrystalline and polycrystalline cells with binary and multi-class labels. A balanced proprietary dataset of 20,000 single-cell EL images was evaluated with identical preprocessing, augmentation, training, and stratified five-fold cross-validation, yielding 150 runs. ConvNeXt-T achieved the highest mean macro-F1 (93.12%) while using about one-third of the parameters of base ViT/DeiT models. On the four-class polycrystalline task, it reached 84.94 ± 0.45% macro-F1, compared with 70.08 ± 1.19% for DenseNet121 and 59.43 ± 1.71% for MobileNetV3-Large. Error analysis revealed conservative missed-defect behavior in lightweight CNNs, especially for surface-level degradation and crack categories. The results provide image-level cross-validation evidence for controlled benchmarking and motivate future module-level grouped validation. Full article

Multimedia recommenders can use behavioral records together with visual and textual item information, but unreliable interactions and sparse histories still make user preference modeling difficult. Most graph-based methods propagate messages over observed user–item edges as if all interactions were equally informative, so incidental or semantically inconsistent behaviors may distort the learned representations. The standard recommendation loss also provides limited context for modeling dependencies within a user’s historical sequence. We propose MGDSL, a MGDSL applies a multimodal-aware topology denoising module to calculate edge reliability weights for historical interactions from collaborative, textual, and visual evidence, and uses these weights for reliability-aware historical aggregation. In parallel, a masked self-supervised auxiliary task reconstructs masked items from sequence context, adding supervision for latent preference learning. Experiments on three benchmark datasets show that MGDSL consistently improves recommendation accuracy over competitive baselines, with particularly clear gains on the sparsest dataset. Full article

With the rapid aging of the global population, constipation has become a major gastrointestinal concern among elderly individuals. Bowel sounds provide a non-invasive acoustic signal for assessing gastrointestinal function, but their automatic analysis remains challenging due to sparsity and non-stationarity. This study proposes a two-stage bowel sound analysis framework based on continuous abdominal recordings. First, a Convolutional Neural Network-MAMBA (CNN-MAMBA) model was used for salient bowel sound detection. Second, a patient-level constipation classification model was developed using multi-view spectral representations and a Convolutional Neural Network-Conformer-Multiple Instance Learning (CNN-Conformer-MIL) architecture. On a held-out test set, the detection model achieved an accuracy of 0.87, an F1-score of 0.78, and a ROC-AUC of 0.93. For patient-level classification under binary Bristol Stool Form Scale (BSFS) grouping, five-fold cross-validation yielded a mean accuracy of 0.665 and an F1-score of 0.755. All BSFS labels were annotated by clinical physicians and temporally aligned with bowel sound recording. Given the modest improvement and cross-validation variability, the patient-level results should be interpreted as preliminary feasibility evidence. These findings suggest that bowel sound analysis may serve as an auxiliary screening or longitudinal monitoring tool rather than a stand-alone diagnostic system. Full article

Loquat (Eriobotrya japonica) is a commercially important subtropical fruit, and its internal sweetness is an important indicator of market quality. Accurate and non-destructive determination of total soluble solids content (TSSC) is therefore essential for fruit grading and quality evaluation. In this study, short-wave infrared hyperspectral imaging (1000–2400 nm) was combined with a multi-scale spectral attention adaptive convolutional neural network (MSSA-ACNN) for rapid TSSC prediction. Spectral data were preprocessed using an SG-MSC-DT strategy to reduce noise and scattering effects, while conventional models (PLSR, Ridge, and SVM) were used for comparison. The proposed model combines multi-scale feature extraction with a dual-path attention mechanism, enabling adaptive enhancement of informative chemical wavebands while suppressing irrelevant variations. Experimental results, rigorously validated through a 5-fold cross-validation strategy, demonstrated that the proposed approach achieved the best predictive performance, with an R_p² of 0.942, RMSEP of 0.505, and RPD of 3.091, outperforming traditional methods. In addition, attention weight analysis revealed that the model mainly focused on spectral regions associated with water and carbohydrate absorption, indicating consistency between the learned features and known chemical information. These results suggest that the proposed method provides an effective and interpretable approach for non-destructive evaluation of loquat quality and shows potential for application in intelligent fruit grading systems. Full article

Multimodal biometric systems have become essential components of modern electronic identity and authentication platforms where robustness under real-world degradation is critical. However, opaque face masks impose severe facial occlusion and attenuate high-frequency spectral components. Conversely, transparent face shields introduce complex specular reflections and act as an acoustic channel distortion source. Addressing these asymmetric degradation challenges, this paper proposes a reliability-aware Dynamic Score Fusion (DSF) for multimodal biometric identification. The proposed method performs sample-level reliability estimation for both face and voice modalities at the input stage. This enables sample-wise adaptive weighting of modality scores based on their estimated reliability. The framework integrates an ElasticFace-Arc backbone for face recognition with an Emphasized Channel Attention, Propagation and Aggregation—Time Delay Neural Network (ECAPA-TDNN) for speaker identification. The proposed approach is evaluated on the FaciaVox dataset, comprising face images and voice recordings acquired under multiple face-covering conditions. Experiments under the Standard to Cross-Condition Protocol (SCCP) and Multi-Condition Protocol (MCP) demonstrate that the proposed DSF consistently outperforms conventional score-level fusion methods, including Weighted Sum Fusion (WSF) and Logistic Regression Fusion (LRF). It achieves average Rank-1 accuracies of 89.6% (SCCP) and 93.7% (MCP), with gains of up to 9.3 percentage points over these baselines. The reliability estimators further demonstrate strong predictive capability, yielding Area Under the Curve (AUC) values above 0.95 for both modalities in distinguishing correctly and incorrectly identified samples under the closed-set identification setting. These findings confirm that sample-wise reliability modeling provides an effective mechanism for enhancing multimodal biometric performance under challenging mask and shield conditions, supporting the deployment of robust AI-driven electronic identification systems. Full article

Radar signal sorting (RSS) and radar emitter recognition (RER) constitute foundational yet challenging operations in electronic reconnaissance, where RSS aims to accurately segregate interleaved radar pulse streams and RER aims to recognize their originating emitters. Existing methods typically address RSS and RER as separate processes within a sequential streaming framework, which neglect the inherent interdependence and collaborative potential between them, thereby resulting in error accumulation and performance bottleneck. In this paper, we redefine the radar signal sorting and recognition (RSSR) problem from an integrated modeling perspective, decomposing it into three sub-problems, i.e., signal pattern detection, signal pattern extraction, and detection result integration. In order to effectively solve these problems, we propose a novel Unified Framework inspired by Object Detection (UFiOD). Firstly, an end-to-end neural network is constructed to simultaneously optimize the regression of signal temporal occurrence regions and the recognition of signal categories. Then, a template matching algorithm is designed to extract corresponding pulses from the regions based on the signal categories. Finally, an integration algorithm based on temporal correlation and direction of arrival (DOA) fuses the detection results to generate object-level sorting and recognition conclusions. We extensively validate the effectiveness of the proposed method on simulation datasets. It demonstrates robust performance under various interleaving scenarios, including the interleaving of homogeneous radar emitters. Notably, it exhibits impressive capability for handling unknown signals, further highlighting its practical utility. Full article

This research investigates atmospheric neutron-induced single event effects (SEEs) on advanced artificial intelligence (AI) chips during natural environment operation. The RK3588 neural processing unit (NPU) is the evaluated target chip, and its SEE is assessed through a combination of irradiation testing and software fault injection. During the irradiation test, the chip was exposed to a spectrum neutron at the China Spallation Neutron Source. Upon reaching a cumulative fluence of 8.25 × 10⁹ n·cm², a total of 14,018 soft errors were detected, of which 99.97% manifested as variations in target recognition accuracy and network inference latency. Among these variations, both detrimental effects (reduced target recognition accuracy or prolonged network inference time) and beneficial effects (enhanced target recognition accuracy or shortened network inference time) caused by single event effects were observed. In addition, atmospheric neutron single event effects were found to cause NPU operation suspension and system crashes. Based on the irradiation test results, failure predictions for neural processing units in real-world environments were estimated, and mitigation recommendations were proposed. Furthermore, software fault injections were employed to conduct in-depth analysis of detected soft errors during irradiation testing. This research provides support and references for the reliable application of artificial intelligence chips in natural environments. Full article