Search Results (3,963)

Landslides are a common type of geological hazard, characterized by sudden onset, high destructiveness, and frequent occurrence, and are widely distributed in mountainous areas with complex terrain. In recent years, due to extreme weather and intensified human activities, both the frequency and intensity of landslide disasters in China have increased significantly, posing serious threats to human life, property, and socio-economic development. Although various methods for landslide susceptibility assessment have been proposed, the accuracy of existing models still needs improvement. In this context, this study takes the landslide-prone Dongchuan District of Kunming City, Yunnan Province, as a case study and proposes a coupled model that integrates an autoencoder and a feedforward neural network (AuFNN). The model uses the autoencoder to extract low-dimensional and highly discriminative feature representations, which are then used as input to the feedforward neural network to perform landslide susceptibility assessment. To evaluate the effectiveness of the proposed model, it is compared with four commonly used models, Support Vector Machine (SVM), Random Forest (RF), XGBoost, and Feedforward Neural Network (FNN), based on performance metrics such as the ROC curve, recall, and F1 score. The results indicate that the AuFNN model provides an alternative representation learning framework and achieves performance comparable to that of established machine learning models in landslide susceptibility assessment, as reflected by similar AUC, accuracy, and F1 score values. Full article

In recent years, unmanned aerial vehicles (UAVs) have become increasingly prevalent across diverse application scenarios due to their high maneuverability, compact size, and cost-effectiveness. However, these advantages also introduce significant challenges for UAV detection in complex environments. This paper proposes an efficient feature perception network (EFPNet) for UAV detection, developed on the foundation of the RT-DETR framework. Specifically, a dual-branch HiLo-ConvMix attention (HCM-Attn) mechanism and a pyramid sparse feature transformer network (PSFT-Net) are introduced, along with the integration of a DySample dynamic upsampling module. The HCM-Attn module facilitates interaction between high- and low-frequency information, effectively suppressing background noise interference. The PSFT-Net is designed to leverage deep-level features to guide the encoding and fusion of shallow features, thereby enhancing the model’s capability to perceive UAV texture characteristics. Furthermore, the integrated DySample dynamic upsampling module ensures efficient reconstruction and restoration of feature representations. On the TIB and Drone-vs-Bird datasets, the proposed EFPNet achieves mAP50 scores of 94.1% and 98.1%, representing improvements of 3.2% and 1.9% over the baseline models, respectively. Our experimental results demonstrate the effectiveness of the proposed method for small UAV detection. Full article

This paper proposes a dynamic voltage restorer (DVR) based on a new three-phase multilevel inverter (MLI). An integral component of DVRs is the power electronic converter. At medium-to-high voltage levels, MLIs are the ideal converters for DVR applications because lower voltage-rated switches are used to generate high voltages, thus minimizing power losses. The proposed three-phase MLI generates 15 levels of load voltage per phase, using a reduced component count: eight lower-rated semiconductor power switches, four primary DC voltage sources, two auxiliary DC sources, and eight driver circuits per phase. Additionally, each phase features a low-frequency transformer with voltage-boosting and galvanic isolation capabilities. The switching sequence of the proposed MLI is simpler to execute using fundamental frequency control; this methodology provides reduced switching stress and reduced switching losses as merits. Structurally, the proposed MLI is less complex and thus scalable. The proposed DVR, based on three-phase MLI, efficiently offsets power quality problems such as voltage swell, voltage sags, and harmonics for balanced and unbalanced loads. The operational performance of the proposed DVR-MLI is verified by a simulation, using PSCAD software and an experimental prototype. Full article

Developmental Language Disorder (DLD) is associated with abnormalities in both intrinsic resting-state brain networks and task-evoked neural responses, yet direct electrophysiological evidence linking these levels remains limited. This study examined multi-level EEG markers in 21 typically developing children and 15 children with DLD across resting-state, a semantic matching task, and an auditory oddball task. Resting-state analyses revealed frequency-specific connectivity imbalances, reduced stability of intrinsic microstate dynamics, and atypical transitions between microstates in the DLD group. During the semantic matching task, DLD children showed weaker occipital P1 and N2 responses (100–300 ms) and lacked the right fronto-central difference wave (500–700 ms) observed in TD children. In the auditory oddball task, DLD children exhibited high-theta/low-alpha event-related desynchronization at left frontal electrodes (400–500 ms), in contrast to TD children. A machine learning framework integrating resting-state and task-based features discriminated DLD from TD children (test-set F1 = 70.3–80.0%) but showed limited generalizability, highlighting the constraints of small clinical samples. These findings support a translational neurophysiological signature for DLD, in which atypical intrinsic network organization constrains emergent neural computations, providing a foundation for future biomarker development and targeted intervention strategies. Full article

Synthetic Aperture Radar (SAR) has unique advantages in ship monitoring at sea due to its all-weather imaging capability. However, its unique imaging mechanism presents two major challenges. First, speckle noise in the frequency domain reduces the contrast between the target and the background. Second, side-lobe scattering blurs the ship outline, especially in nearshore complex scenes, and strong scattering characteristics make it difficult to separate the target from the background. The above two challenges significantly limit the performance of tailored CNN-based detection models in optical images when applied directly to SAR images. To address these challenges, this paper proposes a modulation and perturbation mechanism in the frequency domain based on a lightweight CNN detector. Specifically, the wavelet transform is firstly used to extract high-frequency features in different directions, and feature expression is dynamically adjusted according to the global statistical information to realize the selective enhancement of the ship edge and detail information. In terms of frequency-domain perturbation, a perturbation mechanism guided by frequency-domain weight is introduced to effectively suppress background interference while maintaining key target characteristics, which improves the robustness of the model in complex scenes. Extensive experiments on four widely adopted benchmark datasets, namely LS-SSDD-v1.0, SSDD, SAR-Ship-Dataset, and AIR-SARShip-2.0, demonstrate that our FMP-Net significantly outperforms 18 existing state-of-the-art methods, especially in complex nearshore scenes and sea surface interference scenes. Full article

Electroencephalography (EEG) provides valuable information about a subject’s mental state; however, developing reliable classification models remains challenging. One major difficulty lies in defining an effective feature representation, as the wide range of features proposed in the literature often leads to high-dimensional inputs, increasing the risk of overfitting, reducing generalization, and raising computational cost. A further critical challenge is the strong inter-subject variability inherent to EEG data, where distributional shifts frequently cause models trained on one individual to perform poorly on unseen subjects. This work proposes a novel family of correlation-based feature selection methods that explicitly models inter-feature relationships through correlation structures. The objective is to identify features that are simultaneously discriminative across mental states (relaxed and concentrated) and invariant across subjects, thereby improving cross-subject generalization. The proposed methods are evaluated against established feature selection and dimensionality reduction techniques using a leave-one-subject-out experimental protocol, in which models are trained on multiple participants and tested on unseen individuals. Experimental results demonstrate that the proposed approach consistently achieves superior or competitive performance compared to existing methods, particularly under strong inter-subject distribution shifts. In addition, the analysis reveals how preprocessing parameters—such as window length, overlap, and frequency band decomposition—affect classification performance and generalization. Unlike previous EEG feature selection approaches that primarily focus on feature relevance or redundancy, the proposed framework explicitly promotes domain invariance while preserving feature interpretability, without relying on subject-specific calibration. Full article

Pairs trading via futures calendar spreads offers a robust market-neutral approach to exploiting transient mispricings, yet real-time implementation is hindered by asynchronous trading. This paper introduces a Cointegrated Ising Spin Model, CISM, for real-time signal generation in high-frequency spread trading. The model links the macro-level equilibrium of cointegration with micro-level agent interactions, representing prices as magnetizations in an agent-based system. A novel $\Delta$-weighted arbitrage force dynamically adjusts agents’ corrective behavior to account for information staleness. Calibrated on tick-by-tick Brent crude oil futures, the model produces a time-varying probability of spread reversion, enabling probabilistic trading decisions. Backtesting demonstrates a 74.65% success rate, confirming the CISM’s ability to generate stable, data-driven arbitrage signals in asynchronous environments. The model bridges macro-level cointegration with micro-level agent interactions, representing prices as magnetizations within an agent-based Ising system. A novel feature is a $\Delta$-weighted arbitrage force, where the corrective pressure applied by agents in response to the standard Error Correction Term is dynamically amplified based on information staleness. The model is calibrated on historical tick data and designed to operate in real time, continuously updating its probability-based trading signals as new quotes arrive. The model is framed within the context of Discrete Choice Theory, treating agent transitions as utility-maximizing decisions within a Vector Logistic Autoregressive (VLAR) framework. Full article

Aiming to address insufficient feature extraction, vanishing gradients, and low prediction accuracy in tool wear prediction, this paper proposes a hybrid deep neural network based on a Convolutional Neural Network (CNN), Residual Network (ResNet) residual connections, the Convolutional Block Attention Module (CBAM), and a Bidirectional Gated Recurrent Unit (BiGRU). First, a 34-dimensional multi-domain feature set covering the time domain, frequency domain, and time–frequency domain is constructed, and multi-sensor signals are standardized using z-score normalization. A CNN–BiGRU backbone is then established, where ResNet-style residual connections are introduced to alleviate training degradation and mitigate vanishing-gradient issues in deep networks. Meanwhile, CBAM is integrated into the feature extraction module to adaptively reweight informative features in both channel and spatial dimensions. In addition, a BiGRU layer is embedded for temporal modeling to capture bidirectional dependencies throughout the wear evolution process. Finally, a fully connected layer is used as a regressor to map high-dimensional representations to tool wear values. Experiments on the PHM2010 dataset demonstrate that the proposed hybrid architecture is more stable and achieves better predictive performance than several mainstream deep learning baselines. Systematic ablation studies further quantify the contribution of each component: compared with the baseline CNN model, the mean absolute error (MAE) is reduced by 47.5%, the root mean square error (RMSE) is reduced by 68.5%, and the coefficient of determination (R²) increases by 14.5%, enabling accurate tool wear prediction. Full article

Background/Objectives: Multimodal large language models (LLMs) may directly interpret medical images, including thyroid ultrasounds (USs). Whether these models can reliably assess thyroid nodules—where subtle echogenic and morphological details are critical—remains uncertain. The American College of Radiology (ACR) TI-RADS system provides a structured framework for benchmarking artificial intelligence. This study evaluates GPT-5.0’s ability to interpret thyroid US images according to TI-RADS criteria and contextualizes its performance relative to expert radiologist assessment, using FNA cytology as the reference standard. Methods: This retrospective study included 100 patients (mean age 49.8 ± 12.6 years; 72 women) with cytology-confirmed diagnoses: Bethesda II (benign) or Bethesda V–VI (malignant). Each nodule had longitudinal and transverse US images acquired with high-frequency linear probes. A board-certified radiologist (>10 years’ experience) and GPT-5.0 independently assessed TI-RADS features (composition, echogenicity, shape, margin, echogenic foci) and assigned final categories. Agreement was analyzed using Cohen’s κ, and diagnostic performance was calculated using TR4–TR5 as positive for malignancy. Results: Agreement was substantial for composition (κ = 0.62), shape (κ = 0.70), and margin (κ = 0.68); moderate for echogenicity (κ = 0.48); and poor for echogenic foci (κ = 0.12). GPT-5.0 demonstrated a systematic, risk-averse tendency to up-classify nodules, leading to increased TR4–TR5 assignments. Overall, the TI-RADS agreement was 58% (κ = 0.31). The radiologist showed superior diagnostic performance (sensitivity 89%, specificity 85%) compared with GPT-5.0 (sensitivity 67%, specificity 49%), largely driven by false-positive TR4 classifications among benign nodules. Conclusions: GPT-5.0 recognizes several high-level TI-RADS features but struggles with microcalcifications and tends to overestimate malignancy risk within a risk-stratification framework, limiting its standalone clinical use. Ultrasound-specific training and domain adaptation may enable meaningful adjunctive roles in thyroid nodule assessment. Full article

High-frequency surface wave radar (HFSWR) with a small aperture suffers from limited azimuth resolution, which often leads to association errors and trajectory fragmentation in complex scenarios involving sea clutter and intersecting target tracks. To address this issue, we propose a multi-feature adaptive association method that integrates the target direction cosine features and motion parameters to construct an improved association gate suitable for targets in uniform linear motion. For multiple plots within the association gate, the method evaluates their similarity to the trajectory by combining multiple feature parameters such as great-circle distance and Mahalanobis distance. An adaptive weighting strategy is employed according to the trajectory state to select the most similar plot for association. For trajectories without associated plots, the method maintains them based on a motion model and Kalman predictor. Experimental results demonstrate that the trajectories generated by this method last longer than those produced by traditional association methods, confirming that the proposed approach effectively suppresses trajectory fragmentation and false tracking, thereby enhancing the continuity and reliability of HFSWR target tracking. Full article

Fuel moisture content (FMC) estimation is essential for wildfire danger assessment and fire behavior modeling. This study quantifies the value of integrating satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard Suomi-NPP and the Advanced Baseline Imager (ABI) aboard GOES-16 with High-Resolution Rapid Refresh (HRRR) numerical weather prediction data for hourly 10 h dead FMC estimation across the continental United States. We leverage the complementary characteristics of each system: VIIRS provides enhanced spatial resolution (375–750 m), while ABI contributes high temporal frequency observations (hourly). Using XGBoost machine learning models trained on 2020–2021 data, we systematically evaluate performance improvements stemming from incorporating satellite retrievals individually and in combination with HRRR meteorological variables through eight experimental configurations. Results demonstrate that while both satellite systems individually enhance prediction accuracy beyond HRRR-only models, their combination provides substantially greater improvements: 27% RMSE and MAE reduction and 46.7% increase in explained variance (R${}^2$) relative to HRRR baseline performance. Comprehensive seasonal analysis reveals consistent satellite data contributions across all seasons, with stable median performance throughout the year. Diurnal analysis across the complete 24 h cycle shows sustained improvements during all hours, not only during satellite overpass times, indicating effective integration of temporal information. Spatial analysis reveals improvements in western fire-prone regions where afternoon overpass timing aligns with peak fire danger conditions. Feature importance analysis using multiple explainable AI methods demonstrates that HRRR meteorological variables provide the fundamental physical framework for prediction, while satellite observations contribute fine-scale refinements that improve moisture estimates. The VIIRS lag-hour predictor successfully maintains observational value up to 72 h after acquisition, enabling flexible operational implementation. This research demonstrates the first systematic comparison of VIIRS versus ABI contributions to dead FMC estimation and establishes a framework for hourly, satellite-enhanced FMC products that support more accurate fire danger assessment and enhanced situational awareness for wildfire management operations. Full article

Remote sensing object detection is a critical task in Earth observation. Despite the remarkable progress made in general object detection, existing detectors struggle with remote sensing scenarios due to the prevalence of numerous small objects with limited discriminative cues. Cutting-edge studies have shown that incorporating contextual information effectively enhances the detection performance for small objects. Meanwhile, recent research has revealed that convolution in the frequency domain is capable of capturing long-range spatial dependencies with high efficiency. Inspired by this, we propose a Frequency-aware Feature Pyramid Framework (FFPF) for remote sensing object detection, which consists of a novel Frequency-aware ResNet (F-ResNet) and a Bilateral Spectral-aware Feature Pyramid Network (BS-FPN). Specifically, the F-ResNet is proposed to extract the spectral context information by plugging the frequency domain convolution into each stage of the backbone, thereby enriching features of small objects. In addition, the BS-FPN employs a bilateral sampling strategy and skipping connection to model the association of object features at different scales, enabling the contextual information extracted by the F-ResNet to be fully leveraged. Extensive experiments are conducted for object detection in the public remote sensing image dataset and natural image dataset. The experimental results demonstrate the excellent performance of the FFPF, achieving 73.8% mAP on the DIOR dataset without using any additional training tricks. Full article

Micro-expression recognition (MER) is challenged by a brief duration, low intensity, and heterogeneous spatial frequency patterns. This study introduces a novel MER architecture that reduces computational cost by fine-tuning a large feature extraction model with LoRA, while integrating frequency-domain transformation and graph-based temporal modeling to minimize preprocessing requirements. A Spatial Frequency Adaptive (SFA) module decomposes high- and low-frequency information with dynamic weighting to enhance sensitivity to subtle facial texture variations. A Dynamic Graph Attention Temporal (DGAT) network models video frames as a graph, combining Graph Attention Networks and LSTM with frequency-guided attention for temporal feature fusion. Experiments on the SAMM, CASME II, and SMIC datasets demonstrate superior performance over existing methods. On the SAMM 5-class setting, the proposed approach achieves an unweighted F1 score (UF1) of 81.16% and an unweighted average recall (UAR) of 85.37%, outperforming the next best method by 0.96% and 2.27%, respectively. Full article

In high-speed railways, the reliability of jointless track circuits largely hinges on the operational integrity of compensation capacitors. These capacitors are periodically installed along the track to mitigate rail inductive impedance and stabilize signal transmission. The induced voltage response, referred to as the compensation-capacitor signal, serves as a critical diagnostic indicator of circuit health. Yet it is often distorted by electromagnetic interference and structural resonance, posing significant challenges for robust feature extraction. To address this challenge, we propose a Disturbance-Robust Feature Distillation (DRFD) framework that performs multi-perspective modeling and validation of robust features. The framework formulates a unified multi-objective optimization model that jointly considers statistical significance, environmental stability, and structural separability. These objectives are harmonized through an adaptive Bayesian weighting mechanism, enabling automatic identification of disturbance-resistant and discriminative features under complex operating conditions. Experimental evaluations on real-world datasets collected at a 100 kHz sampling rate from roadbed, tunnel, and bridge environments demonstrate that the DRFD framework achieves 96.2% accuracy and 95.4% F1-score, outperforming the best-performing baseline by 4.2–7.8% in accuracy and 6.5% in F1-score. Moreover, the framework achieves the lowest cross-condition relative variance (RV < 0.015), confirming its high robustness against electromagnetic and structural disturbances. The extracted core features—Root Mean Square (RMS), Peak Factor (PF), and Center Frequency (CF)—faithfully capture the intrinsic electromagnetic behaviors of compensation capacitors, thus linking statistical robustness with physical interpretability for enhanced reliability assessment of railway signal systems. Full article

Focusing on the challenges posed by the strong randomness, weak manifestation, and difficulty in diagnosing intermittent faults (IFs) in high-density integrated circuits (HDICs)—often induced by bonding wire defects—this paper takes the GPIO interfaces of a typical DSP chip as the research object. It systematically analyzes the response characteristics of intermittent short-circuit and open-circuit faults and proposes a hybrid intelligent diagnosis method based on the Sparrow Search Algorithm-optimized Variational Mode Decomposition and Attention-based Support Vector Machine (SSA–VMD–Attention–SVM). A dedicated fault injection circuit is designed to accurately replicate IFs and acquire the power supply current response signals. The Sparrow Search Algorithm (SSA) is employed to adaptively optimize the parameters of Variational Mode Decomposition (VMD) for effective extraction of frequency-domain features from fault signals. A three-level attention mechanism is introduced to adaptively weight multi-domain features, thereby highlighting the key fault components. Finally, the Support Vector Machine (SVM) is utilized to achieve high-precision fault classification under small-sample conditions. Experimental results demonstrate that the proposed method achieves a diagnostic accuracy of 97.78% for intermittent short-circuit and open-circuit faults in the GPIO interfaces of the DSP chip, significantly outperforming traditional methods and exhibiting notable advantages in terms of diagnostic accuracy, robustness, and interpretability. Full article