Search Results (72)

Radio-frequency interference (RFI) severely degrades the imaging quality of synthetic aperture radar (SAR), especially when the interference energy is strongly coupled with ground backscatter in both the time and frequency domains. Existing algorithms typically rely on energy contrast or component decomposition in transform domains, which limits their ability to cleanly separate complex RFI from high-power echoes. Exploiting the fact that kurtosis is insensitive to ground clutter and background noise, this paper proposes an interference suppression network based on the temporal kurtosis guidance mechanism. Specifically, a statistical prior vector capturing the non-Gaussian characteristics of RFI is constructed using kurtosis in the time–frequency domain and is integrated into a multi-scale attention mechanism, allowing the network to more effectively concentrate on interfered regions. Meanwhile, a systematic framework is established for the quantitative assessment of phase fidelity in the reconstruction of complex-valued SAR echoes. On this basis, by exploiting the strong generalization capability and high processing efficiency of data-driven models, the proposed network achieves improved RFI separation and enhanced reconstruction accuracy of underlying scene features. Ablation experiments validated that the design of a kurtosis-guided module can reduce the mean square error (MSE) loss by 14.87% compared to the basic model. Furthermore, regarding the phase fidelity, the correlation coefficient between the suppressed signal and the original true signal reached 0.99. Finally, GF-3 satellite data are used to further demonstrate the effectiveness and practicality of the proposed method. Full article

Satellite-aided multi-unmanned aerial vehicle (UAV) collaborative localization systems combine the extensive coverage of satellites with the flexibility of UAVs, offering new opportunities for locating highly dynamic emitters across large areas. However, the openness of space-air communication links and the increasing complexity of cybersecurity threats make these systems vulnerable to false data injection attacks. Most existing detection approaches focus only on temporal dependencies in time-frequency features and lack diagnostic mechanisms for identifying malicious UAVs, which limits their ability to effectively detect and mitigate such attacks. To address this issue, this paper proposes an intelligent collaborative localization framework that safeguards localization integrity by identifying and correcting false ranging information from malicious UAVs. The framework captures spatio-temporal correlations in multidimensional ranging sequences through a graph attention network (GAT) coupled with a time-attention-based variational autoencoder (VAE) to detect anomalies through anomalous distribution patterns. Malicious UAVs are further diagnosed through an anomaly scoring mechanism based on statistical analysis and reconstruction errors, while detected anomalies are corrected via a K-nearest neighbor-based (KNN) algorithm to enhance localization performance. Simulation results show that the proposed model improves localization accuracy by 25.9%, demonstrating the effectiveness of spatial–temporal feature extraction in securing collaborative localization. Full article

The rapid integration of renewable energy sources and increasing complexity of modern power grids demand advanced forecasting tools capable of simultaneously predicting multiple interconnected variables. While time series foundation models (TSFMs) have demonstrated remarkable zero-shot forecasting capabilities across diverse domains, their application in power grid operations remains limited due to complex coupling relationships between load, price, emissions, and renewable generation. This paper proposes GridFM, a novel physics-informed foundation model specifically designed for multi-task energy forecasting in power systems. GridFM introduces four key innovations: (1) a FreqMixer adaptation layer that transforms pre-trained foundation model representations to power-grid-specific patterns through frequency domain mixing without modifying base weights; (2) a physics-informed constraint module embedding power balance equations and zonal grid topology using graph neural networks; (3) a multi-task learning framework enabling joint forecasting of load demand, locational-based marginal prices (LBMP), carbon emissions, and renewable generation with uncertainty-weighted loss functions; and (4) an explainability module utilizing SHAP values and attention visualization for interpretable predictions. We validate GridFM using over 10 years of real-time data from the New York Independent System Operator (NYISO) at 5 min resolution, comprising more than 10 million data points across 11 load zones. Comprehensive experiments demonstrate that GridFM achieves state-of-the-art performance with an 18.5% improvement in load forecasting MAPE (achieving 2.14%), a 23.2% improvement in price forecasting (achieving 7.8% MAPE), and a 21.7% improvement in emission prediction compared to existing TSFMs including Chronos, TimesFM, and Moirai-MoE. Ablation studies confirm the contribution of each proposed component. The physics-informed constraints reduce physically inconsistent predictions by 67%, while the multi-task framework improves individual task performance by exploiting inter-variable correlations. The proposed model provides interpretable predictions supporting the Climate Leadership and Community Protection Act (CLCPA) 2030/2040 compliance objectives, enabling grid operators to make informed decisions for sustainable energy transition and carbon reduction strategies. Full article

Infantile Epileptic Spasms Syndrome (IESS) is a devastating epileptic encephalopathy of infancy that carries a high risk of lifelong neurodevelopmental disability. Timely diagnosis is critical, as every week of delay in effective treatment is associated with worse cognitive outcomes. Although synchronized electroencephalogram (EEG) and surface electromyography (EMG) recordings capture both the electrophysiological and motor signatures of spasms, accurate automated detection remains challenging due to the non-stationary nature of the signals and the absence of physiologically plausible inter-modal fusion in current deep learning approaches. We introduce IESS-FusionNet, an end-to-end dual-stream framework specifically designed for accurate, real-time IESS detection from simultaneous EEG and EMG. Each modality is processed by a dedicated Unimodal Encoder that hierarchically integrates Continuous Wavelet Transform, Spatio-Temporal Convolution, and Bidirectional Mamba to efficiently extract frequency-specific, spatially structured, local and long-range temporal features within a compact module. A novel Cross Time-Mixing module, built upon the linear recurrent attention of the Receptance Weighted Key Value (RWKV) architecture, subsequently performs efficient, time-decaying, bidirectional cross-modal integration that explicitly respects the causal and physiological properties of cortico-muscular coupling during spasms. Evaluated on an in-house clinical dataset of synchronized EEG-EMG recordings from infants with confirmed IESS, IESS-FusionNet achieves 89.5% accuracy, 90.7% specificity, and 88.3% sensitivity, significantly outperforming recent unimodal and multimodal baselines. Comprehensive ablation studies validate the contribution of each component, while the proposed cross-modal fusion requires approximately 60% fewer parameters than equivalent quadratic cross-attention mechanisms, making it suitable for real-time clinical deployment. IESS-FusionNet delivers an accurate, computationally efficient solution with physiologically inspired cross-modal fusion for the automated detection of infantile epileptic spasms, offering promise for future clinical applications in reducing diagnostic delay. Full article

Against the backdrop of electricity market liberalization and deregulation, accurate electricity price forecasting is critical for optimizing power dispatch and promoting the low-carbon transition of energy structures. However, electricity prices exhibit inherent complexities such as seasonality, high volatility, and non-stationarity, which undermine the efficacy of traditional forecasting methodologies. To address these challenges, this study proposes a four-stage Decomposition-Reconstruction-Optimization-Integration (DROI) framework, coupled with an econometric breakpoint test, to evaluate forecasting performance across distinct time segments of Spanish electricity price data. The framework employs CEEMDAN for signal decomposition, decomposing complex price sequences into intrinsic mode functions to retain essential features while mitigating noise, followed by frequency-based data reconstruction; integrates the Improved Sparrow Search Algorithm (ISSA) to optimize initial model parameters, minimizing errors induced by subjective factors; and leverages Convolutional Neural Networks (CNN) for frequency-domain feature extraction, enhanced by an attention mechanism to weight channels and prioritize critical attributes, paired with Long Short-Term Memory (LSTMs) for temporal sequence forecasting. Experimental results validate the method’s robustness in both interval forecasting (IPCP = 100% and IPNAW is the smallest, Experiment 1.3) and point forecasting tasks (MAPE = 1.3758%, Experiment 1.1), outperforming naive approaches in processing stationary sequence clusters and demonstrating substantial economic utility to inform sustainable power system management. Full article

Epilepsy is increasingly conceptualized as a disorder of large-scale network instability, involving impairments in interhemispheric connectivity, prefrontal inhibitory control, and slow-frequency temporal processing. Rhythmic sensory stimulation—auditory, vibrotactile, or multisensory—can entrain neuronal oscillations and modulate attentional and sensorimotor networks, yet its mechanistic relevance to epileptic network physiology remains insufficiently explored. This conceptual and mechanistic article integrates empirical findings from entrainment research, prefrontal timing theories, multisensory integration, and network-based models of seizure dynamics and uses them to formulate a hypothesis-driven framework for multisensory exogenous rhythmic stimulation (ERS) in focal epilepsy. Rather than presenting a tested intervention, we propose a set of speculative mechanistic pathways through which low-frequency rhythmic cues might serve as an external temporal reference, engage fronto-parietal control systems, facilitate multisensory-driven sensorimotor coupling, and potentially modulate interhemispheric frontal coherence. These putative mechanisms are illustrated by exploratory neurophysiological observations, including a small pilot study reporting frontal coherence changes during mobile ERS exposure, but they have not yet been validated in controlled experimental settings. The framework does not imply therapeutic benefit; instead, it identifies theoretical pathways through which rhythmic sensory cues may transiently interact with epileptic networks. The proposed model is intended as a conceptual foundation for future neurophysiological validation, computational simulations, and early feasibility research in the emerging field of digital neuromodulation, rather than as evidence of clinical efficacy. This Hypothesis article formulates explicitly testable predictions regarding how multisensory ERS may transiently modulate candidate physiological markers of prefrontal network stability in focal epilepsy. Full article

The safe and stable operation of distribution networks relies on the real-time monitoring, analysis, and feedback of power quality data. However, with the continuous advancement of distribution network construction, the number of distributed power electronic devices has increased significantly, leading to frequent power quality issues such as voltage fluctuations, harmonic pollution, and three-phase unbalance in distribution terminals. Therefore, the augmentation and processing of power quality data have become crucial for ensuring the stable operation of distribution networks. Traditional methods for augmenting and processing power quality data fail to consider the differentiated characteristics of burrs in signal sequences and neglect the comprehensive consideration of both time-domain and frequency-domain features in disturbance identification. This results in the distortion of high-frequency fault information, and insufficient robustness and accuracy in identifying Power Quality Disturbance (PQD) against the complex noise background of distribution networks. In response to these issues, we propose a power quality data augmentation and processing method for distribution terminals considering high-frequency sampling. Firstly, a burr removal method of the sampling waveform based on a high-frequency filter operator is proposed. By comprehensively considering the characteristics of concavity and convexity in both burr and normal waveforms, a high-frequency filtering operator is introduced. Additional constraints and parameters are applied to suppress sequences with burr characteristics, thereby accurately eliminating burrs while preserving the key features of valid information. This approach avoids distortion of high-frequency fault information after filtering, which supports subsequent PQD identification. Secondly, a PQD identification method based on a dual-channel time–frequency feature fusion network is proposed. The PQD signals undergo an S-transform and period reconfiguration to construct matrix image features in the time–frequency domain. Finally, these features are input into a Convolutional Neural Network (CNN) and a Transformer encoder to extract highly coupled global features, which are then fused through a cross-attention mechanism. The identification results of PQD are output through a classification layer, thereby enhancing the robustness and accuracy of disturbance identification against the complex noise background of distribution networks. Simulation results demonstrate that the proposed algorithm achieves optimal burr removal and disturbance identification accuracy. Full article

Accurate prediction of photovoltaic power generation is a pivotal factor for enhancing the operational efficiency of electrical grids and facilitating the stable integration of solar energy. This study introduces a holistic forecasting framework that achieves seamless integration of dual-stage decomposition, deep learning architectures, and an advanced metaheuristic algorithm, thereby significantly improving the prediction precision of PV power generation. Initially, the raw PV power sequences are processed using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) to capture multi-scale temporal characteristics. The derived components are subsequently categorized into high-, medium-, and low-frequency groups through K-means clustering to manage complexity. To address residual noise and non-stationary behaviors, the high-frequency constituents are further decomposed via Variational Mode Decomposition (VMD). The refined subsequences are then input into a TCN_BiGRU_Attention network, which employs temporal convolutional operations for hierarchical feature extraction, bidirectional gated recurrent units to model temporal correlations, and a multi-head attention mechanism to prioritize influential time steps. For hyperparameter optimization of the forecasting model, an Improved Crested Porcupine Optimizer (ICPO) is developed, integrating Chebyshev chaotic mapping for initialization, a triangular wandering strategy for local search, and Lévy flight to strengthen global exploration and accelerate convergence. Validation on real-world PV datasets indicates that the proposed model attains a Mean Squared Error (MSE) of 0.3456, Root Mean Squared Error (RMSE) of 0.5879, Mean Absolute Error (MAE) of 0.3396, and a determination coefficient (R²) of 99.59%, surpassing all benchmark models by a significant margin. This research empirically demonstrates the efficacy of the dual decomposition methodology coupled with the optimized hybrid deep learning network in elevating both the accuracy and stability of predictions, thereby offering a reliable and stable forecasting framework for PV power systems. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides all-weather, high-resolution ocean wind speed monitoring that offers additional benefits for forecasting tropical cyclones and severe weather events. However, existing GNSS-R wind retrieval models often lack interpretability and suffer accuracy degradation during high wind conditions. To address these limitations, we leverage a mathematical equivalence between Transformers and graph neural networks (GNNs) on complete graphs, which provides a physically grounded interpretation of self-attention as spatiotemporal influence propagation in GNSS-R data. In our model, each GNSS-R footprint is treated as a graph node whose multi-head self-attention weights quantify localized interactions across space and time. This aligns physical influence propagation with the computational efficiency of GPU-accelerated Transformers. Multi-head attention disentangles processes at multiple scales—capturing local (25–100 km), mesoscale (100 km–500 km), and synoptic (>500 km) circulation patterns. When applied to Level 1 Version 3.2 data (2023–2024) from four Asian sea regions, our Transformer–GNN achieves an overall wind speed RMSE reduction of 32% (to 1.35 m s⁻¹ from 1.98 m s⁻¹) and substantial gains in high-wind regimes (winds >25 m s⁻¹: 3.2 m s⁻¹ RMSE). The model is trained on ERA5 reanalysis 10 m equivalent-neutral wind fields, which serve as the primary reference dataset, with independent validation performed against Stepped Frequency Microwave Radiometer (SFMR) aircraft observations during tropical cyclone events and moored buoy measurements where spatiotemporally coincident data are available. Interpretability analysis with SHAP reveals condition-dependent feature attributions and suggests coupling mechanisms between ocean surface currents and wind fields. These results demonstrate that our model advances both predictive accuracy and interpretability in GNSS-R wind retrieval. With operationally viable inference performance, our framework offers a promising approach toward interpretable, physics-aware Earth system AI applications. Full article

Accurate identification of tool wear states is crucial for ensuring machining quality and reliability. However, non-stationary signal characteristics, feature coupling, and limited use of multimodal information remain major challenges. This study proposes a hybrid framework that integrates a Sparrow Search Algorithm–optimized Continuous Wavelet Transform (SSA-CWT) with a Cross-Modal Time–Frequency Fusion Network (TFF-Net). The SSA-CWT adaptively adjusts Morlet wavelet parameters to enhance energy concentration and suppress noise, generating more discriminative time–frequency representations. TFF-Net further fuses cutting force and vibration signals through a sliding-window multi-head cross-modal attention mechanism, enabling effective multi-scale feature alignment. Experiments on the PHM2010 dataset show that the proposed model achieves classification accuracies of 100%, 98.7%, and 98.7% for initial, normal, and severe wear stages, with F1-score, recall, and precision all exceeding 98%. Ablation results confirm the contributions of SSA optimization and cross-modal fusion. External validation on the HMoTP dataset demonstrates strong generalization across different machining conditions. Overall, the proposed approach provides a reliable and robust solution for intelligent tool condition monitoring. Full article

Rolling Element Bearing (REB) failures represent a critical challenge in rotating machinery maintenance, accounting for approximately 45% of industrial breakdowns. Considering the variable operating conditions of speeds and loads, vibration fault signatures are generally masked by noises. Consequently, traditional diagnostic methods relying on time and frequency analysis or conventional machine learning often fail to capture the nonlinear interactions and phase coupling characteristics essential for accurate fault detection, particularly in noisy industrial environments. In this study, we propose a framework that synergistically combines (1) Empirical Mode Decomposition (EMD) for adaptive handling of non-stationary vibration signals, (2) bispectrum analysis to extract phase-coupled features while inherently suppressing Gaussian noise, and (3) Time-Series Transformer with attention mechanisms to automatically weight discriminative feature interactions. Experimental results based on five different benchmarks show that the proposed BSEMD-Transformer framework is a powerful tool for REB diagnosis, reaching a classification accuracy of at least 98.2% for all tests regardless of the used dataset. The proposed approach is judged to be consistent, robust, and accurate even under variable conditions of speed and loads. Full article

The classification of very-low-frequency and low-frequency (VLF/LF) lightning-radiation electric-field waveforms is of paramount importance for lightning-disaster prevention and mitigation. However, traditional waveform classification methods suffer from the complex characteristics of lightning waveforms, such as non-stationarity, strong noise interference, and feature coupling, limiting classification accuracy and generalization. To address this problem, a novel framework is proposed for VLF/LF lightning-radiated electric-field waveform classification. Firstly, an improved Kalman filter (IKF) is meticulously designed to eliminate possible high-frequency interferences (such as atmospheric noise, electromagnetic radiation from power systems, and electronic noise from measurement equipment) embedded within the waveforms based on the maximum entropy criterion. Subsequently, an attention-based multi-fusion convolutional neural network (AMCNN) is developed for waveform classification. In the AMCNN architecture, waveform information is comprehensively extracted and enhanced through an optimized feature fusion structure, which allows for a more thorough consideration of feature diversity, thereby significantly improving the classification accuracy. An actual dataset from Anhui province in China is used to validate the proposed classification framework. Experimental results demonstrate that our framework achieves a classification accuracy of 98.9% within a processing time of no more than 5.3 ms, proving its superior classification performance for lightning-radiation electric-field waveforms. Full article

Ice loads continue to pose challenges to the structural safety of offshore wind turbines (OWTs), while the rapid development of offshore wind power in cold regions is enabling the deployment of OWTs in deeper waters. To accurately simulate the dynamic response of an OWT under combined ice–wind loading, this paper proposes a Discrete Element Method–Wind Turbine Integrated Analysis (DEM-WTIA) framework. The framework can synchronously simulate discontinuous ice-crushing processes and aeroelastic–structural dynamic responses through a holistic turbine model that incorporates rotor dynamics and control systems. To address the issue of insufficient prediction accuracy for dynamic responses, we introduced a multivariate time series forecasting method that integrates a secondary decomposition strategy with a hybrid prediction model. First, we developed a parallel signal processing mechanism, termed Adaptive Complete Ensemble Empirical Mode Decomposition with Improved Singular Spectrum Analysis (CEEMDAN-ISSA), which achieves adaptive denoising via permutation entropy-driven dynamic window optimization and multi-feature fusion-based anomaly detection, yielding a noise suppression rate of 76.4%. Furthermore, we propose the F-Transformer prediction model, which incorporates a Frequency-Enhanced Channel Attention Mechanism (FECAM). By integrating the Discrete Cosine Transform (DCT) into the Transformer architecture, the F-Transformer mines hidden features in the frequency domain, capturing potential periodicities in discontinuous data. Experimental results demonstrate that signals processed by ISSA exhibit increased signal-to-noise ratios and enhanced fidelity. The F-Transformer achieves a maximum reduction of 31.86% in mean squared error compared to the standard Transformer and maintains a coefficient of determination (R²) above 0.91 under multi-condition coupled testing. By combining adaptive decomposition and frequency-domain enhancement techniques, this framework provides a precise and highly adaptable ultra-short-term response forecasting tool for the safe operation and maintenance of offshore wind power in cold regions. Full article

Bolted connections are extensively utilized in aerospace, civil, and mechanical systems for structural assembly. However, inevitable structural vibrations can induce bolt loosening, leading to preload reduction and potential structural failure. Early-stage preload degradation, particularly during initial loosening, is often undetectable by conventional monitoring methods due to limited sensitivity and poor noise resilience. To address these limitations, this study proposes an intelligent bolt preload monitoring framework that combines electromechanical impedance (EMI) signal analysis with a parallel deep learning architecture. A multiphysics-coupled model of flange joint connections is developed to reveal the nonlinear relationships between preload degradation and changes in EMI conductance spectra, specifically resonance peak shifts and amplitude attenuation. Based on this insight, a parallel convolutional neural network (P-CNN) is designed, employing dual branches with 1 × 3 and 1 × 7 convolutional kernels to extract local and global spectral features, respectively. The architecture integrates dilated convolution to expand frequency–domain receptive fields and an enhanced SENet-based channel attention mechanism to adaptively highlight informative frequency bands. Experimental validation on a flange-bolt platform demonstrates that the proposed P-CNN achieves 99.86% classification accuracy, outperforming traditional CNNs by 20.65%. Moreover, the model maintains over 95% accuracy with only 25% of the original training samples, confirming its robustness and data efficiency. The results demonstrate the feasibility and scalability of the proposed approach for real-time, small-sample, and noise-resilient structural health monitoring of bolted connections. Full article

In response to China’s dual-carbon strategy, this study proposes a comprehensive analytical framework to identify the evolutionary pathways of key policy tasks in developing a new-type power system. A dual-channel data acquisition process was designed to extract, standardize, and segment policy documents and online texts into a unified corpus. A multi-label BERT classification model was then developed, incorporating domain-specific terminology injection, label-wise attention, dynamic threshold scanning, and imbalance-aware weighting. The model was trained and validated on 200 energy news articles, 100 official policy releases, and 10 strategic planning documents. By the 10th epoch, it achieved convergence with a Macro-F1 of 0.831, Micro-F1 of 0.849, and Samples-F1 of 0.855. Ablation studies confirmed the significant performance gain over simplified configurations. Structural label analysis showed “Build system-friendly new energy power stations” was the most frequent label (107 in plans, 80 in news, 24 in policies) and had the highest co-occurrence (81 times) with “Optimize and strengthen the main grid framework.” The label co-occurrence network revealed multi-layered couplings across generation, transmission, and storage. The Priority Evaluation Index (PEI) further identified “Build shared energy storage power stations” as a structurally central task (centrality = 0.71) despite its lower frequency, highlighting its latent strategic importance. Within the domain of national-level public policy and planning documents, the proposed framework shows reliable and reusable performance. Generalization to sub-national and project-level corpora is left for future work, where we will extend the corpus and reassess robustness without altering the core methodology. Full article