Search Results (201)

High-resolution online sensors are now common in environmental process systems, yet turning their non-stationary, heterogeneous observation streams into reliable predictions of the underlying process state remains difficult. The statistical distribution of a sensor stream changes over time, the measured variables do not coincide with the state variables of interest, and repeatedly running a mechanistic process model for forward prediction is computationally costly. We present FT-Crossformer, a time–frequency Transformer that acts as a data-driven surrogate between multi-sensor observations and multivariate process-state prediction. To handle distribution shift in the sensor streams, a time-domain distribution-transformation module, together with an inverse-mapping module, performs an affine bias correction that removes per-window non-stationary statistics at the input and restores them at the output, so the gap between training and test distributions is reduced without discarding non-stationary information. We show that this affine correction, including its learnable per-variable scale and shift, acts in the frequency domain on every non-zero frequency component as one common scaling factor that does not depend on the frequency index, so it cannot change the relative magnitudes among the components. A frequency-stability measurement module and a frequency-weighting module therefore re-weight the spectral components of the observation signal so that the stable, task-relevant ones contribute more to the reconstructed signal. The cross-dimension attention of the Crossformer backbone serves as a multi-sensor fusion mechanism that models the dependencies among the measured variables. We validate the method on public benchmark datasets from different domains as a check of generality and, most relevantly, for environmental modeling on two real cases: a wastewater nitrogen-and-phosphorus-removal process and chlorophyll forecasting from an in situ estuary sensor mooring in San Francisco Bay. On the estuary chlorophyll data, which carries a strong train-to-test distribution shift, the full FT-Crossformer demonstrates superior accuracy among the evaluated models at the next-day nowcasting horizon, and an ablation shows that both the time-domain trans- formation and the frequency-domain weighting contribute to this accuracy. FT-Crossformer produces forward predictions from distribution-shifted sensor data with a single fixed-cost forward pass in place of a repeated mechanistic solve, which makes it a practical building block for sensor-data integration and assimilation in environmental process modeling. Full article

The aim of this study is to implement a custom-designed data acquisition system with a DAQ card, based on the virtual instrument concept in the LabVIEW environment, to investigate and analyze muscle fatigue using surface electromyography (sEMG) signals. The experiment was conducted as a pilot case study on a healthy volunteer, where sEMG signals from the biceps brachii muscle were collected during cyclic weighted exercises. Signal registration was performed across three distinct states: no fatigue, moderate fatigue, and high fatigue. The developed measurement system enabled signal acquisition, filtering, and analysis through both online processing and post-processing. Time-domain parameters (ARV, RMS, ${\mathrm{U}}_{\max }$) and frequency-domain parameters ($\Sigma \mathrm{PS}$, MNF, MDF) were determined from three series of measurements. An analysis of parameter changes was conducted both within and between the series. The results indicated that with the onset of muscle fatigue, the participant exhibited a decrease in amplitude parameters and a shift in the power spectrum toward lower frequencies. Frequency-domain parameters, particularly MNF, exhibited higher diagnostic sensitivity than amplitude parameters. The obtained results confirm the technical feasibility of the developed virtual instrument for sEMG signal analysis. Furthermore, they suggest its potential utility for objective muscle condition assessment, establishing an engineering baseline for future research. Full article

Atmospheric light detection and ranging (lidar) measurements of the attenuated backscatter coefficient (ABSC) form high-dimensional vertical profiles that vary across multiple temporal scales. Directly processing the original time-domain sequence with a single prediction structure may entangle information associated with these different scales. To address this issue, we propose FFT-FDNet, a frequency-domain decomposition network based on fast Fourier transform (FFT). FFT-FDNet explicitly decomposes the input sequence along the temporal dimension into low-, mid-, and high-frequency components. Branch-specific predictors model these components separately, and a learnable fusion layer combines their outputs to forecast future ABSC profiles. Experiments were conducted using continuous single-site lidar observations from the Tokyo station at a temporal resolution of 15 min. FFT-FDNet was compared with eight representative time-series forecasting models at forecast horizons of 2, 4, and 6 h. Across the three horizons, FFT-FDNet achieved the lowest mean ${\mathrm{MAE}}_{\mathrm{std}}$ and ${\mathrm{MSE}}_{\mathrm{std}}$ and the highest mean $R^2$ among the evaluated methods. At the 2 h horizon, these metrics were 0.1221, 0.4116, and 0.7505, respectively. The ablation results showed consistent performance degradation after removing the FFT decomposition, low-frequency branch, or mid-frequency branch, whereas the high-frequency branch provided modest improvements at some forecast horizons. The frequency band sensitivity analysis supported the use of $({\nu }_1,{\nu }_2)=(0.10,0.45)$ among the evaluated cutoff combinations. These results suggest that FFT-based three-band decomposition is useful for the present Tokyo single-station short-term forecasting case. Further validation using data from more stations, seasons, and aerosol conditions is still needed. Full article

Millimeter-wave (mm-wave) near-field imaging using frequency-modulated continuous wave (FMCW) radar has emerged as a pivotal technology for high-resolution applications, including security screening, non-destructive testing, and medical diagnostics. This review evaluates the performance and evolution of key imaging algorithms, categorized into spatial-domain and frequency-domain frameworks. We analyze the delay-and-sum (DAS) beamformer for its real-time utility and the back-projection algorithm (BPA) for its baseline phase precision and robust adaptability to irregular scanning trajectories. To address the high computational demands of standard spatial-domain processing, we examine fast alternatives such as the range migration algorithm (RMA). The exact RMA leverages Fourier-domain operations and Stolt coordinate mapping to achieve optimal computational scaling on uniform grids while preserving diffraction-limited spatial resolutions. Concurrently, we evaluate fast spatial-domain approximations, including Fast Back-Projection (Fast-BPA), which introduces localized Taylor-series expansions to linearize near-field range paths within sub-apertures, accelerating voxel reconstruction times at a reduced computational cost. Furthermore, this study explores advanced modifications designed to overcome physical and operational constraints, such as motion-compensated matched filtering (MF) to eliminate the “stop-and-go” assumption in continuous scanning, and sparse multiple-input multiple-output (MIMO) configurations to mitigate aliasing in undersampled environments. Comparative analysis reveals that while spatial-domain methods (DAS/BPA) generally offer higher robustness to non-uniform aperture perturbations, frequency-domain migration pathways (RMA) maximize the computational throughput required for large-volume three-dimensional (3D) reconstructions. The findings demonstrate that achievable resolution is primarily governed by signal bandwidth and aperture synthesis, though practical performance is often limited by calibration errors and computational overhead. Collectively, these advancements validate the potential of mm-wave FMCW systems to achieve sub-millimeter 3D imaging, bridging the gap between theoretical diffraction limits and real-world indoor sensing challenges. Full article

The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously. This paper presents a unified framework that integrates Proximal Policy Optimization (PPO) for robo-advisory systems, multi-scale time-series prediction models for high-frequency trading, in-context learning mechanisms for dynamic investment advisory, game-theoretic reasoning for competitive banking scenarios, and unified embeddings for cross-modal financial sentiment analysis. Our comprehensive framework addresses the critical gap in the existing literature where these technologies have been developed in isolation, failing to leverage their synergistic potential. Through extensive experimentation across multiple financial datasets and real-world scenarios, we demonstrate that our integrated approach achieves superior performance compared to specialized single-domain systems. Specifically, our framework shows a 23.7% improvement in portfolio optimization metrics, reduces prediction error in high-frequency trading by 31.2%, enhances investment recommendation accuracy by 18.9%, optimizes competitive banking strategies with a 27.4% increase in Nash equilibrium convergence speed, and improves sentiment analysis accuracy by 15.6% through cross-modal fusion. The theoretical foundation of our work establishes convergence guarantees for the integrated optimization problem, while our empirical results validate the practical applicability across diverse financial institutions. This research not only advances the state-of-the-art in financial AI but also provides a blueprint for developing comprehensive intelligent systems that can adapt to the complex, interconnected nature of modern financial markets. Full article

High-quality X-ray pulsar observation profiles are vital for investigating both their physical properties and navigation applications. Conventional profile extraction relies on epoch folding, whose performance is constrained by observation duration and bin size, often leading to poor-quality profiles or even failure under extremely low-photon conditions. This paper proposes a novel method that directly extracts high-quality profile frequency spectra merely by statistical analysis of photon sequences followed by the reconstruction of time domain waveforms. Monte Carlo simulations and real observational data demonstrate that the proposed method exhibits higher correlation coefficients and signal-to-noise ratios than those obtained using traditional epoch folding, and also outperforms the Fourier-series-based frequency cutoff method. Moreover, comparable profile quality can be achieved using an order of magnitude fewer photons than required by epoch folding. The lower the photon count, the more significant the improvement, making the method especially suitable for small-area detectors and resource-constrained observation scenarios. Full article

Electric bicycles are popular due to their environmental benefits and convenience. However, electric bicycle fires caused by series arc faults remain a serious safety concern. This study focuses on series arc fault identification for electric bicycles under complex operating conditions, covering state of charge (SoC), torque, and speed variations, and simultaneously considers normal state, DC-side series arc fault, and AC-side series arc fault conditions. Five time-domain features, namely root mean square (RMS), standard deviation (STD), skewness (SK), kurtosis (KUR), and current amplitude (CA), and three frequency-domain features, namely amplitude–frequency energy (AFE), amplitude–frequency mean (AFM), and amplitude–frequency kurtosis (AFK), are extracted. An improved principal component analysis (PCA)-based feature fusion method transforms the eight original time–frequency features into a five-dimensional PCA-fused feature representation consisting of PC1, PC2, PC3, fused PC4–PC7, and PC8. The fused features are classified using a radial basis function (RBF)-support vector machine (SVM) model. The proposed method achieves 98.68% test accuracy, 0.9869 Macro-F1, and 0.9931 Macro-AUC. A classifier comparison and feature-level latency analysis are also provided to clarify the accuracy–cost tradeoff and deployment feasibility. The results indicate that the proposed method can provide an interpretable and lightweight solution for electric bicycle controllers, battery management systems (BMSs), and onboard safety-monitoring applications. Full article

Time series anomaly detection aims to identify deviations from the normal distribution of temporal data. Reconstruction error is a natural and practical anomaly criterion, and using reconstruction error as an anomaly criterion is a well-established and practical paradigm. However, existing reconstruction-based methods often fail to capture complex structures in high-dimensional time series data and typically lack in-depth analysis of periodicity, limiting detection accuracy. To address these challenges, we propose ReDTF-AD, a novel approach that integrates reconstruction error with time–frequency fusion. Specifically, the input series is decomposed into seasonal and trend components. For seasonal components, we designed a time–frequency fusion block (TFFB) to enhance frequency-domain features while preserving residual time-domain information, ultimately achieving the fusion of time–frequency information. The Top-k transformation converts 1D sequences into 2D representations based on the periodicity of time series data, enabling deeper analysis of intra-period and inter-week variations through our newly proposed Split Concat Block (SCBlock). For the trend term, a linear module captures long-term patterns. In the unsupervised time series anomaly detection experiments based on reconstruction error, ReDTF-AD shows competitive performance on three public benchmark datasets. Full article

This paper proposes a two-stage hybrid framework for biosignal quality validation that produces beat-level or segment-level labels for real-time filtering and offline dataset curation. The framework is quantitatively validated exclusively on ECG data. Its modular architecture is designed to extend to further non-stationary periodic biomedical time-series signals including photoplethysmography (PPG), impedance cardiography (ICG), phonocardiography (PCG), electromyography (EMG), and electroencephalography (EEG) through modality-specific parameter adaptation; however, this broader applicability currently reflects architectural extensibility rather than experimentally validated performance. A prerequisite is synchronized acquisition of the primary biosignal together with inertial motion sensing (IMU/accelerometer) and electrode impedance or lead-off status, with the IMU positioned near the sensing electrodes. The first stage performs sensor-integrity gating to reject intervals corrupted by motion or poor electrode contact. The second stage applies software signal quality indices to the remaining beats, including physiological plausibility constraints (R to R peaks analysis), DTW-based morphological consistency against adaptive templates, frequency domain SNR estimation, and baseline wander quantification. This study systematically evaluates and compares the classification performance of six complementary sensor-level and software-based signal quality assessment methods. When integrated within the proposed hybrid framework, validation against expert-annotated ECG quality labels from 20 healthy participants demonstrates high methodological classification accuracy (98.1%), achieving approximately a 98% F1-score, 99% sensitivity, and 97% specificity. Prospective validation on patient populations with cardiovascular pathology is identified as a necessary step toward clinical deployment. This modular approach improves the reliability of downstream analysis by preventing corrupted data from entering feature extraction and model training pipelines, enabling more stable physiological monitoring in free-living conditions, reducing false alarms in continuous monitoring applications, and generating higher-quality datasets for AI-based diagnostic systems. Full article

Reliable short-term wind power forecasting is crucial for smart grid stability. However, high-dimensional noise and stochastic fluctuations in wind sequences often degrade the accuracy of traditional forecasting models. Moreover, wind power time series typically exhibit asymmetric rising and decaying patterns, which further complicate accurate modeling. To address these challenges, this study proposes a hybrid intelligent system that integrates three components: data preprocessing, heterogeneous ensemble learning, and probabilistic interval forecasting. First, we build a multi-stage preprocessing workflow. Adaptive DBSCAN and Local Outlier Factor (LOF) remove spatial and density anomalies. Then multivariate variational mode decomposition (MVMD) synchronously separates multi-scale oscillatory patterns while preserving cross-channel correlations and frequency-domain symmetry across input variables. SHAP analysis quantifies feature importance, ensuring interpretability. The selected features are fed into a heterogeneous ensemble model consisting of Transformer, BPNN, ELM, XGBoost, and QRLSTM, which collectively capture multi-scale temporal dependencies and diverse data patterns. The ensemble weights are dynamically optimized by a modified multi-objective dragonfly algorithm (MMODA) that balances forecast accuracy and stability. Based on this ensemble, we apply MMODA to tune kernel density estimation for generating high-quality forecast intervals, maximizing coverage while minimizing interval width. Experiments on two wind farms in Shandong show that our MMODA-optimized ensemble reduces mean absolute percentage error by about 44.7% compared to single models, and ablations confirm that MVMD preprocessing adds a further 10.7% reduction. The proposed system provides an interpretable and reliable decision-support tool for sustainable grid operations. Full article

Biologically related time-series data characterize the dynamic evolution of biological systems, including genetic inheritance, disease diagnosis, and the biological microenvironment. However, accurate prediction of these data remains challenging due to their pronounced time-varying, non-stationary, and noisy characteristics. Existing approaches often fail to capture latent shifts of biologically related time series, limiting both predictive performance and time-varying pattern recognition capability. Thus, in this study, we first propose a time-varying neural network (TVNN) model that combines frequency-domain information with Koopman theory. TVNN-model Koopman transition matrices are used to model global dynamics and local time-varying behaviors for pattern extraction. Secondly, a time-varying pattern recognition large language model (TVPRLLM) is introduced to recognize and classify the extracted time-varying patterns, enabling the identification of potential pattern categories. Thirdly, we have developed a biology-related time-series predictive platform that can offer visualization, data analysis, and predictive services. Experimental results demonstrate that the TVNN model outperforms existing mainstream methods in predicting biology-related time-varying time series, and that it achieves competitive forecasting performance, though its behavior depends strongly on the design of the frequency-domain decomposition. Additional robustness analyses reveal that the choice of Fourier masking strategy can materially affect both RMSE and long-horizon stability. We further show that Koopman-derived time-varying representations are highly discriminative for dynamic state recognition. Full article

To address the issue of coordination failure in multi-stage surge protective devices (SPDs) under lightning surges in communication power systems, this study employs traveling wave propagation theory and electromagnetic transient simulations using the PSCAD/EMTDC platform. It systematically evaluates how lightning strike location, interstage cable length, and load type affect energy coordination and overvoltage response in a two-stage SPD configuration. By combining time-domain and frequency-domain analysis, the coupling mechanism of SPD conduction timing is revealed. There exists a critical length for the interstage cable to ensure coordinated operation of the SPDs. This critical length decreases with increasing surge intensity but increases significantly with greater lightning strike distance. Incorporating an appropriate series inductor can provide the necessary time delay, serving as an alternative to using a long cable. For capacitive loads, although an excessively short cable can reduce the amplitude of oscillatory voltage spikes, it aggravates the surge steepness, thereby stressing the SPD. These oscillations can be effectively suppressed by installing a damping resistor in front of the SPD2. Furthermore, the study reveals a strong coupling between energy coordination and overvoltage behavior under capacitive load conditions, indicating that the two must be jointly optimized. The parameter configurations and practical recommendations presented offer quantitative design guidance for SPD selection, cable layout, and resonance suppression in communication power systems. Full article

Robust decoding of latent cognitive states from non-stationary physiological time series is a challenging high-dimensional signal processing problem. Traditional unimodal frameworks based only on electroencephalography often show covariate shift and weak cross-task generalization. This study presents the Neuro-Cardiac Symbiotic Engine, a multimodal fusion architecture that combines high-frequency cortical EEG dynamics with low-frequency autonomic regulation derived from heart rate variability within a unified discriminative feature space. The pipeline integrates spectral decomposition and autonomic quadratic descriptors through a memory-optimized high-performance computing workflow on the CEDIA supercomputer. To reduce domain discrepancy between memory and piloting tasks, we design a few-shot calibration strategy based on affine manifold alignment and probabilistic ensemble inference. Validation on 29 subjects reaches a mean classification accuracy of 99.13 percent, far above the zero-shot baseline near 38 percent. Topological analysis also indicates phase-space contraction under high workload, where fused vagal and frontal-parietal biomarkers concentrate system dynamics into a low-entropy attractor. The results establish a mathematically grounded framework for passive brain–computer interfaces and show that orthogonal neuro-visceral integration is critical for reliable cognitive state estimation. Full article

This paper proposes an optimized variable-frequency phase-shift modulation strategy based on frequency-domain analysis to address the issues of large reactive circulating current and low transmission efficiency in dual-active-bridge (DAB) converters under voltage mismatch conditions. First, a unified frequency-domain analytical model for extended phase-shift (EPS) modulation is established using Fourier series, which avoids the complexity introduced by mode division in traditional time-domain analysis. The Karush–Kuhn–Tucker (KKT) conditions are then utilized to analytically derive the optimal phase-shift angles that minimize the RMS current over the entire power range. Based on this, a control method is proposed to suppress the reactive circulating current by adjusting the switching frequency. Experimental results demonstrate that the proposed strategy significantly reduces the RMS current and reactive circulating current, thereby improving efficiency across a wide voltage gain and full load range, compared to traditional single phase-shift and extended phase-shift strategies. Full article

Traditional diagnostics of power transformers heavily rely on signal transformations, such as Welch’s method, to analyze low-frequency noise signals. This study proposes a novel approach using Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, for direct time-series analysis of raw low-frequency signals without frequency-domain transformation. By training and testing multiple LSTM architectures on transformer vibroacoustic data, the proposed approach achieved approximately 86% accuracy in the fine-grained multi-class benchmark and up to 95.54% in the broader grouped categorization scenario. The model further demonstrated near-perfect classification accuracy in distinguishing transformer types (normal vs. overload) using a simplified RNN architecture. These findings illustrate that RNN-based models can streamline transformer diagnostics and improve accuracy in identifying operational states and types, potentially advancing non-invasive monitoring techniques in power system infrastructure. Full article