Search Results (98)

To address the high non-stationarity of COVID-19 pandemic time-series data and the severe error accumulation issue in long-horizon forecasting, a spatiotemporal two-branch multi-step forecasting model named CFC-FLCA is proposed. This model integrates closed-form continuous-time neural networks (CFC), a hybrid decomposition strategy combining variational mode decomposition (VMD) and jump plus AM-FM mode decomposition (JMD), and a cross-attention (CA) mechanism. First, the VMD-JMD hybrid mode decomposition method is applied to preprocess raw new case sequences. By leveraging the complementary advantages of the two decomposition algorithms, non-stationary sequences are adaptively decomposed into high-frequency noise components and low-to-mid-frequency trend-periodic components, eliminating random disturbance interference at the data source. On this basis, a time–frequency dual-branch feature extraction network is constructed. CFC provides ultra-long-range temporal dependency modeling capability; the time-domain branch adopts Legendre projection units (LPU) to extract robust temporal evolution features, while the frequency-domain branch employs frequency-enhanced units (FEU) to uncover latent periodic patterns that are difficult to capture using traditional time-domain methods. A cross-attention mechanism is introduced to dynamically learn the importance weights of time–frequency-domain features, enabling the adaptive deep integration of complementary information and effectively mitigating error accumulation in long-horizon forecasting. Multi-step forecasting experiments are conducted on real-world COVID-19 datasets from Belgium, the Czech Republic, and Ireland, with comprehensive comparisons against mainstream time series forecasting models. The experimental results demonstrate that the CFC-FLCA model outperforms all comparison models across all evaluation metrics for all prediction horizons. Full article

Rolling element bearing degradation monitoring is critical for predictive maintenance in rotating machinery systems. Existing methods predominantly address fault classification and remaining useful life (RUL) estimation as separate tasks, thereby failing to capture the progressive and multistage nature of bearing deterioration. This paper proposes a physics-aware multi-modal fusion framework for continuous RUL prediction from vibration signals, organized around a stage-aware representation of the bearing life cycle. The proposed pipeline integrates two complementary preprocessing branches: Hilbert envelope demodulation followed by short-time Fourier transform (STFT) to generate degradation-sensitive time–frequency spectrograms, and handcrafted statistical feature extraction to yield compact global severity descriptors. A dual-head convolutional neural network-multilayer perceptron (CNN-MLP) architecture is designed to learn discriminative representations from both modalities and fuse them for end-to-end normalized RUL regression. The bearing life cycle is further partitioned into four ordered degradation stages based on normalized life–progress ratios, providing an interpretable health representation that complements the continuous prognosis target. Experiments conducted on the PRONOSTIA/FEMTO-ST benchmark dataset demonstrate that the proposed framework achieves an RMSE of 0.1597, an MAE of 0.1328, and an $R^2$ of 0.7487 on normalized RUL prediction, with stable error behavior across most of the life cycle. Feature importance analysis confirms that the CNN branch captures localized low-to-mid-frequency spectral evolution while the MLP branch encodes amplitude variability and impulsive indicators, validating the complementarity of the dual-branch design. The proposed method offers a unified, interpretable, and engineering-relevant solution for intelligent bearing condition monitoring and prognostic health management. Full article

Grain moisture content is a key variable for safe storage, drying control, and quality management. Microwave sensing is attractive because water strongly modulates the complex relative permittivity ($\epsilon *={\epsilon }^{\prime }-j{\epsilon }^{″}$) of granular agricultural products, thereby shaping broadband scattering-parameter spectra. This study presents a meter-referenced feasibility evaluation of an interpretable S-parameter–permittivity–moisture chain using a vector network analyzer over 2–18 GHz. Wheat, maize, and mung bean were prepared at six moisture levels, and the moisture values were referenced to two commercial grain moisture meters (MC_ref) to represent rapid on-site benchmarking rather than absolute gravimetric moisture determination. Therefore, the reported errors should be interpreted as commercial-meter-referenced calibration indicators rather than absolute gravimetric moisture prediction accuracy. Two free-space configurations were compared on the same platform: a two-horn transmission setup under controlled packing and a metal-backed double-pass reflection setup intended to represent single-sided access under loose bulk packing. After SOLT calibration and empty-holder background normalization, ${\epsilon }^{\prime }$ and ${\epsilon }^{″}$ were retrieved via complex-domain nonlinear least-squares fitting of physics-based slab models to measured S21 spectra. The results show that moisture-dependent dielectric responses were grain- and configuration-dependent. In particular, ${\epsilon }^{″}$ generally provided a more robust moisture-sensitive feature in the free-space transmission configuration, whereas the optimal single-parameter predictor in the metal-backed configuration differed among grains. A mid-band frequency window of approximately 8–16 GHz provided more stable inversion by avoiding low-frequency coupling artefacts and high-frequency signal-to-noise degradation. The metal-backed configuration preserved moisture trends but yielded lower effective ${\epsilon }^{\prime }$ values, likely due to increased air fraction under loose packing. These results indicate that packing state, grain type, and frequency-window selection are critical factors for transferring microwave moisture calibration from laboratory measurements to practical grain-handling scenarios. Full article

The ionosphere is a major error source in single-frequency GNSS positioning, and Satellite-Based Augmentation Systems (SBAS) mitigate this effect by providing real-time correction information. However, the performance of SBAS under different latitude regions and geomagnetic activity levels still requires further evaluation. Taking EGNOS as an example, this study assesses SBAS-enhanced positioning performance using data from nine IGS stations across Europe. The experiments cover relatively low-, mid-, and high-latitude regions within the EGNOS service area, four representative quarters in 2023, and two disturbed geomagnetic events. Results show: (1) SBAS significantly improves positioning accuracy in all latitude regions, with overall improvement rates ranging from 62.11% to 83.51%. (2) The relatively low-latitude region achieves the largest performance gains, while the mid-latitude region provides the most stable and accurate results. (3) Under disturbed geomagnetic conditions, SBAS still outperforms conventional SPP, but its performance decreases compared with low-Kp periods, with latitude-dependent degradation observed at both MAS1 and SOD3. (4) The integrity analysis further shows that APV II availability reaches 91.85%, whereas CAT I availability decreases to 86.95% under disturbed conditions. Overall, SBAS effectively improves single-frequency positioning accuracy, stability, and integrity, but its performance remains affected by latitude and geomagnetic activity. Full article

This paper presents a new plane stress model for the dynamic analysis of multilayer composite beams with interlayer slip effects. In this model, the cross section of a multilayer composite beam is transformed into an equivalent plane stress cross section. Based on the equilibrium, constitutive and geometric equations of the plane stress problem, state equations are derived in terms of a set of state variables. The state variables are then expanded in Fourier series, and the state equations are solved using the state-space method. The proposed computational model makes it convenient to account for slip at each interface and can represent the entire transition of an interface from fully slipped to fully bonded. Interlayer slip and the corresponding interaction forces are incorporated naturally into the derivation of the governing equations, and the model gives accurate results. A steel–concrete–steel composite beam, a four-layer composite beam and a laminated timber beam are analyzed as examples of multilayer composite beams under both static and dynamic loading. The static analysis results are in good agreement with the literature results, with a maximum error of 0.63% for the maximum mid-span deflection and only 0.143% for the maximum interlayer slip value. Compared with finite element results, the natural frequencies and buckling loads obtained from the dynamic analysis exhibit maximum relative errors of 2.87% and 3.77%, respectively. The relationship between axial force and natural frequency is also presented, which verifies the accuracy and reliability of the proposed model and calculation method. Full article

This paper introduces an electrocardiogram (ECG) noise removal front-end amplifier circuit based on a current-feedback operational amplifier (CFOA) that uses the current feedback to detect error signals and control the output. This ECG circuit focuses on denoising the ECG noise to accentuate the ECG electrical signals from the heart. Noises in ECG refer to baseline wander (BW), powerline interference (PLI) and motion artifacts. We proposed a CFOA-based ECG pre-amplifier using the AD844 commercial operational amplifier built inside with a positive second-generation current conveyor (CCII+) and a voltage follower circuit. This work introduces an ECG noise removal front-end amplifier based on a CFOA. The primary innovation lies in the balancing instrumentation amplifier architecture that utilizes the high-speed and robust properties of the AD844 commercial operational amplifier to achieve superior noise rejection. To protect against high-frequency interference, we introduce a novel cascaded low-pass filter (LPF) stage that ensures a sharper cut-off compared to traditional single-stage designs. Experimental results validate the design’s effectiveness, achieving a high common-mode rejection ratio (CMRR) of 75.4 dB and a mid-band gain of 46.5 dB. These performance metrics, combined with the circuit’s ability to eliminate BW and PLI, confirm its robust suitability for high-fidelity wearable ECG monitoring. Full article

Ultra-short-term electricity load forecasting is crucial for real-time power system operation, but its accuracy is limited by the nonstationary and multiscale characteristics of load data. To address this issue, this study proposes a multi-scale decomposition–clustering and heterogeneous gated fusion framework. The original load sequence is decomposed by ICEEMDAN and then grouped into high-, mid-, and low-frequency components using K-means clustering. MS-gTCN is used to capture high-frequency fluctuations, adaptive DLinear is used to model low-frequency trends, and a gated fusion mechanism is designed for mid-frequency components. A lightweight error correction network is further introduced to reduce residual prediction errors. Experiments on two real-world datasets show that the proposed method achieves the best performance across 1-, 4-, 8-, and 12-step horizons. For the 12-step task, it reduces MAE by 29.3% on Dataset A and 26.2% on Dataset B compared with the second-best baselines. Compared with ICEEMDAN-LSTM on Dataset A, it reduces MAE by 17.7% and improves $R^2$ from 0.9127 to 0.9418. Ablation, sensitivity, significance, and complexity analyses further verify the effectiveness, robustness, and real-time feasibility of the proposed framework. Full article

This study presents the development and optimisation of sustainable composite materials derived from automotive polymeric waste (dashboard crumbs). The influence of key formulation parameters on material performance was investigated using experimental analysis combined with model-based prediction. Acoustic behaviour was evaluated using an impedance tube method, while predictive modelling was performed using the Johnson–Champoux–Allard (JCA) model and a Padé approximation for efficient computation. Model performance was assessed using quantitative metrics, including root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R²), demonstrating good agreement with experimental data across the investigated frequency range. The results show that catalyst concentration is a critical parameter, with an optimal value of 5 wt% yielding near-unity absorption within the mid-frequency range (1200–1800 Hz). Further increase in catalyst content resulted in reduced performance due to changes in pore structure and reaction kinetics. In contrast, particle size variation exhibited a limited effect on overall performance. The proposed modelling framework enables efficient prediction of material behaviour and supports optimisation of formulation parameters. This study highlights the potential of recycled polymeric materials for sustainable engineering applications and provides a practical approach for performance-driven material design. Full article

Methods of single-point diamond turning and chemical mechanical polishing can achieve an ultra-flat substrate. However, these methods which rely on mechanical interactions to achieve material removal can easily lead to defects such as abrasive embedding and scratches on the surface. In addition, for low-rigidity and thin-plate workpieces, clamping deformation and force deformation are critical factors affecting the machining accuracy. This paper proposes a two-step polishing chain that uses controllable electrochemical and chemical etching to correct the shape error of the workpiece. With the optimized parameters, the jet electrochemical machining (Jet-ECM), which uses the electrochemical etching mechanism, is applied to the computer-controlled optical surfacing (CCOS) to achieve the rapid convergence of the shape accuracy. In addition, electrogenerated chemical polishing (EGCP) is implemented as a follow-up process which uses the mechanism of diffusion-controlled chemical etching to reduce the mid-spatial-frequency (MSF) error caused by the computer-controlled optical surfacing. Based on this two-step polishing chain and the self-developed devices, the peak-to-valley (PV) value of the φ 50 mm workpiece (valid dimensions = 90% of the central region) is reduced from 2.678 μm to 0.384 μm. This study has great implications for further understanding the mechanism of Jet-ECM and EGCP, which expands the applications of stress-free polishing to solve the processing problems of the low-rigidity workpiece. Full article

This study evaluates the application of deep learning techniques to the design of a microwave transmitter–receiver system operating in the 5G mid-band. The proposed architecture consists of four stages—signal generation, amplification, mixing, and filtering—each initially designed using conventional microwave methods and subsequently integrated into a complete transceiver. Simulation data were generated and component-specific convolutional neural networks (CNNs) were implemented in Python using TensorFlow/Keras. Across all models, an average error reduction exceeding 90% was achieved, with most networks converging after the third training cycle. System-level integration shows that the baseline design achieved a transmitted power of −32.637 dBm and a gain of 1.116 dB, while the deep learning-based design yielded −33.912 dBm and 0.738 dB. Additional analysis of S-parameters confirms acceptable impedance matching and a frequency response of around 3.5 GHz. These results illustrate that deep learning provides an effective complementary methodology for multi-component microwave system modeling and optimization in 5G applications. Full article

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems. Full article

The hyperspectral infrared observations of the Geostationary Interferometric Infrared Sounder (GIIRS) on the Fengyun-4A (FY-4A) satellite are an important data source for numerical weather prediction (NWP) assimilation. However, there are systematic differences between observed and simulated brightness temperatures (i.e., the observation increments contain predictable systematic bias components). To address the issue that traditional linear methods struggle to capture the nonlinear relationships between biases and forecast predictors, this study proposes an intelligent bias correction method that integrates ensemble learning and explainable artificial intelligence. First, the entropy reduction method is used to select 69 mid-wave channels. Then, Random Forest, XGBoost, LightGBM, Decision Tree, and Extra Tree are used as base learners to construct a weighted average ensemble model. Training and validation are conducted using high-frequency clear-sky observation data from FY-4A/GIIRS during Typhoon Lekima. The results show that: (1) the ensemble learning correction method outperforms single models and traditional offline methods, with root mean square errors of brightness temperature bias of less than 0.9209 K for the training set and 1.4447 K for the test set; (2) Shapley Additive Explanations (SHAP)-based interpretability analysis reveals the contribution and nonlinear influence mechanisms of factors such as longitude, atmospheric thickness, surface temperature, and total precipitable water on bias correction. This study provides an intelligent bias correction framework with both high precision and explainability, offering a reference for the bias correction and assimilation applications of hyperspectral satellite observations like GIIRS. Full article

The 12 November 2025 G4 geomagnetic storm—the third most intense of solar cycle 25—was triggered by a complex shock-ICME (interplanetary coronal mass ejection) structure as a result of three ICMEs and driven shocks that arrived on 11–12 November. The main enhancement in the interplanetary magnetic field occurred in the sheath region behind the shock driven by the second ICME. The Dst index reached −217 nT (the SYM-H index reached −254 nT) and the maximum Kp index was 9-. To comprehensively analyze the causes of the storm and its complex effects on near-Earth space, we used a multi-instrumental data set, involving data from satellite missions (ACE, SDO, PROBA2), GNSS networks, ionosondes, optical instruments, high-frequency radars (SuperDARN-like), and cosmic ray monitors. The auroral oval expanded equatorward (down to ~35° N in America). We recorded a super equatorial plasma bubble that almost reached the auroral oval boundary. The equatorial anomaly crests intensified, exceeding 175 TECU, and shifted poleward (8–10°). At mid-latitudes, the F2 layer critical frequency exhibited a strong negative disturbance (−50%) during the main phase, followed by an unusually prolonged and intense positive phase (+100%). GPS Precise Point Positioning errors increased to 2–3 m at high latitudes and in regions affected by the equatorial bubble. The event also featured a Forbush decrease and ground-level enhancement (GLE 77 according to the database hosted by the University of Oulu) associated with the X5.1 solar flare. The results underscore the complex chain of processes from solar storm to geomagnetic and ionospheric responses, highlighting the risks to satellite-based navigation and communication systems. Full article

High-precision thermal regulation in semiconductor process equipment is critical for product quality, yet it is challenged by actuator transport delays, limited actuator bandwidth due to hardware dynamics, and broadband inlet disturbances in temperature-controlled process fluids. This paper presents a systematic solution integrating architecture optimization with a physics-informed hybrid prediction model to enable effective feedforward compensation. Frequency-domain analysis justifies placing the temperature fluctuation attenuator (TFA) upstream of the heater to filter mid-to-high-frequency disturbances without compromising feedback stability. To address actuation delays, a Physics-CNN-LSTM predictor is developed using a residual learning strategy. This framework employs a mechanism model for baseline estimation and a deep learning network to correct persistent low-frequency residuals caused by unmodeled dynamics. Comparative experiments on industrial data demonstrate that the model achieves a Root Mean Square Error (RMSE) of $3.56\times {10}^{-5}$ K under low-to-mid-frequency inlet disturbances, reducing error by approximately 51.8% compared to a standard LSTM. The model also exhibits strong robustness against disturbance frequency shifts ($R^2>0.996$ on unseen data). Furthermore, closed-loop simulations confirm that the proposed feedforward compensation enhances temperature stability in high-precision thermal control. Full article

The transition to clean energy and electrification of transportation requires accurate, real-time monitoring of the state of health (SoH) of lithium-ion batteries, which serve as critical components for energy storage. Conventional SoH estimation methods typically rely on fixed statistical feature extraction, have poor generalization ability, and are unsuitable for multiple battery chemistry and temperature conditions. In this work, we propose a deep learning framework based on a transformer encoder and XGBoost to extract ageing-related electrochemical impedance spectroscopy (EIS) features, capturing low-, mid-, and high-frequency ageing characteristics, directly from daily operation profiles for capacity estimation. The approach requires only current, voltage, and temperature time-series data, making it suitable for edge deployment without the need for explicit EIS measurements. Validation on a dataset with two battery chemistries and three temperature conditions yields a root-mean-square error of 0.16% to 0.20% in capacity estimation. These results establish the feasibility of accurate SoH estimation during multiple operation of battery energy storage systems and electric vehicles. Full article