Search Results (941)

Electrocardiograms (ECGs) are critical for cardiovascular disease diagnosis, but their accuracy is often compromised by electrode motion (EM) artifacts—large, nonstationary distortions caused by patient movement and electrode–skin interface shifts. These artifacts overlap in frequency with genuine cardiac signals, rendering traditional filtering methods ineffective and increasing the risk of false alarms and misdiagnosis, particularly in wearable and ambulatory ECG applications. To address this, we propose the Feature–Shuffle Multi–Head Attention Autoencoder (FMHA–AE), a novel architecture integrating multi-head self–attention (MHSA) and a feature–shuffle mechanism to enhance ECG denoising. MHSA captures long–range temporal and spatial dependencies, while feature shuffling improves representation robustness and generalization. Experimental results show that FMHA–AE achieves an average signal–to–noise ratio (SNR) improvement of 25.34 dB and a percentage root mean square difference (PRD) of 10.29%, outperforming conventional wavelet–based and deep learning baselines. These results confirm the model’s ability to retain critical ECG morphology while effectively removing noise. FMHA–AE demonstrates strong potential for real–time ECG monitoring in mobile and clinical environments. This work contributes an efficient deep learning approach for noise–robust ECG analysis, supporting accurate cardiovascular assessment under motion–prone conditions. Full article

Despite its increasing use in veterinary rehabilitation, practical constraints—such as skin preparation and single-use electrodes—limit the wider adoption of surface electromyography (sEMG). Having conventional pre-gelled Ag/AgCl electrodes as reference, we made a pioneering comparison of the performance of reusable soft polymeric dry electrodes for recording paraspinal muscle activity in dogs during treadmill walking. Twelve clinically healthy Dachshunds from both genders were evaluated under two conditions, namely: (i) dry electrodes on untrimmed hair; and (ii) pre-gelled electrodes after trichotomy. Signals were acquired from the longissimus dorsi muscle at 1 kHz, processed with standardized filtering and rectification, and analyzed in both time and frequency domains. Dry electrodes yielded higher amplitude and Root Mean Square (RMS) values, but slightly lower power spectral density metrics when compared to pre-gelled electrodes. Nevertheless, frequency-domain results were broadly comparable between configurations. Dry electrodes reduce the preparation time, avoid hair clipping, and allow reusability without major signal degradation. While pre-gelled electrodes may still offer marginally superior stability during movement, our results suggest that soft polymeric dry electrodes present a feasible, less invasive, and more sustainable alternative for canine sEMG. These findings support further validation of dry electrodes in clinical populations, particularly for neuromuscular assessment in intervertebral disk disease. Full article

Accurate short-term photovoltaic (PV) power generation forecasting is critical for the stable integration of renewable energy into the grid. This study proposes a Transformer model enhanced with an adaptive sparse self-attention (ASSA) mechanism for PV power forecasting. The ASSA framework employs a dual-branch attention structure that combines sparse and dense attention paths with adaptive weighting to effectively filter noise while preserving essential spatiotemporal features. This design addresses the critical issues of computational redundancy and noise amplification in standard self-attention by adaptively filtering irrelevant interactions while maintaining global dependencies in Transformer-based PV forecasting. In addition, a deep feedforward network and a feature refinement feedforward network (FRFN) adapted from the ASSA–Transformer are incorporated to further improve feature extraction. The proposed algorithms are evaluated using time-series data from the Desert Knowledge Australia Solar Centre (DKASC), with input features including temperature, relative humidity, and other environmental variables. Comprehensive experiments demonstrate that the ASSA models’ accuracy in short-term PV power forecasting increases with longer forecast horizons. For 1 h ahead forecasts, it achieves an R² of 0.9115, outperforming all other models. Under challenging rainfall conditions, the model maintains a high prediction accuracy, with an R² of 0.7463, a mean absolute error of 0.4416, and a root mean square error of 0.6767, surpassing all compared models. The ASSA attention mechanism enhances the accuracy and stability in short-term PV power forecasting with minimal computational overhead, increasing the training time by only 1.2% compared to that for the standard Transformer. Full article

The reliability of hydraulic valves is critical for the safety and efficiency of industrial systems. While vibration and pressure sensors are widely deployed for condition monitoring, leveraging the heterogeneous data from these multi-sensor systems for accurate remaining useful life (RUL) prediction remains challenging due to noise, outliers, and inconsistent sampling rates. This study proposes a sensor data-driven framework that integrates multi-step signal preprocessing, time–frequency feature fusion, and a machine learning model to address these challenges. Specifically, raw data from vibration and pressure sensors are first harmonized through a multi-step preprocessing pipeline including Hampel filtering for impulse noise, Robust Scaler for outlier mitigation, Butterworth low-pass filtering for effective frequency band retention, and resampling to a unified rate. Subsequently, vibro-acoustic features are extracted from the preprocessed sensor signals, including Fast Fourier Transform (FFT)-based frequency domain features and Wavelet Packet Decomposition (WPD)-based time–frequency features, to comprehensively characterize the valve’s degradation. A health indicator (HI) is constructed by fusing the most sensitive features. Finally, a Kernel Principal Component Analysis (KPCA)-optimized Random Forest model is developed for HI prediction, which strongly correlates with RUL. Validated on the UCI hydraulic condition monitoring dataset through 20-run Monte-Carlo cross-validation, our method achieves a root mean square error (RMSE) of 0.0319 ± 0.0090, a mean absolute error (MAE) of 0.0109 ± 0.0014, and a coefficient of determination (R²) of 0.9828 ± 0.0097, demonstrating consistent performance across different data partitions. These results confirm the framework’s effectiveness in translating multi-sensor data into actionable insights for predictive maintenance, offering a viable solution for industrial health management systems. Full article

Based on a fractional-order equivalent circuit model, this paper proposes a multi-timescale collaborative State of Energy (SOE) and State of Health (SOH) estimation method (FOASTFREKF-EKF) for lithium batteries to mitigate the influence of model inaccuracies and battery aging on SOE estimation. Initially, a fractional-order equivalent circuit model is built, and its parameters are identified offline using the Starfish Optimization Algorithm (SFOA) to establish a high-fidelity battery model. An H∞ filter is then integrated to improve the algorithm’s resilience to external disturbances. Furthermore, an adaptive noise covariance adjustment mechanism is employed to reduce the effect of operational noise, and a time-varying attenuation factor is introduced to improve the algorithm’s tracking and convergence capabilities during abrupt system-state changes. A joint estimator is subsequently constructed, which uses an Extended Kalman Filter (EKF) for the online determination of battery parameters and SOH assessment. This approach minimizes the effect of varying model parameters on SOE accuracy while reducing computational load through multi-timescale methods. Experimental validation under diverse operating conditions shows that the proposed algorithm achieves root mean square errors (RMSE) of less than 0.21% for SOE and 0.31% for SOH. These findings demonstrate that the method provides high accuracy and reliability under complex operating conditions. Full article

The accurate estimation of the state of health (SOH) of lithium-ion batteries is critical for ensuring the safety, reliability, and efficiency of modern energy storage systems. Traditional model-based and data-driven methods often struggle to capture complex spatiotemporal degradation patterns, leading to reduced accuracy and robustness. To address these limitations, this paper proposes a novel dynamic graph pruning neural network with self-attention mechanism (DynaGPNN-SAM) for SOH estimation. The method transforms sequential battery features into graph-structured representations, enabling the explicit modeling of spatial dependencies among operational variables. A self-attention-guided pruning strategy is introduced to dynamically preserve informative nodes while filtering redundant ones, thereby enhancing interpretability and computational efficiency. The framework is validated on the NASA lithium-ion battery dataset, with extensive experiments and ablation studies demonstrating superior performance compared to conventional approaches. Results show that DynaGPNN-SAM achieves lower root mean square error (RMSE) and mean absolute error (MAE) values across multiple batteries, particularly excelling during rapid degradation phases. Overall, the proposed approach provides an accurate, robust, and scalable solution for real-world battery management systems. Full article

Indirect detection phase control algorithms, such as the dithering algorithm and the stochastic parallel gradient descent algorithm (SPGD), have simple system structures and are applicable to filled-aperture coherent combinations with high efficiency. The performances of these algorithms are limited when applied to a coherent combination of pulsed fiber lasers with a low repetition rate (≤5 kHz). Firstly, due to the overlap of the phase noise frequency and repetition rate, conventional algorithms cannot effectively distinguish the phase noise from the pulse fluctuation, and directly applying filtering will result in the phase information being filtered out. Secondly, if the pulse fluctuation is ignored and only the continuous part of the phase information is utilized, it relies on the presetting of conditions to separate the pulse from the continuous part and loses the phase information of the pulse part. In this article, we propose a waveform self-referencing algorithm (WSRA) based on a two-channel near-infrared laser coherent combination system to overcome the above challenges. Firstly, by modelling a self-referencing waveform, a nonlinear scaling operation is performed on the combined signal to generate a pseudo-continuous signal, which removes the intrinsic pulse fluctuation while preserving the phase noise information. Secondly, the phase control signal is calculated based on the pseudo-continuous signals after parallel perturbation. Finally, the phase noise is corrected by optimization. The results show that our method effectively suppresses the waveform fluctuation at a 5 kHz repetition rate, the light intensity reaches an ideal value (0.9939 $I_{\max }$), and the root-mean-square (RMS) phase error is only 0.0130 $\lambda$. This method does not require the presetting of pulse detection thresholds or conditions, and solves the challenge of coherent combination at a low repetition rate, with adaptability to different pulse waveforms. Full article

Multi-view data, captured from various perspectives, is crucial for training view-invariant human action recognition models, yet its acquisition is hindered by spatio-temporal constraints and high costs. This study aims to develop the Pose Scene EveryWhere (PSEW) framework, which automatically generates temporally consistent, multi-view 3D human action data from a single monocular video. The proposed framework first predicts 3D human parameters from each video frame using a deep learning-based Human Mesh Recovery (HMR) model. Subsequently, it applies tracking, linear interpolation, and Kalman filtering to refine temporal consistency and produce naturalistic motion. The refined human meshes are then reconstructed into a virtual 3D scene by estimating a stable floor plane for alignment, and finally, novel-view videos are rendered using user-defined virtual cameras. As a result, the framework successfully generated multi-view data with realistic, jitter-free motion from a single video input. To assess fidelity to the original motion, we used Root Mean Square Error (RMSE) and Mean Per Joint Position Error (MPJPE) as metrics, achieving low average errors in both 2D (RMSE: 0.172; MPJPE: 0.202) and 3D (RMSE: 0.145; MPJPE: 0.206) space. PSEW provides an efficient, scalable, and low-cost solution that overcomes the limitations of traditional data collection methods, offering a remedy for the scarcity of training data for action recognition models. Full article

Target tracking in typical radar applications faces critical challenges in complex environments, including nonlinear dynamics, non-Gaussian noise, and sensor outliers. Current robustness-enhanced approaches remain constrained by empirical kernel tuning and computational trade-offs, failing to achieve balanced noise suppression and real-time efficiency. To address these limitations, this paper proposes the variational Bayesian-based maximum correntropy criterion cubature Kalman filter (VBMCC-CKF), which integrates variational Bayesian inference with CKF to establish a fully adaptive robust filtering framework for nonlinear systems. The core innovation lies in constructing a joint estimation framework of state and kernel size, where the kernel size is modeled as an inverse-gamma distributed random variable. Leveraging the conjugate properties of Gaussian-inverse gamma distributions, the method synchronously optimizes the state posterior distribution and kernel size parameters via variational Bayesian inference, eliminating reliance on manual empirical adjustments inherent to conventional correntropy-based filters. Simulation confirms the robust performance of the VBMCC-CKF framework in both single and multi-target tracking under non-Gaussian noise conditions. For the single-target case, it achieves a reduction in trajectory average root mean square error (Avg-RMSE) by at least 14.33% compared to benchmark methods while maintaining real-time computational efficiency. Integrated with multi-Bernoulli filtering, the method achieves a 40% lower optimal subpattern assignment (OSPA) distance even under 10-fold covariance mutations, accompanied by superior hit rates (HRs) and minimal trajectory position RMSEs in cluttered environments. These results substantiate its precision and adaptability for dynamic tracking scenarios. Full article

In this work, we tested the performance of automated atmospheric PM_2.5 monitoring instruments and contrasted the data from automated measurements with those from filter-based reference measurements. The tested instruments include four brands of beta attenuation instruments (two were made in China, D1 and D2; the other two were imported from other countries, I1 and I2) and one brand of a light scattering instrument (also imported from another country, I3). The automated monitoring data were corrected based on the reference tests. The total testing period lasted 18 months. The objective of this work is to evaluate the influences of environmental factors on the performance of different automated instruments, and to improve the accuracy of the automated instruments by using a correction method. The results showed that contrasted with the reference tests, the absolute errors (MAE, mean absolute error; SD, standard deviation; and RMSE, root mean square error) of the automated monitoring instruments werehigher for temperature (T ≤ 10 °C), humidity (60% ≤ RH < 80%), and PM_2.5 concentrations (PM_2.5 ≥ 75 μg/m³). Meanwhile, the relative errors (CV, coefficient of variation; and NRMSE, normalized root mean square error) of the automated monitoring instruments were higher for humidity (RH > 80%) and PM_2.5 concentrations (PM_2.5 < 15 μg/m³). For winter data, it proved challenging to pass the reference test, which was based on a linear regression between 24-h average automated monitoring data and the integrated filter-based PM_2.5 data (aka the KBR test). Before corrections, the pass rates of D1, D2, I1, I2, and I3 in the rolling KBR tests are 57.7%, 51.3%, 41.1%, 21%, and 90.2%, respectively. After corrections, the rates increase to 79.6%, 86.6%, 81.8%, 58.9%, and 91.8%, respectively. The coefficient corrections (corrections of system errors) have made the most prominent contribution to improving the pass rates of the winter samples. The quarterly correction method can significantly improve the data accuracy of automated monitoring instruments. Full article

This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UAV)-photogrammetry point clouds, offering an alternative to traditional ground filtering methods. Unlike conventional ground filters that rely on point cloud classification, the proposed approach employs heteroscedastic regression. The U-Net model predicts the conditional expected values of the elevation corrections, aiming to reduce the impact of vegetation on determined ground surface elevations. Concurrently, it estimates the logarithm of the elevation correction variance, allowing for direct quantification of the uncertainty associated with each elevation correction value. The algorithm was evaluated using three metrics: the root mean square error (RMSE) of vertical displacements, the percentage of nodes with determined displacement values, and the percentage of outliers among those values. Performance was assessed using the technique for order of preference by similarity to ideal solution (TOPSIS) method and compared against several ground-filter-based algorithms across four datasets, each including at least two time intervals. In most cases, the U-Net-based approach demonstrated a slight performance advantage over traditional ground filtering techniques. For example, for the U-Net-based algorithm, for one of the test datasets, the RMSE of the determined subsidences was 6.1 cm, the percentage of nodes with determined subsidences was 80.5%, and the percentage of outliers was 0.2%. For the same case, the algorithm based on the next best model (SMRF) allowed an RMSE of 7.7 cm to be obtained; for 77.3% of nodes, the subsidences were determined; and the percentage of outliers was 0.3%. Full article

To address the issue of poor yaw stability in distributed-drive electric pickup trucks at medium-to-high speeds, particularly under the influence of continuously varying tire forces and road adhesion coefficients, a novel Kalman filter-based method for estimating the road adhesion coefficient, combined with a Tube-based Model Predictive Control (Tube-MPC) algorithm, is proposed. This integrated approach enables real-time estimation of the dynamically changing road adhesion coefficient while simultaneously ensuring vehicle yaw stability is maintained under rapid response requirements. The developed hierarchical yaw stability control architecture for distributed-drive electric pickup trucks employs a square root cubature Kalman filter (SRCKF) in its upper layer for accurate road adhesion coefficient estimation; this estimated coefficient is subsequently fed into the intermediate layer’s corrective yaw moment solver where Tube-based Model Predictive Control (Tube-MPC) tracks desired sideslip angle and yaw rate trajectories to derive the stability-critical corrective yaw moment, while the lower layer utilizes a quadratic programming (QP) algorithm for precise four-wheel torque distribution. The proposed control strategy was verified through co-simulation using Simulink and Carsim, with results demonstrating that, compared to conventional MPC and PID algorithms, it significantly improves both the driving stability and control responsiveness of distributed-drive electric pickup trucks under medium- to high-speed conditions. Full article

The large size and metal-enclosed casings of oil-immersed power transformers present significant challenges for patrol robots attempting to accurately locate their position within the transformer. Therefore, this paper proposes a three-dimensional spatial localization method for transformer patrol robots using a nine-element ultrasonic array. This method is based on wavelet decomposition and weighted filter beamforming (WD-WFB) algorithms. To address the issue of strong noise interference in the field, the ultrasonic localization signals are adaptively decomposed into wavelet coefficients at different frequencies and scales. An improved semi-soft thresholding function is applied to the decomposed wavelet coefficients to reduce noise and reconstruct the localization signals, resulting in localization signals with low distortion and a high signal-to-noise ratio(SNR). To overcome the limitations of traditional beamforming algorithms regarding interference resistance and signal resolution, this paper presents an improved WFB algorithm. By obtaining the energy distribution of the scanning area and determining the position of the maximum energy point, the spatial position of the transformer patrol robot can be determined. The test results show that the proposed improved semi-soft threshold function demonstrates superior denoising performance compared to traditional threshold functions. When compared to the soft threshold function, it achieves improvements of 15.32% in SNR and 15.57% in normalized correlation coefficient (NCC), along with a 48.91% reduction in root mean square error (RMSE). Compared with the hard threshold function, the improvement is even more significant: the SNR is improved by 60.55%, the NCC is improved by 24.90%, and the RMSE is reduced by 58.77%. The denoising effect was significantly improved compared to the traditional threshold function. In a 1200 mm × 1000 mm × 1000 mm transformer test box, the improved WFB algorithm in this paper was used to perform multiple localizations of the transformer patrol robot at different positions after denoising the field signals using the semi-soft threshold function. The maximum relative localization error was 3.47%, and the absolute error was within 2.6 cm, meeting engineering application requirements. Full article

In this study, we introduce an advanced framework for state estimation in electro-hydraulic systems, utilizing a structurally adapted Kalman filter. The proposed model was designed to enhance estimation accuracy and robustness under dynamic load variations and evolving measurement conditions. A notable feature of the approach is the algebraic resolution of one system state during each iteration, enabling the seamless inclusion of variables that are otherwise difficult to measure, without disrupting the model’s linear formulation. In addition, the dynamics of the load torque are empirically characterized through a regression-based model derived from experimental observations. The framework integrates adaptive mechanisms for updating the model and measurement error covariance matrices, facilitating the real-time accommodation of system nonlinearities and environmental changes. Experimental results are presented in different operating modes, reflecting characteristic dynamic movements. They show that the method reduced the root mean square error (RMSE) when estimating angular velocity between five and more than six times, depending on the mode. When evaluating the load torque, even in modes with a sharply changing load, the RMSE value remains stable below 0.05 Nm, which indicates the absence of systematic drift and high stability of the estimates. This confirms the stable operation of the algorithm in dynamic conditions and its applicability in real systems. Full article

The dynamic load caused by vehicle vibration due to an uneven pavement surface is a primary factor affecting the structural performance and service life of asphalt pavement. As the principles of vibration mechanics, in conjunction with the coherence function of the vehicle’s left and right wheels, along with the lag between front and rear wheels, the entire vehicle vibration model for three-axle and four-axle heavy-load vehicles was developed using Simulink software. Through simulation, the root-mean-square value of the dynamic load and the dynamic load coefficient of the vehicle with different pavement roughness grades, speeds, loads, and cornering radii were analyzed. The outcomes demonstrate that a nonlinear rise in the wheel dynamic load occurs when pavement roughness increases. The greater the speed, the greater the impact of pavement roughness on the dynamic load. An increase in vehicle load tends to reduce vehicle vibrations. The interaction between vehicle vibration frequency and road excitation frequency is essential in figuring out the loads, and a negative influence on the pavement structure should be given more attention when the vehicle is driving at low speed. The dynamic load coefficient of the left and right wheels is greatly affected when the vehicle is in a lateral tilt. The findings offer valuable insights for selecting appropriate loads in pavement structure design. By constructing 11 degrees of freedom for a three-axle vehicle and 16 degrees of freedom for a four-axle heavy-duty vehicle model, the dynamic load variation law under different roughness excitation conditions is systematically analyzed. The results can be applied to the selection of vehicle load in asphalt pavement design to make it closer to the actual driving state, which will be helpful for improving accuracy in the design of pavement structure and avoiding early damage to the pavement. Full article