Search Results (1,328)

Lithium-ion batteries serve as critical energy storage devices and are extensively utilized across diverse applications. The accurate estimation of State of Charge (SOC) is critically important for Battery Management Systems. Traditional SOC estimation methods have achieved progress, such as the Extended Kalman Filter (EKF) and particle filter. However, when there exist uncertainties in battery model parameters and the parameters change dynamically with operating conditions, the EKF tends to produce accumulated errors, which leads to a decline in estimation accuracy. This paper proposes a hybrid approach integrating the EKF with a Hidden Markov Factor Graph (HMM-FG). First, this method uses the EKF to achieve a real-time estimation of the SOC. Then, it treats the EKF-estimated value as an observation through the HMM-FG and combines current and voltage measurement data. It also introduces a factor function to describe the temporal correlation of the SOC and the uncertainty of EKF modeling errors, thereby performing Maximum A Posteriori (MAP) estimation correction on the SOC. Different from the traditional EKF, this method can use future observation information to suppress the error accumulation of the EKF under dynamic parameter changes. Experiments were conducted under different temperatures (0 °C, 25 °C, 45 °C), and a variety of different dynamic operating conditions (FUDS, DST), and comparisons were made with the EKF, Extended Kalman Smoother (EKS), and data-driven method based on LSTM. Full article

This study presents the Fourier–Gegenbauer integral Galerkin (FGIG) method, a new numerical framework that uniquely integrates Fourier series and Gegenbauer polynomials to solve the one-dimensional advection–diffusion (AD) equation with spatially symmetric periodic boundary conditions, achieving exponential convergence and reduced computational cost compared to traditional methods. The FGIG method uniquely combines Fourier series for spatial periodicity and Gegenbauer polynomials for temporal integration within a Galerkin framework, resulting in highly accurate numerical and semi-analytical solutions. Unlike traditional approaches, this method eliminates the need for time-stepping procedures by reformulating the problem as a system of integral equations, reducing error accumulation over long-time simulations and improving computational efficiency. Key contributions include exponential convergence rates for smooth solutions, robustness under oscillatory conditions, and an inherently parallelizable structure, enabling scalable computation for large-scale problems. Additionally, the method introduces a barycentric formulation of the shifted Gegenbauer–Gauss (SGG) quadrature to ensure high accuracy and stability for relatively low Péclet numbers. This approach simplifies calculations of integrals, making the method faster and more reliable for diverse problems. Numerical experiments presented validate the method’s superior performance over traditional techniques, such as finite difference, finite element, and spline-based methods, achieving near-machine precision with significantly fewer mesh points. These results demonstrate its potential for extending to higher-dimensional problems and diverse applications in computational mathematics and engineering. The method’s fusion of spectral precision and integral reformulation marks a significant advancement in numerical PDE solvers, offering a scalable, high-fidelity alternative to conventional time-stepping techniques. Full article

Peridynamics (PD) exhibits inherent advantages in solving solid mechanics problems involving strong discontinuities, such as crack propagation. However, the significant magnitude discrepancy between the micro-modulus and bond stretch in the nonlocal modelling, the extensive accumulation operations during nonlocal interaction integration, and the calculation methods for surface-correction coefficients can all introduce or amplify numerical errors, thereby reducing the confidence in numerical results. To address these sources of error and enhance the numerical accuracy of the PD models, this study derived a dimensionless bond-based PD formulation and proposed computational strategies to mitigate numerical errors during model implementation. The correctness of the dimensionless bond-based PD model was validated through investigating an elastic-wave propagation problem and a crack-branching problem, and comparing the numerical results with that from the finite-element method and the referenced literature. The effectiveness of the dimensionless model and the numerical strategies in enhancing numerical accuracy was verified through comparing the numerical performance of the model while investigating symmetrical mechanical problems under extreme computational conditions and load conditions. This study provides an effective modelling framework and numerical processing strategies for accurate computations in PD. Full article

To address frost heave in winter-lined canals and sediment accumulation in the Hetao Irrigation District of Inner Mongolia Autonomous Region, while reducing long-term maintenance costs of canal linings and relocating sediment as solid waste, this study proposes the use of low-toxicity, environmentally friendly octadecyltrichlorosilane (OTS) to modify channel sediment. This approach aims to improve the frost heave resistance of canal sediment and investigate optimal modification conditions and their impact on frost heave phenomena, aligning with sustainable development goals of low energy consumption and economic efficiency. Water Droplet Penetration Time (WDPT) tests and unidirectional freezing experiments were conducted to analyze frost heave magnitude, temperature distribution, and moisture variation in modified sediment. A coupled thermal–hydraulic–mechanical (THM) model established using COMSOL Multiphysics 6.2 software was employed for numerical simulations. Experimental results demonstrate that the hydrophobicity of channel sediment increases with higher OTS concentrations. The optimal modification effect is achieved at 50 °C with a silane-to-sediment mass ratio of 0.001, aligning with the economic efficiency of sustainable development. The unidirectional freezing test results indicate that compared to the 0% modified sediment content, the 40% modified sediment proportion reduces frost heave magnitude by 71.3% and decreases water accumulation at the freezing front by 21.1%. The comparison between numerical simulation results and experimental data demonstrates that the model can accurately simulate the frost heave behavior of modified sediment, with the error margin maintained within 15%. In conclusion, OTS-modified channel sediment demonstrates significant advantages in enhancing frost heave resistance while aligning with the economic and environmental sustainability requirements. Furthermore, the coupled thermal–hydraulic–mechanical (THM) model provides a reliable tool to guide sustainable infrastructure development for hydraulic engineering in the cold and arid regions of Inner Mongolia, effectively reducing long-term maintenance energy consumption. Full article

This work proposes a 5G-based synchronized measurement method for urban distribution networks. First, downlink frequency synchronization is achieved by cross-correlating the Primary and Secondary Synchronization Signals (PSSs/SSSs) within gNB-broadcast Synchronization Signal Blocks (SSBs), enabling accurate alignment with the 5G system clock. Then, uplink phase synchronization is refined using Timing Advance (TA) feedback to compensate for propagation delays. Based on the recovered 5G Pulse Per Second (PPS) signal, a dynamic compensation algorithm is applied to discipline the SAR ADC sampling process. This algorithm tracks crystal oscillator drift, accumulates sub-cycle deviations, and corrects integer timer counts only when the error exceeds $\pm 0.5$. Simulations under a 228 MHz oscillator and 1200 samples per cycle demonstrate that the accumulated phase error remains below 0.00008°, satisfying IEEE C37.118 precision requirements. Compared with traditional GPS-based synchronization methods, the proposed solution offers greater deployment flexibility and can operate reliably in GPS-denied environments such as indoors and urban canyons. Full article

The influence of polar ice-covered environments on the corrosion-related static electric field (CRSE) of underwater vehicles is critical for understanding and applying the characteristics of underwater electric fields in polar regions. This study utilizes a combined methodology involving COMSOL Multiphysics 6.1 simulations and laboratory-simulated experiments to systematically investigate the distribution characteristics of underwater vehicle electric fields under ice-covered conditions. By comparing the electric field distributions in scenarios with and without ice coverage, this study clarifies the effect of ice presence on the behavior of underwater electric fields. The simulation results demonstrate that the existence of ice layers enhances both the electric potential and field strength, with the degree of influence depending on the ice layer conductivity, thickness, and proximity of the measurement points to the ice layer. The accumulation of error analysis and laboratory experiments corroborates the reliability of the simulation results, demonstrating that ice layers enhance electric field signals by modifying the conductive properties of the surrounding medium, whereas the overall spatial distribution characteristics remain largely consistent. These findings offer a theoretical and technical basis for the optimization of stealth strategies in polar underwater vehicles and contribute to the advancement of electric field detection technologies. Full article

Retinal vessel segmentation is crucial for analyzing medical images, where symmetry in vascular structures plays a fundamental role in diagnostic accuracy. In recent years, the rapid advancements in deep learning have provided robust tools for predicting detailed images. However, within many scenarios of medical image analysis, the task of data annotation remains costly and challenging to acquire. By leveraging symmetry-aware semi-supervised learning frameworks, our approach requires only a small portion of annotated data to achieve remarkable segmentation outcomes, significantly diminishing the costs associated with data labeling. At present, most semi-supervised approaches rely on pseudo-label update strategies. Nonetheless, while these methods generate high-quality pseudo-label images, they inevitably contain minor prediction errors in a few pixels, which can accumulate during iterative training, ultimately impacting learner performance. To address these challenges, we propose an enhanced semi-supervised vessel semantic segmentation approach that employs a symmetry-preserving pixel-level filtering strategy. This method retains highly reliable pixels in pseudo labels while eliminating those with low reliability, ensuring spatial symmetry coherence without altering the intrinsic spatial information of the images. The filtering strategy integrates various techniques, including probability-based filtering, edge detection, image filtering, mathematical morphology methods, and adaptive thresholding strategies. Each technique plays a unique role in refining the pseudo labels. Extensive experimental results demonstrate the superiority of our proposed method, showing that each filtering strategy contributes to enhancing learner performance through symmetry-constrained optimization. Full article

Air pollution, particularly particulate matter (PM₁, PM_2.5, and PM₁₀), poses a significant environmental health risk globally. This study evaluates the predictive performance of three machine learning algorithms, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), for forecasting particulate matter concentrations in four Brazilian cities (Porto Alegre, Recife, Goiânia, and Belém), which share similar demographic and urbanization characteristics but differ in geographic and climatic conditions. Using data from the Copernicus Atmosphere Monitoring Service, daily concentrations of PM₁, PM_2.5, and PM₁₀ were modeled based on meteorological variables, including air temperature, relative humidity, wind speed, atmospheric pressure, and accumulated precipitation. The models were tested under two climate change scenarios (+2 °C and +4 °C temperature increases). The results indicate that RF consistently outperformed the other models, achieving low RMSE values, around 0.3 µg/m³, across all cities, regardless of their geographic and climatic differences. KNN showed stable performance under moderate temperature increases (+2 °C) but exhibited higher errors under more extreme warming, while SVM demonstrated higher sensitivity to temperature changes, leading to greater variability in bivariate contexts. However, in multivariate contexts, SVM adjusted better, improving its predictive performance by accounting for the combined influence of multiple meteorological variables. These findings underscore the importance of selecting suitable machine learning models, with RF proving to be the most robust approach for particulate matter prediction across diverse environmental contexts. This study contributes valuable insights for the development of region-specific air quality management strategies in the face of climate change. Full article

This paper presents the study of integrating a deep motion model into simple online and real-time tracking for video multi-object tracking. The tracking-by-detection paradigm faces significant challenges in handling nonlinear motion and occlusions. Although conventional Kalman-filter-based methods such as the SORT are efficient, they suffer from error accumulation because of their linear motion assumption. We propose KalmanFormer, a novel framework that enhances Kalman-filter-based tracking through adaptive motion modeling for video sequences. KalmanFormer consists of three key components. First, the inner-trajectory motion corrector, built upon the transformer architecture, refines Kalman filter predictions by learning nonlinear residuals from historical trajectories, thereby improving adaptability to complex motion patterns in videos. Second, the cross-trajectory attention module captures interobject motion correlations, significantly boosting object association under occlusions. Third, a pseudo-observation generator is integrated to provide neural-based predictions when detections are missing, stabilizing the Kalman filter update process. To validate our approach, we conduct comprehensive evaluations on the video benchmarks DanceTrack, MOT17, and MOT20 to demonstrate its effectiveness in handling complex motion and occlusion. The experimental results on the DanceTrack, MOT17, and MOT20 benchmarks demonstrate that KalmanFormer achieves competitive performance, with HOTA scores of 66.6 on MOT17 and 63.2 on MOT20, and strong identity preservation, IDF1: 82.0% and 80.1%, respectively. Full article

WIFI base stations have full indoor coverage, and the inertial navigation system (INS) is independent and autonomous, with high short-term positioning accuracy. However, errors accumulate over time, and an INS/WIFI combination has become the mainstream research direction regarding indoor positioning technology. The accuracy of WIFI fingerprint maps deteriorates significantly with changes in the environment or time, and there is an urgent need to solve the problem of automatic real-time updating of fingerprint maps. This article addresses the issue that the existing real-time acquisition technology for fingerprint point locations has severely restricted the real-time updating of fingerprint maps. For the first time, landmark points are introduced into the fingerprint map, and landmark point fingerprints are defined to construct a new fingerprint map database structure. A method for automatic recognition of landmark points (turning points) based on inertial technology is proposed, which achieves automatic and accurate collection of landmark point fingerprints and improves the reliability of crowdsourcing data. Real-time automatic monitoring of fingerprint signal fluctuations at landmark points and construction of error models have achieved real-time and accurate updates of fingerprint maps. Real scene experiments have shown that the proposed solution significantly improves the long-term stability and reliability of fingerprint maps. Full article

Understanding spatial variability in crop yields across fields is critical for developing precision agricultural strategies that optimize productivity while reducing negative environmental impacts. This variability often arises from a complex interplay of topographic features, soil characteristics, and hydrological conditions. This study investigates the influence of hydro-topographic factors on corn and soybean yield variability from 2016 to 2023 at the well-managed experimental sites in Beltsville, Maryland. A high-resolution surface digital elevation model (DEM) and subsurface DEM derived from ground-penetrating radar (GPR) were used to quantify topographic factors (elevation, slope, and aspect) and hydrological factors (surface flow accumulation, depth from the surface to the subsurface-restricting layer, and distance from each crop pixel to the nearest subsurface flow pathway). Topographic variables alone explained yield variation, with a relative root mean square error (RRMSE) of 23.7% (r² = 0.38). Adding hydrological variables reduced the error to 15.3% (r² = 0.73), and further combining with remote sensing data improved the explanatory power to an RRMSE of 10.0% (r² = 0.87). Notably, even without subsurface data, incorporating surface-derived flow accumulation reduced the RRMSE to 18.4% (r² = 0.62), which is especially important for large-scale cropland applications where subsurface data are often unavailable. Annual spatial yield variation maps were generated using hydro-topographic variables, enabling the identification of long-term persistent yield regions (LTRs), which served as stable references to reduce spatial anomalies and enhance model robustness. In addition, by combining remote sensing data with interannual meteorological variables, prediction models were evaluated with and without hydro-topographic inputs. The inclusion of hydro-topographic variables improved spatial characterization and enhanced prediction accuracy, reducing error by an average of 4.5% across multiple model combinations. These findings highlight the critical role of hydro-topography in explaining spatial yield variation for corn and soybean and support the development of precise, site-specific management strategies to enhance productivity and resource efficiency. Full article

This paper presents a semi-analytical, mesh-free space–time Collocation Trefftz Method (SCTM) for solving two-dimensional (2D) wave equations. Given prescribed initial and boundary data, collocation points are placed on the space–time (ST) boundary, reformulating the initial value problem as an equivalent boundary value problem and enabling accurate reconstruction of wave propagation in complex domains. The main contributions of this work are twofold: (i) a unified ST Trefftz basis that treats time as an analytic variable and enforces the wave equation in the full ST domain, thereby eliminating time marching and its associated truncation-error accumulation; and (ii) a Multiple-Scale Characteristic-Length (MSCL) grading strategy that systematically regularizes the collocation linear system. Several numerical examples, including benchmark tests, validate the method’s feasibility, effectiveness, and accuracy. For both forward and inverse problems, the solutions produced by the method closely match exact results, confirming its accuracy. Overall, the results reveal the method’s feasibility, accuracy, and stability across both forward and inverse problems and for varied geometries. Full article

Cemented filling technology is an effective approach to solving tailings accumulation and goaf, with rheological properties serving as key indicators of slurry fluidity. Since slurry rheology is influenced by multiple factors, accurate prediction of its parameters is essential for optimizing filling design. In this study, we developed a model to predict static and dynamic yield stress using the extreme gradient boosting (XGBoost) algorithm, trained on 140 experimental samples (105 for training and 35 for validation, split 75:25). For comparison, adaptive boosting tree (ADBT), gradient boosting decision tree (GBDT), and random forest (RF) algorithms were also applied. Model performance was evaluated using four metrics: coefficient of determination (R²), mean absolute error (MAE), root mean square error (RMSE), and explained variance score (EVS). The Shapley additive explanation (SHAP) method was employed to interpret model outputs. The results show that XGBoost achieved superior predictive accuracy for slurry yield stress compared with other models. Analysis of importance revealed that underflow concentration had the strongest influence on predictions, followed by the binder-to-tailings ratio, while the fine-to-coarse tailings ratio contributed least. These findings highlight the potential of machine learning as a powerful tool for modeling the rheological parameters of filling slurry, offering valuable guidance for engineering applications. Full article

During the service life of steel bridges, the structural stress histories display combined cyclic characteristics due to the superposition of low-frequency thermal loading and high-frequency vehicle loading. To investigate the fatigue performance under such loading patterns, a series of constant-amplitude and high-low frequency superimposed loading fatigue (HLSF) tests were conducted on notched specimens fabricated from Q355D bridge steel. The influence of HLSF waveform parameters on fatigue life was systematically investigated. Based on the fracture evolution mechanism, a concept of low-frequency periodic damage acceleration factor was proposed to effectively model the block nonlinear damage effects, and the applicability of existing fatigue life prediction models was discussed. The results show that the effect of average stress on the fatigue life under HLSF can be effectively considered by Walker’s formula. Low-amplitude ratios and low-frequency ratios indicate unfavorable loading conditions that may accelerate the Q355D fatigue damage accumulation, and these conditions are not adequately accounted for in current life prediction models. Compared to constant amplitude loading, HLSF can lead to a 66% and 46% reduction in high-frequency life when the amplitude ratio reaches 0.12 and the frequency ratio reaches 100. Compared to Miner’s rule, the proposed damage correction method reduces the life prediction error for HLSF by 11%. These findings provide valuable references for the fatigue assessment of bridge steel structures under the coupled effects of temperature and vehicle loading. Full article

The development of efficient strategies for optimizing and controlling nonlinear bioprocesses remains a significant challenge due to their complex dynamics and sensitivity to operating conditions. This work addresses the problem by proposing a two-step methodology applied to a laboratory-scale fed-batch bioethanol process. The first step employs a dynamic optimization approach based on Fourier parameterization and orthonormal polynomials, which generates smooth and continuous substrate-feed profiles using only three parameters instead of the ten required by piecewise approaches. The second step introduces a controller formulated through basic linear algebra operations, which ensures accurate trajectory tracking of the optimized state variables. Simulation results demonstrate a $3.65\%$ increase in ethanol concentration at the end of the process, together with an accumulated tracking error of only $0.0189$ under nominal conditions. In addition, the closed-loop strategy outperforms open-loop implementation when the initial conditions deviate from their nominal values. These findings highlight that the proposed methodology reduces mathematical complexity and computational effort while producing continuous control profiles suitable for practical application. The combination of optimization and algebraic control thus provides a promising alternative for improving the efficiency of bioethanol-production processes. Full article