Search Results (708)

The continuous growth in both the number of phenotypic records and the range of traits included in beef cattle genetic evaluations poses substantial statistical and computational challenges for the estimation of genetic and residual (co)variance matrices required for breeding value estimation. Structural equation models (SEM), implemented using either factor analysis (FA) or recursive model (REC) structures, provide a flexible framework to model genetic and residual (co)variance matrices while yielding more parsimonious and computationally efficient parameterizations. Here, SEM was applied to estimate parameters for growth and ultrasound-measured carcass traits in beef cattle. The dataset comprised 2,942 animals, and six traits were evaluated using standard multiple-trait mixed models (SMTM) and SEM. We considered FA and REC models implemented with six alternative parameterizations, in which random effects were represented as linear combinations of fewer unobservable random variables. Relative to the SMTM, both the model with two factors in the genetic covariance matrix (FA2G) and the model in which six recursive effects were constrained to zero in the residual covariance matrix (REC1) demonstrated a strong ability to capture genetic variability, as reflected by comparable heritability estimates. Correlations between estimated breeding values (EBV) for the same traits across models were consistently high, ranging from 0.94 to 1.00, indicating strong agreement among model estimates. The FA2G model was the most parsimonious in terms of the effective number of parameters ($p_D$), with 431.2 $p_D$, corresponding to a reduction of 25.3 parameters relative to the SMTM. The REC1 model also emerged as a competitive alternative for this dataset, exhibiting a lower $p_D$ (443.6) than the SMTM (456.5) and the most favorable deviance information criterion among all models evaluated (e.g., 37,868.6 for REC1 versus 37,874.7 for SMTM). Overall, these results demonstrate that mixed-effects multi-trait models for beef cattle genetic evaluation can be effectively implemented using FA or REC structures, which provide parsimonious representations of the underlying covariance patterns while maintaining high agreement in EBV. Full article

Optical and passive remote sensing-based estimation of aboveground biomass (AGB) using forest structural stratification has shown improvements over global models. This study investigated whether stratification by human-mediated disturbances improves prediction accuracy. Disturbance variables included proximity to mines, roads, and settlements, evaluated across three regimes: the full Atewa landscape (“FSR”), the Atewa Range Forest Reserve (“FR”), and the surrounding disturbed area (“SR”). Predictor selection for regimes was performed using recursive feature elimination with cross-validation, applied to random forest (RF) and support vector machine (SVM) algorithms. AGB was then estimated using local, global, and retuned global models, and the results were compared using the coefficient of determination (r²) and root mean square error (RMSE). The global RF model achieved the best performance (r² = 0.54; RMSE = 57.71 Mg/ha), likely due to structured heterogeneity captured across combined regimes. The “SR” models, however, performed poorly, indicating that excessive unstructured heterogeneity introduces noise and redundancy that weaken predictions. The low performance of the “FR” regime was attributed to spectral saturation and limited variance in observed AGB. Although disturbance factors added minimal bias, heteroscedasticity was evident in the “SR” and “FSR” regimes. Overall, this study indicates that disturbance-based stratification may not necessarily improve AGB estimation accurately compared to global models. However, it highlights the value of disturbance information for AGB modeling in heterogeneous forest landscapes. Full article

The absolute positioning accuracy of industrial manipulators is frequently bottlenecked by the interplay of geometric tolerances and complex, unmodeled non-geometric parameter drifts. Traditional static kinematic models, predicated on rigid-body assumptions, often struggle to characterize these state-dependent dynamic behaviors. To bridge this gap, this study introduces a PSO-Driven Neuro-Symbolic State-Space Framework incorporating Adaptive Wavelet Networks, drawing inspiration from two biological principles: the collective swarm intelligence observed in bird flocking and fish schooling, and the localized receptive field structure of mammalian visual cortex neurons. By reformulating calibration as a latent state estimation problem, we model kinematic parameters as stochastic states. Crucially, the observation model fuses symbolic Denavit–Hartenberg (D–H) predictions with an Adaptive Wavelet Network (AWNN). The AWNN utilizes Mexican Hat kernels, whose morphology mirrors the center-surround antagonism of cortical receptive fields, and leverages their precise time–frequency localization to effectively learn complex, configuration-dependent residuals. The framework employs a robust decoupled strategy. First, Particle Swarm Optimization (PSO) executes meta-optimization to autonomously determine hyperparameters, thereby mitigating initialization sensitivity. Second, a recursive inference engine estimates the hybrid states. Third, a global batch optimization refines the symbolic parameters against a frozen non-geometric error field. Experimental validation on an ABB IRB 120 robot (400 datasets) yielded a test RMSE of 0.73 mm. Compared to the standard Levenberg–Marquardt method, our approach reduced the RMSE by 40.16% and the maximum error by 35.71% (down to 0.99 mm). Moreover, it outperforms the state-of-the-art RPSO-DCFNN baseline by 12.05% while maintaining high computational efficiency (convergence within 20.15 s). These findings underscore the superiority of the proposed bio-inspired state-space fusion strategy for high-precision industrial applications. Full article

Adaptive filtering algorithms based on tensor decomposition represent appealing choices for system identification problems, especially when dealing with the estimation of long-length impulse responses, like in acoustic echo cancellation. The topic has recently been addressed in the literature, showing that the gain (compared to the conventional approach) is twofold in terms of both better performance and lower complexity. The main idea is that a system identification problem with a large parameter space (i.e., a long-length filter) is reformulated based on a group of shorter filters, while their coefficients are combined using the Kronecker product. Nevertheless, one of the main challenges is related to handling the tensor rank, which is particularly addressed for each specific decomposition order. Previous solutions have been designed for second-order (matrix case) and third-order tensorial decompositions. In this paper, we develop a recursive least-squares adaptive filtering algorithm that exploits a fourth-order tensor decomposition, aiming for further performance improvements compared to the existing solutions. In this framework, the influence of the decomposition setup is investigated, which is also related to the main parameters of the algorithm, i.e., the forgetting factors. Simulations performed in the context of acoustic echo cancellation support the theoretical findings and indicate the good performance of the proposed algorithm. Full article

Practical robotic systems require control methods that remain reliable under limited computational resources, uncertain environments, and frequent changes in operating conditions. Although model-based control forms the foundation of high-performance robotics, real-world deployment is often hindered by model uncertainty, time-varying dynamics, and costly identification. As a result, low-order and intuitive control schemes remain dominant, yet such approaches often fail to sustain consistent performance under disturbances and parameter variations. Robust and adaptive control provide representative paradigms to address this gap, where a Disturbance Observer (DOB) suppresses uncertainty through disturbance rejection and a Parameter Adaptation Algorithm (PAA) improves model fidelity through online identification. However, direct integration of a DOB and a PAA often introduces functional interference, including mutual masking between disturbance compensation and parameter estimation, which compromises closed-loop stability. This paper proposes an Adaptive Disturbance Observer (ADOB) that integrates a DOB with online parameter adaptation. The ADOB updates the nominal model of the DOB in real time using a Recursive Least Squares (RLS)-based PAA, while a dual-filtering structure separates disturbance rejection and parameter identification. Stability is analyzed using hyperstability theory, where a smoothing mechanism enforces the slowly varying parameter assumption. Experiments on a one-Degree-of-Freedom (DOF) electromagnetic actuator and a three-DOF robotic manipulator demonstrate reductions in model uncertainty and tracking error compared with a conventional DOB. Full article

Accurate state-of-charge (SoC) estimation is essential for ensuring the safety, efficiency, and longevity of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional methods such as ampere-hour (AH) integration and the extended Kalman filter (EKF) often suffer from error accumulation, sensitivity to initial conditions, and inadequate handling of strong nonlinearities and time-varying noise. To overcome these limitations, this paper proposes a novel LDL-Decomposition-Based Multi-Innovation Adaptive Unscented Kalman Filter (LDL-MIAUKF) algorithm that integrates three key innovations: (1) multi-innovation theory to exploit historical measurement sequences for enhanced state correction; (2) an adaptive mechanism to dynamically adjust process and observation noise covariances in real time; and (3) LDL decomposition (instead of Cholesky) to guarantee numerical stability and positive definiteness of the covariance matrix during sigma point generation. A second-order RC equivalent circuit model is established for the lithium battery, and its parameters are identified online using the forgetting factor recursive least squares (FFRLS) method under Hybrid Pulse Power Characterization (HPPC) test conditions. The proposed LDL-MIAUKF algorithm is then applied to estimate SoC using real battery data. Experimental results demonstrate that the LDL-MIAUKF achieves a maximum SoC estimation error of less than 1% at 25 °C and effectively tracks the reference SoC with high robustness. Furthermore, the terminal voltage prediction error of the identified model remains within ±0.1 V, confirming model accuracy. These results validate that the proposed LDL-MIAUKF algorithm significantly improves estimation accuracy, stability, and adaptability, making it a promising solution for advanced battery management systems. Full article

Visible Light Positioning (VLP) is widely regarded as a promising technology for high-precision indoor localization due to its immunity to radio-frequency interference and compatibility with existing Light-Emitting Diode (LED) lighting infrastructure. Despite recent progress, current VLP systems remain fundamentally limited by nonlinear received signal strength (RSS) characteristics, unknown transmitter orientations, and dynamic indoor disturbances. Existing solutions typically address these challenges in isolation, resulting in limited robustness and scalability. This paper proposes SCENE-VLP (Sequential Deep Learning with Feature Compression and Optimal State Estimation), a structured positioning framework that integrates feature compression, temporal sequence modeling, and probabilistic state refinement within a unified estimation pipeline. Specifically, SCENE-VLP combines Principal Component Analysis (PCA) and Denoising Autoencoders (DAE) for linear and nonlinear observation conditioning, Gated Recurrent Units (GRU) for modeling temporal dependencies in RSS sequences, and Kalman-based filtering (KF/EKF) for recursive state-space refinement. The framework is formulated as a hierarchical approximation of the nonlinear observation model, linking data-driven measurement learning with Bayesian state estimation. A systematic ablation study across multiple scenarios, including same-dataset evaluation and cross-dataset generalization, demonstrates that each component provides complementary benefits. Feature compression reduces redundancy while preserving dominant signal structure; GRU significantly improves robustness over static regression; and recursive filtering consistently reduces positioning error compared to unfiltered predictions. While both KF and EKF improve performance, EKF provides incremental refinement under mild nonlinearities. Extensive simulations conducted on an indoor dataset collected from a realistic deployment with eight ceiling-mounted LEDs and a single photodetector (PD) show that SCENE-VLP achieves sub-decimeter localization accuracy, with P50 and P95 errors of 1.84 cm and 6.52 cm, respectively. Cross-scenario evaluation further confirms stable generalization and statistically consistent improvements. These results demonstrate that the structured integration of observation conditioning, temporal modeling, and Bayesian refinement yields measurable gains beyond partial pipeline configurations, establishing SCENE-VLP as a robust and scalable solution for next-generation indoor visible light positioning systems. Full article

Industry 5.0 emphasizes human-centered integration of artificial intelligence in industrial contexts, yet successful adoption depends critically on workforce perception and acceptance. This research develops and validates a machine learning framework for predicting AI-related perceptions and expected impacts in the construction industry under small sample constraints typical of specialized industrial surveys. Specifically, the study aims to develop and empirically validate a predictive AI decision support model that estimates the expected impact of AI adoption in the construction sector based on digital competencies, ICT utilization, AI training and experience, and AI usage at both individual and organizational levels, operationalized through a composite AI Impact Index and two process-oriented outcomes (perceived task automation and perceived cost reduction). Using a dataset of 51 survey responses from Slovak construction professionals collected in 2025, we implement a methodologically rigorous approach specifically designed for limited-data regimes. The framework encompasses ordinal target simplification from five to three classes, dimensionality reduction through theoretically grounded composite indices reducing features from 15 to 7, exclusive deployment of low variance regularized models, and leave-one-out cross-validation for unbiased performance estimation. The optimal model (Lasso regression with recursive feature elimination) predicts cost reduction perception with R² = 0.501, MAE = 0.551, and RMSE = 0.709, while six classification targets achieve weighted F1 = 0.681, representing statistically optimal performance given sample constraints and perception measurement variability. Comparative evaluation confirms regularized models outperform high variance alternatives: random forest (R² = 0.412) and gradient boosting (R² = 0.292) exhibit substantially lower generalization performance, empirically validating the bias-variance trade-off rationale. Key methodological contributions include explicit bias-variance optimization preventing overfitting, feature selection via RFE reducing input space to six predictors (personal AI usage, AI impact on budgeting, ICT utilization, AI training, company size, and age), and demonstration that principled statistical approaches achieve meaningful predictions without requiring large-scale datasets or complex architectures. The framework provides a replicable blueprint for perception and impact prediction in data-constrained Industry 5.0 contexts, enabling targeted interventions, including customized training programs, strategic communication prioritization, and resource allocation for change management initiatives aligned with predicted adoption patterns. Full article

Background: Measuring glycated hemoglobin (HbA1c) is essential for assessing long-term glycemic control, yet direct testing remains expensive and underutilized in many large-scale health surveys and resource-constrained settings. This study aims to (i) deliver a highly accurate and interpretable ML model for predicting HbA1c from routinely collected clinical, biochemical, and demographic data, (ii) reduce dependency on extensive laboratory panels by identifying a compact, cost-efficient subset of key predictors, and (iii) establish a transferable, explainable modeling framework applicable across chronic disease biomarkers. Unlike prior HbA1c prediction studies that focus primarily on classification or accuracy-driven models, this work introduces a unified framework for continuous HbA1c regression that jointly integrates cost-oriented feature parsimony, stratified regression validation, and explainability by design. Methods: We aggregated data from the National Health and Nutrition Examination Survey (NHANES) cycles 2007–2020, encompassing 66,148 records and 224 candidate features. We implemented a two-stage feature selection pipeline: Incremental Correlation Selection (ICS) to narrow the variable space, followed by Recursive Feature Elimination with Cross-Validation (RFECV) to isolate the most informative features. Model interpretability was assessed using partial dependence plots and feature importance analysis. Results: The optimal model, LightGBMRegressor with most-frequent imputation, achieved R² = 0.7161, MAE = 0.334, MSE = 0.304, and MAPE = 5.56%, while using only 40 selected features. Interpretability analysis revealed clinically coherent relationships that align with physiological expectations. Discussion: The proposed framework maintains robust predictive performance while substantially reducing the number of required input features, enabling cost-efficient HbA1c estimation together with transparent, physiologically coherent model insights. By consolidating continuous HbA1c prediction, cost-aware feature selection, stratified evaluation, and explainability within a single pipeline are enhanced. Conclusions: This study advances beyond existing approaches and offers a practical blueprint for scalable biomarker estimation in population health and clinical decision-support applications. Its explainable, efficient, and generalizable design positions it as a strong candidate for clinical decision-support and population-health applications. Full article

This study addresses a critical knowledge gap in quantifying strategic mineral resources within hyper-arid, tectonically complex terrains by establishing a recursive framework that reconciles deterministic resource estimation with the nonlinear dynamics of tectonically mediated metamorphic systems. Using Libya’s Wadi El Shati as a case study, legacy lithological misclassifications are rectified through the fusion of Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, and Digital Elevation Model analytics within a unified geospatial workflow. The methodology synergizes atmospherically corrected optical data, processed via supervised Maximum Likelihood Classification, with calibrated radar-derived structural lineaments. Classified marble-bearing zones within the Al Mahruqah Formation are integrated with DEM data and field-validated thickness measurements using Triangulated Irregular Network models to resolve surface–subsurface dependencies and compute volumes. The results demonstrate a 91% lithological classification accuracy, rectifying a 22% error in legacy maps. Structural analysis of 1213 lineaments confirms a dominant NE–SW extensional regime (σ₃) that facilitated fluid conduits. The quantified marble-bearing horizon spans ~334 km² with a volume of 6.0 km³ (±9%). Spatial analysis reveals a causal link between high-grade marble clusters, basaltic intrusions, and NE–SW fault systems, refining models of contact metamorphism in rift-related settings. Full article

Accurate state estimation is a key technology for improving battery utilization and ensuring operational safety in electric vehicles. The joint estimation of the state of charge (SOC) and the state of power (SOP) over a wide temperature range is therefore essential for intelligent battery management systems. To address modeling uncertainties and estimation accuracy degradation induced by ambient temperature variations, a dual-polarization equivalent circuit thermal model incorporating temperature bias is proposed, and online parameter updating is achieved using the forgetting factor recursive least squares (FFRLS) algorithm. Furthermore, an unscented particle filter (UPF) is constructed by employing the unscented Kalman filter (UKF) as the proposal density function of the particle filter, thereby improving the estimation accuracy and convergence speed of SOC under wide temperature conditions. Based on the coupling relationship between SOC and SOP, a stepwise progressive strategy is then developed to predict the peak power state under multiple constraints, enhancing the robustness of SOP estimation. Simulation and experimental results demonstrate that the proposed method can accurately estimate SOC and SOP under complex operating conditions over a wide temperature range from −5 °C to 45 °C, exhibiting favorable convergence performance and estimation accuracy, which contributes to the safe operation and performance optimization of electric vehicle battery systems. Full article

Electroencephalogram (EEG) signals serve as a primary input for brain–computer interface (BCI) systems, and extensive research has been conducted on EEG-based emotion recognition. However, because EEG signals are inherently contaminated with various types of noise, the performance of emotion recognition is often degraded. Furthermore, the use of a Band Feature Extraction Neural Network (BFE-Net), a state-of-the-art (SOTA) method in this field, has limitations with respect to independent band-wise feature extraction and a simplistic band aggregation process to obtain final classification results. To address these problems, this study proposes the noise-robust band-attention BFE-Net framework, aiming to improve the conventional BFE-Net from two perspectives. First, we implement multiple-input, multiple-output (MIMO)-based preprocessing. Specifically, we utilize multichannel minima-controlled recursive averaging for precise non-stationary noise covariance estimation and generalized eigenvalue decomposition for subspace filtering to enhance the signal-to-noise ratio. Second, we propose an attention-based band aggregation mechanism. By integrating a band-wise self-attention mechanism, the model learns dynamic inter-band dependencies for more sophisticated feature fusion for classification. Experimental results on the SEED and SEED-IV datasets under a subject-independent protocol show that our model outperforms the SOTA BFE-Net by 3.27% and 3.34%, respectively. This confirms that rigorous MIMO noise reduction, combined with frequency-centric attention, significantly enhances the reliability and generalization of BCI systems. Full article

Lower-limb exoskeletons require accurate dynamic models to achieve stable and compliant human–robot interactions. However, least-squares-based identification often relies on demanding experiments and may yield limited accuracy for exoskeletons with non-standard structures and actuator-induced uncertainties. This paper proposes a two-stage dynamic parameter identification method that integrates recursive least squares (RLS) and a genetic algorithm (GA), denoted as RLS-GA. RLS is first executed offline to estimate the variation ranges of the inertial parameter vector and to construct a finite, physically meaningful search space. GA then refines the parameters within these bounds by minimizing the regression residual norm. Experiments on a hip exoskeleton show that RLS-GA achieves higher identification accuracy than LS and unconstrained GA, while converging faster than GA under identical conditions. Full article

Managing unknown, time-varying noise and outliers presents a critical challenge in GPS applications. Variational Bayesian (VB) inference effectively estimates unknown noise statistics but lacks robustness to outliers, while robust filters such as the centered error entropy (CEE) suppress outliers but rely on fixed noise assumptions. To address both limitations, we propose the centered error entropy-based variational Bayesian extended Kalman filter (CEEVB-EKF), which integrates VB inference with the CEE criterion in a unified framework. The method estimates time-varying noise covariance via recursive VB updates and applies the CEE cost function for robustness to heavy-tailed disturbances and outliers. This dual-stage design improves adaptability and reliability, with simulations showing superior, stable state estimation, making CEEVB-EKF suitable for positioning, tracking, and autonomous navigation. Full article

This paper proposes a complex fast recursive least-squares (FRLS) channel-estimation algorithm for single-carrier electromagnetic (EM) communications across the seawater–air interface, where severe attenuation and multipath cause strong SNR fluctuations. By redesigning the input-data structure and using forward–backward joint estimation, FRLS reduces the per-iteration complexity from the quadratic cost of classical RLS to a linear form (14L + 20 operations per iteration, where L is the channel length). Simulations under representative one- to three-path channels show that FRLS achieves the lowest steady-state mean-square deviation (MSD) at low SNR, outperforming LMS, IPNLMS, RLS, and PRLS. Offshore experiments further validate the practicality: after MMSE equalization, FRLS yields higher OSNR and improves the BER distribution, demonstrating an effective accuracy–complexity trade-off for hardware-constrained cross-medium EM links. Full article