Search Results (219)

Because of their great energy density and efficiency, lithium-ion batteries (LIBs) are essential to renewable energy systems and electric vehicles. Effective battery management requires precise estimation of the state of health (SoH) and state of charge (SoC). In order to overcome the difficulties caused by parameter fluctuations and real-world disturbances, this work presents a novel μ-analysis-based methodology designed to improve the resilience and accuracy of online SoC and SoH estimations in LIBs. In contrast to conventional techniques, the suggested strategy successfully manages both structured and unstructured uncertainties in battery systems by combining μ-analysis with model-based estimation. The framework creates an estimator that is resistant to parameter drift and outside perturbations by combining model-based estimation approaches with μ-analysis tools. Simulations using UDDS, US06, and HWFET driving cycles are used to verify its performance. When evaluating battery health and condition in dynamic and uncertain operating scenarios, the μ-analysis-based estimator demonstrates superior accuracy compared to conventional H∞-pole placement filter methods. The proposed approach enhances system robustness, achieving an 8 dB improvement in disturbance attenuation, as verified through MATLAB/Simulink. Stability analysis reveals the μ-analysis controller maintains robust performance up to ‖∆‖∞ = 3.5 at 10 Hz, compared to only ‖∆‖∞ = 1.5 for the H∞-pole placement controller—demonstrating significantly greater tolerance to parameter variations and unmodeled dynamics. These capabilities make the μ-analysis approach particularly suitable for electric vehicle applications requiring next-generation battery management systems. Full article

Background/Objectives: Perceptual awareness and decision formation unfold gradually as sensory evidence increases. Near the threshold of awareness, small differences in neural processing efficiency can be amplified into marked behavioral variability. Open-skill athletes are trained to make rapid decisions under dynamic and uncertain conditions, yet it remains unclear whether their perceptual advantage reflects enhanced early sensory sensitivity or more efficient late-stage evidence accumulation. This study aimed to identify the processing stage at which open-skill sports expertise exerts its influence. Methods: Twenty-five open-skill athletes and twenty-three non-athlete controls completed a visual orientation discrimination task with eight graded levels of stimulus visibility, ranging from subliminal to clearly visible. Behavioral performance was analyzed together with hierarchical drift–diffusion modeling to estimate latent decision parameters. Event-related potentials (ERPs) were recorded using a 64-channel EEG system during an active decision task and a passive viewing task, focusing on early (N2, P2) and late (P3) components. ERP–behavior correlations were examined across visibility levels. Results: No group differences were observed at the lowest visibility levels. Group differences emerged selectively at intermediate to high visibility levels, where athletes showed higher accuracy and a tendency toward faster responses. Drift–diffusion modeling revealed that this advantage was driven by higher drift rates in athletes, with no group differences in non-decision time, boundary separation, or starting point. Early ERP components (N2, P2) were strongly modulated by stimulus visibility but showed no consistent group differences. In contrast, the P3 component exhibited earlier and more pronounced differentiation across visibility levels in athletes. In the passive viewing task, group differences were substantially reduced. ERP–behavior analyses showed stronger and earlier P3–behavior coupling in athletes. Conclusions: Open-skill sports expertise selectively optimizes late-stage evidence accumulation and its translation into behavior, rather than enhancing unconscious or early sensory processing. Full article

Background/Objectives: The Bedouins (nomads) and the Fellahin (farmers) of Jordan represent two distinct subpopulations, characterized by unique lifestyles, settlement patterns, and linguistic features. This study aims to estimate the frequency of 27 Y-STRs in these two Jordanian subpopulations, along with various forensic parameters and paternal lineage comparisons with neighboring populations. Methods: Twenty-seven Y-STRs were typed in two major Jordanian subpopulations: Bedouin nomads (n = 101) and Fellahin farmers (n = 98). The forensic and paternal genetic lineage parameters and Y-haplogroup predictions were estimated. In addition, we conducted multidimensional scaling (MDS) and centroid analyses based on the Fst distance matrix to compare the sampled communities with neighboring populations from the MENA region, East Africa, Southeast Europe, and South Asia. Results: The Y-haplogroup predictions revealed differences in the predicted lineage composition based on the Y-STR profiles. The predicted J1a2a1a2 haplogroup predominated among the Bedouins (74.3%), whereas the Fellahin displayed a more heterogeneous profile, with notable frequencies of J1 (40%) and J2 (17.3%). Furthermore, the Fellahin exhibited remarkable genetic diversity and significant gene flow, providing plausible evidence of kinship with neighboring Levantine and Arabian groups. In contrast, the Bedouins showed consistently lower diversity across multiple loci, indicating long-term tribal isolation and, therefore, the potential effects of genetic drift. The MDS and centroid analyses positioned the Fellahin among the genetically interconnected Middle Eastern populations, while the Bedouins were clustered with the Arabian Peninsula populations. Conclusions: Overall, the contrasting genetic signatures of the two Jordanian subpopulations reflect their settlement patterns and sociocultural practices. In addition, the Y-STR dataset generated in this study enhances the Jordanian forensic database and to extends our understanding of paternal lineage structures in the West Asian/Levantine region. Full article

Reliable state estimation is critical for the safe operation of second-life battery systems but is severely hindered by significant parameter heterogeneity arising from diverse historical aging conditions. Traditional static models struggle to adapt to such variability, while online identification methods are prone to divergence under dynamic loads. To overcome these challenges, this paper proposes a Dual-Anchor Adaptive Fusion Framework for robust State of Charge (SOC) estimation and State of Health (SOH) soft-sensing. Specifically, to establish a reliable physical baseline, an automated Dynamic Relaxation Interval Selection (DRIS) strategy is introduced. By minimizing the fitting Root Mean Square Error (RMSE), DRIS systematically extracts high-fidelity parameters to construct two “anchor models” that rigorously define the boundaries of the aging space. Subsequently, a residual-driven Bayesian fusion mechanism is developed to seamlessly interpolate between these anchors based on real-time voltage feedback, enabling the model to adapt to uncalibrated target batteries. Concurrently, a novel “SOH Soft-Sensing” capability is unlocked by interpreting the adaptive fusion weights as real-time health indicators. Experimental results demonstrate that the proposed framework achieves robust SOC estimation with an RMSE of 0.42%, significantly outperforming the standard Adaptive Extended Kalman Filter (A-EKF, RMSE 1.53%), which exhibits parameter drift under dynamic loading. Moreover, the a posteriori voltage tracking residual is compressed to ~0.085 mV, effectively approaching the hardware’s ADC quantization limit. Furthermore, SOH is inferred with a relative error of 0.84% without additional capacity tests. This work establishes a robust methodological foundation for calibration-free state estimation in heterogeneous retired battery packs. Full article

To address the increasing demands for faster response and higher control accuracy in the braking systems of electric and intelligent vehicles, a novel brake-by-wire actuation unit and its braking force control methods are proposed. The braking unit employs a permanent-magnet linear motor as the driving actuator and utilizes the lever-based force-amplification mechanism to directly generate the caliper force. Compared with the “rotary motor and motion conversion mechanism” configuration in other electromechanical braking systems, the proposed scheme significantly simplifies the force-transmission path, reduces friction and structural complexity, thereby enhancing the overall dynamic response and control accuracy. Due to the strong nonlinearity, time-varying parameters, and significant thermal effects of the linear motor, the braking force is prone to drift. As a result, achieving accurate force control becomes challenging. This paper proposes a model-free adaptive control method based on compact-form dynamic linearization. This method does not require an accurate mathematical model. It achieves dynamic linearization and direct control of complex nonlinear systems by online estimation of pseudo partial derivatives. Finally, the proposed control method is validated through comparative simulations and experiments against the fuzzy PID controller. The results show that the model-free adaptive control method exhibits significantly faster braking force response, smaller steady-state error, and stronger robustness against external disturbances. It enables faster dynamic response and higher braking force tracking accuracy. The study demonstrates that the proposed brake-by-wire scheme and its control method provide a potentially new approach for next-generation high-performance brake-by-wire systems. Full article

The accurate retrieval of the raindrop size distribution (DSD) is a longstanding objective in meteorology because it underpins reliable quantitative precipitation estimation. Among remote sensors, weather radars are the primary tool for mapping DSD over wide areas, and phased-array systems in particular have demonstrated unique advantages owing to their high temporal and spatial resolution together with agile beam steering. Exploiting the underused high-resolution capability of an X-band phased-array radar, this study induced a Rainfall Regression Model (RRM). The RRM assumes a normalized gamma DSD model and retrieves its three parameters. It was then applied to a rain event influenced by the remnant circulation of Typhoon Haikui that affected Guangzhou on 8 September 2023. First, collocated disdrometer observations and T-matrix scattering simulations are used to build polynomial regressions between DSD parameters (D₀, N_w, μ) and the polarimetric variables. Validation against independent disdrometer samples yields Nash–Sutcliffe efficiencies of 0.93 for D₀ and 0.91 for log₁₀N_w. The RRM is then applied to the full volumetric radar data. Horizontal maps reveal that the surface elevation angle consistently exhibited the largest standard deviation for all three parameters. A vertical profile analysis shows that large-drop cores (D₀ > 2 mm) can reside above 2 km and that iso-value contours tilt rather than align vertically, implying an appreciable horizontal drift of raindrops within the complex remnant typhoon–monsoon wind field. By demonstrating the ability of X-band phased-array radar to resolve the three-dimensional microphysical structure of remnant typhoon precipitation, this study advances our understanding of the vertical characteristics of raindrops and provides high-resolution DSD information that can be directly ingested into severe weather monitoring and nowcasting systems. Full article

Understanding the spatial structure of large mammals is critical for conservation planning, especially under increasing habitat fragmentation. This study applies an integrated spatial analysis combining the DBSCAN density-based clustering algorithm and the α-hull method to delineate non-convex geographic ranges of the maned wolf (Chrysocyon brachyurus) across South America. Using 454 occurrence records filtered for ecological reliability, we identified 11 geographically isolated α-populations distributed across five countries and multiple biomes, including the Cerrado, Chaco, and Atlantic Forest. The sensitivity analysis of the α parameter demonstrated that values below 2 failed to generate viable polygons, while α = 2 provided the best balance between geometric detail and ecological plausibility. Our results reveal a highly fragmented distribution, with α-populations varying in area from 43,077 km² to 566,154.7 km² and separated by distances up to 994.755 km. Smaller and peripheral α-populations are likely more vulnerable to stochastic processes, genetic drift, and inbreeding, while larger clusters remain functionally isolated due to anthropogenic barriers. We propose the concept of ‘α-population’ as an operational unit to describe geographically and functionally isolated groups identified through combined spatial clustering and non-convex hull analysis. This approach offers a reproducible and biologically meaningful framework for refining range estimates, identifying conservation units, and guiding targeted management actions. Overall, integrating α-hulls with density-based clustering improves our understanding of the species’ fragmented spatial structure and supports evidence-based conservation strategies aimed at maintaining habitat connectivity and long-term viability of C. brachyurus populations. Full article

Base isolation is known as a useful and popular technique for seismic upgrading of reinforced concrete buildings. Predicting damage levels based on relative inter-story drift plays an important role for designing optimal base isolation systems. However, the existing codes usually rely on the acceleration spectrum for calculating the relative inter-story drift, and they do not provide an accurate estimation of the relative inter-story drift. Consequently, to cover the research gap, machine learning algorithms are being trained and used for identification of damage levels in retrofitted reinforced concrete buildings. More than 7000 datasets were derived by using nonlinear time-history and incremental dynamic analysis. A total of 48 reinforced concrete buildings with different stories and bay numbers were designed based on an older version of existing building codes, and then, base isolation systems were designed for the seismic retrofit. The machine learning algorithms used here were Decision Tree, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and an Artificial Neural Network. Based on the results, four of the mentioned algorithms have the capability of predicting the damage level with an accuracy of more than 85%, with the best performance being reached by extreme gradient boosting with an accuracy of 89%. Finally, the most important parameters affecting the damage levels of retrofitted reinforced concrete buildings were derived. Full article

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article

This study utilizes the drift–flux model to develop a new flow pattern map designed to facilitate an accurate estimation of gas void fraction (${\alpha }_g$) in vertical upward flow. The map is parameterized by mixture velocity ($u_m$) and gas volumetric quality (${\beta }_g$), integrating transition criteria from the established literature. For applications characterized by significant pressure gradients, such as gas lift, these criteria were reformulated as functions of pressure, enabling direct estimation from operational data. A critical component of this methodology for the estimation of ${\alpha }_g$ is the estimation of the distribution parameter ($C_0$). An analysis of experimental data, spanning pipe diameters from 1.27 to 15 cm across the full void fraction ranges (${0<\alpha }_g<1$), reveals a critical ${\alpha }_g$ threshold beyond which $C_0$ exhibits a distinct decreasing trend. To characterize this phenomenon, the parameter of the distribution-weighted void fraction (${\alpha }_c={\alpha }_g{C}_0$) is introduced. This parameter, representing the dynamically effective void fraction, identifies the critical threshold at its inflection point. The proposed model subsequently defines $C_0$ using a two-part function of ${\alpha }_c$. This generalized approach simplifies the complexity inherent in existing correlations and demonstrates superior predictive accuracy, reducing the average error in ${\alpha }_g$ estimations to 5.4% and outperforming established methods. Furthermore, the model’s parametric architecture is explicitly designed to support the optimization and fine-tuning of coefficients, enabling future use of machine learning for various fluids and complex industrial cases. Full article

This study presents a framework for assessing the resilience of steel special moment-resisting frame (SMRF) buildings under sequential earthquake–flood hazards. Surrogate models, including a stacked attention-based LSTM network (Stack-AttenLSTM) and CatBoost, are developed to predict key engineering demand parameters (EDPs), particularly maximum inter-storey drift ratios (MIDRs), avoiding the need for computationally expensive nonlinear time history analysis (NLTHA). The predicted EDPs are integrated with the FEMA P-58 methodology to estimate repair costs and durations, while the REDi framework is used to capture recovery delays and functionality loss. A two-storey code-compliant SMRF building is evaluated under a design-basis earthquake (DBE) with and without a subsequent 4.0 m flood. Results show that the combined hazard nearly doubles repair costs (from 0.33 to 0.77 of replacement value), increases downtime from 194 to over 411 days, and reduces the resilience index (R_i) from 0.873 to 0.265. These findings highlight the severe impacts of cascading multi-hazard events and the need to extend performance-based design toward resilience-focused strategies. The proposed surrogate-based framework provides a practical tool for evaluating multi-hazard risks and guiding the design of more resilient structures. Full article

Accurate modeling of proton exchange membrane fuel cells (PEMFCs) is essential for predicting system performance under diverse operating conditions. This study introduces a refined semi-empirical modeling that combines experimental validation with an enhanced parameter estimation method based on the Cloud Drift Optimization (CDO) algorithm. The approach focuses on identifying seven key parameters of the nonlinear PEMFC model by minimizing the difference between experimentally measured and simulated cell voltages. To assess its effectiveness, the proposed CDO-based estimator was compared with several established metaheuristic algorithms, including the particle swarm optimizer and the tetragonula carbonaria optimization algorithm. The evaluation was performed using three commercial PEMFC stacks rated at 250 W, 500 W, and the NedStack PS6, as well as experimental data obtained from the Renewable Energy Laboratory at A’Sharqiyah University. Results demonstrate that the CDO algorithm consistently produced the lowest sum of squared errors (SSE) of 1.0337 and exhibited stable convergence across multiple independent runs with a standard deviation of 1.2114 × 10⁻⁷. Its reliable performance under both normal and degraded conditions confirms the algorithm’s robustness and adaptability, establishing CDO as an efficient and dependable technique for PEMFC modeling and parameter identification. Full article

Inspired by the adaptive flexible motion coordination of biological systems, this study presents a bioinspired control strategy that enables robotic manipulators to achieve precise and compliant motion–force coordination for embodied intelligence and dexterous interaction in physically constrained environments. To this end, a learning-based motion–force hybrid control (LMFC) framework is proposed, which unifies learning and kinematic-level control to regulate both motion and interaction forces under incomplete or implicit kinematic information, thereby enhancing robustness and precision. The LMFC formulation recasts motion–force coordination as a time-varying quadratic programming (TVQP) problem, seamlessly incorporating multiple practical constraints—including joint limits, end-effector orientation maintenance, and obstacle avoidance—at the acceleration level, while determining control decisions at the velocity level. An RNN-based controller is further designed to integrate adaptive learning and control, enabling online estimation of uncertain kinematic parameters and mitigating joint drift. Simulation and experimental results demonstrate the effectiveness and practicality of the proposed framework, highlighting its potential for adaptive and compliant robotic control in constraint-rich environments. Full article

This paper presents a physically constrained grey-box identification framework for electromechanical systems, illustrated through the dynamics of brushed DC motors. The method estimates all electromechanical parameters by minimizing a normalized residual that combines current, velocity, and steady-state algebraic constraints under a current-limit condition. Classical approaches such as least-squares and black-box identification often lack physical interpretability and do not explicitly enforce steady-state consistency, making their estimates susceptible to nonphysical parameter drift. The proposed formulation incorporates these physical constraints within a Levenberg–Marquardt scheme with signal normalization, enabling the joint minimization of current and velocity errors. Validation was performed using step-response data from two DC motors under both synthetic and experimental conditions. When applied to unfiltered measurements, the method maintained steady-state relative errors below 1% and achieved low trajectory discrepancies, with NRMSE in velocity between 2.6 and 3.2% and NRMSE in current between 0.9 and 1.2% across both motors. Embedding physical and steady-state constraints directly into the cost function improves robustness and ensures physically consistent parameter estimates, even under high measurement noise and without filtering. The approach provides a general strategy for dynamic system identification under physical consistency requirements and is suitable for rapid calibration, diagnostic monitoring, and controller tuning in robotic and mechatronic applications. Full article

Background/Objectives: Computational studies using drift diffusion models on go/no-go escape tasks consistently show that individuals with suicidal ideation (SI) preferentially engage in active escape from negative emotional states. This study extends these findings by examining how individuals with SI update beliefs about action–outcome contingencies and uncertainty when trying to escape an aversive state. Methods: Undergraduate students with (n = 58) and without (n = 62) a lifetime history of SI made active (go) or passive (no-go) choices in response to stimuli to escape or avoid an unpleasant state in a laboratory-based negative reinforcement task. A Hierarchical Gaussian Filter (HGF) was used to estimate trial-by-trial trajectories of contingency and volatility beliefs, along with their uncertainties, prediction errors (precision-weighted), and dynamic learning rates, as well as fixed parameters at the person level. Bayesian mixed-effects models were used to examine the relationship between trial number, SI history, trial type, and all two-way interactions on HGF parameters. Results: We did not find an effect of SI history, trial type, or their interactions on perceived volatility of reward contingencies. At the trial level, however, participants with a history of SI developed progressively stronger contingency beliefs while simultaneously perceiving the environment as increasingly stable compared to those without SI experiences. Despite this rigidity, they maintained higher uncertainty during escape trials. Participants with an SI history had higher dynamic learning rates during escape trials compared to those without SI experiences. Conclusions: Individuals with an SI history showed a combination of cognitive inflexibility and hyper-reactivity to prediction errors in escape-related contexts. This combination may help explain difficulties in adapting to changing environments and in regulating responses to stress, both of which are relevant for suicide risk. Full article