Search Results (4,576)

In real-world operation, battery electric buses often encounter conditions with significant and rapid load variations. To improve regenerative braking energy recovery efficiency under such dynamic load conditions, this paper proposes a braking control strategy based on dynamic load estimation. First, a load estimation method based on a time-varying interactive multiple-model unscented Kalman filter (TVIMM-UKF) is developed by leveraging the vehicle longitudinal dynamics model and IMU sensor data, achieving high-accuracy online load estimation. Second, a multi-objective constrained optimization model is established, and an improved artificial bee colony algorithm is introduced to realize optimal brake force distribution under time-varying loads. Based on this, a regenerative braking control strategy is designed by incorporating motor characteristics and system-level operational constraints, enabling precise adjustment of braking torque across the full load range. Finally, simulation studies are conducted under two typical driving cycles, CHTC-B and C-WTVC, to verify the effectiveness of the proposed strategy. The results show that under dynamic load conditions, the proposed strategy can effectively improve braking energy recovery efficiency in both driving cycles. Full article

Despite the widespread deployment of quadrotor unmanned aerial vehicles (UAVs), ensuring their flight stability under asymmetric environmental disturbances, such as concurrent wind and rain, remains a significant challenge. To address the trajectory tracking problem under these severe conditions, this paper proposes a Composite Continuous Rapid Nonsingular Adaptive Terminal Sliding Mode (CCRNATSM) control strategy. First, a composite dynamic model is developed, integrating wind aerodynamics with rain impact characteristics to accurately simulate realistic flight environments. A High-Order Sliding Mode Observer (HOSMO) is then employed for the real-time, accurate estimation of these lumped disturbances. Subsequently, this observer is integrated with an adaptive control law to ensure rapid and precise system stabilization. Comparative simulations conducted under strong disturbance conditions demonstrate that the proposed method exhibits superior performance over existing strategies, reducing roll angle deviation by 75% and shortening the recovery time to 1.5 s. Ultimately, this control strategy significantly enhances the robustness and safety of quadrotor UAVs operating in harsh, asymmetric environments. Full article

As the complexity of power systems continues to increase and the penetration rate of distributed generation (DG) rises, traditional distance protection schemes face a dual, severe challenge. Specifically, the non-negligible fault transition resistance in grounding faults often leads to underreach, compromising protection speed, while the fault current contribution from integrated DG units severely distorts the measured impedance, increasing the risk of maloperation or failure to trip. To overcome these critical limitations, this study proposes an improved distance protection scheme that simultaneously accounts for and effectively compensates for both fault transition resistance and the impact of DG integration. By leveraging the known $R/X$ ratios of transmission lines and employing voltage–current phasor analysis, the proposed method enables the accurate and rapid estimation/correction of the line impedance between the relay and the fault point. This work provides a robust and low-cost solution for protective decision-making in contemporary power systems. Full article

Existing appearance-based and video-based gaze estimation methods mainly rely on frame-wise prediction or local-window temporal fusion, which limits their ability to model long-range dependencies and to explicitly suppress output-level jitter. This leaves a gap in unified temporal gaze estimation frameworks that jointly address contextual feature aggregation and prediction-level stabilization. To address this limitation, we propose a unified state-space temporal gaze estimation framework to improve both angular accuracy and temporal consistency. Specifically, consecutive eye image sequences are mapped into a shared latent state space, where spatial appearance cues and inter-frame dynamics are jointly modeled. A feature-level temporal aggregation module is further designed to adaptively reweight historical observations for the current estimate, and a prediction-level temporal correction module is introduced to suppress short-term fluctuations while preserving rapid gaze shifts. On the TEyeD dataset after quality screening, the proposed method achieves a 3D gaze MAE of $0.533°$, compared with $0.96°$ for Model-aware and $3.18°$–$3.47°$ for the ResNet baselines reported in the original TEyeD paper, while maintaining manageable deployment overhead. These results indicate that the proposed framework provides a favorable balance between estimation accuracy, temporal stability, and practical efficiency. Full article

In low-altitude complex environments, accurately reconstructing the three-dimensional (3D) flight trajectories of small unmanned aerial vehicles (UAV) without onboard positioning modules remains challenging. To address this issue, this paper proposes a multi-view ground camera-based UAV 3D trajectory detection method founded on an optical imaging geometric model. Multiple ground cameras are used to synchronously observe UAV flight, enabling stable 3D trajectory reconstruction without relying on onboard Global Navigation Satellite System (GNSS). At the two-dimensional (2D) observation level, a lightweight object detection model is employed for rapid UAV detection. Foreground segmentation is further introduced to extract accurate UAV contours, and geometric centroids are computed to obtain precise image plane coordinates. At the 3D reconstruction stage, camera extrinsic parameters are estimated using a back intersection method with ground control points, and the UAV spatial position in the world coordinate system is recovered via multi-view forward intersection. Field experiments demonstrate that the proposed method achieves stable 3D trajectory reconstruction in real urban environments, with a median error of 4.93 m and a mean error of 5.83 m. The mean errors along the X, Y, and Z axes are 2.28 m, 4.58 m, and 1.09 m, respectively, confirming its effectiveness for low-cost UAV trajectory monitoring. Full article

Accurate measurement of low-level radon concentrations in the environment is increasingly important for climate research, radon priority area delineation, and atmospheric studies. Adsorbent-based radon detectors offer high sensitivity but suffer from strong temperature dependence of radon adsorption and rapid degradation under humid conditions, limiting their applicability in long-term environmental monitoring. This work presents a universal design methodology for temperature- and moisture-compensated radon detectors based on hermetically packaged adsorbents enclosed by radon-permeable polymer foils. Analytical models describing the opposing temperature dependences of radon adsorption in adsorbents and radon permeability in polymers are combined to derive a general optimization criterion that minimizes temperature-induced response variations over a defined temperature range. The method is applicable to arbitrary combinations of adsorbent materials and polymer foils, provided their radon adsorption and permeability characteristics are known. The approach is demonstrated using activated carbon fabrics and common polymers (LDPE, HDPE, and polypropylene), for which optimal design parameters are identified. In addition, water vapor permeation through polymer foils is modeled to estimate moisture protection and permissible exposure durations under high humidity. The results demonstrate that appropriately designed compensation modules can significantly reduce temperature sensitivity while extending operational stability in humid environments, enabling next-generation high-sensitivity radon detectors suitable for environmental applications. Full article

Generating high-precision normalized digital surface models (nDSMs) from a single remote sensing image remains a challenging and ill-posed problem due to the absence of reliable geometric constraints. In this work, we show that monocular depth provides structurally stable cues of local geometry but lacks the global scale and vertical reference required for absolute height recovery. This intrinsic mismatch limits direct depth-to-height regression, particularly when transferring across heterogeneous terrains, land-cover compositions, and imaging conditions. Building on this idea, we propose the Relative Depth–Absolute Height Prediction Network (RDAH-Net), a framework that exploits relative depth as a geometry-aware prior while learning terrain-dependent height mappings from image appearance to absolute height. As the backbone, we employ a lightweight MobileNetV2 enhanced with a Convolutional Block Attention Module (CBAM), and further incorporate a cross-modal bidirectional attention fusion scheme with positional encoding to achieve a deep and effective fusion of image appearance and depth prior cues. Finally, a PixelShuffle-based upsampling strategy is used to sharpen prediction details and mitigate typical upsampling artifacts. Extensive experiments across diverse regions demonstrate that RDAH-Net achieves robust and generalizable height estimation, providing a practical alternative for large-scale mapping and rapid update scenarios. Full article

Gaze estimation plays a crucial role in human-computer interaction and behavior analysis. However, in dynamic scenes, rigid head movements and rapid gaze shifts pose significant challenges to accurate gaze prediction. Most existing methods either process single-frame images independently or rely on long video sequences, making it difficult to simultaneously achieve strong performance and high computational efficiency. To address this issue, we propose DGAGaze, a gaze estimation framework based on a difference-driven spatiotemporal attention mechanism. This framework uses a geometry-aware temporal alignment module to mitigate interference from rigid head movements, compensating for them through pose estimation and affine feature warping, thereby achieving explicit decoupling between global head motion and local eye motion. Based on the aligned features, inter-frame differences are used to adjust spatial and channel attention weights, enhancing motion-sensitive representations without introducing an additional temporal modeling layer. Extensive experiments on the EyeDiap and Gaze360 datasets demonstrate the effectiveness of the proposed approach. DGAGaze achieves improved gaze estimation accuracy while maintaining a lightweight architecture based on a ResNet-18 backbone, outperforming existing state-of-the-art methods. Full article

Background: Short-term facilitation (STF) is a key form of synaptic plasticity driven by activity-dependent increases in presynaptic release probability. However, estimating core synaptic parameters—quantal size ($q$), vesicle pool size ($N$), and release probability ($p_i$)—remains challenging due to nonlinear dynamics and unobservable presynaptic states, limiting the applicability of conventional methods. Methods: We developed a fast analytical framework based on first- and second-order statistical moments of evoked EPSCs, including mean, variance, and cross-stimulus covariance. By constructing composite moment relationships, latent variables were algebraically eliminated, yielding closed-form estimators of synaptic parameters. To improve robustness under strong facilitation, a Tsodyks–Markram (T–M) model-based calibration step was introduced to refine $N$ and $p_i$ using the estimated $q$ as a constraint. Results: Applied to hippocampal CA3–CA1 synapses, the method produced accurate and stable estimates of q across varying noise and sampling conditions. Incorporation of cross-stimulus covariance enabled effective characterization of structured variability that is neglected in classical approaches. While direct estimates of $N$ and $p_i$ showed dispersion, T–M calibration significantly improved stability and physiological consistency. Compared with mean–variance analysis, the proposed method achieved superior performance under facilitating conditions. Conclusions: This hybrid framework enables rapid and reliable estimation of synaptic parameters in STF synapses by exploiting second-order statistical structure. It provides a practical tool for investigating presynaptic mechanisms and may facilitate quantitative studies of synaptic dysfunction in neurological and psychiatric disorders. Full article

Fluid–structure interaction (FSI) effects may significantly influence the dynamic response of blast-loaded structures, particularly in lightweight configurations where the structural motion modifies the pressure loading. Despite their relevance, FSI phenomena are often neglected in engineering practice, mainly due to the computational cost of fully coupled simulations and the lack of simple predictive tools. This study presents a semi-analytical framework for estimating FSI effects in free-standing blast-loaded plates. The framework relies on one-dimensional theories accounting for non-linear gas compressibility and includes both coupled and uncoupled formulations. Their comparison provides a direct quantification of the FSI contribution to the structural response. The framework was applied to two case studies from the literature, involving different blast intensities and plate areal masses. They were selected to highlight conditions in which the reflected pressure exhibits significant temporal decay while the plate is in motion, indicating relevant FSI effects. In both cases, the coupled formulation achieves excellent agreement with the observed reference data, whereas the uncoupled solution overestimates the plate velocity. These results validate the governing equations of the coupled formulation and demonstrate that they can be reliably applied to blast-loading scenarios characterised by time-decaying pressure profiles. Thus, unlike other methods in the literature, the framework extends beyond simplified loading assumptions and offers a robust basis for rapid and cost-effective estimation of FSI effects in blast-loaded plates. Full article

This study presents a calibrated analytical micromechanical framework for predicting the linear elastic behavior of unidirectional glass fiber/epoxy and carbon fiber/epoxy composites over a wide range of fiber volume fractions. The approach combines the classical rule of mixtures for the longitudinal Young’s modulus with the semi empirical Halpin–Tsai equations to estimate the transverse Young’s modulus and the in-plane shear modulus. The framework is specifically formulated to support durability-oriented composite design through rapid and physically consistent estimation of elastic properties governing load transfer and stress distribution. Material parameters, including fiber and matrix Young’s moduli (Ef, Em), shear moduli (Gf, Gm), Poisson’s ratios (νf, νm), and fiber volume fraction (Vf up to 0.80), are taken from established material property databases and implemented within a literature-informed modeling scheme. To preserve physical realism at high fiber contents, a shear correction factor is introduced for Vf > 0.50 to account for microstructural interaction and fiber clustering effects. The predicted effective elastic constants (E₁, E₂, G₁₂, ν₁₂) exhibit consistent and physically meaningful trends across the full fiber volume fraction range. The model predictions were evaluated against trends widely reported in the composite micromechanics literature, and the results showed overall agreement in the nonlinear reduction in stiffness gains at elevated fiber volume fractions. Comparative results indicate that carbon fiber/epoxy composites achieve up to approximately 30% higher stiffness than glass fiber/epoxy systems at equivalent fiber contents, reflecting the influence of stiffness contrast on composite response. The analysis further indicates that stiffness saturation begins approximately in the Vf = 0.60–0.70 range, where the incremental gains in E2 and G12 become noticeably smaller for both composite systems. This behavior provides design-relevant guidance by showing that, beyond this range, further increases in fiber content may offer limited stiffness improvement relative to the associated manufacturing complexity. Overall, the calibrated Halpin–Tsai methodology offers a practical and computationally efficient tool for preliminary evaluation and design-stage optimization of the elastic performance of high-performance composite structures. Full article

Vertical docking of the autonomous underwater shuttle (AUS) for deep-sea data relay relies heavily on ultra-short baseline (USBL) acoustic positioning, whose measurements can be intermittently unavailable and contaminated by outliers in complex underwater environments. This paper proposes a neural network-enhanced robust navigation framework to improve AUS navigation reliability during acoustically guided vertical docking under USBL outages. First, a model-aided batch maximum a posteriori trajectory estimation method (MA-BMAP) is developed to generate learning quality supervision under sensor-limited conditions. Based on the estimated trajectories, a long short-term memory (LSTM)-based horizontal velocity predictor is integrated into a robust fusion filter with online ocean current estimation, enabling stable state estimation during USBL outages and robust rejection of abnormal USBL measurements. The proposed framework is validated through simulations and field trials in lake and sea environments. In sea trials, during two representative 200 s USBL outage intervals, the end-of-window horizontal position errors are 7.86 m and 4.14 m, respectively, corresponding to AUS-to-docking station distances of 244 m and 51 m. In addition, the introduced USBL outliers are successfully detected and rejected. The results indicate that the proposed method enables accurate and stable navigation during USBL unavailability and rapid recovery once USBL measurements resume, demonstrating its practicality for vertical docking missions. Full article

Magnetorheological (MR) semi-active suspensions offer clear advantages in improving ride comfort and handling stability, yet their engineering applications are often hindered by strong nonlinear hysteresis of the damper, the randomness of road excitations, and the reliance on manual tuning of controller parameters. To address these issues, this paper proposes an integrated framework of “experimental modeling–semi-active implementation–adaptive control.” First, characteristic tests of the MR damper are conducted, based on which a current-dependent Bouc–Wen forward model is established. Tianji’s Horse Racing Optimization (THRO) is then employed for parameter identification to reproduce the hysteresis behavior accurately. Second, a back propagation (BP) neural network-based inverse current model is developed to achieve rapid mapping from “desired damping force” to “driving current,” enabling semi-active actuation. Furthermore, a radial basis function (RBF) neural network is embedded into the active disturbance rejection control (ADRC) structure to estimate the system Jacobian online and to tune key extended state observer (ESO) gains in real time, forming the proposed RBF-ADRC strategy and thereby enhancing disturbance observation and compensation capability. Simulation results under pulse-road and Class-C random-road excitations show that, compared with the passive suspension, the proposed method reduces the root mean square error values of sprung-mass acceleration, suspension dynamic deflection, and tire dynamic load by 25.14%, 18.71%, and 11.61%, respectively, while also outperforming skyhook control and fixed-gain ADRC. Frequency-domain results further show stronger attenuation in the low-frequency band relevant to body vibration. Under pulse excitation, RBF-ADRC yields smaller peak and trough body accelerations and faster post-impact recovery. Under ±30% sprung-mass variations, it achieves the best worst-case and fluctuation-range robustness among the compared strategies and remains close to offline retuning. These results demonstrate that the proposed method improves both control performance and robustness while reducing the need for repeated manual calibration. Full article

Underwater acoustic sensor networks (UASNs) have emerged as a pivotal technology for ocean exploration, tactical surveillance, and environmental monitoring. However, the underwater acoustic channel poses severe challenges, including high propagation delay, limited bandwidth, and rapid time-varying multipath fading, which significantly degrade communication reliability. Cooperative communication, which exploits spatial diversity via relay nodes, offers a promising solution to these impairments. In this paper, we investigate the joint optimization of relay selection and power allocation in UASNs to maximize the long-term system energy efficiency and throughput. This problem is inherently complex due to the hybrid action space, which couples the discrete selection of relay nodes with the continuous allocation of transmission power, and the absence of real-time, perfect channel state information (CSI). To address these challenges, we propose a novel deep hybrid reinforcement learning (DHRL) framework utilizing a parameterized deep Q-Network (P-DQN) architecture. Unlike traditional approaches that discretize power levels or relax discrete constraints, our approach seamlessly integrates a deterministic policy network for continuous power control and a value-based network for discrete relay evaluation. Furthermore, we incorporate a prioritized experience replay (PER) mechanism to improve sample efficiency by focusing on rare but significant channel transition events. We provide a comprehensive theoretical analysis of the algorithm’s complexity and convergence properties. Extensive simulation results demonstrate that the proposed DHRL algorithm outperforms state-of-the-art combinatorial bandit algorithms and conventional deep reinforcement learning baselines in terms of system energy efficiency, and also exhibits superior robustness against channel estimation errors. Full article

Background: Sub-Saharan Africa is undergoing a rapid epidemiological transition marked by a growing burden of non-communicable diseases, including breast, cervical, ovarian, and uterine cancers, which constitute major causes of morbidity and mortality among women in the region; however, comprehensive assessments of long-term trends and regional heterogeneity remain limited. This study examines the burden and temporal trends of breast, cervical, ovarian, and uterine cancers across sub-Saharan Africa from 1990 to 2023. Methods: A retrospective ecological analysis was conducted using data from the latest Global Burden of Disease 2023 study. Age-standardised incidence rates, mortality rates, and disability-adjusted life year rates were estimated for breast, cervical, ovarian, and uterine cancers across 48 sub-Saharan African countries and four sub-regions. Temporal trends were assessed from 1990 to 2023, with percentage changes calculated to characterise epidemiological transitions. Geographic variation and age-specific patterns were examined to identify high-burden settings and priority populations. Results: Between 1990 and 2023, the burden of all four cancers increased substantially across sub-Saharan Africa, with significant regional and country-level heterogeneity. Breast cancer exhibited the largest absolute burden, with incidence increasing by over 120 percent and mortality by more than 80 percent, particularly in Central and Western Africa. Cervical cancer remained the leading cause of cancer-related mortality among women in Eastern and Southern Africa, despite evidence of stabilisation or decline in selected countries. Ovarian and uterine cancers demonstrated sustained upward trends, especially in Central Africa, with high mortality-to-incidence ratios indicating late diagnosis and limited treatment access. Across all cancer types, Central and Eastern sub-Saharan Africa consistently experienced the highest disability-adjusted life year burdens. Conclusions: The burden of the selected cancers in sub-Saharan Africa has increased markedly over the past three decades, with persistent regional inequities reflecting gaps in prevention, early detection, and treatment capacity. Strengthening cancer surveillance systems, expanding equitable access to screening and vaccination programmes, and improving diagnostic and treatment infrastructure are critical to reversing current trends. These findings provide region-specific evidence to guide cancer control priorities and resource allocation across sub-Saharan Africa. Full article