Search Results (1,615)

Aiming at the problem of the insufficient passability and stability of the tracked chassis of silage harvesters caused by complex hilly and mountainous areas and a severe working environment, the crawler chassis of self-propelled silage harvesters was taken as the research object, the straight-line driving, longitudinal climbing, and lateral climbing processes of the chassis were theoretically analyzed, and the critical parameters that affect the normal climbing of the chassis were calculated. Meanwhile, the multi-body dynamics model of the tracked chassis was established by using the software SolidWorks 2020 and RecurDyn 2023, and its climbing and obstacle crossing performance were analyzed. The relevant motion parameters of the tracked chassis suitable for longitudinal and transverse slopes in hilly and mountainous areas were obtained, and field tests were conducted on the tracked chassis to verify the reliability of the simulation model. According to the simulation results, the tracked chassis achieves ultimate slope angles of 28° longitudinally and 23° laterally. It demonstrates the capability to navigate 140 mm high ridges and 250 mm wide trenches smoothly, while its straight-line driving offset rate conforms to prevailing agricultural machinery industry standards. Field test results indicated that the tracked chassis achieved a maximum longitudinal climbing angle of 26°. The relative error of less than 8% between the experimental and simulated data confirms a strong correlation. The maximum offset rate for straight-line travel is 1.95%, meeting the requirements of the agricultural machinery industry standards. The test verified the feasibility of the dynamic model of the crawler chassis of the silage harvester, providing a theoretical basis and technical support for the optimal design of the crawler chassis of the self-propelled silage harvester in hilly and mountainous areas. Full article

The dynamic response of ancient multi-drum columns, commonly found in historical monuments, is characterized by complex nonlinear mechanisms including rocking, sliding, and wobbling. Unlike modern monolithic columns, these structures consist of large, unbonded stone drums that rotate and interact dynamically during ground motion, resulting in highly nonlinear behavior due to intermittent impacts and evolving contact surfaces. The objective of this study is to evaluate the influence of the friction coefficient at the interfaces on the dynamic response of multi-drum columns. Two structural configurations are considered: (i) simple free-standing multi-drum columns, and (ii) multi-drum columns connected with iron dowels, replicating ancient Greek construction techniques. The columns analyzed are representative of the colonnade system of the Gymnasium of Ancient Messene, Greece. Sinusoidal base excitations with varying characteristics are applied, and parametric study is conducted by varying the interfacial friction coefficient. The results indicate that in the first configuration, low friction promotes interfacial sliding, leading to enhanced energy dissipation, a softened rocking response, and a reduced overturning frequency range. In the second configuration, variations in friction have a limited effect on the collapse frequency range, because at lower friction levels strong excitations lead to dowel reinsertion failure over a wide frequency range. Full article

With the increasing demand for high speed and high-precision motion control in CNC machine tools, permanent magnet linear synchronous motors (PMLSMs) have been widely adopted in feed drive systems due to their excellent dynamic performance and positioning accuracy. However, existing model predictive current control (MPCC) variants still face challenges regarding high computational overhead and strong dependency on accurate motor parameters, which limit their industrial applicability. To address these issues, this paper proposes an adaptive recursive MPCC for PMLSM drives based on the Cartesian distance minimization principle. An adaptive recursive prediction scheme that is inspired by the feedback structure of recurrent architectures is first introduced. By cyclically utilizing the previously sampled current to predict the next period’s state, the strategy effectively decouples the control law from inductance variations. The dependence on resistance is further mitigated by analyzing the correlation between the ideal current vector and voltage vector deviations. Second, the selection of the optimal voltage vector is transformed into a geometric problem: minimizing the Cartesian distance between the reference voltage and 19 candidate deviations within a proposed virtual voltage vector hexagon. To minimize the computational burden, the vector space is partitioned into eight regions, allowing the optimal candidate to be selected from only two pre-derived deviations. The experimental results demonstrate that the proposed method significantly outperforms existing MPCC benchmarks. Specifically, the execution time is reduced by 63.6%. Under severe parameter mismatch, the current THD is reduced from 14.82% to 6.35%, and the thrust ripple is improved from 12.06 N to 5.25 N, validating its superior robustness and efficiency. Full article

The rapid growth of the elderly population worldwide demands reliable activity recognition technologies to support independent living and continuous health supervision. However, conventional wearable sensor-based human activity recognition (HAR) techniques often fail to capture the complex temporal behaviour and subtle motion patterns characteristic of the elderly. To address these limitations, this study introduces a hybrid deep residual architecture—CNN-CBAM-BiGRU—that integrates convolutional neural networks (CNNs), the convolutional block attention module (CBAM), and bidirectional gated recurrent units (BiGRUs) to improve activity recognition using inertial measurement unit (IMU) data. In the proposed CNN-CBAM-BiGRU framework, CNN layers automatically derive representative features from raw sensor signals, CBAM applies adaptive channel and spatial attention to highlight informative patterns, and BiGRU captures long-range temporal relationships within activity sequences. The approach was evaluated on three benchmark datasets designed for elderly populations—HAR70+, HARTH, and SisFall—covering daily activities and fall events. The proposed model consistently outperforms existing methods across all datasets, achieving accuracies exceeding 96%, F1-scores above 93%, and a fall detection recall of 93.74%, confirming its robustness and suitability for safety-critical monitoring applications. Class-level evaluation indicates excellent recognition of static postures and consistent performance for dynamic actions. Convergence analysis further confirms efficient learning with limited overfitting across datasets. The proposed framework thus provides a robust and accurate solution for wearable-based elderly activity recognition, with strong potential for deployment in fall detection, health monitoring, and ambient assisted living systems. Full article

A hybrid aquatic–aerial vehicle (HAAV) is a novel type of aircraft capable of both aerial flight and underwater navigation. Inspired by the swan’s gliding and landing motion on water surfaces, this study investigates the dynamic modeling and integrated optimization design of an HAAV equipped with a biomimetic skipping plate. By comprehensively accounting for the aerodynamic, impact, hydrodynamic, and frictional forces during the water entry process, a dynamic model for the HAAV’s gliding water entry is established. The reliability of the model is verified through comparisons between numerical simulations and theoretical predictions. Parametric modeling of the skipping plate’s configuration and layout is performed to analyze the influence of different parameters on the water entry dynamics. With the objectives of minimizing the overload and pitch angle variation, a hybrid infilling strategy based on a radial basis function neural network (RBFNN) surrogate model is constructed to improve optimization efficiency. This is combined with a quantum-behaved particle swarm optimization (QPSO) algorithm to conduct the multi-objective optimization of the biomimetic plate, thereby obtaining its optimal configuration and layout parameters. The results demonstrate that the established dynamic model is effective and can accurately capture the kinematic characteristics of the gliding water entry process. The error between the peak load and the pitch angle variation is less than 5%. Compared with the direct QPSO algorithm, the proposed method reduces the number of model evaluations by 66.7%, the computational time by 52.1%, and the optimal solution response value by 12.01%, demonstrating strong potential for engineering applications. Full article

While Graph Convolutional Networks (GCNs) have revolutionized skeleton-based action recognition, existing methods face a critical efficiency–accuracy dilemma: state-of-the-art approaches achieve high performance through computationally expensive multi-stream fusion (joint, bone, joint motion, and bone motion) and deep architectures, limiting real-world deployment on resource-constrained devices. We propose LST-AGCN (Lightweight Spatial–Temporal Attention Graph Convolutional Network), introducing three technical contributions that address this challenge: (1) Unified Attention Module (UAM)—a framework that integrates channel, spatial, and temporal attention through a single compact operation, significantly reducing attention parameters compared to separate attention mechanisms; (2) Depthwise Separable Attention Mechanism (DSAM)—a factorization using depthwise separable convolutions that achieves linear complexity reduction from $O\left(C^2\right)$ to $O\left(C\right)$ in attention operations; and (3) Efficient Topology-Aware Fusion (ETAF)—an adaptive Joint-wise Attention strategy that captures fine-grained spatial relationships without quadratic complexity growth. Extensive experiments on NTU RGB+D 60 and NTU RGB+D 120 datasets demonstrate that LST-AGCN achieves strong performance using only joint modality (86.14%/94.0% and 79.5%/82.0% Top-1 accuracy with 99.0% Top-5 on cross-view) while requiring 14.11 M parameters and 19.02 GFLOPs, delivering efficient inference suitable for edge deployment. Full article

Accurate assessment of mitral valve (MV) and pulmonary vein (PV) flow velocities is important for left ventricular diastolic function testing. This study investigated the scan–rescan reproducibility of 4D Flow MRI-assessed MV and PV flow velocities in 21 healthy volunteers (25 ± 4 years). Participants underwent repeated whole-heart 3T 4D Flow MRI involving repositioning and different respiratory compensation strategies (motion-uncompensated free-breathing vs. respiratory motion-compensated navigator gating). MV parameters (net flow volume (NFV), E-wave velocity, A-wave velocity, E/A ratio, E deceleration time (DT), annular e’ velocity, E/e’ ratio) and PV parameters (NFV, S-wave velocity, D-wave velocity, S/D ratio, atrial reversal (AR) wave velocity) were derived from velocity–time curves and compared using intraclass correlation coefficients (ICCs), Bland–Altman analysis, and Pearson’s correlation (r). Results showed significant moderate-to-strong scan–rescan agreement and correlation for most MV and PV parameters (ICC = 0.51–0.92; r = 0.51–0.92; all p < 0.05), except E DT, e’ velocity, E/e’ ratio, PV NFV, and AR velocity (ICC = −0.13–0.47; r = −0.14–0.47). Subanalysis of respiratory motion strategies showed moderate-to-strong agreement and correlation for MV and PV parameters (ICC = 0.61–0.99; r = 0.52–0.99; all p < 0.05 excluding E DT), except E DT (ICC = 0.44) and PV NFV (ICC = 0.46; r = 0.46). While intraobserver agreement was mostly moderate-to-excellent (ICC = 0.58–0.97; ICC = 0.41 for E DT), interobserver agreement was poor for E DT and PV parameters (ICC = −0.12–0.34). Overall, 4D Flow MRI shows acceptable reproducibility for selected diastolic flow parameters, particularly mitral inflow indices, but substantial variability and limited robustness for key indices currently restrict its clinical applicability. Full article

Multi-line-scan camera systems provide high-frequency sampling and wide field-of-view coverage, making them valuable for three-dimensional measurement and dynamic reconstruction. However, their one-dimensional projection property introduces scale ambiguity and strong parameter coupling during calibration, which limits the consistency and stability of local optimization in multi-camera systems. To address this issue, this paper proposes a global calibration method based on physical constraints and hierarchical optimization. A unified imaging and motion model is constructed by incorporating physical scale constraints and structural priors, and geometric scale information is introduced into the joint optimization to reduce scale ambiguity and parameter coupling. Parameter normalization and staged optimization are further adopted to improve numerical stability for variables of different magnitudes and enable consistent estimation of multi-camera parameters within a unified framework. Simulation and experimental results show that the method achieves stable convergence under focal-length initialization perturbation, baseline deviation, and noise interference, with a three-dimensional reconstruction error below 0.67 mm and a convergence probability of at least 99.7%. These results indicate that the proposed method effectively reduces calibration uncertainty in multi-line-scan camera systems and supports high-precision online measurement and dynamic three-dimensional perception. Full article

Accurate needle insertion during epidural anesthesia is challenging due to strong dependence on clinician experience and the limited integration of guidance modalities that simultaneously provide visual feedback and physical motion constraints. Current approaches, including ultrasound guidance and augmented reality visualization, mainly offer passive assistance and do not actively regulate insertion trajectory and depth, which may lead to variability in accuracy and increased risk of complications. This work presents a multimodal human–machine assistance system that combines augmented reality guidance with virtual fixtures to support lumbar epidural needle placement. A Tuohy needle is coupled to a haptic device interacting with a patient-specific L3–L4 lumbar phantom fabricated using 3D printing and ballistic gel. A model-based force profile reproduces the mechanical response of anatomical layers during insertion. Three experimental conditions are evaluated: freehand execution, augmented reality guidance with trajectory and depth visualization, and cooperative guidance using virtual fixtures defined by a cylindrical corridor and a depth-limiting plane. Results show a progressive reduction in mean depth error from 6.82 ± 3.46 mm (freehand) to 4.96 ± 2.41 mm (augmented reality) and 2.21 ± 1.73 mm (virtual fixtures). These findings indicate that the integration of visual and haptic guidance significantly enhances insertion precision and control. The proposed approach highlights the potential of multimodal human–machine cooperation for safer training and assisted interventions. Full article

This study examines the electrohydrodynamic (EHD) behavior of air bubbles rising in deionized water under a non-uniform electric field, with particular emphasis on the influence of applied voltage (0.5–3.0 kV) and gas flow rates of 30 and 40 mL ${\min }^{-1}$ (corresponding to Reynolds numbers of $R{e}_g=107$–142) on bubble dynamics. High-speed imaging reveals bubbles with equivalent diameters in the range of $d_{eq}\approx 0.8$–3.5 mm, enabling a detailed characterization of their deformation, trajectory, and interfacial response under coupled hydrodynamic and electric stresses. At $R{e}_g=107$, bubbles exhibited stable vertical trajectories with negligible lateral displacement, whereas at $R{e}_g=142$, inertial and wake effects induced deviations. Increasing $B{o}_E$ reduced lateral displacement, restoring alignment with the electric field. Bubble rise velocities increased by ∼20–30% with applied voltage due to polarization-driven EHD forces. A transition from hydrodynamically dominated to EHD-dominated regimes was identified. While polarization forces govern the initial bubble motion under a strong electric field, bubbles progressively transition downstream to a hydrodynamic regime as the electric field weakens, reducing the influence of polarization effects. These findings provide quantitative insight into coupled hydrodynamic–electrohydrodynamic interactions and support the development of predictive models for controlling bubble trajectories, with implications for electrically tunable multiphase and microfluidic systems. Full article

Video-based action recognition for neural rehabilitation—spanning stroke recovery, Parkinsonian gait assessment, and cerebral palsy monitoring—faces critical challenges, including temporal ambiguity, non-causal motion correlations, and the absence of causally grounded dynamics modeling. While transformer-based architectures achieve strong performance, they often exploit spurious temporal and environmental cues, limiting reliability in safety-critical clinical settings. We propose NeuroPrisma, a neuro-prismatic video framework that integrates frequency-domain spectral decomposition with causal intervention under Structural Causal Models (SCMs) via the backdoor criterion. NeuroPrisma introduces (i) a Prismatic Spectral Attention (PSA) module, which applies discrete Fourier transforms to decompose temporal features into multi-scale frequency bands, disentangling slow postural dynamics from rapid corrective movements, and (ii) a Causal Intervention Layer (CIL), which performs do-calculus-based backdoor adjustment to remove confounding influences and produce causally invariant representations. PSA preconditions representations prior to intervention, improving confounder estimation and causal robustness. Extensive evaluation against seven state-of-the-art models (I3D, SlowFast, TimeSformer, ViViT, Video Swin Transformer, UniFormerV2, and VideoMAE) demonstrates that NeuroPrisma achieves 98.7% Top-1 accuracy on UCF101, 82.4% on HMDB51, 71.2% on Something-Something V2, and 91.5%/95.8% on NTU RGB+D (Cross-Subject/Cross-View), consistently outperforming prior methods. It further reduces the Causal Confusion Score (CCS) by 42.3%, indicating substantially lower reliance on spurious correlations, while maintaining real-time performance with 23.4 ms latency per 16-frame clip on an NVIDIA A100 GPU. All improvements are statistically significant (p < 0.001, Cohen’s d = 0.72–1.24). Evaluation was conducted exclusively on benchmark datasets (UCF101, HMDB51, Something-Something V2, and NTU RGB+D) under controlled conditions, without direct clinical validation on neurological patient cohorts. Overfitting was mitigated using three random seeds (42, 123, 456), RandAugment, Mixup (α = 0.8), weight decay (0.05), and early stopping. Cross-dataset generalization from UCF101 to HMDB51 without fine-tuning achieved 76.2% Top-1 accuracy. Future work will focus on prospective clinical validation across stroke, Parkinson’s disease, and cerebral palsy populations, including correlation with standardized clinical assessment scales such as Fugl–Meyer, UPDRS, and GMFCS. These results establish NeuroPrisma as a causally grounded and computationally efficient framework for reliable, real-time movement assessment in clinical rehabilitation systems. Full article

Transonic buffet is a critical self-sustained shock/boundary-layer instability limiting the flight envelope of modern transport aircraft. This study investigates the interaction between shock buffet and forced wing motion on the Airbus XRF-1 wind tunnel model, using unsteady Reynolds-Averaged Navier–Stokes (URANS) simulations with the DLR TAU code. The investigation is carried out in deep buffet condition ($\mathrm{Ma}=0.84$, $\alpha =4.5^{\circ }$, $\mathrm{Re}=25\times {10}^6$) and validated against wind tunnel data at the same flow condition. The buffet flow is superimposed with forced wing motions derived from a symmetric wing eigenmode at $\mathrm{Sr}=0.164$. Two different amplitudes scaled with the half-span s are considered: $A_{tip}=0.0025·s$ and $0.01·s$. The baseline no-forcing URANS captures the buffet flow quite well with only small deviations in the standard deviation of the surface pressure coefficient $c_{p,rms}$. A special variant of the Discrete Fourier Transformation for the whole wing upper surface $c_p$ distribution revealed that the typical buffet frequencies are also matched. The analysis of the forced simulations revealed a strong influence of the local wing motion on the increase of $c_{p,rms}$. The spectral content showed a shift and damping or amplification of different buffet modes, which is relevant for the interaction of motion induced and buffed induced aerodynamic forces. Full article

Background/Objectives: The Gait Variable Score (GVS) and Gait Abnormality Score (GAS) have been proposed as methods for quantitatively evaluating deviations from normal gait patterns. This study aimed to investigate whether the GVS or GAS is more useful for evaluating gait in stroke survivors. Methods: We used open-access motion capture datasets from 43 stroke survivors and 82 healthy individuals. Nine kinematics and seven muscle activities were extracted. The GVS was calculated as the root mean square difference between the pathological and healthy gait patterns. The modified GAS (mGAS) newly defined in this study was calculated as the mean value of the absolute differences between the pathological and healthy gait patterns divided by the standard deviation of healthy gait patterns. The amplitudes of kinematics and muscle activities were calculated. Results: Both the GVS and mGAS were significantly higher in stroke survivors than in healthy individuals. A significant strong correlation for 16 variables (nine kinematics and seven muscle activities) was observed between the GVS and amplitude (r = 0.921), but no significant correlation was found between the mGAS and amplitude (r = 0.167). Conclusions: As the mGAS is not affected by the amplitude of kinematics and muscle activities, it allows for a comprehensive comparison of abnormalities in both kinematics and muscle activities. The mGAS may be more useful than the GVS for evaluating gait abnormalities in stroke survivors. Full article

This study examines the 3D attributes of submesoscale eddies identified over a 12-month period within the Western Pacific Ocean. Composite parameters of cyclonic submesoscale eddies (CSMEs) occurring within and away from the Kuroshio Current system are compared and analyzed for their surface and subsurface features, as well as the seasonality of their core properties. Within the Kuroshio Current (KC) region, CSMEs are faster, stronger and deeper than in the open water (OW) region, with composite eddy depths of 97.5 m and 77.5 m, or 2.8 and 2.0 times the mixed layer depth, respectively. Prominent dipolar divergence patterns both at the surface and at depth reveal the presence of ageostrophic influence, with KC CSME cores deviating 48% and OW CSMEs deviating 40% from geostrophic balance at the surface. This imbalance drives strong vertical motion with maximum upward velocities of 19.2 m day⁻¹ at 57.7 m and 9.3 m day⁻¹ at 157.1 m within the KC and OW region CSME cores, respectively. Subsurface extrema analysis reveals structural differences in CSMEs between dynamic regions. These results provide a useful model-based estimate for subsurface CSME features which are difficult to quantify with observations. Full article

To facilitate the practical deployment and engineering implementation of multi-robot coordination for biomimetic underwater spherical robots (BUSRs), it is imperative to develop a formation tracking control method with a simple structure, a small number of tunable parameters, convenient parameter tuning and strong anti-disturbance capability. This study proposes a formation controller integrating virtual structure (VS), consensus protocol, and parallel output-velocity-type active disturbance rejection control (POV-ADRC), denoted as VS-C-POV-ADRC. A rotating global (RG) coordinate system is established to decouple robot positions from heading angles, which makes the parameter tuning more convenient. A double-loop control architecture is constructed, where the outer consensus control loop generates the desired velocity for each robot based on virtual-structure reference positions, and the inner POV-ADRC loop achieves high-precision velocity tracking. The proposed controller features a compact structure with only five adjustable parameters per motion direction, realizing easy engineering implementation and adaptation to the limited computing capacity of BUSRs. The simulation and experiment results demonstrate that the proposed algorithm enables robots to maintain a stable formation and achieve trajectory tracking accuracy within one body length, while exhibiting superior disturbance rejection. The proposed method provides a feasible and practical solution for BUSR formation control. Full article