Search Results (456)

In this study, a multiphysics coupling numerical model was developed to investigate the thermal-fluid dynamics and microstructure evolution during the laser metal deposition of AlCoCrFeNi high-entropy alloy (HEA) coatings on 430 stainless steel substrates. The model integrated laser-powder interactions, temperature-dependent material properties, and the coupled effects of buoyancy and Marangoni convection on melt pool dynamics. The simulation results were compared with experimental data to validate the model’s effectiveness. The simulations revealed a strong bidirectional coupling between temperature and flow fields in the molten pool: the temperature distribution governed surface tension gradients that drove Marangoni convection patterns, while the resulting fluid motion dominated heat redistribution and pool morphology. Initially, the Peclet number (Pe_T) remained below 5, indicating conduction-controlled heat transfer with a hemispherical melt pool. As the process progressed, Pe_T exceeded 50 at maximum flow velocities of 2.31 mm/s, transitioning the pool from a circular to an elliptical geometry with peak temperatures reaching 2850 K, where Marangoni convection became the primary heat transfer mechanism. Solidification parameter distributions (G and R) were computed and quantitatively correlated with scanning electron microscopy (SEM)-observed microstructures to elucidate the columnar-to-equiaxed transition (CET). X-ray diffraction (XRD) analysis identified body-centered cubic (BCC), face-centered cubic (FCC), and ordered B2 phases within the coating. The resulting hierarchical microstructure, transitioning from fine equiaxed surface grains to coarse columnar interfacial grains, synergistically enhanced surface properties and established robust metallurgical bonding with the substrate. Full article

This study addresses the system identification of a small autonomous surface vehicle (ASV) under moored conditions using Hankel dynamic mode decomposition with control (HDMDc) and its Bayesian extension (BHDMDc). Experiments were carried out on a Codevintec CK-14e ASV in the CNR-INM towing tank, under both irregular and regular head wave conditions. The ASV under investigation features a recessed moon pool, which induces nonlinear responses due to sloshing, thereby increasing the modeling challenge. Data-driven reduced-order models were built from measurements of vessel motions and mooring loads. The HDMDc framework provided accurate deterministic predictions of vessel dynamics, while the Bayesian formulation enabled uncertainty-aware characterization of the model response by accounting for variability in hyperparameter selection. Validation against experimental data demonstrated that both HDMDc and BHDMDc can predict the vessel’s response under unseen regular and irregular wave excitations. In conclusion, this study shows that HDMDc-based ROMs are a viable data-driven alternative for system identification, demonstrating for the first time their generalization capability for an unseen sea condition different from the training set, achieving high accuracy in reproducing the vessel dynamics. Full article

Coral reefs play a crucial role in tropical coastal ecosystems, even though these environments are difficult to monitor due to their diversity and morphological complexity and due to their shallowness in some cases. This study used two approaches for acquiring very-high-resolution bathymetric data: underwater structure-from-motion (SfM) photogrammetry collected from a low-cost platform and unmanned/uncrewed aerial vehicle (UAV)-based SfM photogrammetry. While underwater photogrammetry avoids the distortions caused by refraction at air/water interface, it remains limited in spatial coverage (about 0.04 ha in 1 h of survey). In contrast, UAV photogrammetry allows for covering extensive areas (more than 20 ha/h) but requires applying refraction correction in order to accurately compute bathymetry and roughness values. An analytical approach based on Snell laws and an empirical approach based on linear regression (calibrated using a batch of points whose depths are representative of the depth range of the surveyed areas) are tested to correct the apparent depth on the raw UAV digital elevation model (DEM). Comparison to underwater photogrammetry shows that correcting refraction reduces the root mean square error (RMSE) by more than 50% (up to 62%) on bathymetric models, with RMSE lower than 0.13 m for the analytical approach and down to 0.09 m for the regression method. The linear-regression-based refraction correction proved most effective in restoring accurate seabed roughness, with a mean error on roughness lower than 17% (vs. 30% for analytical refraction correction and 48% for apparent bathymetry). Full article

For unmanned aerial vehicle (UAV) ground crew marshalling tasks, the accuracy of skeleton-based action recognition is often limited by the high similarity of motion patterns across action categories as well as variations in individual performance. To address this issue, we propose an adaptive refined graph convolutional network with enhanced features for action recognition. First, a multi-order and motion feature modeling module is constructed, which integrates joint positions, skeletal structures, and angular encodings for multi-granularity representation. Static-domain and dynamic-domain features are then fused to enhance the diversity and expressiveness of the input representations. Second, a data-driven adaptive graph convolution module is designed, where inter-joint interactions are dynamically modeled through a learnable topology. Furthermore, an adaptive refinement feature activation mechanism is introduced to optimize information flow between nodes, enabling fine-grained modeling of skeletal spatial information. Finally, a frame-index semantic temporal modeling module is incorporated, where joint-type semantics and frame-index semantics are introduced in the spatial and temporal dimensions, respectively, to capture the temporal evolution of actions and comprehensively exploit spatio-temporal semantic correlations. On the NTU-RGB+D 60 and NTU-RGB+D 120 benchmark datasets, the proposed method achieves accuracies of 89.4% and 94.2% under X-Sub and X-View settings, respectively, as well as 81.7% and 83.3% on the respective benchmarks. On the self-constructed UAV Airfield Ground Crew Dataset, the proposed method attains accuracies of 90.71% and 96.09% under X-Sub and HO settings, respectively. Environmental robustness experiments demonstrate that under complex environmental conditions including illumination variations, haze, rain, shadows, and occlusions, the adoption of the Test + Train strategy reduces the maximum performance degradation from 3.1 percentage points to within 1 percentage point. Real-time performance testing shows that the system achieves an end-to-end inference latency of 24.5 ms (40.8 FPS) on the edge device NVIDIA Jetson Xavier NX, meeting real-time processing requirements and validating the efficiency and practicality of the proposed method on edge computing platforms. Full article

Second-order damping oscillation models are incapable of precisely predicting superimposed and multi-fruit collision-induced oscillations. In view of this problem, an ARIMA-EEMD-LSTM hybrid model for predicting the oscillation trajectories of trellised tomatoes was proposed in this study. First, the oscillation trajectories of trellised tomatoes under different picking forces were captured with the aid of the Nokov motion capture system, and then the collected oscillation trajectory datasets were then divided into training and test subsets. Afterwards, the ensemble empirical mode decomposition (EEMD) method was employed to decompose oscillation signals into multiple intrinsic mode function (IMF) components, of which different components were predicted by different models. Specifically, high-frequency components were predicted by the long short-term memory (LSTM) model while low-frequency components were predicted by the autoregressive integrated moving average (ARIMA) model. The final oscillation trajectory prediction model for trellised tomatoes was constructed by integrating these components. Finally, the constructed model was experimentally validated and applied to an analysis of single-fruit oscillations and multi-fruit oscillations (including collision oscillations and superposition oscillations). The following experimental results were yielded: Under single-fruit oscillation conditions, the prediction accuracy reached an RMSE of 0.1008–0.2429 mm, an MAE of 0.0751–0.1840 mm, and an MAPE of 0.01–0.06%. Under multi-fruit oscillation conditions, the prediction accuracy yielded an RMSE of 0.1521–0.6740 mm, an MAE of 0.1084–0.5323 mm, and an MAPE of 0.01–0.27%. The research results serve as a reference for the dynamic harvesting prediction of tomato-picking robots and contribute to improvement of harvesting efficiency and success rates. Full article

(1) Aim: This study aims to assess the repeatability and accuracy of a 9-axis IMU-based glove and an IR camera system, in order to determine their potential to replace traditional goniometry. (2) Background: Traditional methods for assessing hand function, such as goniometry, are time-consuming and limited by subjectivity, inter-rater variability and external factors that compromise accuracy and reliability. Recent advancements in motion capture technology and sensor-based devices offer potential improvements in efficiency and accuracy for hand rehabilitation assessment; (3) Methods: To evaluate the repeatability of an IMU-based glove and an IR camera, measurements were taken using a silicone hand model under controlled conditions, while accuracy assessments involved a volunteer without movement constraints. Bland–Altman plots were employed for visual comparison and accuracy evaluation; (4) Results: The Nuada glove exhibited high repeatability, with standard deviations below two degrees across all joints, surpassing the goniometer’s accuracy threshold of five degrees. The UltraLeap system demonstrated comparable repeatability, with deviations consistently under 3.5 degrees. Accuracy assessments revealed limitations: over 50% of the Nuada glove’s measurements and over 80% of UltraLeap’s measurements deviated by more than five degrees compared to the goniometer. However, the Nuada glove and UltraLeap system were more consistent with each other than with the goniometer, suggesting limitations in the goniometer’s reliability for modern mobility assessment; (5) Conclusions: Both devices exhibited excellent repeatability, highlighting their strong potential for clinical application. However, their accuracy compared to the goniometer requires further refinement. These findings suggest that these technologies could enhance traditional assessment methods, offering more efficient and accurate solutions for evaluating hand mobility in clinical settings. Full article

In geosynchronous orbit satellite synthetic aperture radar (GEO SAR) maritime surveillance, imaging of ship targets with complex motion is a hard task. The difficulty lies in how to process the received signal with an extremely low signal-to-noise ratio (SNR), long synthetic aperture time, high-order phase term and range migration. To address the issue, this paper proposes a GEO SAR refocusing algorithm for ship targets with complex motion via ISAR technique, which is based on complex fast-time slow-time frequency distribution (CFSFD). First, the received signal of ship targets with complex motion is derived and modeled as a multicomponent 2-D joint sine-series-polynomial phase signal (2-D JSPS), where 2-D signal is used to describe the signal with range migration induced by complex motion. To deal with the signal, a joint envelope-phase estimation named CFSFD is proposed. Even under low SNR and long synthetic aperture time, CFSFD can achieve directly instantaneous frequency analysis for 2-D JSPS accurately. Finally, a hybrid SAR/ISAR refocusing algorithm is proposed, in which CFSFD-based ISAR technique replaces the range migration correction followed by time–frequency transform approach, yielding clear refocused results of ship targets with complex motion. Simulation and real data experiments validate the effectiveness of the proposed algorithm. Full article

Physical therapy is a critical component of medical rehabilitation, aiding recovery from conditions such as stroke, spinal cord injuries, and musculoskeletal disorders. Effective rehabilitation requires precise monitoring of patient performance to ensure exercises are executed correctly and progress is accurately assessed. This paper presents a novel automated system for controlling the rehabilitation process and evaluating physical therapy exercise quality using computer vision and a customized Human Skeleton-based Balanced Time Warping algorithm. The proposed method quantitatively assesses the similarity between a physiotherapist and patient performance by analyzing skeletal motion data extracted from RGB-D video sequences without requiring pre-alignment or sensor-specific calibration. A motion-dependent, weighted Euclidean distance between 3D skeletal models is used to compute pose dissimilarity, while a modified DTW approach aligns temporal sequences and evaluates dynamic consistency. The total dissimilarity measure is a balanced combination of posture (DP) and dynamics (DT) components. Evaluated on a custom dataset of 136 video recordings from 23 participants performing exercises in sitting and standing positions under varying performance accuracy levels (“good,” “intermediate,” and “bad”), the system demonstrates the strong clustering of accuracy levels. Proposed dissimilarity, together with a fixed reference element (physiotherapist), induces a natural non-strict order on the set of distances between patients and physiotherapists. A high value of Spearman’s rank correlation coefficient between computed dissimilarity and execution accuracy (0.977) indicates that this method is suitable for assessing exercise performance accuracy and for adequately evaluating the patient’s rehabilitation progress. The method enables objective, real-time feedback, reduces therapist workload, and supports remote monitoring, offering a scalable solution for personalized rehabilitation. Future work will involve clinical validation with post-stroke and cardiac patients. Full article

A continuum model for describing pseudo-turbulent flows of a dispersed phase is developed using a statistical approach based on the kinetic equation for the probability density of particle velocity and temperature. The introduction of the probability density function enables a statistical description of the particle ensemble through equations for the first and second moments, replacing the dynamic description of individual particles derived from Langevin-type equations of motion and heat transfer. The lack of detailed dynamic information on individual particle behavior is compensated by a richer statistical characterization of the motion and heat transfer within the particle continuum. A numerical simulation of the unsteady flow of a gas–particle suspension generated by the interaction of a shock wave with a particle cloud is performed using an interpenetrating continua model and equations for the first and second moments of both gas and particles. Numerical methods for solving the two-phase gas dynamics equations—formulated using a two-velocity and two-temperature model—are discussed. Each phase is governed by conservation equations for mass, momentum, and energy, written in a conservative hyperbolic form. These equations are solved using a high-order Godunov-type numerical method, with time discretization performed by a third-order Runge–Kutta scheme. The study analyzes the influence of two-dimensional effects on the formation of shock-wave flow structures and explores the spatial and temporal evolution of particle concentration and other flow parameters. The results enable an estimation of shock wave attenuation by a granular backfill. The extended pressure relaxation region is observed behind the cloud of particles. Full article

When the spaceborne satellite target rotates non-uniformly relative to the ladar, the high-order space-variant phase will be introduced into the echo phase along both the range and azimuth direction, which will cause the degree of defocusing of the scatterers on the target to rely on their locations. Traditional imaging algorithms usually assume that the target is in uniform motion and only compensate for second-order phase errors, ignoring spatial phase variations caused by higher-order non-uniform rotation. Consequently, these algorithms are ineffective in accurately focusing on edge scatterers, leading to image blurring at the target boundaries. To solve this problem, an ISAL imaging algorithm for spaceborne non-uniformly rotating targets based on matched Fourier transform (MFT) and a genetic algorithm is proposed in this paper. First, the echo signal model of the non-uniform rotation target is established. Second, the corresponding higher-order space-variant phase compensation method based on the estimated parameters is proposed, with time-domain higher-order phase compensation along the range direction and MFT algorithm along the azimuth direction. Then, the genetic algorithm is employed for parameter estimation. Finally, the results obtained from both simulation experiments and real data experiments verify that the proposed algorithm has good compensation accuracy and robustness. Full article

The accurate motion of roller-bearing-supported rings is critically influenced by shape and positional tolerances, which are often underestimated in conventional modeling approaches. The aim of this study is to develop and validate a multibody dynamic framework capable of quantifying the impact of roundness and positional errors on the motion accuracy of roller-bearing-supported rings. Shape errors are modeled using Fourier series and incorporated into a high-fidelity multibody simulation environment. Experimental validation using laser triangulation reveals a maximum runout error of 72.9 μm, compared to a numerically predicted value of 88.6 μm, resulting in a quantified numerical overestimation of 21.5%. Parametric studies investigated the effects of harmonic order, error amplitude, and combined error scenarios on key performance metrics, including trajectory runout and initial offset displacement. Results reveal that the trajectory errors range between 0.29 mm and 0.63 mm for shape error orders and can escalate to 2.84 mm for high amplitude errors, demonstrating the critical role of error order and amplitude. Furthermore, combined simulations show that bearing position errors exert a more pronounced effect on radial accuracy than shape deviations alone. The proposed approach enables precision design evaluation and tolerance optimization in high-accuracy applications, including robotics, aerospace mechanisms, and optical alignment systems. Full article

In this study, a nonlinear dynamic model is developed to examine the stability and vibration behavior of a self-balancing electric Segway operating over irregular stochastic terrains. The Segway is treated as a three-degrees-of-freedom cart–inverted pendulum system, incorporating elastic and damping effects at the wheel–ground interface. Road irregularities are generated in accordance with international standard using high-order filtered noise, allowing for representation of surface classes from smooth to highly degraded. The governing equations, formulated via Lagrange’s method, are transformed into a Lorenz-like state-space form for nonlinear analysis. Numerical simulations employ the fourth-order Runge–Kutta scheme to compute translational and angular responses under varying speeds and terrain conditions. Frequency-domain analysis using Fast Fourier Transform (FFT) identifies resonant excitation bands linked to road spectral content, while Kernel Density Estimation (KDE) maps the probability distribution of displacement states to distinguish stable from variable regimes. The Lyapunov stability assessment and bifurcation analysis reveal critical velocity thresholds and parameter regions marking transitions from stable operation to chaotic motion. The study quantifies the influence of the gravity–damping ratio, mass–damping coupling, control torque ratio, and vertical excitation on dynamic stability. The results provide a methodology for designing stability control systems that ensure safe and comfortable Segway operation across diverse terrains. Full article

In order to address the significant nonlinear dynamic characteristics and limited trajectory tracking accuracy of unmanned vehicles under cornering conditions, this paper proposes a trajectory tracking control strategy based on Adaptive Model Predictive Control (AMPC). First, to enhance the accuracy of the vehicle model, an 11-degree-of-freedom vehicle dynamics model is established, incorporating pitch, roll, yaw, rotation around the Z-axis, and wheel-axis rotation. The vehicle motion equations are derived using Lagrangian analytical mechanics. Meanwhile, the tire model is optimized by accounting for the influence of vehicle attitude changes on tire mechanical properties. Based on the principles of nonlinear model predictive control (NMPC) and adaptive control, the AMPC algorithm is developed, its prediction model is constructed, and appropriate control constraints are defined to ensure improved accuracy and stability in trajectory tracking. Finally, simulations under double-lane-change and serpentine driving conditions are conducted using a co-simulation platform involving Carsim and Matlab/Simulink. The results demonstrate that the proposed controller achieves high trajectory tracking accuracy, effectively suppresses vehicle yaw, pitch, and roll motions, and enhances both the smoothness of trajectory tracking and the overall dynamic stability of the vehicle. Full article

In order to optimize the preparation process of microlens arrays, improve preparation efficiency, and reduce preparation costs, 248 nm KrF excimer laser direct writing is combined with a motion mask to prepare microlens arrays on PMMA substrates. Firstly, a specific exposure mask based on the contour characteristics of the microlens unit was designed, and the preparation principle was analyzed. Using COMSOL Multiphysics 6.3 simulation software, a microlens preparation model was built to intuitively describe the process of preparing microlenses by the motion mask method. Secondly, a preparation system was built, and the laser processing technology was optimized. Finally, microlens arrays were prepared based on the optimized process, and an optical microscope and white-light interferometer were used to observe their morphology. The experimental results show that this method can effectively prepare cylindrical and circular microlens arrays. The width of the cylindrical microlens array unit exceeded 90 μm, the height was 7.08 μm, and the roughness was 0.09 μm. The diameter of the circular microlens array unit was φ100 μm, the height was 4 μm, and the curvature radius was 230 μm. The geometric dimensions of the mask can be adjusted to obtain microlens units of the desired size, achieving personalized preparation of microlens arrays. The excimer laser motion mask method can prepare various types of microlens arrays, and the array units have a high consistency and high surface quality, which helps to improve the efficiency, flexibility, stability, and specificity of microlens array preparation. Full article

This paper addresses the issue of rotor swings in a high-power synchronous generator during stable operation with a stiff power grid. The analysis of electromechanical swings was conducted using a circuit model incorporating fractional-order derivatives. Assuming that variations in the load angle under small disturbances from a stable equilibrium are minor, a linearized differential equation describing the electrodynamic state of the synchronous machine was derived. Based on this linearized equation of motion and the identified parameters of the equivalent circuit, calculations were performed for a 200 MW turbogenerator. The results indicate that the electromechanical swings are characterized by a constant pulsation and a low damping factor. Calculations were also carried out using a lumped-parameter equivalent circuit model. Based on the obtained results, it can be stated that the fractional-order model provides a more accurate fit of the frequency characteristics compared with the classical model with the same number of rotor equivalent circuits. The relative approximation errors for the fractional-order model are, for the d-axis (one rotor equivalent circuit), relative magnitude error δ_m = 1.53% and relative phase error δ_φ = 6.32%, and for the q-axis (two rotor equivalent circuits), δ_m = 3.2% and δ_φ = 8.3%. To achieve comparable approximation accuracy for the classical model, the rotor electrical circuit must be replaced with two equivalent circuits in the d-axis and four equivalent circuits in the q-axis, yielding relative errors of δ_m = 2.85% and δ_φ = 6.51% for the d-axis, and δ_m = 1.86% and δ_φ = 5.49% for the q-axis. Full article