Search Results (1,315)

Multi-sensor fusion of GNSS, IMU, and vision sensors has been extensively applied in urban Internet of Things systems and automated driving to improve positioning accuracy in complex environments. However, conventional FGO algorithms are based on fixed sensor weights, which limit their adaptability to fluctuations in sensor errors caused by environmental changes, thereby compromising positioning performance. To overcome this limitation, a novel multi-sensor adaptive weighted localization algorithm based on joint residuals detection was proposed in this study. The algorithm computes joint residuals by the sliding window accumulation of GNSS, IMU, and vision sensor measurements. By integrating a global weight decay factor into the M-estimation framework, the weights of each sensor were dynamically adjusted, thereby suppressing the effects of outliers on the state estimation. This approach enables high-precision and robust estimation of position, velocity, and attitude. Experimental results demonstrate that, based on validation with the GNSS–Visual–Inertial Navigation System (GVINS) public datasets sports field and complex environments, the proposed method exhibits superior performance in challenging low-altitude economic scenarios such as weak GNSS signals and significant IMU drift—specifically, it improves positioning accuracy by 32.3% and reduces velocity error by 32% compared to traditional FGO algorithms. In scenarios with GNSS signal interference, the system effectively mitigates error accumulation and maintains the stability of position and velocity estimation. The proposed algorithm demonstrates exceptional positioning accuracy and robustness in complex and dynamic environments, making it highly suitable for advanced urban IoT and automated driving applications. Full article

The rapid development of Internet of Things technologies has accelerated the deployment of smart home systems. However, perception solutions based on visual sensors remain constrained by illumination sensitivity, occlusion, and privacy concerns. Frequency-modulated continuous-wave (FMCW) millimeter-wave radar provides a promising alternative because it operates independently of lighting conditions, is robust to environmental changes, and preserves user privacy. To address multiple-extended-target tracking in cluttered indoor environments, this paper proposes a high-accuracy tracking algorithm that combines an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, an optimized Nearest-Neighbor Data Association (NNDA) scheme, and an Extended Kalman Filter (EKF). The improved DBSCAN algorithm introduces spatial-extent constraints, velocity-consistency checks, and candidate-cluster validation to cluster raw radar point clouds and convert extended targets into representative point targets with little additional computational cost. The optimized NNDA scheme then integrates clustering information into the association process, improving the matching accuracy between existing tracks and current measurements. Finally, the EKF estimates the state of each target from the associated measurements. Real-world experiments show that the proposed algorithm achieves tracking errors below 0.4 m in typical motion scenarios, maintains continuous tracking in two-person crossing scenarios, and reaches 93.3% counting accuracy in five-person scenarios. These results outperform the tracking system based on the commercial Texas Instruments (TI) IWR6843ISK millimeter-wave radar evaluation board. The proposed method offers a reliable and privacy-preserving sensing solution for smart homes, elderly care, and intelligent building applications. Full article

In this paper, a composite control framework integrating feedback linearization, an extended state observer, and an adaptive fractional-order sliding-mode controller is presented for autonomous underwater vehicles operating under uncertain hydrodynamics and external disturbances. The proposed algorithm, named adaptive fractional-order sliding-mode control with extended state observer, aims to enhance trajectory-tracking accuracy, disturbance rejection, and robustness against model uncertainties beyond what is offered by conventional active disturbance rejection control and integer-order sliding-mode control. First, a fractional-order sliding surface with an extended state observer is introduced to estimate and compensate lumped disturbances, where the fractional operator provides intrinsic filtering and memory effects to reduce chattering. Second, an adaptive exponential reaching law with smooth switching is formulated to overcome the trade-off between convergence speed and chattering, and a Levant differentiator is employed for sensorless velocity estimation. Finally, the uniform ultimate boundedness of the closed-loop system is proved via Lyapunov stability theory. Comparative simulation studies on step, sinusoidal, and circular trajectories under external disturbances, measurement noise, and 50% parametric uncertainties demonstrate that the proposed controller achieves zero overshoot, suppresses position fluctuations by 97%, and reduces root mean square tracking errors by 38–70% relative to conventional methods, confirming its superior performance. Full article

Accurate estimation of shear-wave velocity (Vs) is fundamental for reservoir geomechanics, as it directly influences the calculation of elastic properties used in Mechanical Earth Models (MEMs). However, shear-wave sonic logs are frequently unavailable in legacy or data-limited wells due to high operational costs and technical constraints. Therefore, reliable prediction of Vs has become essential. This study proposes a physically guided porosity-compensated compressional-wave predictor, Vp (1 − PHIT), derived from the Wyllie time-average equation, to mitigate porosity-induced variability and enhance sensitivity to rock-frame stiffness. The proposed model was evaluated using a multi-well sandstone and shaly sand dataset comprising 29,426 data points from 19 wells in the Sirte Basin, Libya. Its performance was benchmarked against five widely used global correlations using statistical metrics including R², RMSE, MAE, and MAPE. The results demonstrate that the proposed model achieves superior predictive performance: R² = 0.908, root-mean-square error (RMSE) = 0.00047 ft/µs, mean absolute error (MAE) = 0.00037 ft/µs, and mean absolute percentage error (MAPE) = 4.05%, outperforming conventional empirical correlations. The developed correlation provides a simple, physically interpretable, and field-applicable solution for predicting Vs in sandstone reservoirs and similar formations where shear-wave measurements are unavailable. Full article

Tidal turbine wake predictions in regional-scale hydrodynamic models typically account for rotor thrust but neglect the drag of support structures. This study introduces a method for representing turbine support structures as permeable drag volumes within TELEMAC-3D and evaluates their influence on wake characteristics. The method is demonstrated for the 1 MW DeepGen-IV turbine deployed at the Fall of Warness test site at the European Marine Energy Centre, Scotland. The tripod foundation, tower, and nacelle are each implemented as momentum source terms alongside an actuator disc rotor in a regional-scale model with mesh resolution down to 1.5 m with 24 sigma layers and output at 60 s intervals (1 s at instrument locations), validated against seabed-mounted ADCP measurements. Including the support structures improves the agreement with measured wake profiles by 6–18% in root-mean-square error at 3.7 rotor diameters downstream and extends the hub-height 5% velocity deficit distance by an average of three rotor diameters (~54 m), with substantial variability across tidal conditions. The tripod and tower drag also extend the velocity deficit into the lower water column, a feature absent from the rotor-only formulation, with potential relevance to near-bed processes such as bed shear stress and sediment transport which are not examined in the present study. The implementation is in principle extendable to other support concepts and multi-device studies, and the results indicate that support structure drag should be considered in regional wake models where wake persistence and downstream interactions are important. Full article

Research on human motion representation commonly investigates portable, wearable, and ergonomic sensing systems. Cameras, infrared sensors, and inertial measurement units (IMUs) are widely used to reproduce and validate human movement. Known limitations persist, including increased error during slow movements, the gimbal lock effect in Euler space, and the requirement for one sensor per joint. The objective of this work is twofold: first, to characterize the measurement accuracy of a commercial IMU sensor (BWT901BLE) under controlled conditions using a fixed-arm pendulum model that replicates the single-degree-of-freedom planar kinematics of elbow flexion–extension, comparing angular position, angular velocity, and angular acceleration outputs against a video-based reference system; and second, to describe and publish a complete data processing pipeline—from raw sensor readings to real-time biomechanical motion visualization within OpenSim—demonstrated through upper limb motion recordings from 6 participants, whose data were used to generate motion files and estimate muscle fiber lengths and activation patterns within OpenSim. Regarding sensor characterization, experiments compared sensor data against the video-based reference. The inter-sensor angular position mean error was 0.765° (100 Hz) and 0.445° (200 Hz); angular velocity mean error was 0.124°/s (100 Hz) and 0.277°/s (200 Hz). Direct Euler angle measurements outperformed quaternion-to-Euler conversion (mean RMSE 5.69° vs. 53.1° at 100 Hz; 5.08° vs. 41.8° at 200 Hz). Angular velocity showed the highest agreement with the video-based reference (mean RMSE 0.60 rad/s at 100 Hz and 0.43 rad/s at 200 Hz; mean R = 0.982 and 0.991). Raw accelerometer output showed negligible correlation with the video-based angular acceleration reference (mean R ≈ 0.00–0.05); however, acceleration derived from angular velocity differentiation achieved high accuracy (mean RMSE 4.43 rad/s² at 100 Hz and 3.06 rad/s² at 200 Hz; mean R = 0.976 and 0.989). Regarding the OpenSim integration, the real-time visualization pipeline achieved an effective frame rate of 40–50 fps with an estimated end-to-end latency of 35–50 ms, and the recorded motion data were used to estimate muscle fiber lengths and activation patterns through OpenSim’s analysis tools. These findings confirm that angular velocity is the most reliable output of this sensor class. Full article

Anabranching, sedimentation, island growth, and bank scouring are key morphological processes occurring in the Tigris River. These processes can disrupt navigation, affect water intake, and compromise the safety of infrastructure near the riverbanks. This study aims to simulate and assess the responses of a 4.75 km meandering–anabranching reach of the Tigris River in Baghdad city center to various alternative groyne dimensions designed to control natural morphological processes, using a depth-averaged hydro-morphodynamic model (Delft3D-FM). Bathymetric and field measurements, including sediment load, velocity, water level, and discharge, were conducted and used for model calibration and validation. The model demonstrated good agreement with observed water levels (Root Mean Square Error (RMSE) = 0.02 m) and depth-averaged velocities (RMSE = 0.068–0.142 m/s), and it reproduced morphological changes with a maximum bed-level error of approximately 13% at control sections. More than 20 groyne configurations, varying in orientation, length (L), and spacing (S), were simulated and assessed. In this study, the selection of the best groyne design for controlling morphological processes in the target reach was carried out using a proposed composite Groyne Performance Index (GPI). The index is based on weighted contributions from flow partitioning, thalweg stability, cross-channel infilling, island-margin response, and corridor deposition. While the straight–groyne configuration with L = 0.25 W (river width) and S = 2 L achieved the highest GPI, the L = 0.25 W and S = 3 L configuration is selected as the preferred design as it provided a more balanced response in terms of flow redirection, thalweg stability, reduced anabranching and deposition, and lower scour risk. The adopted selection methodology demonstrates a valuable indicator-based framework for selecting river-training layouts in low-slope, sand-bed, meandering–anabranching reaches of alluvial rivers. Full article

Quantifying river surface velocity and discharge is essential for flood control and mitigation. Traditional contact measurement methods are capable of providing precise results, yet they demand considerable manpower and material resources and face implementation challenges in flood seasons. Image velocimetry methods have attracted extensive attention due to their low cost, simplicity in operation, and safety. However, most of them lack a physical basis and interpretability. This paper introduces a river flow estimation algorithm combined with Physics-Informed Neural Networks (PINNs). The introduction of the convection–diffusion equation based on optical flow enables the model to better fit the flow characteristics of water and provides stronger physical support for the measurement results. The adoption of this equation as the loss function and the introduction of multiple scenarios eliminate the need for labeled data in the PINNs training process. The experimental results in both artificial and natural river channels demonstrate that the relative errors of the discharge measured by the proposed method are 0.66% and −1.75%, and the relative errors of the mean velocity are 0.64% and −2.33%. Compared with other methods, the proposed method exhibits superior performance. Full article

With the rapid development of low-altitude UAVs, accurate wind detection is crucial for ensuring flight safety and enabling broader applications. To address this need, this paper introduces a highly integrated CDWL system specifically designed for compact UAV platforms. The system incorporates a self-compensating Harris Hawks Optimization (SC-HHO) retrieval algorithm, which is tailored to the high-dynamic flight environment and stringent payload constraints of UAVs. This algorithm enables real-time wind retrieval with low dependence on external reference data while effectively compensating for platform motion. The performance of the proposed system was validated through the comparative experiment and the UAV-borne experiment. In the comparative experiment, the CDWL showed correlation coefficients above 0.976 in horizontal wind speed and 0.987 in horizontal wind direction relative to a benchmark airborne CDWL system, with corresponding root-mean-square errors better than 0.395 m/s and 4.135°, respectively. During the UAV-borne experiment, the CDWL retrieved platform velocity using the self-compensating mechanism, achieving a standard deviation of 0.080 m/s relative to global navigation satellite system (GNSS) measurements, and successfully acquired wind field information. These results confirm that the developed system provides a viable and practical technical solution for UAV-based remote wind sensing. Full article

This paper presents an extended combined experimental and numerical study on the vibration comfort assessment of a modern timber-framed public utility building. The research focuses on a lightweight skeleton floor system, representing a typical high-frequency floor. In situ vibration measurements were conducted under various walking excitations (single and multiple pedestrians) to determine key vibration parameters. Post-processing, which yielded root mean square accelerations and velocities, was performed using a custom-developed code in the Mathematica package. A finite element model was prepared in Dlubal RFEM 6 using shell and beam elements with offsets. The dynamic characteristics obtained from the FE modal analysis showed high consistency with the experimental data, with a relative error of approximately 5 % for the fundamental frequency. The vibration comfort was assessed using two distinct methodologies: the JRC report and the SCI P354 guide. Both approaches positively verified the floor’s vibration comfort, confirming its suitability for the intended use. The study demonstrates that the JRC methodology is more straightforward and unambiguous for engineering practice. Furthermore, the results indicate that simplified FE models provide a reliable basis for predicting vibration modes and calculating mode shape factors, which are essential for the correct interpretation of local measurements in existing buildings. Full article

Ship transit in vertical ship lift chambers represents a highly confined flow regime characterized by extreme blockage (N < 2), where ship-induced piston effects can significantly influence navigational safety and structural loads. This study presents an experimental investigation of the unsteady hydrodynamic responses of a 1000 t class ship operating in the Baise vertical ship lift. A 1:10 large-scale physical model was constructed to reproduce the ship lift chamber and auxiliary lock geometry under Froude similarity. Tests were conducted for prototype water depths of 3.7–3.9 m and sailing velocities between 0.4 and 1.1 m/s. Ship sinkage, free-surface oscillations, and dynamic chamber weight variations were synchronously measured. Results revealed a profound process asymmetry: the exit maneuver induced significantly higher sinkage (0.92 m at 1.1 m/s) and chamber weight fluctuations (810 t) than the entry process due to restricted return flow replenishment. A non-dimensional predictive P–K relationship was derived with a regression coefficient α = 1.9121. Based on safety margins and mechanical load limits, critical speed thresholds were established at 0.6 m/s for exit and 0.7 m/s for entry to ensure a minimum safety clearance of 0.48 m even under docking error conditions. Full article

This study examined whether barbell velocity measured under standardized loading conditions during routine resistance training may provide useful contextual information for athlete monitoring in elite track-and-field athletes. Although velocity-based training has been widely used for load prescription, its utility as contextual monitoring information under fixed external loads remains unclear. Eight national-level jump and throw athletes were observed over a six-week in-season period. Mean concentric velocity (MCV) was recorded during back squat exercises performed at a consistent external load (~60% 1RM) across 95 regularly scheduled training sessions. The overall mean MCV was 0.783 ± 0.057 m·s⁻¹ (range: 0.68–0.89 m·s⁻¹). Within-athlete session-to-session fluctuations ranged from 6.13% to 11.20%, exceeding both the coefficient of variation (2.06–3.80%) and the pooled typical error of measurement (0.019 m·s⁻¹), suggesting that the observed variability was unlikely to be explained solely by measurement noise. Distinct individual velocity trajectories were observed under fixed individualized loading conditions, reflecting notable intra- and inter-individual variability within an ecologically valid training environment. These findings suggest that barbell velocity may provide contextual information regarding session-to-session fluctuations relevant to athlete monitoring under standardized loading conditions. Collectively, the findings suggest that barbell velocity measured under standardized loading conditions may provide contextual information regarding day-to-day variability during routine elite training. Full article

Stroke is a leading cause of chronic disability, with heterogeneous sensorimotor impairments that are not well captured by standard clinical assessments. While advanced motion capture and robotic systems provide precise measurements, they are not scalable for widespread clinical use. We developed C-MORE (Computer Vision for Movement Observation and Recovery Enhancement), a smartphone-based framework that uses computer vision and machine learning to automatically score the Box and Blocks Test (BBT) and extract quantitative kinematic metrics. The system combines hand tracking with a custom machine learning (ML) architecture to identify valid block transfers and segment task phases. We evaluated C-MORE in 7 individuals with chronic stroke and a cohort of 10 healthy adults. The system achieved 99.0% agreement with ground-truth scoring, with errors below clinically meaningful thresholds. Kinematic measures derived from the system were sensitive to stroke-related impairments, including reduced movement velocity and increased task duration in affected limbs. Exploratory analyses indicated that grasp-related metrics, particularly the ratio of grasp-to-transfer duration, were correlated with independent measures of proprioception. These findings demonstrate that smartphone-based computer vision can provide accurate, scalable assessment of upper-extremity function. C-MORE offers a practical approach for enhancing clinical evaluation and enabling more precise, individualized rehabilitation strategies. Full article

For nonlinear problems in robotic systems, such as parametric uncertainties and external disturbances, this paper proposes a control method based on bilateral dual-kernel fuzzy control. To address the issue that joint angular velocities cannot be directly measured, an extended state observer (ESO) is introduced to simultaneously estimate the joint positions, velocities, and system nonlinearities, thereby achieving effective reconstruction of the system states. In terms of controller design, a dual-kernel function is adopted instead of the conventional single-kernel function. By exploiting its enhanced feature representation capability and fast response characteristics, the proposed approach improves the system dynamic response speed and reduces the settling time. For nonlinear residuals, the bilateral parallel control strategy further improves the approximation accuracy of the control system. Multiple dual-kernel fuzzy sub-controllers are integrated in a bilateral parallel manner, and the weighting parameters of both the fuzzy system and the bilateral structure are updated in real time based on the approximation error. This enables accurate approximation and compensation of the residuals estimated by the extended state observer. The stability of the closed-loop system is rigorously proved based on Lyapunov theory. Finally, simulations on the MATLAB R2022b platform and experiments on a robotic experimental platform are conducted to verify that the proposed bilateral dual-kernel fuzzy controller achieves significantly improved control accuracy for a two-degree-of-freedom robotic manipulator system compared with conventional controllers, thereby demonstrating the effectiveness and superiority of the proposed algorithm. Full article

Background/Objectives: Children with Autism Spectrum Disorder (ASD, here on termed autistic children) exhibit motor difficulties in social and non-social contexts. Although previous studies have reported behavioral and neural characteristics, their relationship remains largely unexplored. The current study aimed to investigate the behavioral and neural mechanisms underlying interpersonal synchrony in autistic children using simultaneous kinematic and Functional Near-Infrared Spectroscopy (fNIRS) recordings. Methods: Fifty-eight autistic or non-autistic children participated (mean age = 10.1, standard error = 0.3). fNIRS and an inertial measurement unit were used simultaneously to record the neural activity over frontotemporal and parietal regions and arm movement kinematics during a reach-to-clean-up task across three conditions: Watch—the child observed the tester clean up the blocks; Do—the child cleaned up the blocks independently; and Together—the child and tester cleaned up the blocks synchronously. Results: Behaviorally, autistic children demonstrated longer movement displacement, higher average velocity and acceleration, and a greater number of movement units. In terms of cortical activation, autistic children showed hypoactivation in the bilateral precentral gyrus and right inferior parietal lobe, along with hyperactivation in the right middle frontal gyrus, left inferior frontal gyrus, and left inferior parietal lobule. Correlations between kinematic and neural measures suggest that autistic children rely more on online/feedback control to compensate for reduced feedforward control. Conclusions: This study reveals unique compensatory strategies in autistic children, highlighting the connections between neural and behavioral characteristics. These findings have strong potential to inform the development of ASD screening tools and to guide targeted intervention strategies. Full article