Search Results (4,693)

Overhauser magnetometer (OVM) is a proton precession magnetometer (PM) enhanced by electron resonance, and it is widely used in earthquake prediction, UXO detection, geological exploration, etc. For fast measurement, high cycling rate is necessary for OVM to enhance spatial resolution. Due to the impossibility to receive Larmor signal during the polarization process, traditional intermittent measurement is limited in fast mobile measurement applications owing to the long polarization time. Since it is difficult for proton magnetization to align rapidly for the long longitudinal relaxation time of liquid proton, we combined RF continuous excitation with a series 90° pulse to achieve fast measurement. To achieve the best alignment, a dynamic equation of Larmor precession is constructed and calculated, and the influences such as pulse waveform, pulse strength, and pulse duration on the proton magnetization alignment were investigated. The influence of different waveform pulses on the Larmor signal was studied experimentally, and the experimental results verified that the polarization time can be significantly shortened and fast measurement can be achieved by optimizing the waveform, strength, and duration of the 90° pulse. By using the optimized 90° pulse, the proton magnetization can be saturated within 3 ms, and 0.02 nT sensitivity was observed at 1 Hz cycling rate. Consistency between theory and the experiment indicates that the dynamic equation of Larmor motion can provide theoretical guidance for the investigation of fast measurement. Full article

Robust vehicle detection in real-world traffic surveillance remains challenging due to degraded imagery caused by motion blur, adverse weather, and low illumination, which significantly increases detector sensitivity to hyperparameter configurations. This study proposes a “Frugal AI” distributed multi-GPU framework that optimizes hyperparameters via a stochastic simplex-based search coupled with five-fold cross-validation. Utilizing three low-cost NVIDIA GTX 1050 Ti GPUs, the framework performs parallel candidate exploration with an asynchronous model-level exchange mechanism to escape local optima without the overhead of gradient synchronization. Seven CNN backbones—VGG16, VGG19, GoogLeNet, MobileNetV2, ResNet18, ResNet50, and ResNet101—were evaluated within YOLOv2 and Faster R-CNN detectors. To address memory constraints (4 GB VRAM), YOLOv2 was selected for extensive benchmarking. Performance was measured using a harmonic precision–recall-based cost metric to strictly penalize imbalanced outcomes. Experimental results demonstrate that under identical wall-clock time budgets, the proposed framework achieves an average 1.38% reduction in aggregated cost across all models, with the highly sensitive VGG19 backbone showing a 4.00% improvement. Benchmarking against Bayesian optimization, genetic algorithms, and random search confirms that our method achieves superior optimization quality with statistical significance ($p < 0.05$). Under a rigorous IoU = 0.75 threshold, the optimized models consistently yielded F1-scores 0.8444 $\pm$ 0.0346. Ablation studies further validate that the collaborative model exchange is essential for accelerating convergence in rugged loss landscapes. This research offers a practical, scalable, and cost-efficient solution for deploying robust AI surveillance in resource-constrained smart city infrastructure. Full article

This study develops a reduced-order predictive model of the Sit-To-Stand (STS) task to examine whether a simplified biomechanical representation can reproduce key STS patterns reported in the literature and to investigate the role played in movement by a flexible trunk. The model represents the human body as a planar multibody system and formulates STS as an optimization problem within a discrete mechanics framework. This formulation combines reduced model complexity, explicit torso flexibility, and a structure-preserving numerical approach for trajectory generation. Simulations were used to evaluate the effects of movement duration, reduced joint strength, and seat height on joint torques, kinematics, trunk motion, and ground reaction forces (GRFs). The results reproduced several qualitative trends reported in previous experimental studies, including increased peak joint torques and GRFs with shorter movement duration, lower joint strength, and reduced seat height, as well as greater compensatory trunk motion under more demanding conditions. These findings suggest that the proposed framework captures key adaptive features of STS mechanics and may provide useful insights for rehabilitation analysis and the design of assistive technologies such as lower-limb exoskeletons and rehabilitation devices. At the same time, the present work should be regarded as an initial methodological study, since validation is currently qualitative and further experimental calibration, quantitative validation, and sensitivity analysis remain part of ongoing work. Full article

The integration of automated learning and video analysis enables the development of intelligent systems that can operate effectively in uncertain scenarios. These systems can autonomously identify dominant motion dynamics, depending on the theoretical framework used for representation and the learning process used for pattern identification. Current literature offers a state-based approach to describe the key temporal and spatial relationships required to understand motion dynamics. An important aspect of this approach is determining when the number of positively learned rules from a given information source is sufficient to detect dominant motion in automatic surveillance scenarios. This is crucial, as it affects both the variability of movements that monitored subjects can exhibit within the camera’s field of view and the resources needed for effective implementation. This study addresses these gaps through a grammar-based sufficiency criterion, which posits that learning is complete when production rule growth stabilizes, under the assumption of system stationarity. The stability criterion evaluates whether the most probable rules are learned over time, and whenever a high-growth rule is added, it is used to update the criterion. We outline several benefits of having a formal criterion for determining when a symbolic surveillance system has a robust model that explains the observed motion dynamics. Our hypothesis is that a correct model can consistently account for the majority of motion dynamics over time in an automated learning process. The proposed approach is evaluated by modeling motion dynamics in several scenarios using the SEQUITUR algorithm as input and computing the probability of stability along the learning curve, which indicates when the model reaches a steady state of consistent learning. Experimental validation was conducted in real-world scenarios under varying acquisition conditions. The results show that the proposed method achieves robust modeling performance, with accuracy values ranging from 83.56% to 95.92% in dynamic environments. Full article

Air flotation is widely used in wastewater treatment for the removal of emulsified oils and suspended solids. The complex flow disturbances generated during the flotation process play a critical role in determining separation efficiency. This study employs the volume-of-fluid (VOF) method within the OpenFOAM framework to simulate the aggregation and rising behavior of microbubbles (40–100 μm) and oil droplets under various perturbation conditions. The effects of different airflow disturbance patterns on the flotation dynamics of oil–gas compounds are systematically investigated. Results show that negative pulsation promotes the rising of bubble–oil aggregates, whereas positive pulsation hinders their coalescence and upward motion. Furthermore, recirculation vortices induced by surface disturbances increase the residence time of oil–gas compounds in the water column, thereby affecting overall separation performance. The findings demonstrate that introducing vertical upward flow and bilateral oblique upward airflow can enhance flotation efficiency. This work provides insights into optimizing airflow configurations for improved oil removal in wastewater treatment applications. Full article

Background/Objectives: Instrument fracture remains a significant complication in endodontics. This study compared the resistance to cyclic fatigue failure between rotary and reciprocating nickel–titanium file systems, as well as differences related to file size and taper. Methods: Nineteen rotary and reciprocating file types (n = 10 per group) were evaluated in three independent test series, harmonized according to file size and system. Cyclic fatigue testing was conducted using a static model with a stainless-steel artificial canal, with an internal diameter of 0.9 mm, a 75° curvature angle, and a fixed radius for each series. Files were operated using preset programs on the X-Smart Plus, Rooter X3000, and Sendoline Endo torque-controlled motors. Time to fracture was recorded digitally, and the total number of full rotations to failure was calculated. The fractured fragments were examined with scanning electron microscopy and fractographic analysis. The data were analyzed using linear models in Stata version 19, with significance set at p ≤ 0.05. Results: Reciprocating file systems demonstrated greater time-to-fracture fatigue resistance than rotary systems. However, these differences were diminished or, in some cases, eliminated when normalized to the number of complete rotations. Fractographic analysis indicated that fractures predominantly resulted from tensile stress rather than shear forces. Conclusions: Reciprocating kinematics generally enhanced fatigue resistance compared with continuous rotation. The results suggest that fatigue resistance in machine-driven nickel–titanium instruments cannot be predicted by motion type or file design alone but reflects a complex interaction between alloy composition, heat treatment, and cross-sectional geometry. Full article

In order to investigate the application potential of lightweight agricultural robots utilizing carbon fiber-reinforced polymer (CFRP) as the primary structural material, this study developed a dedicated rubber-tracked chassis tailored for peanut pest and disease monitoring robots. The chassis design is anchored to the widely applied “single ridge with double rows” cultivation pattern in peanut production and incorporates a real-time track tension monitoring mechanism integrated with pressure sensors. The overall structural configuration of the chassis fully conforms to the standard ridge parameters of mechanized peanut planting while fully considering the intrinsic material properties of CFRP. Additionally, a sprocketless drive wheel structure is specifically adopted to realize higher-precision motion control performance. A mathematical model was constructed to quantitatively characterize the tension correlation between the tight side and slack side of the rubber track, as well as the variation law of initial tension influenced by multiple factors including the total mass of the robot platform. With the curb weight of the robot platform set at 45 kg, the theoretical initial tension is calculated to be 24.5 N (equivalent to approximately 2.5 kg, taking the gravitational acceleration g = 9.8 m/s²). The prototype shows potential for maintaining consistent tension, though a mechanical weakness was identified and will be addressed in future work. Performance validation tests show that the chassis maintains stable operation with no sprocket slippage during field visual inspection. Full article

This study presents a simulation-based evaluation of a wall-following and mapping framework for autonomous underwater vehicles (AUVs) equipped with a single-line laser, targeting structured environments such as rectangular tanks and dam interiors. A hardware-in-the-loop (HIL) simulation platform is developed to integrate sensor emulation, vehicle dynamics, and image-based control while preserving the onboard data formats, update rates, and communication protocols of the AUV system. Using a single camera–laser pair, the framework estimates yaw angle and lateral wall distance from laser image geometry to support real-time wall-following and frontal obstacle avoidance. Wall mapping is performed by transforming laser image features into spatial coordinates and estimating the dimensions of geometric protrusions. The framework is evaluated on simulated walls with protruding features under two navigation conditions: ideal-motion and dynamic-control operation. Simulation results show stable wall-following performance, with lateral distance errors typically below 0.1 m. Under ideal-motion conditions, mapping errors range from 1% to 13%, while under dynamic-control navigation they increase to 10–35% due to attitude fluctuations and control-induced motion. Frontal obstacle avoidance maintains a minimum clearance of 1.04 m. The results demonstrate the feasibility of using a single-line laser and a unified image stream for both real-time wall-following control and post-mission geometric mapping within the defined simulation conditions. While the evaluation is limited to simulation and assumes idealized optical conditions without modeling hydrodynamic disturbances or optical degradation effects, the framework provides a system-level reference for laser-guided inspection strategies in confined underwater environments such as tanks, reservoirs, and dams. Full article

Omnidirectional mobile robots are increasingly used in industrial and service applications due to their high maneuverability and ability to perform combined translational and rotational motions in confined spaces. However, these locomotion advantages are often accompanied by additional wheel–ground interaction losses, making power consumption an important design criterion in the design of efficient mobile platforms. This study presents a dynamic modeling and experimental-power benchmarking framework for a modular mobile robot equipped with three omnidirectional wheels, using a four-omni-wheel configuration as a baseline reference for comparison. A CAD-consistent multibody dynamic model of the three-wheel architecture is developed in the MATLAB/Simulink–Simscape Multibody R2024benvironment to estimate motor currents and electrical-power demand during motion. Experimental validation is carried out on the physical prototype using Hall-effect current sensors integrated into the drive modules, enabling real-time current acquisition for each motor. Both the simulation and experiments are performed on a standardized 1 m square-path benchmark at a constant 12 V supply. The results show that the proposed three-omni-wheel configuration reaches a total measured power of 14.43 W and a simulated power of 12.72 W, corresponding to a robot-level deviation of 11.85%. By comparison, the four-omni-wheel baseline exhibits a total measured power of 25.75 W and a simulated power of 24.92 W. Therefore, the proposed three-wheel architecture reduces the measured power demand by approximately 43.96% relative to the baseline, while the four-wheel configuration provides higher model fidelity. The proposed methodology supports power-oriented evaluation and informed design selection of omnidirectional locomotion architectures for modular mobile robots intended for industrial applications. Full article

This study compared the kinematic and neuromuscular characteristics of the tennis forehand drive between high-performance (HP) and intermediate (INT) players. Eighteen right-handed male players (HP: n = 9; INT: n = 9) performed cross-court forehands while three-dimensional motion capture and surface electromyography (EMG) were recorded from the dominant upper limb and trunk. Kinematic and EMG data were time-normalized to the forward swing. One-dimensional statistical parametric mapping two-sample t-tests were used to compare joint angles, angular and linear velocities, and EMG amplitude waveforms between groups. Bonferroni-corrected significance levels were set at α = 0.0017 for kinematic variables and α = 0.0063 for EMG data. HP players exhibited greater racket linear velocity during the final part of the forward swing, accompanied by higher shoulder, elbow and wrist linear velocities, whereas hip linear velocity did not differ between groups. Joint angles were broadly similar, with SPM revealing only slightly greater early knee flexion in HP players. In contrast, HP players showed higher hip and knee angular velocities and greater wrist angular velocities in both flexion/extension and radial/ulnar deviation towards impact. EMG patterns were generally comparable, but HP players displayed higher biceps brachii activation in two significant clusters during the mid-to-late forward swing and greater triceps brachii activation in the late forward swing. No significant differences were observed for deltoid, pectoralis major, latissimus dorsi, flexor carpi radialis or extensor carpi radialis. These findings indicate that superior forehand performance in HP players is associated primarily with refined segmental coordination, greater lower-limb and distal segment velocities, and locally increased elbow muscle activation, rather than with widespread increases in upper-limb or trunk muscle activity. Full article

This paper presents a low-cost, lightweight wearable sensing module for real-time multi-degree-of-freedom motion analysis, which is validated using ankle movements from a representative case study. The system is based on a compact inertial measurement unit integrated into a custom-made enclosure and employs Kalman filter-based sensor fusion to estimate three-dimensional joint orientation. An experimental campaign involving sixteen healthy participants was conducted, and measurements were compared against a gold-standard optical motion capture system, Optitrack V120 Trio. Ankle kinematics were analysed across all anatomical planes, including dorsiflexion/plantarflexion, inversion/eversion, and adduction/abduction. Quantitative metrics, including cosine similarity consistently above 0.98 across all movements and root mean square error within 4° on average, demonstrate strong agreement between the angular measuring device and motion capture data, with errors remaining within clinically acceptable limits. The results confirm the feasibility of the proposed system as a reliable, portable, and affordable alternative to laboratory-based measurement technologies. Beyond ankle assessment, the sensing approach is applicable to a wide range of motion-assistive and rehabilitation systems, supporting continuous monitoring, personalised therapy, and future integration into intelligent wearable devices. Full article

Polyurethane O-ring seals are vital for the service life and sealing reliability of hydraulic systems, yet internal hysteresis heat generation under reciprocating motion causes localized temperature rise, altering contact pressure distribution and impairing sealing performance. This study aimed to clarify the coupled effects of reciprocating motion parameters on O-ring hysteresis heat generation and sealing performance. A unified hysteresis heat generation rate expression was derived by combining the time–temperature superposition principle with the Maier–Göritz model, and the heat source model was integrated into a thermo-mechanically coupled finite element analysis (FEA) framework, validated by matching simulated and experimental temperature rise histories. Under baseline conditions, hysteresis heating causes the O-ring’s peak contact pressure to decrease by approximately 0.4 MPa during the outward stroke. Parametric analysis revealed that elevated operating parameters increase contact pressure to maintain effective sealing, but simultaneously intensify hysteresis heating. Quantitatively, the maximum O-ring temperature was highly sensitive to operating conditions, reaching 63.6 °C at 8 MPa hydraulic pressure, 60.0 °C at a 90 Hz reciprocating frequency, and up to 81.5 °C for a friction coefficient of 0.2. Although the current framework is limited by the exclusion of interfacial frictional heating, it enables the reliable quantitative prediction of thermal loads. Ultimately, this study provides a robust method for assessing sealing safety margins and offers theoretical guidance for the structural optimization of hydraulic sealing systems. Full article

Background/Objectives: Prosthesis optimization after transfemoral amputation is often guided by clinical experience, yet quantitative evidence isolating how prosthesis mass, inertial properties, and alignment affect mechanical load transmission remains limited. Musculoskeletal modeling can be used as a controlled framework for examining relative sensitivity rankings of constraint force transmission across prosthetic junctions under fixed gait inputs. Methods: A model was modified to incorporate a transfemoral prosthesis. Experimental walking data from a healthy adult reference subject (Qualisys motion capture, synchronized AMTI force plates) provided kinematics and ground reaction forces for model scaling, inverse kinematics, and loading. These inputs provided a standardized mechanical reference and were not intended to represent transfemoral amputee gait. Prosthesis mass (2.625, 3.50, 4.375 kg), inertia (0.5×, 1.0×, 1.5×), and mediolateral alignment (−10, 0, +10 mm) were varied while keeping kinematics and ground reaction forces identical across conditions. Constraint reaction forces at the socket–residual limb junction and prosthetic ankle were computed and normalized to body weight. Results: Increasing mass produced the largest monotonic increases in peak resultant constraint reactions, most prominently at the socket-level junction (8.51 → 10.48 → 12.29 BW), with smaller changes at the ankle and unchanged peak timing. Inertia caused joint-specific effects, whereas mediolateral alignment minimally affected constraint reaction forces and redistributed force components. Conclusions: This study quantified the one-factor-at-a-time effects of prosthesis mass, inertia, and mediolateral alignment on inter-segment constraint reaction forces. The reported reactions should be interpreted as net rigid-body constraint reactions under fixed inputs, not as physiological joint contact forces or direct interface loads. Full article

Autonomous guided vehicle (AGV) navigation in high-density warehouses faces significant challenges due to narrow aisles and complex U-shaped traps. In such environments, traditional sampling-based path planning algorithms often converge slowly and produce suboptimal paths. To solve these issues, a novel hierarchical hybrid planning framework named MEG-RRT* (Multi-objective Evolutionary Guided RRT*) is proposed in this study. The proposed MEG-RRT* integrates an optimization engine based on NSGA-II into the sampling process. It guides exploration direction away from local minima by jointly optimizing convergence efficiency and safety-related objectives. Furthermore, a geometry-aware execution layer is introduced to improve motion through narrow passages and to refine the path structure. This layer includes radar-guided steering, adaptive step-size control, and ancestor shortcut operations. Comparative experiments were conducted in simulated scenarios of complex narrow passages and high-density warehouses to verify the superiority of the proposed MEG-RRT*. In complex narrow passages, the proposed algorithm achieves a 100% success rate; it also reduces convergence time by 43.5% compared to standard RRT* and by 44.9% compared to Informed-RRT*. In warehouse environments, it generates smooth, kinematically favorable paths that are 39% shorter than those produced by RRT-Connect. These results demonstrate that MEG-RRT* balances exploration efficiency and solution optimality, making it well suited for automated logistics applications. Full article

The motion prediction of semi-submersible platforms is of significant importance for improving operational efficiency, ensuring platform safety, and providing early warning information for potential risks. Traditional prediction methods, such as those based on hydrodynamic simulations combined with Kalman filters, often face limitations due to their reliance on precise hydrodynamic parameters, which are difficult to obtain in practice. More recently, data-driven approaches, particularly deep learning models like Long Short-Term Memory (LSTM) networks, have shown promise in predicting complex motions. However, these methods often treat the prediction process as a “black box,” leading to issues such as a lack of generalization ability, overfitting, and an inability to quantify the uncertainty of prediction results. To address these challenges, this paper proposes a novel motion prediction method for semi-submersible platforms based on a Bayesian neural network (BNN). The BNN incorporates Bayesian inference to effectively integrate prior knowledge and measured data, thereby quantifying uncertainties and improving prediction accuracy. The method is validated using field-measured motion data from a semi-submersible platform in the South China Sea. Compared with LSTM and feedforward neural network, the BNN demonstrates superior anti-noise performance and prediction accuracy, achieving an accuracy rate (R²) of up to 91.5%. Moreover, over 92% of the true values are captured within the 95% confidence interval of the prediction results. This study highlights the potential of BNNs for the real-time motion prediction of offshore platforms, providing valuable support for early warning systems and operational decision-making. Full article