Single Particle Tracking (SPT) is a crucial tool for analysing material deformation, providing sub-pixel accuracy in tracking individual particles. However, the influence of particle shape and imaging parameters on tracking accuracy remains underexplored. This study represents the first systematic examination of location errors in relation to particle shapes and sizes, and provides practical recommendations for optimizing tracking accuracy. We investigate static location errors associated with various particle shapes, including spheres, hollow spheres, and cubes. Two versions of spheres were investigated: the first scaled the grey value according to the volume occupied by the sphere, while the second version additionally scaled the grey values using a Gaussian function, depending on the distance to the particle centroid. Equivalent versions were used for cubes. Using simulated, noise-free 3D-images, we assessed the influence of particle shape, image resolution, and algorithm-specific parameters on location accuracy and the resulting strain measurement errors. Results indicate that particle shape significantly affects accuracy. Solid spheres showed the lowest mean location error — under 0.0075 voxel-size for diameters of 7 voxel-size – while hollow spheres exhibited errors up to 2.5 voxel-size. Strain error analysis revealed that a particle spacing of 50 voxel-size suffices to keep strain errors below 0.01% (suitable for engineering purposes) for spheres between 7 and 15 voxel-size in diameter. These findings provide valuable insight into improving SPT techniques in material deformation analysis, highlighting optimal particle geometries and imaging settings for accurate strain quantification. Future work should address the impact of noise and explore tracking-performance for more complex particle shapes. • Particle shape has a noticeable influence on the accuracy of single particle tracking. • A larger refinement area often leads to better accuracy in single particle tracking. • A particle size of more than 3 voxels is recommended to improve accuracy.

Fully integrated AI-enhanced flexible wearable sensor for real-time movement evaluation and table tennis training.

To address high-dimensional coupling and extrapolation errors in vehicle lightweighting, this paper proposes a “Macroscopic Topology—Microscopic Data-Driven Size Synergy” methodology. Macroscopically, strain-energy-driven topology optimization on a simplified skeleton reduces mass by 9.4% (2835.8 kg to 2566.9 kg). Microscopically, a global ANOVA mechanism compresses 169 thickness variables to 39 core dimensions, mitigating the curse of dimensionality. Crucially, an active learning-based sequential approximate optimization (SAO) framework rectifies severe static model extrapolation errors (up to 475%) by injecting high-entropy boundary samples, boosting the R2 accuracy to near 0.90. Consequently, this approach secures the true Pareto solution, reducing full vehicle mass by 2.59% (to 6229.4 kg) while strictly adhering to EN12663 and EN15227 standards. This paradigm effectively resolves epistemic uncertainties, unlocking extreme lightweighting potential in complex systems.

Towards a Unified SCADA-Based Workflow for Wind Turbine Performance Analysis and Static Errors Diagnosis

Reducers used in robotic joints often provide only a limited range of transmission ratios, while remaining bulky and imposing relatively high sliding velocities on the meshing tooth pairs. This work presents the mechanism design and dynamic analysis of a novel Double Differential Reducer intended for compact high-ratio transmission. The reducer employs a special internal planetary arrangement that substantially reduces the input speed, and the desired transmission ratio can be obtained by finely adjusting the tooth numbers. A symmetric transmission layout further enhances power density while preserving a compact overall envelope. To investigate the dynamic behaviour of the transmission system, a nonlinear dynamic model is developed that incorporates backlash, time-varying mesh stiffness, meshing damping and static transmission error. The resulting equations of motion are numerically integrated using a classical fourth-order Runge–Kutta scheme, and the dynamic response under different rotational speed excitations is examined to clarify the global vibration characteristics of the reducer. A dedicated test bench is constructed and prototype tests are carried out to validate the model. The comparison between numerical and experimental results shows that the proposed dynamic model predicts the vibration characteristics of the Double Differential Reducer with good accuracy and provides a useful basis for the design of stable and reliable operation. The results also indicate that the transmission system exhibits stable periodic vibration under high-speed excitation, which supports the use of the proposed reducer in high-speed transmission applications.

This study presents a multi-objective macro-geometry optimization of a compound planetary geartrain for an electric tractor powertrain. The proposed framework simultaneously minimizes peak-to-peak static transmission error and maximizes gear mesh efficiency under representative agricultural load conditions derived from load duration distribution data. The macro-geometry design variables include normal module, pressure angle, helix angle, and face width for two planetary gear sets integrated into a developed electric tractor prototype. A detailed geartrain model was developed using commercial analysis software, and the optimization was performed using the nondominated sorting genetic algorithm II with strength constraints based on ISO 6336 to ensure durability. The Pareto-optimal solutions were ranked using a criterion importance method based on variability and intercriteria correlation. Compared with the baseline prototype configuration, the optimized designs achieved a 14-16% reduction in transmission error across all gear pairs while maintaining or slightly improving mesh efficiency (up to + 0.16%p). The results demonstrate that macro-geometry refinement within fixed gear ratio and packaging constraints can effectively reduce excitation-related transmission error without compromising efficiency. Experimental validation of the optimized gear sets is planned in future work.

The direct imaging of potentially habitable exoplanets is one prime science case for high-contrast imaging (HCI) instruments on ground-based, extremely large telescopes. Most such exoplanets orbit close to their host stars, where their observation is limited by fast-moving atmospheric speckles and quasi-static noncommon path aberrations (NCPA). Conventional NCPA correction methods often use mechanical mirror probes, which compromise performance during operation. This work presents machine-learning-based NCPA control methods that automatically detect and correct both dynamic and static NCPA errors by leveraging past telemetry data and sequential phase diversity. We extend previous work in reinforcement learning (RL) for adaptive optics (AO) to focal plane wavefront control. A new model‑based RL algorithm, Policy Optimization for Noncommon Path Aberrations (PO4NCPA), interprets the focal plane image as input data and, through sequential phase diversity, determines phase corrections that optimize both non‑coronagraphic and post‑coronagraphic point spread functions (PSFs) without prior system knowledge. Furthermore, we demonstrate the effectiveness of this approach by numerically simulating static NCPA errors on a ground-based telescope and an infrared imager affected by water vapor-induced seeing (dynamic NCPAs). Simulations show that PO4NCPA robustly compensates static and dynamic NCPAs. In static cases, it achieves near-optimal focal plane light suppression with a coronagraph and near-optimal Strehl without one. With dynamic NCPA, it matches the performance of the modal least-squares reconstruction combined with a 1-step delay integrator in these metrics, though with a higher wavefront root mean square error (RMSE), especially for high-order modes. The method remains effective for the Extremely Large Telescope (ELT) pupil, the vector vortex coronagraph, under photon and background noise. PO4NCPA is model-free and can be directly applied to standard imaging as well as to any type of coronagraphy; its submillisecond inference times and performance also make it suitable for real-time low-order correction of atmospheric turbulence beyond HCI requirements.

This study presents the analysis and modeling of the thermal regime of a furnace lining at an industrial copper smelting facility using a combined approach based on neural network (NN) technologies and the finite element method (FEM). Experimental temperature data were collected from a laboratory setup equipped with three thermocouples (TP-2488/1 and TCRosemount 0065), with a sampling frequency of 1 Hz over a total duration of 5 hours, resulting in 18,000 measurement points. The measurement uncertainty of the thermocouples did not exceed ±1.5 °C. These data were used both for model development and for validating the numerical FEM simulations. A feedforward neural network was trained using 70% of the dataset, while 15% and 15% were used for validation and testing, respectively. The prediction error of the neural network remained within 3% with a 95% confidence interval of [2.6%, 3.4%]. The results show that the proposed hybrid approach improves temperature prediction accuracy and reduces static control error by 15% when combined with a proportional-integral controller. The methodology demonstrates significant potential for improving thermal process stability and reducing energy consumption in high-temperature metallurgical systems.

Abstract To improve the calibration accuracy of quadratic error coefficients in the static error model of gyroscopes, a whole-period vibration calibration method on a linear vibration table is proposed. By considering parasitic rotation and attitude error of the vibration table, angular vibration during testing, and gyroscope installation error, a calibration model for the gyroscope is established. A multi-position calibration method is adopted, where the quadratic error coefficients are calibrated by combining the stationary and whole-period vibration states of the vibration table. Error analysis demonstrates that the proposed method accurately identifies the quadratic error model coefficients of the gyroscope, achieving a calibration accuracy of 10 −4 (°/h/g 2 ).

Optimising the control of wind power systems based on Doubly-Fed Induction Generators (DFIG) raises complex technical challenges, intrinsically linked to the non-linear natureof these machines. With this in mind, this study presents a comparison of two distinct control approaches: Proportional-Integral (PI) control, and Backstepping control, designed specifically to address the challenges posed by unstable and variable dynamics.The methodological approach is based on a DFIG model built on the foundations of vector control. This theoretical framework is implemented into a MATLAB/Simulink environment. Backstepping control, in particular, is stabilised by means of a rigorous construction of the Lyapunov function, guaranteeing error convergence and robustness in the face of disturbances. The simulation results highlight the differences in performance. Whilethe classic PI control approach is robust to parametric variations, it results in a slower response time (27.6 ms) and higher static error (0.2%). Its simple structure and efficient implementation make it a reliable choice in industrial environments with limitedresources. In contrast, the Backstepping method significantly reduces overshoot, improves system response time (0.18 ms), and achieves a notable reduction in static error (0.064%), demonstrating its superiority in dynamic and unstable environments. This approach excels in managing the non-linearities inherent in wind energy systems, giving it a clear advantage in unstable or fluctuating environments. In short, this study does not simply juxtapose two methods; it outlines the future of more adaptive, more responsive control. While PI remains a faithful ally in simplicity, Backstepping technology offers a promising approachto the development of smart energy systems.

In order to solve the problem of non-ideal properties in a high-resolution Time-Interleaved Analog-to-Digital Converter (TIADC) system, various mismatch errors of the TIADC system need to be calibrated. In this paper, a single pre-Sample-Hold Amplifier (SHA) is used to avoid the timing mismatch error of this system, and a Back Propagation Neural Network (BPNN) calibration method for the static nonlinear error of this system is proposed, which fits nonlinear errors to achieve calibration by training the network parameters. The 4-channel 24bit, 10MSPS TIADC system is calibrated and simulated, this method can increase the Signal-to-Noise Ratio (SNR) of the system by more than 70 dBc. This error calibration method can improve the accuracy of the TIADC system.

Fall-from-height fatalities in underground construction are closely associated with formwork scaffold operations, where dense steel members cause severe non-line-of-sight (NLOS) and multipath effects that degrade positioning performance. Although ultra-wideband (UWB) technology offers high theoretical ranging accuracy, its deployment-dependent performance in metal-rich scaffold environments remains insufficiently quantified. This study focuses on physical deployment optimization rather than algorithmic compensation. A full-scale formwork scaffold was constructed, and a stepwise one-factor controlled experimental design was employed to quantify the effects of anchor height (H) and horizontal spacing (S) on 3D positioning accuracy. The results show that sub-meter accuracy can be achieved through appropriate deployment, with a minimum 3D RMSE of 0.317 m and over 80% of single-axis errors confined within a 0.2 m engineering-valid region. For this specific setup, the optimal S = 1.5 m correlates with the scaffold grid size (approximately 0.8 times the 1.8 m bay width). While we hypothesize this ratio dependency applies to other geometries, this remains a site-specific observation requiring future cross-validation. Further analysis indicates that this deployment balances vertical signal visibility and multipath suppression. In addition, while the Position Dilution of Precision (PDOP) metric reflects geometric sensitivity, it does not linearly correlate with actual positioning errors under coplanar UWB deployments. These findings provide a rigorous static error model, serving as a critical prerequisite for developing robust real-time safety monitoring systems in scaffold-intensive construction environments.

Active-matrix (AM) resistive sensor arrays are widely used because they suppress the crosstalk currents that plague passive matrices. In conventional feedback-based readout circuit (FBROC), the static voltage-drop error across the pixel switch is cancelled; however, as the pixel-transistor on-resistance (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ON</sub>) is increased to shrink switch size and raise pixel density, parasitic capacitances push the feedback loop’s poles to lower frequencies, degrade phase margin, and induce oscillation—producing large sensing errors and limiting accuracy. This work proposes a stability-enhanced FBROC that augments the conventional architecture with a single RC compensator placed outside the array. The compensator introduces tunable zeros in the loop transfer function, restoring phase margin and suppressing oscillation without any in-pixel modification. Simulation and measurement results show that the proposed FBROC maintains a phase margin greater than 50° and reduces <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ERROR</sub> from 1393.2% to less than 0.1% under worst-case conditions. The allowable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ON</sub> increases by a factor of ten, enabling smaller pixel switches (higher pixel density) and scaling to larger row/column counts while keeping higher accuracy despite increased parasitic capacitances. Because the compensator is passive and placed outside the array, the approach is simple, area-efficient, and compatible with existing AM sensor array implementations—breaking the device-size/loop-stability trade-off and supporting higher-resolution resistive sensing.

Accurately predicting gear meshing excitation is crucial for high-performance transmission fatigue design and noise optimization. To address the frequently overlooked topography-lubrication coupling, this paper presents a loaded tooth contact analysis (LTCA) model integrating ground surface features and oil film stiffness. Utilizing the finite element substructure method, the model combines a non-uniform sinusoidal function for surface waviness with the Dowson-Higginson film thickness formula. Results show that higher grinding wheel speeds enhance surface quality, bringing time-varying meshing stiffness (TVMS) and static transmission error (STE) levels closer to ideal smooth surfaces. Conversely, increased axial feed rates or fluctuation amplitudes deepen surface textures, causing stiffness attenuation and a significant STE amplitude surge. Additionally, higher lubricant viscosity, pressure-viscosity coefficients, or driving gear speeds thicken the oil film, reducing equivalent stiffness and elevating global transmission error. Crucially, the roughness-oil film coupling causes the most severe stiffness loss. This study offers a precise theoretical foundation for predicting precision gear system contact characteristics.

Research on static error identification of three-axis machine tool based on multi-flexible system theory

A static current error elimination algorithm for predictive current control in PMSM for electric vehicles

Accurately predicting gear meshing excitation is crucial for high-performance transmission fatigue design and noise optimization. To address the frequently overlooked topography-lubrication coupling, this paper presents a loaded tooth contact analysis (LTCA) model integrating ground surface features and oil film stiffness. Utilizing the finite element substructure method, the model combines a non-uniform sinusoidal function for surface waviness with the Dowson-Higginson film thickness formula. Results show that higher grinding wheel speeds enhance surface quality, bringing time-varying meshing stiffness (TVMS) and static transmission error (STE) levels closer to ideal smooth surfaces. Conversely, increased axial feed rates or fluctuation amplitudes deepen surface textures, causing stiffness attenuation and a significant STE amplitude surge. Additionally, higher lubricant viscosity, pressure-viscosity coefficients, or driving gear speeds thicken the oil film, reducing equivalent stiffness and elevating global transmission error. Crucially, the roughness-oil film coupling causes the most severe stiffness loss. This study offers a precise theoretical foundation for predicting precision gear system contact characteristics.

This paper systematically investigated the mechanical behavior of welded-plate lifting lugs subjected to dynamic and eccentric loadings in steel structure hoisting applications. By integrating on-site stress monitoring throughout the hoisting process with finite element numerical simulations, the dynamic response characteristics of the lugs were comprehensively analyzed. The results indicated that the stress response followed a three-stage evolution comprising elastic growth stage, peak fluctuation stage, and gradual decay stage. Non-uniform loading significantly intensified stress concentrations at the edges of the lifting holes and in the lug–stiffener transition region, with local impact parameters ranging from 1.02 to 1.12 and exhibiting a distinctly non-uniform spatial distribution. A refined finite element model was established, and comparisons with experimental data confirmed that static and dynamic prediction errors were controlled within 5 MPa and 5%, respectively. The optimal lifting angle of 75° was identified, resulting in a significant reduction in dynamic amplification. Furthermore, a small-sample Bootstrap method was introduced to probabilistically correct the dynamic parameter, enhancing design reliability by approximately 10%. Overall, this research provided a more rigorous theoretical foundation and practical design tool for evaluating the safety of lifting lugs subjected to dynamic loading.

Abstract The non-orthogonal shafting design is motived by the growing demand for large-scale measurement instrument, and the matching laser tracking technology is required to be completed to improve the automation and flexibility. While, due to the complexity and nonlinearity of non-orthogonal shafting architecture, it is difficult to calculate the rotation angles of the two axes based on the tracking deviation in the absence of distance information, and traditional proportional, integral and differential method cannot enable the system to adapt to unknown sudden target movement efficiently. Thus, the enhanced cascade control method for non-orthogonal shafting laser tracking system based on fuzzy control theories is proposed in this paper. Firstly, a position loop fuzzy prediction and tracking algorithm is proposed. Based on the current motion state of the target, the fuzzy controller predict the motion trend and calculate the tracking speed of the two axes. By implementing a two-stage model of coarse adjustment and fine adjustment, the responsiveness and stability of the laser tracking system have been improved. Secondly, the particle swarm optimization algorithm is adopted to conduct offline iterative optimization of the parameters of the scaling factor in the variable universe fuzzy control, achieving the rapid adaptability of the speed loop. The simulation results show that, compared with cascade PI control, this method has a faster response speed and a smaller tracking error. The experimental results show that the average dynamic tracking error and average static tracking error of this method are 1.873 58 mm and 0.026 89 mm respectively, and the fastest tracking speed of the non-orthogonal system is better than 7° s −1 .

Accurate monitoring of high-frequency upset force during friction stir welding (FSW) is a critical step for optimizing welding process parameters and ensuring joint quality. However, traditional platform-type force gauges cannot measure the upset force in the bobbin tool friction stir welding (BT-FSW) process, which poses a technical challenge. To resolve this issue, a high-frequency spindle-integrated rotational piezoelectric testing system (SIR-PTS) mounted on the equipment spindle was designed. This system uses piezoelectric sensors as measuring elements and comprises a force gauge, a built-in signal conditioning circuit, and monitoring software. First, a theoretical mechanical model under rotational conditions was established to determine the dimensional parameters of the piezoelectric quartz wafers. Subsequently, ANSYS finite element simulation was utilized to analyze the static structural characteristics and dynamic response of the testing system. Meanwhile, a modular hardware circuit integrating charge conversion, filtering, and wireless transmission functions was developed. A series of verification experiments were conducted to evaluate the system performance. The experimental results demonstrate that the system exhibits a static linearity error of 0.64% and a repeatability error of 0.34%. Its main-direction natural frequency is 2082.52 Hz, which meets the requirements of high-frequency dynamic testing. These results confirm that the system can realize high-precision, real-time dynamic monitoring of upset force in BT-FSW processes.