Search Results (2,104)

This study proposes a driver monitoring and active rear-wheel assist steering control scheme integrating Model Predictive Control (MPC) and Recursive Least Squares (RLS) to enhance path-following precision and facilitate seamless control authority distribution in autonomous mobility. Rather than attempting to directly measure internal physiological cognitive states, the proposed approach quantifies physical steering performance degradation by employing a dual RLS algorithm to estimate a Steering Performance Degradation Index, which systematically fuses temporal response delay and spatial tracking deviation. Based on this real-time index, an assist MPC dynamically computes the auxiliary rear-wheel steering angle by adapting its tracking input weights according to three candidate weighting functions: exponential, linear, and threshold-based. High-fidelity co-simulations in IPG CarMaker and MATLAB/Simulink are conducted under various velocities and road curvatures with systematic driver delays. The evaluation results demonstrate that the proposed assist controller effectively enhances path-tracking precision, reducing the maximum lateral error and yaw angle error by up to approximately 82.24% and 73.53%, respectively, compared to the unassisted delayed driver. These findings verify that the proposed steering control architecture successfully mitigates transient trajectory deviation during driver performance degradation, establishing a promising candidate fail-safe strategy for advanced automated driving systems. Full article

Extended-reach and highly deviated wells often adopt a double build-up trajectory. This profile contains two curvature transition zones: the build-to-tangent transition and the tangent-to-build transition. The annular flow undergoes severe restructuring in these zones. They are potential bottlenecks for cuttings transport. This work uses a 215.9 mm wellbore with 127.0 mm drill pipe as the reference case. Analytical expressions for the cuttings accumulation ratio in both transition zones are developed based on the three-layer transport model. For power-law drilling fluids, a generalized Reynolds number is introduced to reformulate the Dean number. A dynamic disturbance coefficient is constructed from the along-hole Dean number gradient. This coefficient captures the contrasting behavior of secondary flow. In one transition zone, the secondary-flow decays. In the other, it suddenly emerges. The results show that when the two curvature radii are equal, the accumulation ratio in the tangent-to-build transition is roughly 1.15 times that in the build-to-tangent transition. Reducing the curvature radius from 250 m to 100 m increases the accumulation ratio by about 2.5 times. The ratio rises with the inclination angle. The disturbance coefficient increases monotonically with the build rate. After accounting for drill pipe eccentricity, the recommended minimum curvature radii are 160 m for the first build section and 220 m for the second. These findings offer a theoretical basis for trajectory design in sections with abrupt curvature changes. Full article

Background: Measurement of anterior chamber depth (ACD) is an important screening method for angle-closure glaucoma risk. We aimed to determine the precision and agreement of a smartphone-based method for measuring ACD when compared with anterior segment optical coherence tomography (AS-OCT) and Smith’s technique. Methods: A total of 43 participants (22 female, 21 male) were recruited. The mean age ± standard deviation of participants was 21.9 ± 2.1 years (range = 19–27 years). Three measurements were taken on each participant’s right eye in a random order for each technique. For this comparative study, differences in ACD, test–retest variability of a given method, and Bland–Altman level of agreement were computed to compare differences in performance between all three methods. Results: Smith’s technique over-estimated ACD, while smartphone photography under-estimated ACD, when compared to the reference AS-OCT. There was proportional bias between all three techniques. Smith’s technique had the largest degree of variability, whereas there was no statistically significant difference in test–retest variability between AS-OCT and smartphone photography. Conclusions: Zamir’s smartphone photography technique could be used for measuring ACD. Although there are some limitations compared to other established approaches, smartphone photography may have potential as a low-cost screening tool for estimating ACD in settings where specialised equipment is not available. Further studies are required to establish diagnostic performance for angle-closure screening in clinically relevant populations. Full article

Accurately measuring geometric parameters of high-aspect-ratio blind holes is a critical requirement for microelectronics and micro–nanooptics applications. Conventional measurement techniques are restricted by limited measurement precision, incomplete measurable geometric indicators, bulky optical hardware, and potential irreversible damage to test specimens. To tackle these drawbacks, this paper proposes a deep-learning-driven image reconstruction method for full-parameter geometric measurement of high-aspect-ratio blind holes. In the proposed method, a Denoising Convolutional Neural Network (DnCNN) is deployed to restore low signal-to-noise ratio (SNR) microscopic images captured at blind hole bottoms. Meanwhile, Laplacian variance sharpness evaluation coupled with local quadratic polynomial fitting is adopted to boost the precision of Z-axis focal positioning and depth calculation. A dedicated machine vision measurement system is custom-developed, where annular ring illumination is integrated to strengthen light irradiation at hole bottoms and guarantee high-quality image acquisition. To address the scarcity of authentic paired clean-noisy training data, high-clarity surface micrographs are artificially degraded in reverse to synthesize low-quality counterparts, forming a dataset containing 2000 image pairs for DnCNN training and optimization. Furthermore, a dedicated feature extraction pipeline combining image preprocessing, Otsu-based adaptive threshold segmentation and least-squares ellipse fitting is designed to extract core geometric metrics, including microhole diameter, depth, taper angle, and relative ellipticity deviation. Experimental validation reveals that the absolute measurement deviations of the mean measured top diameter, bottom diameter, and depth reach 4 μm, 7 μm, and 8 μm, respectively, with a calculated depth-to-diameter-ratio error of 0.06. The results demonstrate that this method delivers a high-precision, low-cost non-destructive micrometer-scale solution for comprehensive geometric inspection of high-aspect-ratio blind holes. Full article

Traditional stochastic models that rely solely on elevation angle and signal-to-noise ratio (SNR) struggle to adapt to the precision differences in BDS-3 multi-frequency observations, making it difficult to support high-precision positioning in complex scenarios. To address the issues that existing models fail to adapt to the differentiated error characteristics of BDS-3 five-frequency observations, lack adaptive modeling capabilities, and cannot support high-precision five-frequency PPP-AR in complex environments, this study proposes a Transformer-based adaptive stochastic model for five-frequency precise point positioning ambiguity resolution (PPP-AR). Satellite elevation angle, the SNR, and position dilution of precision (PDOP) are used as inputs, while observation noise labels derived from pseudorange post-fit residuals support supervised training. The predicted noise standard deviations are introduced into the observation covariance matrix for adaptive weighting. To distinguish generalization from memorization, the model was evaluated using observations from different days. On day of year (DOY) 244, the Transformer model achieved a mean post-convergence three-dimensional root-mean-square (3D RMS) error of 0.029 m, outperforming the comparison models, which yielded errors of 0.0037–0.0039 m. It also reduced the mean convergence time to 20.33 min, compared with 21.22–22.17 min for the comparison models. At the HARB station, the Transformer and elevation angle models both converged in 7 min, only one 30 s epoch faster than the multilayer perceptron (MLP) and SNR models. At the GAMG station, the ambiguity fix rate reached 31.25%, exceeding those of the elevation angle and SNR models by 18.28 and 15.40 percentage points, respectively. The results for DOY 245 and DOY 246 further support short-term transferability, but not long-term temporal generalization. Overall, the proposed model improves aggregate positioning accuracy and convergence efficiency while maintaining competitive ambiguity fixing performance. Full article

Sustained uphill motion of two-wheeled self-balancing wheelchairs is challenging. Slope-induced gravity changes the equilibrium condition, driving-torque demand, and velocity response. This paper proposes a smoothly weighted hybrid control method. The method combines nonlinear model predictive control and a linear quadratic regulator for pitch stabilization and uphill velocity tracking. The control design accounts for the slope-dependent equilibrium condition and steady-state torque demand. NMPC handles large-deviation recovery, velocity regulation, and actuator constraints. LQR improves local stabilization near the equilibrium point. The two controller outputs are coordinated by a continuously varying weight, which provides a smooth transfer of control authority across the transition region. MATLAB/Simulink simulations compare the proposed method with standalone NMPC, LQR, and SMC under several slope angles. Disturbance-recovery tests are also conducted under external torque disturbances. The results show stable uphill motion under the tested slope conditions. After finite-duration disturbances, the controller recovers both pitch posture and uphill velocity. Under a sustained torque disturbance, pitch stability is retained, but velocity regulation degrades. The proposed method improves pitch stabilization and velocity maintenance under the tested conditions. The recorded wheel-end torque remains bounded without sustained saturation in the three hybrid-controller cases. These results demonstrate numerical feasibility under the specified nominal simulation conditions, while uncertainty-robust and real-time performance remain to be validated. Full article

Amid the ongoing decarbonization of the international shipping industry, methanol has emerged as a promising alternative fuel for marine auxiliary boilers owing to its environmental advantages and engineering feasibility. However, its low viscosity and surface tension make the atomization process highly sensitive to operating conditions, posing challenges to stable and efficient burner operation. Existing studies have predominantly focused on engine applications, whereas systematic investigations into the atomization characteristics and operating-parameter matching of primary-air swirl-cup nozzles for marine auxiliary boilers remain limited. To address this gap, the present study combines experimental measurements and numerical simulations to investigate the effects of fuel flow rate, atomizing-cup rotational speed, and primary-air damper opening on spray characteristics. Spray imaging was employed to characterize the spray cone angle and macroscopic morphology, while PIV and PDA were used to measure the outer-flow-field velocity and droplet-size characteristics, respectively. Numerical simulations of liquid-film formation and breakup were performed using a coupled VOF-DPM framework. The predicted spray angle and outer-flow-field velocity showed good agreement with the experimental measurements, with overall deviations within 3–12%. Increasing the atomizing-cup speed generally promoted droplet refinement, while adjustment of the primary-air supply further influenced the droplet-size distribution. Under high-speed operating conditions, the atomized droplet size was generally maintained below 100 μm, and the SMD in the investigated near-field region was approximately 60–80 μm. Based on the multi-load experimental results, primary-air parameter-matching relationships were established for fuel flow rates ranging from 100 to 500 kg/h, providing guidance for maintaining stable atomization performance over a wide operating-load range. This study provides a quantitative basis for the operating-parameter design and stable operation of primary-air swirl-cup nozzles in marine methanol-fired auxiliary boilers and offers useful guidance for their engineering application. Full article

This paper proposes a retrodirective cross-eye jamming (RCJ) recognition and angle estimation algorithm based on directional modulation and multiple signal classification (DM-MUSIC). Because RCJ intercepts the radar transmit waveform to produce a monopulse angle measurement result that deviates from the true target, the traditional phased-array (TPA) radar that radiates identical transmit waveforms across the spatial domain fails to recognize RCJ by calculating the normalized cross-correlation function (NCCF). In DM-MUSIC, the monopulse angle measurement result induced by RCJ is derived, and the transmit phase matrix synthesis criterion in digital array radar is then formulated to minimize the NCCFs between the transmit waveform for the detection direction and RCJ-induced monopulse angle deception directions by utilizing the flexibility of DM in the waveform domain. Sequential quadratic programming combined with the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm is employed to calculate the transmit phase matrix. The RCJ system is then recognized by comparing the NCCFs of the received jamming signal associated with the DM transmit waveform in the detection direction and RCJ-induced monopulse angle deception directions. Finally, the synthesized DM transmit waveform and forward–backward spatial smoothing are used to decorrelate the jamming signals, and the RCJ angle is estimated by MUSIC. The simulation results demonstrate that DM-MUSIC achieves high recognition probability and accurate RCJ angle estimation. The recognition probability reaches $98.1\%$ at a jamming-to-noise ratio (JNR) of $5\mathrm{dB}$, with an amplitude gain of 1 and a phase shift of $179°$. At a JNR of $20\mathrm{dB}$, with an amplitude gain of 0.97 and a phase shift of $179°$, the Root Mean Square Error of angle estimation result is reduced from $0.13°$ for FBSS-MUSIC to $0.063°$ for DM-MUSIC. Full article

Satellite active-fire products may miss fires that occupy only a small fraction of a pixel and produce limited absolute thermal responses. In this study, weak thermal anomalies are operationally defined as confirmed fire-affected VIIRS pixels with relatively low absolute $B{T}_4$ and $\mathsf{\Delta }BT$ responses but positive deviations from their recent temporal and local spatial backgrounds. STAR-FD (Stratified Spatiotemporal Adaptive Residual Fire Detection) was developed to detect such anomalies from VIIRS 375 m observations. The method reconstructs a 14-day recent thermal background and jointly evaluates single-pixel temporal residuals and spatial-contrast temporal residuals. Stable non-fire samples are stratified by day/night condition, season, macroclimate zone, land cover, and view zenith angle, and residual thresholds are estimated within each stratum. Validation was conducted in Heilongjiang, northwestern India, and California using Sentinel-2 MSI, Landsat 8/9 OLI, and available external fire information. STAR-FD detected 927 overpass-level thermal-anomaly events, of which 855 were confirmed as Fire, corresponding to a confirmation rate of 92.23%. VNP14IMG detected 343 events, of which 333 were confirmed as Fire (97.08%). STAR-FD identified 522 more confirmed fire-related thermal-anomaly events than VNP14IMG, corresponding to a 156.76% increase, while retaining 88.05% of VNP14IMG events. In northwestern India and Heilongjiang, STAR-FD-added daytime confirmed fire pixels had median $B{T}_4$ values 9.49 K and 11.88 K lower, respectively, and median $\mathsf{\Delta }BT$ values 8.10 K and 10.78 K lower than detections shared by both methods. These results show that STAR-FD provides complementary detection of confirmed fire-related thermal anomalies with weaker absolute thermal signals. Full article

A two-level closed-loop calibration framework is proposed to reduce odometry scale errors during motion and pose drift during stationary periods in Mecanum-wheeled mobile robots. At the upper calibration level, the planar displacement between the initial and final poses is calculated using the L2-norm, which reduces the influence of lateral deviation on distance measurements based on a single coordinate axis. Rotational displacement is obtained by accumulating normalized angular increments, thereby avoiding discontinuities when the yaw angle crosses the ±π boundary. A relay controller with a tolerance deadband is also introduced to reduce static-friction-induced stalling and oscillation near the target during low-speed calibration. At the lower odometry interface, the covariance assigned to wheel odometry measurements is adjusted according to the commanded zero-velocity state. During stationary periods, this adjustment increases the contribution of near-zero velocity measurements and limits the effect of residual velocity estimates and sensor noise on the fused pose. The identified longitudinal and rotational compensation factors are then updated online in the dead-reckoning node through an ROS 2 service. Unlike conventional ZUPT implementations, the proposed method does not require an additional zero-velocity pseudo-measurement node. Experiments were conducted on three near-horizontal surfaces: ceramic tile, epoxy resin, and asphalt. Across 720 bidirectional in-place rotation trials, the angular Error Reduction Rate ranged from (59.13%) to (96.58%). In 540 straight-line trials covering nine combinations of surface type and target distance, the overall mean absolute error decreased from 53.22 mm before calibration to 9.69 mm after calibration. Intermittent stop-and-go experiments were further performed using the EKF, UKF, RCKF, and a graph-based SLAM optimization framework implemented by slam_toolbox. For each estimation back-end, the estimated trajectory was evaluated by calculating its deviation from the corresponding synchronized /odom trajectory under the fixed-covariance and proposed ZUPT-based adaptive-covariance configurations; /odom was used as a common comparison baseline rather than as an absolute localization ground truth. The adaptive covariance strategy reduced the positional RMSE by (19.38%–67.44%) across the evaluated filtering back-ends. These results show that the proposed framework can reduce both motion-dependent odometry scale errors and stationary pose drift under the tested surface conditions. Full article

Advancements in solid rocket propulsion have intensified the demand for higher specific impulse. Incorporating high-density, high-calorific metal additives into propellant formulations offers a viable pathway to meet these performance targets. Nevertheless, the ablation response of C/C-SiC thermal protection systems (TPS) exposed to high-temperature, high-velocity, dense gas-particle flows remains inadequately characterized. To address this, we employ high-fidelity numerical simulations to resolve the gas-side ablation behavior under extreme multiphase conditions. By calibrating against experimental data, we quantify the erosive mechanisms induced by boron-laden exhaust plumes and derive an empirical correlation for the linear ablation rate. This model serves as a predictive tool for TPS design in severe operational environments. Parametric investigations were conducted across three key variables: particle diameter (30–50 μm), condensed-phase mass loading (28–68%), and impact angle (18–27°). Validation against ground testing yielded a mean deviation of 3.87%, confirming its applicability to the conditions studied in this paper. Results indicate a positive correlation between ablation rate and particle concentration, impact velocity, incident angle (within the tested range), and particle size. During sensitivity analyses, a single-variable control protocol was enforced to isolate individual parameter effects. Full article

Aiming at the path tracking oscillation problem caused by sideslip of navigation agricultural machinery in complex farmland environments, this paper takes the Yanmar YR-70D transplanter as the research object and proposes an adaptive control method based on deep belief network (DBN) sideslip identification. Based on the preview tracking model, this method dynamically adjusts the preview distance by real-time identifying the sideslip status via DBN. In this research, the Optuna framework is utilized to optimize the DBN model, and the SHAP framework is introduced to quantify the feature contribution. The results show that, considering both real-time performance and accuracy, when the combined variable of “lateral deviation + heading deviation” with a data length of 20 is adopted, the prediction accuracy of the model reaches 88.04%, among which heading deviation (HD) has the highest contribution with a SHAP value of 0.957. Comparative experiments indicate that the accuracy of DBN is comparable to that of mainstream models such as LSTM and Transformer, but its forward propagation speed (0.0487 ms) is better than other models’. Upland field experiments verify that under different test factors of vehicle speed and slope angle, the line-on time (1.83–5.60 s), line-on distance (1.46–5.07 m), and maximum overshoot (0.32–15.75 cm) of the adaptive control group are all superior to those of the fixed-parameter control group. Paddy field experiments further confirm that under the working condition of 1.0 m/s vehicle speed and 15° slope, the above three indicators of the experimental group are 0.85–3.1 s, 0.75–3.14 m and 0–7.44 cm, respectively, all of which are better than those of the control group. The proposed method demonstrated superior path tracking performance under the operating conditions of the tested rice transplanting machines, indicating its potential application in lateral slip perception adaptive navigation control. However, further verification is still needed with different types of transplanting machines, field environments, and operating conditions to assess its general applicability. Full article

Background: Wrist joint position and hand anthropometric measurements may affect grip strength and hand performance. This pilot, analytical cross-sectional study aimed to investigate the relationship between different wrist positions and grip strength, as well as the association between grip strength and hand size in healthy individuals. Methods: An analytic cross-sectional study was conducted with undergraduate medical students. We assessed grip strength using a hand dynamometer in different wrist positions, including wrist flexion, extension, ulnar deviation, and radial deviation. Hand anthropometric measurements, including HB, MHB, PL, TFL, IFL, MFL, RFL, and LFL, were performed using a sliding digital caliper. Results: Grip strength varied significantly across wrist positions, with the highest mean value observed in the neutral position (30.53 kg), followed by ulnar deviation (28.94 kg), radial deviation (18.02 kg), extension (14.75 kg), and flexion (14.67 kg). The repeated-measures ANOVA demonstrated significant main effects of wrist position and sex, as well as a significant interaction between wrist position and sex (all p < 0.001). Males demonstrated higher mean grip strength than females in the neutral position (42.56 ± 8.98 kg vs. 18.54 ± 4.20 kg, respectively). Grip strength measurements across wrist positions showed strong positive correlations, with the strongest associations observed between flexion and neutral grip strength (ρ = 0.84) and between ulnar deviation and neutral grip strength (ρ = 0.81). The wrist angle was negatively correlated with grip strength during flexion (ρ = −0.24, p < 0.05) and extension (ρ = −0.41, p < 0.05), representing weak and moderate associations, respectively. Females demonstrated greater mean flexion and extension than males, whereas males demonstrated higher mean values for ulnar and radial deviation. Among males, grip strength was significantly positively correlated with maximum hand breadth (ρ = 0.365, p = 0.001), hand breadth (ρ = 0.326, p = 0.003), palm length (ρ = 0.304, p = 0.006), thumb length (ρ = 0.289, p = 0.009), and index finger length (ρ = 0.229, p = 0.039). No statistically significant associations were observed between grip strength and the assessed hand anthropometric measurements among females. Conclusions: Grip strength is markedly influenced by wrist position, with the highest grip strength measured in the neutral position and decreased in flexion, extension, or radial deviation. The results of this study may have implications for rehabilitation, ergonomic design, and sports performance in the future. Full article

Space-constrained articulated mechanisms integrated into mechatronic assemblies require rigorous kinematic characterisation during preliminary design, since spatial limitations and the avoidance of kinematic locking in the transient regime constrain the admissible geometry. An original planar guidance-and-retraction linkage is analysed, composed of three fixed joints, a linear hydraulic actuator, a rigid block of six interconnected elements, and two guiding links that suppress the out-of-plane degrees of freedom; the retractable landing gear of a light training aircraft is adopted as the application case. All mobile joints are expressed in closed form as explicit functions of a single input parameter, the actuator length, without decomposition into Assur groups or iterative compatibility equations. The analytical positions, velocities and accelerations were cross-verified against two independent packages, Linkage v.3.16.14 and GIM v.2025.4; deviations remain below 0.25% of the amplitude of each quantity, with velocity root-mean-square deviations below 0.16 mm/s. The guiding dyad reaches neither dead-centre over the stroke, although its link lengths are treated as preliminary because the 35° minimum transmission-angle margin is not maintained over the first 2.2% of the stroke, for which a corrective dimensioning is provided. The closed-form characterisation provides a basis for subsequent structural, dynamic and experimental analysis. Full article

Single-axis solar tracker arrays are commonly installed within the lower atmospheric boundary layer. Their wind loads are governed not only by the module tilt angle, row-to-row sheltering and wind-direction angle but also by the inflow turbulence intensity. To clarify the variation in PV array wind loads under different turbulent environments, large-eddy simulation (LES) was used to investigate the mean pressure coefficient, the standard deviation pressure coefficient and the flow field of a multi-row tracker array subjected to three inflow turbulence intensities. The model scale was 1:240, with a chord length C = 0.02 m, a ground clearance h = 0.0375 m and a row spacing of 0.047 m. The target turbulent inflow was generated in ANSYS Fluent using the narrowband synthesis random flow generation (NSRFG) method, and the inlet spectra, mean-velocity profile and turbulence-intensity profile were verified in an empty domain. Turbulence intensity had a limited overall effect on the mean pressure coefficient but slightly increased its chordwise non-uniformity. In contrast, it markedly increased the standard deviation of the wind pressure coefficient while making its chordwise distribution more uniform. The outer trackers showed greater non-uniformity in both the mean pressure coefficient and the standard deviation pressure coefficient than the inner trackers. Increasing the module tilt angle strengthened the sheltering effect on standard deviation wind loads. Oblique wind weakened the sheltering effect but generally produced greater mean wind loads and wind load standard deviations at the leeward-end modules. Instantaneous vorticity fields showed that a high turbulence intensity weakened periodic vortex shedding behind the modules. These results provide a basis for evaluating the combined effects of the turbulence intensity, tilt angle and wind-direction angle in the wind-resistant design of PV trackers. Full article