Search Results (716)

The AZ31 magnesium alloy is an attractive lightweight metallic material, but its low corrosion resistance and wear resistance significantly limit its widespread application in fields such as aerospace, the automotive industry, and mechanical engineering. Moreover, most coating systems currently cannot restore their original functions and dimensions after localized damage. Based on this, this study combined cold spray (CS), micro-arc oxidation (MAO), and magnetron sputtering (MS) to develop a high-performance and repairable composite modification strategy. First, a 5056 aluminum alloy coating was prepared on AZ31 via CS, followed by the growth of a hard alumina (Al₂O₃) coating via MAO and a diamond-like carbon (DLC) coating via MS on the 5056 aluminum alloy surface. The microstructure, phase composition, hardness, tribological properties, and electrochemical corrosion behavior of the coatings were evaluated using scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDS), X-ray diffraction (XRD), Vickers hardness testing, ball-on-disk dry sliding wear testing, and potentiodynamic polarization testing in a 3.5% sodium chloride solution. The CS 5056 aluminum alloy coating reduced the corrosion current density of AZ31 from 4.098 × 10⁻⁵ A/cm² to 2.714 × 10⁻⁶ A/cm². The MAO alumina coating increased the hardness of AZ31 from 68.60 HV_0.05 to 1614.00 HV_0.05 and decreased the wear rate from 1.703 × 10⁶ μm³/(N·m) to 2.038 × 10³ μm³/(N·m). The DLC coating further reduced the average coefficient of friction of the alumina coating from 0.48 to 0.27, decreased the wear rate to 6.979 × 10² μm³/(N·m), and lowered the corrosion current density from 3.020 × 10⁻⁶ A/cm² to 8.860 × 10⁻⁹ A/cm². This indicates that the three-phase composite coating achieves synergistic improvements in the corrosion and wear resistance of AZ31 through complementary advantages. Additionally, the thick CS aluminum alloy underlayer provides potential repairability, enabling the restoration of function and dimensions after damage without compromising the magnesium substrate. Overall, the proposed 5056Al/Al₂O₃/DLC composite coating strategy offers a reliable protective approach for AZ31 components and is expected to further expand their application fields. Full article

Accurately assessing collapse risks of high-elevation, concealed rock mass blocks within the steep cliffs of Bijiashan, Three Gorges Reservoir Area, is challenging. This study employed a multidimensional approach—integrating airborne Light Detection and Ranging (LiDAR), the transient electromagnetic method (TEM), close-range photogrammetry, horizontal drilling, and borehole optical imaging—to characterize the rock mass structure of the WD1 cliff belt and delineate 52 individual blocks. Stability analysis incorporated stereographic projection for macro-scale assessment and employed mechanical models specific to three primary failure modes (toppling, sliding, falling). Finite element strength reduction quantified the stress–strain response of a representative block under natural and rainstorm conditions. Particle Flow Code (PFC) simulated dynamic instability of the exceptionally large block W1-37. Results indicate the WD1 rock mass is highly fractured, with base sections prone to weakness. Toppling failure dominates (90.4%). Under rainstorm conditions, the average Factor of Safety (FOS) decreased by 14.7%, and 73.1% of the blocks that were stable under natural conditions were destabilized—specifically transitioning to marginally stable or substable states—often triggering chain-reaction instability characterized by “crack propagation—base buckling”. W1-37 exhibited staged failure under rainstorm: “strain localization at fissure tips—penetration of basal cracks—overturning of the upper rock mass”. Its frontal rock reached a peak sliding velocity of 15.17 m/s, indicative of base-breaking toppling. The integrated “multi-technology survey—multi-method evaluation—multi-scale simulation” framework provides a quantitative basis for risk assessment of rock mass disasters in the Three Gorges Reservoir Area and offers a technical paradigm for similar high-steep canyon regions. Full article

With the rapid development of unmanned agricultural machinery technology, the accuracy and stability of agricultural machinery path tracking have become key challenges in achieving precision agriculture. To address the issues of insufficient accuracy and stability in path tracking for rice transplanters in paddy fields, this study proposes a composite control strategy that integrates the extended line-of-sight (LOS) guidance law with an adaptive fuzzy sliding mode control law. By establishing a two degree of freedom dynamic model of the rice transplanter, two extended state observers are designed to estimate the longitudinal and lateral velocities of the rice transplanter in real time. A dynamic compensation mechanism for the sideslip angle is introduced, significantly enhancing the adaptability of the traditional look-ahead guidance law to soil slippage. Furthermore, by combining the approximation capability of fuzzy systems with the adaptive adjustment method of sliding mode control gains, a front wheel steering control law is designed to suppress complex environmental disturbances. The global stability of the closed-loop system is rigorously verified using the Lyapunov theory. Simulation results show that compared to the traditional Stanley algorithm, the proposed method reduces the maximum lateral error by 38.3%, shortens the online time by 23.9%, and decreases the steady-state error by 15.5% in straight-line path tracking. In curved path tracking, the lateral and heading steady-state errors are reduced by 19.2% and 14.6%, respectively. Field experiments validate the effectiveness of this method in paddy fields, with the absolute lateral error stably controlled within 0.1 m, an average error of 0.04 m, and a variance of 0.0027 m². Full article

Fiber laser surface texturing was applied to AA1050 aluminum to improve friction and wear performance of PTFE coatings. A Taguchi L16 design varied texture geometry (square, diamond, hexagon, circle), scanned area ratio (20% to 80%), and laser power (40 to 100 W) prior to primer plus PTFE topcoat deposition (25 to 35 µm). Dry reciprocating sliding against a 6 mm 100Cr6 ball was conducted at 20 N, 1 Hz, and 50 m, and wear track geometry was measured by non-contact profilometry. The non-textured reference exhibited an average COF of 0.143, whereas the lowest mean COF was achieved with diamond 60% and 40 W (0.095) and the highest with hexagon 60% and 100 W (0.156); hexagon 20% and 60 W matched the reference. ANOVA indicated scanned area ratio as the dominant contributor to COF (39.72%), followed by geometry (35.07%) and power (25.21%). Profilometry confirmed reduced coating penetration for optimized textures: the reference wear track was approximately 1240 µm wide and 82 µm deep, compared with 930 µm and 34 µm for square 80% and 40 W, 997 µm and 39 µm for diamond 60% and 40 W, and 965 µm and 36 µm for hexagon 40% and 40 W. Full article

With the continuous growth of the global population, cities worldwide face the challenge of limited surface land area, making the utilization of underground space increasingly important. The structural stability of underground tunnels is a critical component of underground space safety, influenced by the distribution of the surrounding composite strata and hydrogeological environment. To better analyze the structural stability of underground tunnels, this study proposes a method for estimating the distribution of composite strata that considers the surrounding hydrogeological conditions. The method uses a hydrogeological analysis of the tunnel area to determine the spatial estimation range and unit scale to meet the actual project requirements and then uses the geostatistical kriging method to obtain a distance-weighted interpolation algorithm for the impact area. First, the spatial data are used to obtain the statistical characteristics. Second, the statistical data are interpolated, multifractal theory is used to compensate for the kriging method of sliding weighted average defects, and the local singularity of the regionalized variables is measured. Finally, the mean results of 100 simulations are compared with the empirical results for the tunnel. The interpolation results reveal that this method can be used to quickly obtain good interpolation results. Full article

To address the challenges in unstructured orchard environments, including high geometric similarity between fruit trees (with the measured average Euclidean distance difference between point cloud descriptors of adjacent trees being less than 0.5 m), significant dynamic interference (e.g., interference from pedestrians or moving equipment can occur every 5 min), and uneven terrain, this paper proposes an improved mapping algorithm named OSC-LIO (Orchard Scan Context Lidar Inertial Odometry via Smoothing and Mapping). The algorithm designs a dynamic point filtering strategy based on Euclidean clustering and spatiotemporal consistency within a 5-frame sliding window to reduce the interference of dynamic objects in point cloud registration. By integrating local semantic features such as fruit tree trunk diameter and canopy height difference, a two-tier verification mechanism combining “global and local information” is constructed to enhance the distinctiveness and robustness of loop closure detection. Motion compensation is achieved by fusing data from an Inertial Measurement Unit (IMU) and a wheel odometer to correct point cloud distortion. A three-level hierarchical indexing structure—”path partitioning, time window, KD-Tree (K-Dimension Tree)”—is built to reduce the time required for loop closure retrieval and improve the system’s real-time performance. Experimental results show that the improved OSC-LIO system reduces the Absolute Trajectory Error (ATE) by approximately 23.5% compared to the original LIO-SAM (Tightly coupled Lidar Inertial Odometry via Smoothing and Mapping) in a simulated orchard environment, while enabling stable and reliable path planning and autonomous navigation. This study provides a high-precision, lightweight technical solution for autonomous navigation in orchard scenarios. Full article

This paper describes a composite control approach that improves the accuracy and dynamic performance of the control of a voice-coil-driven, two-dimensional fast steering mirror (FSM). Strong nonlinearity, perturbation of parameters, unmodeled dynamics and external disturbances typically compromise the performance of the FSM. The proposed controller combines a tracking differentiator (TD), linear extended state observer (LESO), and a double-integral global fast terminal-sliding mode control (DIGFTSMC). The TD corrects the reference command signal, and the LESO approximates and counteracts system disturbances. The sliding surface is then equipped with the double-integral operators and an improved adaptive reaching law (IARL) to enhance tracking accuracy, response speed and robustness. Prior to physical experiments, systematic numerical simulations were conducted for five control algorithms across four typical test scenarios, verifying the proposed controller’s feasibility and preliminary performance advantages. It is found through experimentation that the proposed controller lowers the time esterified by the step response adjustment by 81.0% and 48.4% more than the PID controller and the DIGFTSMC approach with no IARL, respectively, and the proposed controller enhances error control when tracking sinuoidal signals and multisinusoidal signals. Simulation results consistently align with experimental trends, confirming the proposed controller’s superior convergence speed, tracking precision, and disturbance rejection capability. Furthermore, it cuts the angular movement swing by an average of over 44% through dismissing needless vibration interruptions as compared to other sliding mode control techniques. Experimental results demonstrate that the proposed composite control approach significantly enhances the disturbance rejection, control accuracy, and dynamic tracking performance of the voice-coil-driven FSM system. Full article

Significant wave height (SWH) is a key descriptor of sea state, yet providing accurate, site-specific forecasts at low computational cost remains challenging. This study proposes a transfer-learning-based framework for SWH forecasting that combines Seasonal and Trend decomposition using Loess (STL), a stacked long short-term memory (LSTM) network, and an efficient sliding-window updating scheme. First, STL is applied to decompose the SWH time series into trend, seasonal, and remainder components; the resulting sub-series are then fed into a transfer-learning architecture in which the parameters of the stacked LSTM backbone are kept fixed, and only a fully connected output layer is updated in each window. Using multi-year observations from five National Data Buoy Center (NDBC) buoys, the proposed STL-LSTM-T model is compared with a STL-LSTM configuration that is fully retrained after each STL decomposition. For example, the transfer-learning setup reduces MAE, MSE, and RMSE by up to 11.2%, 19.2%, and 14.5% at buoy 46244, respectively, while reducing the average training time per update to about one-fifth of the baseline. Parameter analyses indicate that a two-layer LSTM backbone and moderate continuous forecast step (6–12 steps) provide a good balance between predictive accuracy, error accumulation, and computational cost, making STL-LSTM-T suitable for SWH forecasting on resource-constrained platforms. Full article

Control of elbow exoskeletons using muscular signals, although promising for the rehabilitation of millions of patients, has not yet been widely commercialized due to challenges in real-time intention estimation and management of dynamic uncertainties. From a practical perspective, millions of patients with stroke, spinal cord injury, or neuromuscular disorders annually require active rehabilitation, and elbow exoskeletons with precise and safe motion intention tracking capabilities can restore functional independence, reduce muscle atrophy, and lower treatment costs. In this research, an intelligent control framework was developed for an elbow joint exoskeleton, designed with the aim of precise and safe real-time tracking of the user’s motion intention. The proposed framework consists of two main stages: (a) real-time estimation of desired joint angle (as a proxy for movement intention) from High-Density Surface Electromyography (HD-sEMG) signals using an LSTM network and (b) implementation and comparison of three PID, impedance, and sliding mode controllers. A public EMG dataset including signals from 12 healthy individuals in four isometric tasks (flexion, extension, pronation, supination) and three effort levels (10, 30, 50 percent MVC) is utilized. After comprehensive preprocessing (Butterworth filter, 50 Hz notch, removal of faulty channels) and extraction of 13 time-domain features with 99 percent overlapping windows, the LSTM network with optimal architecture (128 units, Dropout, batch normalization) is trained. The model attained an RMSE of 0.630 Nm, R² of 0.965, and a Pearson correlation of 0.985 for the full dataset, indicating a 47% improvement in R² relative to traditional statistical approaches, where EMG is converted to desired angle via joint stiffness. An assessment of 12 motion–effort combinations reveals that the sliding mode controller consistently surpassed the alternatives, achieving the minimal tracking errors (average RMSE = 0.21 Nm, R² ≈ 0.96) and showing superior resilience across all tasks and effort levels. The impedance controller demonstrates superior performance in flexion/extension (average RMSE ≈ 0.22 Nm, R² > 0.94) but experiences moderate deterioration in pronation/supination under increased loads, while the classical PID controller shows significant errors (RMSE reaching 17.24 Nm, negative R² in multiple scenarios) and so it is inappropriate for direct myoelectric control. The proposed LSTM–sliding mode hybrid architecture shows exceptional accuracy, robustness, and transparency in real-time intention monitoring, demonstrating promising performance in offline simulation, with potential for real-time clinical applications pending hardware validation for advanced upper-limb exoskeletons in neurorehabilitation and assistive applications. Full article

This work presents a lightweight and interpretable framework for the early warning of voltage stability degradation in distribution networks, based on fractal and spectral features from flow measurements. We propose a Fast Voltage Stability Index (FVSI), which combines four independent indicators: the Detrended Fluctuation Analysis (DFA) exponent α (a proxy for long-term correlation), the width of the multifractal spectrum Δα, the slope of the spectral density β in the low-frequency range, and the $c_2$ curvature of multiscale structure functions. The indicators are calculated in sliding windows on per-node series of voltage in per unit Vpu and reactive power Q, standardized against an adaptive rolling/first-N baseline, and anomalies over time are accumulated using the Exponentially Weighted Moving Average (EWMA) and Cumulative SUM (CUSUM). A full online pipeline is implemented with robust preprocessing, automatic scaling, thresholding, and visualizations at the system level with an overview and heat maps and at the node level and panel graphs. Based on the standard IEEE 13-node scheme, we demonstrate that the Fractal Voltage Stability Index (FVSI_Fr) responds sensitively before reaching limit states by increasing α, widening Δα, a more negative $c_2$, and increasing β, locating the most vulnerable nodes and intervals. The approach is of low computational complexity, robust to noise and gaps, and compatible with real-time Phasor Measurement Unit (PMU)/Supervisory Control and Data Acquisition (SCADA) streams. The results suggest that FVSI_Fr is a useful operational signal for preventive actions (Q-support, load management/Photovoltaic System (PV)). Future work includes the calibration of weights and thresholds based on data and validation based on long field series. Full article

This paper proposes a data-driven model-free robust predictive control strategy for parallel three-level NPC inverters based on finite control set model predictive control (FCS-MPC), focusing on the zero-sequence circulating current (ZSCC) problem under parameter mismatch conditions. A set of virtual voltage vectors with zero average common-mode voltage (CMV) is introduced to effectively suppress ZSCC without adding additional constraints to the cost function. Meanwhile, an Integral Sliding Mode Observer (ISMO) is integrated into the predictive control framework to enhance robustness and enable reliable control using only input–output data. Unlike existing studies that primarily consider ZSCC suppression under an ideal system, this work specifically addresses the practical scenario in which system parameters deviate from their nominal values. Even when ZSCC suppression strategies are employed, parameter mismatch can still lead to noticeable circulating currents, motivating the need for a more robust solution. Simulation and experimental results validate that the proposed approach achieves excellent current tracking, neutral-point voltage balance, and effective ZSCC suppression under parameter variations, demonstrating strong robustness and feasibility for practical applications. Full article

Knee flexion refers to the relative motion between the tibia and femur including rolling and sliding (rollback motion). Notwithstanding the individual variations in knee motion, conventional wearable knee-assistive devices use hinge joints—resulting in nonnegligible mismatched movements, particularly during deep flexion. Therefore, we proposed a biomimetic knee joint (BKJ) that adapts to individual knee motion. A polycentric BKJ, integrating two gears with different radii, was designed to match the trajectory of the rotational axes of the knee. In this study, we developed a semi-active polycentric BKJ (SA-BKJ) incorporating an adjustable reaction-force mechanism (ARFM). In the ARFM, the combined spring constant can be adjusted using a shape-memory alloy actuator owing to its compact size, lightweight nature, and low energy consumption. In addition, the geared joint of the SA-BKJ (which integrates two gears with different radii) was designed to match the average trajectory of the rotational axes of the knee (of 22 healthy men). Applying the genetic algorithm, the radius of the femur and tibia gears were determined to be 25.5 and 40.0 mm. Misalignments of the designed SA-BKJ were measured in three healthy male participants. The error measurements averaged 20 degrees in the control device and 10 degrees in the optimized device. These results indicated that the optimized gears of the SA-BKJ totally reduced the misalignment. Full article

This study develops a machine-learning-based framework for multiclass soil classification using Cone Penetration Test (CPT) data, aiming to overcome the limitations of traditional empirical Soil Behavior Type (SBT) charts and improve the automation, continuity, robustness, and reliability of stratigraphic interpretation. A dataset of 340 CPT soundings from 26 sites in Shanghai is compiled, and a sliding-window feature engineering strategy is introduced to transform point measurements into local pattern descriptors. An XGBoost-based multiclass classifier is then constructed using fifteen engineered features, integrating second-order optimization, regularized tree structures, and probability-based decision functions. Results demonstrate that the proposed method achieves strong classification performance across nine soil categories, with an overall classification accuracy of approximately 92.6%, an average F1-score exceeding 0.905, and a mean Average Precision (mAP) of 0.954. The confusion matrix, P–R curves, and prediction probabilities show that soil types with distinctive CPT signatures are classified with near-perfect confidence, whereas transitional clay–silt facies exhibit moderate but geologically consistent misclassification. To evaluate depth-wise prediction reliability, an Accuracy Coverage Rate (ACR) metric is proposed. Analysis of all CPTs reveals a mean ACR of 0.924, and the ACR follows a Weibull distribution. Feature importance analysis indicates that depth-dependent variables and smoothed p_s statistics are the dominant predictors governing soil behavior differentiation. The proposed XGBoost-based framework effectively captures nonlinear CPT–soil relationships, offering a practical and interpretable tool for high-resolution soil classification in subsurface investigations. Full article

Silicon is commonly used in micro/nano-electromechanical system (MEMS/NEMS) devices. Because detailed information about the friction interface in these systems is lacking, the relationship between texture shape and friction remains unclear. In this study, molecular dynamics simulations were performed to investigate the dry-friction tribological behavior of crystalline silicon, focusing on the effects of surface roughness, normal load, and sliding speed. The results show that between normal loads of 4 GPa and 8 GPa, the average frictional force exhibits significant nonlinear behavior under a sliding speed of 0.2 Å/ps. The approximate steady value of the friction coefficient is 0.39, which is in good agreement with the experimental result of 0.37. Under a normal load of 5 GPa, the friction force increases linearly from 110 nN at 0.05 Å/ps to 311 nN at 2 Å/ps. In addition, in systems with sinusoidal surface roughness, the amplitude has a greater effect on the frictional properties than the period. Among the four rough surfaces studied, A10T32 exhibits the lowest friction force and friction coefficient. This provides theoretical support for the further design of MEMS/NEMS devices with long operational lifetimes. Full article

A robust methodology for parametric fault diagnosis in photovoltaic systems is proposed, focusing on DC-DC boost converters. The methodology uses Adaptive Sliding Mode Observers (ASMO) combined with adaptive thresholding. Specifically, an observer-based scheme detects and isolates faults in passive components of the converter, achieving complete isolation in about 0.05 s, even under varying environmental conditions. In addition, a dynamic fault discrimination approach is introduced, based on adaptive thresholds derived from Exponentially Weighted Moving Average (EWMA). This minimizes false alarms caused by transient conditions. Stability and robustness are guaranteed through Lyapunov-based conditions. Simulation results under sequential and simultaneous fault scenarios confirm rapid and precise fault detection, highly specific isolation, and exceptional resilience against environmental disturbances. Full article