石家庄铁路职业技术学院

An adaptive control strategy is developed and analyzed for trajectory tracking of mechanical systems subject to simultaneous model uncertainties and full-state constraints. To overcome the significant hurdle of guaranteeing both transient and steady-state performance within a user-defined time, a novel predefined-time adaptive neural network (NN) control scheme is proposed. By integrating predefined-time stability theory with a nonlinear mapping framework, a control scheme is developed to rigorously enforce full-state constraints while achieving predefined-time convergence. Radial basis function neural networks (RBFNNs) are employed to approximate the unknown system dynamics, with adaptive laws designed for online learning. The nonlinear mapping is strategically incorporated to ensure that the full-state constraints are never violated throughout the entire operation. Furthermore, through Lyapunov stability theory, it is proved that all signals of the resulting closed-loop system are uniformly ultimately bounded, and most importantly, the trajectory tracking error converges to a small neighborhood of zero within a predefined time, which can be explicitly set regardless of initial conditions. Comparative simulation results on a representative mechanical system are provided to demonstrate the superiority of the proposed controller, showcasing its faster convergence, higher tracking accuracy, and guaranteed constraint satisfaction compared to conventional finite-time and adaptive NN control methods.

Water immersion significantly affects the rutting resistance of asphalt mixtures. While existing research has predominantly focused on macro-scale performance tests, it has often failed to reveal the mechanical impact of water immersion at the meso scale. This study employs the Discrete Element Method (DEM) to investigate the damage evolution mechanism of water-immersed asphalt mixtures under rutting from a meso mechanical perspective. A meso mechanical model of AC-13 asphalt mixture was developed using PFC2D software. The meso mechanical parameters of the Burgers model, which characterizes the viscoelastic properties of asphalt mortar, were obtained through asphalt creep tests. A Discrete Fracture Network (DFN) was incorporated to simulate the initial contact failure behavior induced by water damage. The accuracy of the model was validated against laboratory rutting tests. The meso mechanical evolution of the asphalt mixture under varying immersion durations was examined. Results indicate that the virtual rutting test can effectively predict the rutting behavior of water-immersed asphalt mixtures. The study elucidates how water damage redistributes the load-bearing force chains and clarifies the underlying mechanism through multiple dimensions, including meso scale contact forces, aggregate movement, and crack propagation.

ObjectiveTo achieve the automatic sorting of citrus fruits. A smart sorting system combining machine vision algorithms and an intelligent camera is designed.MethodsThe system is composed of a detection room, an intelligent camera, a mirror, a lighting system and rollers (including conveyor belts). The intelligent camera is equipped with a microprocessor, which can capture and process images without a dedicated computer. The lighting system consists of LED light strips positioned above the fruits, operating in stroboscopic mode synchronized with the camera to ensure uniform and stable illumination. For the captured fruit images, machine vision algorithms are applied to perform image segmentation and image labeling, achieving fruit size estimation, circle detection, color index estimation and local defect recognition.ResultsExperimental results based on a dataset of oranges collected from a certain orchard demonstrate that the proposed system achieves high prediction accuracy in fruit size and color index estimation.ConclusionThe designed online smart sorting system can efficiently complete the sorting and detection of citrus fruits. Compared with other advanced computer vision algorithms, including automated grading systems based on the Raspberry Pi platform and nondestructive quality detection systems integrating machine vision and spectroscopy, the proposed system achieves higher accuracy in local defect classification.

Surface deformation poses significant safety hazards to high-speed railways, necessitating high-precision monitoring and predictive analysis. Focusing on the Jining section of the Lunan High-Speed Railway (HSR), this study analyzes 36 Sentinel-1A ascending-track images using a DS-InSAR approach. One major subsidence zone was identified along the railway corridor, with a maximum subsidence rate of − 25 mm/a and cumulative subsidence of up to 75 mm during the monitoring period. Based on the monitored deformation time series, a Random Forest regression model was developed to predict subsidence at six representative points. The predictive performance of the Random Forest model was compared with that of the GM (1,1) model, showing substantially lower prediction errors for the Random Forest approach (RMSE ≤ 4.1 mm and MAE ≤ 3.2 mm). These results indicate that the proposed integrated framework effectively combines DS-InSAR monitoring with data-driven prediction, providing a practical and robust reference for subsidence assessment and decision support in the operation and maintenance of high-speed railway corridors.

Single-axis solar tracker arrays experience significant torsional vibrations at strong winds. The aerodynamic interference mechanisms associated with this instability remain inadequately characterized. This study employs numerical simulations to investigate the aerodynamic behavior of a six-row solar tracker array, focusing on the interference mechanisms that trigger aerodynamic instability. The results indicate that the first three windward rows have low critical wind speeds for aerodynamic instability and large vibration amplitudes. The number of rows experiencing aerodynamic instability decreases with increasing tilt angle. Under aerodynamic interference, the displacement power spectra of the solar trackers in each row exhibit distinct characteristics. Variations in the tilt angle alter both the fluid-structure coupling intensity and the frequency characteristics of the solar trackers. The formation and shedding of separated vortices are key indicators of aerodynamic instability in solar trackers. During aerodynamic instability, solar trackers experience coupled effects from self-generated vortices and vortices shed by upstream rows ahead of them. An increased torsional amplitude generates significant shading effects. When the amplitude becomes sufficiently large, this shading effect suppresses the aerodynamic instability of downstream solar trackers.