Light field cameras capture synchronized spatial-angular information of lights, offering a new paradigm for 3D visual perception. Depth estimation, as a fundamental task in light field analysis, underpins the critical applications such as 3D reconstruction and visual odometry. However, occlusion-induced estimation errors remain a persistent challenge. This paper proposes an occlusion-aware depth estimation framework featuring two novel modules: an entropy-based occlusion mask pre-computation method and a viewpoint screening-driven depth estimation algorithm. In the investigation, first, the light field occlusion in the polar plane diagram is modeled by analyzing the information entropy of micro-images array, and a local entropy extremum-based occlusion mask pre-computation approach is constructed, thus overcoming the limitations of conventional techniques in characterizing occluded regions. Subsequently, viewpoint screening is employed to eliminate the interference from occluded perspectives, thus effectively reducing estimation errors and decreasing the proportion of disparity outliers exceeding 0.03 pixels compared to other methods. The core contribution of this paper lies in establishing a theoretical connection between information entropy and light field occlusion, enabling a robust occlusion-aware depth estimation framework based on the information entropy of micro-images array. Experimental results on light field benchmark dataset demonstrate that the proposed method achieves superior performance in mean absolute error and 25th error percentile. Comparative ablation studies confirm the efficacy of the entropy-driven occlusion mask, thus highlighting the critical role of information entropy theory in the framework of light field depth estimation.

To overcome the relatively high sound pressure level noise generated by the elevator traction machine during the braking process, this study proposes a vibration and noise reduction solution based on particle dampers. Firstly, the vibration characteristics of the brake wheel and brake pads are investigated through finite element analysis, and the key vibration modes are identified by correlating the principal vibration frequencies obtained from whole-machine vibration and noise tests. On the basis of these findings and by considering the symmetry and spatial layout of the brake wheel structure, an innovative cavity design is introduced in the brake wheel to accommodate particle dampers. Then, coupled EDEM-ADAMS simulation technology is employed to optimize the parameters of solid particles, with a focus on addressing three critical technical issues:(1) the selection of damping particle material (to avoid the interference from magnetic fields, pure aluminum is ultimately selected); (2) the analysis of energy dissipation process of the particle damper within the cavity (with the adoption of discrete element analysis); and (3) the optimization of particle radius and filling ratio of the damper (by integrating the discrete element analysis with the multi-body dynamics simulation). Finally, an experimental validation is conducted in a semi-anechoic chamber, by setting a timed braking control strategy with a 5-second cycle, using an acceleration sensor array to collect vibration signals, and synchronously recording sound pressure level data. The results indicate that, the installation of particle dampers helps to reduce the average sound pressure level during the braking process by 20.7%, thus confirming the effectiveness of the proposed solution. This research provides a novel technical approach to noise control of electromagnetic braking systems and is of significant engineering application value.

Multi-modal data fusion of LiDAR (Laser Imaging, Detection, and Ranging) and binocular camera is important in the research on 3D reconstruction. The two sensors have their own advantages and disadvantages, and they can complement each other through data fusion to obtain better reconstruction results. In order to achieve data fusion, firstly it is necessary to unify the two data into the same coordinate system. The calibration results of the external parameters between the LiDAR and the camera are very important to 3D reconstruction. Due to sparse LiDAR point cloud and its positioning error, it is a challenge to extract feature points accurately for constructing accurate point correspondences when calibrating extrinsic parameters between LiDAR and stereo camera. In addition, most calibration methods ignore that LiDAR works on spherical coordinate system and directly use the Cartesian coordinate measurement results for calibration, which introduces anisotropic coordinates error and reduces the calibration accuracy. This paper proposed a calibration method by minimizing isotropic spherical coordinate error. Firstly, a novel calibration object using centroid feature points was proposed to improve the extraction accuracy of feature points. Secondly, the anisotropic LiDAR Cartesian coordinate error were convert into the isotropic spherical coordinate error, and the extrinsic parameters were solved through directly minimizing the spherical coordinate error. The experiments show that the proposed method has advantages over the anisotropic weighting method. The method ensures that the solution is globally optimal and the number of calibration samples required is greatly reduced on the premise of sacrificing some accuracy. With the optimal calibration error of 2.75 mm, the amount of calibration data can be reduced by about 54.5% by sacrificing 3.6% accuracy using the proposed method.

Near-infrared optical tracking systems can restore the movement of tracked objects in real time based on the markers attached to the tracked objects. This technology has now been widely adopted across numerous fields. This paper proposed a real-time tracking method for muli-objects that is robust to target loss. First, based on the imaging characteristics of reflective marker balls in near-infrared cameras, the geometric center of each marker was extracted using the grayscale centroid method. Then, the SORT algorithm was used as a multi-objetcs tracking method in each monocular camera to match each marker point between frames. The matching relationship of the image points of the markers in each camera was determined based on the principle of epipolar geometry combined with the weighted bipartite graph matching method, and the three-dimensional spatial coordinates of each tracked marker were calculated in real time based on the triangulation method. Next, the markers were grouped based on their spatial relationships during motion to identify markers belonging to the same object. Spatial feature vectors were established for tracked objects using the Euclidean distances between markers within the same group, serving as matching references for reappearing lost objects. When a fully lost object reproduced , re-matching is performed using cosine distance of these feature vectors. Finally, the proposed algorithm was experimentally verified. The experiment shows that the tracking accuracy of the proposed algorithm can reach about 0.5 mm at a speed of not less than 60 f/s. In addition, the lost reproduced objects and markers can be correctly re-matched.

In the absence of sufficient escalator motor bearing failure data, to address the issue of unstable bearing fault characteristics during frequent load and speed variations in escalator operation, this paper proposed a transfer diagnosis method for escalator motor bearings using Stockwell (S) transformation combined with subdomain adaptation. Firstly, for the fault characteristics of escalator motor bearings, a time-frequency image of vibration signals was generated using the S transform combined with bilinear interpolation. This time-frequency image effectively reflects bearing fault features and is subsequently aligned with the requirements of the feature extraction network. Secondly, local maximum mean discrepancy (LMMD) was introduced at the output end of the feature extraction network layer based on the deep residual neural network ResNet-50. It incorporates the confidence of bearing fault sample categories as weights in the mapped maximum mean discrepancy (MMD), aligning the distributions of subdomains belonging to the same category, thereby expanding the scope of transfer learning. Next, the network was constructed to minimize both LMMD and cross-entropy loss functions, and network training was performed using mini-batch gradient descent. Consequently, by refining the feature differences between different fault categories, fault subdomain self-adaptation was achieved, overcoming the problem of low transfer diagnosis accuracy. Finally, based on two publicly available bearing fault datasets and a limited amount of escalator motor bearing fault data, the S-transformed time-frequency dataset was constructed, and transfer diagnosis experiments were conducted. The results demonstrate that the proposed method achieves an average accuracy of 99.1% and 95.49% for transfer diagnosis in two different source-to-target domain scenarios of escalator bearings, outperforming five commonly used diagnostic methods in terms of recognition accuracy and robustness.

A compliant precision positioning platform is a core component of precision equipment. The high-speed and high-precision positioning operation requires the platform to possess high response speed and good regulation capabilities. Passive damping can effectively enhance the platform’s rapid response capabilities. To improve the rapid response capability of an XY compliant positioning platform with local resonance damping, this paper proposes an optimization design method that comprehensively enhances modal damping and natural frequency. In the investigation, firstly, based on elasticity theory and Castigliano’s second theorem, the platform stiffness is analytically modeled and synthesized, and the expression for the platform’s natural frequency is derived. Subsequently, aiming at the maximum control gain, a single-objective optimization function composed of natural frequency and the frequency response curve area of the resonant region is formulated, along with the mathematical expression for the platform optimization design problem and a ABAQUS-Python-Matlab joint optimization model. Then, to simplify the complexity of finite element calculations, an equivalent structure for the optimized platform based on the firstorder fixed frequency equivalence is established. Moreover, simulation analysis of the optimized platform is conducted, and the optimization design results are compared and analyzed to verify the correctness of the natural frequency analytical expression. Finally, an experimental platform is constructed to perform static, dynamic and trajectory tracking experiments on the XY compliant platform with local resonance damping. The results demonstrate that the proposed comprehensive optimization method can increase the X-axis and Y-axis control bandwidths of the platform respectively by 7. 42% and 24. 70%, and effectively enhance the trajectory tracking performance of the system.