Since the swing of the lifting load and the positioning of the trolley during the operation of a bridge crane seriously affect the safety and reliability of its work, we have not only designed Proportional Integral Derivative (PID) controllers for the anti-swing and positioning control but also proposed a hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithm to optimize the gains of the controllers. In updating the PSO algorithm, a nonlinear adaptive method is utilized to update the inertia weight and learning coefficients, and the SA algorithm is also integrated when the PSO algorithm is searching for a global optimal solution, to reduce the probability of falling into the local optimal solution. The simulation results demonstrate that the PSO–SA algorithm proposed in this paper is prone to be a more effective method in searching for the optimal parameters for the controllers, compared with three other algorithms. As shown by the experimental results, the swing angle stabilization time of the novel algorithm is 6.9 s, while the values of the other algorithms range from 10.3 to 13.1 s under a common working condition. Simultaneously, the maximum swing angle of the novel algorithm is 7.8°, which is also better than the other algorithms.

The bridge-type bridge crane is a common lifting equipment used in modern factories and workshops. During the crane’s operation, the positioning of the trolley and the swing of the load can significantly impact the bridge crane’s safety and reliability. In this paper, we propose a variable universe fuzzy multi-parameter self-tuning PID (VUFMS-PID) control strategy for controlling the trolley’s movement. Our control strategy uses scaling factor variation to dynamically adjust the number of fuzzy control rules based on the system error and error rate of change. This approach improves control accuracy and enhances the crane’s stability and safety. Simulation results demonstrate that our proposed control strategy outperforms both the fuzzy PID and traditional PID control strategies. Specifically, it reduces the crane trolley’s positioning time and the maximum swing angle of the load. Our control strategy exhibits good adaptive ability and robustness, which further improves the stability and safety of the bridge-type bridge crane operation.

Lateral flow immunoassay (LFIA) is extensively utilized for point-of-care testing due to its ease of operation, cost-effectiveness, and swift results. This study investigates the flow dynamics and reaction mechanisms in LFIA by developing a three-dimensional model using the Richards equation and porous media transport, and employing numerical simulations through the finite element method. The study delves into the transport and diffusion behaviors of each reaction component in both sandwich LFIA and competitive LFIA under non-uniform flow conditions. Additionally, the impact of various parameters (such as reporter particle concentration, initial capture probe concentrations for the T-line and C-line, and reaction rate constants) on LFIA performance is analyzed. The findings reveal that, in sandwich LFIA, optimizing parameters like increasing reporter particle concentration and initial capture probe concentration for the T-line, as well as adjusting reaction rate constants, can effectively enhance detection sensitivity and broaden the working range. Conversely, in competitive LFIA, the effects are inverse. This model offers valuable insights for the design and enhancement of LFIA assays.

Position determination is a critical technical challenge to be addressed in the unmanned and intelligent advancement of crane systems. Traditional positioning techniques, such as those based on magnetic grating or encoders, are limited to measuring the positions of the main carriage and trolley. However, during crane operations, accurately determining the position of the load becomes problematic when it undergoes swinging motions. To overcome this limitation, this paper proposes a novel Ultra-Wide-Band (UWB) positioning method for unmanned crane systems, leveraging the Snake Optimizer Long Short-Term Memory (SO-LSTM) framework. The objective is to achieve real-time and precise localization of the crane hook. The proposed method establishes a multi-base station and multi-tag UWB positioning system using a Time Division Multiple Access (TDMA) combined with Two-Way Ranging (TWR) scheme. This system enables the acquisition of distance measurements between the mobile tag and UWB base stations. Furthermore, the hyperparameters of the LSTM network are optimized using the Snake Optimizer algorithm to enhance the accuracy and effectiveness of UWB positioning estimation. Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.

Point cloud segmentation, as a core technology in 3D data processing, is essential in applications such as autonomous driving, smart cities, and industrial inspection. However, large-scale point cloud segmentation tasks often encounter boundary ambiguities among object categories. To address these challenges, this study introduces a large-scale segmentation framework combining local geometric features with an improved Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm. Initially, precise preprocessing of far-field noise is conducted using the spatial distribution characteristics of the 3D point cloud. Subsequently, Cloth Simulation Filtering (CSF) is utilized to extract initial ground points, further optimized by leveraging local ensemble features. Finally, non-ground points are pre-clustered through the enhanced HDBSCAN algorithm, which constructs a radius adjacency graph, quantifies spatial correlation via neighborhood overlap frequency, and incorporates a dual dynamic constraint mechanism based on connection weight thresholds and centroid distance thresholds. Experimental results demonstrate that integrating the geometric feature module significantly improves the ground segmentation method, increasing the F1 score from 86.73% to 97.34%. Furthermore, continuous multi-frame testing yielded an average F1 score of 94.31%. Notably, the improved HDBSCAN algorithm reduced the total number of clusters by 42.7% compared to the original method. By effectively combining geometric information with density-based clustering, the proposed framework significantly enhances segmentation accuracy and provides a robust and efficient solution for point cloud segmentation tasks in complex environments.

To meet the demand for real-time and accurate diamond particle size measurement in industrial scenarios—where traditional image processing methods lack robustness in complex environments and existing deep learning models struggle to balance accuracy and efficiency—this paper proposes an integrated framework for dynamic segmentation and morphological analysis of diamond particles based on video streams. A fully automated data acquisition system consisting of a high-precision motion stage, an industrial camera, and an optical microscope is first constructed to capture dynamic particle images. Based on YOLOv8n-seg, a lightweight SPR-YOLOv8 instance segmentation model is then developed with three key improvements: a Large Separable Kernel Attention (LSKA) mechanism is introduced into the SPPF module to enhance feature discriminability; the RepBlock module is adopted in the neck network to improve feature fusion efficiency through structural re-parameterization; and a P2 small-object detection head is introduced while large-object detection branches are removed, enabling the model to focus on tiny, densely distributed particles. Finally, a contour-based geometric analysis method is proposed for particle size estimation based on segmentation results. Experimental results show that the proposed model achieves an mAP@0.9 of 0.861 while maintaining a low parameter count (0.97 M) and a high inference speed of 500 FPS. Compared with the conventional OpenCV-based method (CADPS), the proposed DPSCA framework reduces the mean absolute percentage error in particle size measurement by over 70%, while also demonstrating strong accuracy and stability in consecutive-frame tracking. Overall, this study provides a practical and efficient automated inspection solution for online quality control in superhard material manufacturing, and supplementary cross-scale experiments further demonstrate promising robustness on diamond particles beyond the primary 180–250 μm range.