AVIC Optronics (China)

Abstract Aiming at the trajectory clustering problem of observed data, an improved Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm, named FB-HDBSCAN, is proposed. Firstly, the spatiotemporal features of the data are thoroughly exploited to construct a distance matrix based on Fast Dynamic Time Warping (FastDTW), ensuring an accurate measurement of similarity between data points. Then, the Bayesian Optimization (BO) algorithm is introduced for adaptive parameter optimization, and the optimal parameters, along with the distance matrix, are input into the HDBSCAN algorithm framework to achieve automatic clustering of trajectory data. Experimental results demonstrate that the proposed algorithm achieves superior performance across multiple datasets, validating its feasibility and applicability.

Given the significant impact of the initial surface layer of materials on their tribological performance, this study uses a wire–plate reciprocating friction pair to investigate the effects of surface mechanical rolling process on the elastic current-carrying friction performance. The plate specimens were subjected to rolling processing with varying feed rates under different load conditions, using a self-designed current-carrying friction and wear testing machine. The results show that as the feed rate and load increase, the contact resistance varies within the range of 0.0065 Ω to 0.0310 Ω, with a standard deviation ranging from 0.01 Ω to 0.07 Ω, indicating good electrical conductivity. As the feed rate of the surface mechanical rolling increases, the wear rate of the material significantly decreases. Under all test conditions, the material wear marks exhibit plowing wear, and with the increase in surface mechanical rolling feed rate, the occurrence and intensification of adhesive wear are delayed. When the feed rate is 100 μm and the load is 0.025 N, the material wear rate is the lowest, reduced by 63.1% compared to the untreated condition.