Batch similarity comparison of line traces based on multi-strategy machine learning

Abstract

Conventional methods can scarcely achieve the rapid comparison of line traces. In this study, trace detection signals based on a single-point laser were collected and smoothed. The multi-scale wavelet coefficient of the trace detection signals was obtained after noise reduction by the dual-tree complex wavelet algorithm to minimize the adverse effects of data size on successive comparison calculations. Batch similarity comparison of trace features was achieved using multiple comparison strategies, including an optimized dynamic time regulation algorithm and the recognition of the changing rate gradient. Based on the results of boosting fusion multi-strategy comparison, optimized comparisons were achieved by machine learning, and a model for the rapid comparison of trace features was established. Finally, the viability of the proposed algorithm was verified by experiments.