Affiliations: [a]
Aviation College, Kunming University of Science & Technology, Kunming, P.R. China | [b] Faculty of Mechanical & Electrical Engineering, Kunming University of Science and Technology, Kunming, P.R. China | [c] Kunming SNLab Tech Co., Ltd., Kunming, P.R. China | [d]
Xiangyang Public Security Department Wuhan Railway Public Security Bureau, Xiangyang, P.R. China | [e]
Criminal Investigation Department of Railway Public Security Bureau, Beijing, P.R. China | [f]
Department of Mechanical Engineering, Blekinge Institute of Technology, Karlskrona, Sweden
| [g]
Institute of Forensic Science, Shijiazhuang Public Security Bureau, Shijiazhuang, P.R. China
Abstract: There are lots of line traces on the surface of the broken ends which left in the cable cutting case crime scene along the high-speed railway in China. The line traces usually present nonlinear morphological features and has strong randomness. It is not very effective when using existing image-processing and three-dimensional scanning methods to do the trace comparison, therefore, a fast algorithm based on wavelet domain feature aiming at the nonlinear line traces is put forward to make fast trace analysis and infer the criminal tools. The proposed algorithm first applies wavelet decomposition to the 1-D signals which picked up by single point laser displacement sensor to partially reduce noises. After that, the dynamic time warping is employed to do trace feature similarity matching. Finally, using linear regression machine learning algorithm based on gradient descent method to do constant iteration. The experiment results of cutting line traces sample data comparison demonstrate the accuracy and reliability of the proposed algorithm.
Keywords: Signal detection, wavelet transforms, lasers, machine learning
