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Nonlinear tool traces fast tracing algorithm based on single point laser detection
Blekinge Institute of Technology, Faculty of Engineering, Department of Mechanical Engineering.
Kunming University of Science and Technology, CHI.
Kunming SNLab Tech Co., CHI.
Xiangyang Public Security Department Wuhan Railway Public Security Bureau, CHI.
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2019 (English)In: Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, E-ISSN 1875-8967, Vol. 36, no 2, p. 1109-1120Article in journal (Refereed) Published
Abstract [en]

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. © 2019 - IOS Press and the authors. All rights reserved

Place, publisher, year, edition, pages
IOS Press , 2019. Vol. 36, no 2, p. 1109-1120
Keywords [en]
Lasers, Machine learning, Signal detection, Wavelet transforms, Image processing, Iterative methods, Learning algorithms, Learning systems, Railroad plant and structures, Railroad transportation, Wavelet decomposition, Dynamic time warping, Feature similarities, Gradient Descent method, High - speed railways, Laser displacement sensors, Morphological features, Three-dimensional scanning, Wavelet domain features
National Category
Other Mechanical Engineering
Identifiers
URN: urn:nbn:se:bth-17779DOI: 10.3233/JIFS-169885ISI: 000461770000025Scopus ID: 2-s2.0-85063326312OAI: oai:DiVA.org:bth-17779DiVA, id: diva2:1302765
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-04-18Bibliographically approved

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