电子测量与仪器学报

潘楠,阚立峰,潘地林.非线性线条痕迹小波域特征快速溯源算法研究[J].电子测量与仪器学报,2017,31(6):899-908

非线性线条痕迹小波域特征快速溯源算法研究

Study of fast tracing algorithm based on wavelet domain features of nonlinear line trace

DOI：10.13382/j.jemi.2017.06.012

英文关键词:line trace wavelet de nosing dynamic time warping machine learning fast tracing

基金项目:云南省科技计划(2014SC030,2016RA042,2017EH028)、公安部技术研究计划 (2014JSYJA020，2016JSYJA03)、昆明市科技计划(2015 1 S 00284)、昆明理工大学分析测试基金 (2016T20130030)、国家留学基金委创新型人才国际合作培养项目（201608740005）资助

作者	单位
潘楠	昆明理工大学航空学院昆明650500
阚立峰	昆明理工大学机电工程学院昆明650500
潘地林	昆明信诺莱伯科技有限公司昆明650228

Author	Institution
Pan Nan	Faculty of Aviation, Kunming University of Science & Technology, Kunming 650500, China
Kan Lifeng	Faculty of Mechanical and Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China
Pan Dilin	Kunming SNlab Co. Ltd., Kunming 650228, China

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中文摘要:

高铁线缆盗割案件案发现场遗留的断头承痕面上存在着大量线条痕迹，其往往呈现非线性形态特征，随机性较强。为了更快速地进行痕迹特征分析及所属工具推断，设计出一种针对非线性线条痕迹的小波域特征快速溯源算法,该算法首先将单点激光位移传感器检测断头表面拾取的一维信号利用小波分解进行降噪，随后使用基于小波特征的动态时间规整算法实现痕迹特征相似重合度匹配，最后使用基于梯度下降法的线性回归机器学习算法不断的迭代使得代价函数值代价最小，从而实现对应工具快速推断。最终通过实际痕迹推断剪切工具试验验证了本算法的实用性和有效性。

英文摘要:

There is a large number of line traces on the surface of the broken end which left in the cable cutting case crime scene of high speed railway, they often present nonlinear morphological feature and has strong randomness. In order to make rapid trace analysis and infer the tools, a fast algorithm based on wavelet domain feature aiming at the nonlinear line traces is presented. The proposed algorithm applies wavelet decomposition to the 1 D signal which picked up by single point laser displacement sensor to partially reduce noises. After that, the dynamic time warping is employed to realize the trace feature similarity matching. Finally, the linear regression machine learning algorithm based on gradient descent method is used to realize the constant iteration. The experiment results of cut traces sample data comparison demonstrate the accuracy and reliability of the proposed algorithm.

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