非线性线条痕迹小波域特征快速溯源算法研究

发布时间：2018-07-11 11:17

  本文选题：线条痕迹 + 小波降噪　； 参考：《电子测量与仪器学报》2017年06期

【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。
[Abstract]:There are a large number of line marks on the face of the severed head mark left on the scene of the high speed wire and cable theft and cutting case, which often show nonlinear morphological characteristics and strong randomness. In order to analyze trace features more quickly and to infer their own tools, a fast traceability algorithm in wavelet domain for nonlinear line trace is designed. Firstly, the one-dimensional signal picked up by a single point laser displacement sensor is de-noised by wavelet decomposition, and then the dynamic time warping algorithm based on wavelet feature is used to match the similarity coincidence of trace features. Finally, the linear regression machine learning algorithm based on gradient descent method is used to iterate the cost function value to minimize the cost, so that the corresponding tool can be inferred quickly. Finally, the practicability and validity of the algorithm are verified by the actual trace inference and cutting tool test.
【作者单位】： 昆明理工大学航空学院;昆明理工大学机电工程学院;昆明信诺莱伯科技有限公司;
【基金】：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
【分类号】：TN911.7;U298
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本文编号：2114999