摘要
电缆线护套是爆炸案件现场的最常见物证之一,亦是绑架、盗窃、杀人、强奸等其他犯罪现场的常见物证。为了建立一种快速有效鉴别电缆护套种类的方法,利用X射线荧光光谱法对40个电缆线护套样品进行了检验。本文针对爆炸等各类犯罪现场可能遗留的电缆线护套进行检验分析,通过K均值聚类对样品进行初步聚类区分,可将样品分为5类。在此基础上构建多层感知器和Fisher判别分析分类模型。结果表明,多层感知器分类模型训练集和测试集的准确率均为100%,Fisher判别分析留一交叉验证的准确率为90%。从分类准确度可以看出,X射线荧光光谱与机器学习方法相结合可以有效地对电缆线护套进行快速、准确地分类,从而对以爆炸案件为典型代表的各类涉及电缆线的犯罪案件提供高效的溯源支撑。
Cable sheath is one of the most common physical evidence at the scene of an explosion, as well as at other crime scenes such as kidnapping, theft, homicide and rape. In order to establish a method to identify cable sheath quickly and effectively, 40 cable sheath samples were tested by X-ray fluorescence spectrometer. In this paper, we examined and analyzed the cable sheath that may be left behind at various crime scenes such as explosions. The samples were preliminarily clustered by K-means clustering into 5 categories. On this basis, the multi-layer perceptron and Fisher discriminant analysis classification models are constructed. The results show that the accuracy of training set and test set of multi-layer perceptron classification model is 100%, and the accuracy of Fisher discriminant analysis classification model is 90% using the leave-one-out cross-validation method. It can be seen from the classification accuracy that the combination of X-ray fluorescence and machine learning methods can effectively classify cable sheaths quickly and accurately. Thus, this method can provide efficient traceability support for all kinds of crimes involving cable lines, such as explosion cases.
作者
陈争
李春宇
吕航
姜红
满吉
Chen Zheng;Li Chunyu;Lü Hang;Jiang Hong;Man Ji(Institute of Criminal Investigation,Peoples Public Security University of China,Beijing 100038,China;Beijing Hua yi Honrizon Technology Co.Ltd.,Beijing 100123,China)
出处
《应用激光》
CSCD
北大核心
2022年第10期146-155,共10页
Applied Laser
基金
中央高校基本科研业务费项目(2021JKF204)
公安部技术研究计划(2019JSYJC21)。
关键词
爆炸现场
电缆线护套
X射线荧光光谱
机器学习
scene of the explosion
cable sheath
X-ray fluorescence spectrum
machine learning
