摘要
为建立一种快速分类及认定案件现场的皮鞋鞋底物证的分类预测模型,采用X射线荧光光谱法(X-ray Fluorescence),工作模式,电压:50 kV,电流:200μA,测试时间:60 s的实验条件下对样品进行了分析,提取了55个皮鞋鞋底样品中的无机元素成分及含量。根据提取样品所含元素种类及含量进行系统聚类,以系统聚类结果作为建立了判别分析、RBF神经网络、MLP神经网络三种分类预测模型。最终,55个样品被分为5类,经验证,多层感知器神经网络的分类准确率相对较高,为94.4%。结果表明:该方法快速无损检材,操作简单,所建立的分类预测模型可以为公安实际办案提供新思路、新方法。
In order to establish a classification and prediction model for quickly classifying and identifying the physical evidence of leather shoes and soles at the scene of the case,X-ray fluorescence spectroscopy is used and the working mode includes voltage 50 kV,current 200μA.test time 60 s.The samples were analyzed and the components and contents of inorganic elements in 55 leather shoe sole samples were extracted.According to the types and contents of elements in the extracted samples,systematic clustering was carried out.Taking the results of systematic clustering as an example,three classification and prediction models of discriminant analysis,RBF neural network and MLP neural network were established.Finally,55 samples were divided into 5 categories.After verification,the classification accuracy of multilayer perceptron neural network is relatively high,which is 94.4%.The results show that this method is fast and easy to operate,and the established classification prediction model can provide new ideas and methods for the actual handling of public security cases.
作者
田陆川
张馨艺
姜红
王丹
满吉
TIAN Luchuan;ZHANG Xinyi;JIANG Hong;WANG Dan;MAN Ji(People's Public Security University of China,Investigation Institute,Beijing 100038,China;Nanning Normal University,Applied Chemistry Department,Nanning 530100,China;Beijing Huayi Hongsheng Technology Co.Ltd,Beijing 100123,China)
出处
《皮革科学与工程》
CAS
北大核心
2022年第3期90-95,共6页
Leather Science and Engineering
基金
中国人民公安大学2021年度基科费重点项目(2021JKF212)。
关键词
X射线荧光光谱
人工神经网络
判别分析
系统聚类
皮鞋
X-ray fluorescence spectrum
artificial neural network
discriminant analysis
systematic clustering
leather shoes