闪火条件下防火服装热收缩形变的数据挖掘  

Data Mining of Protective Clothing Shrinkage During Flash Fire

作　　者：翟丽娜[1,2] 李俊[1,2,3] 

机构地区：[1]东华大学服装艺术设计学院,上海200051 [2]东华大学功能防护服装研究中心,上海200051 [3]东华大学现代服装设计与技术教育部重点实验室,上海200051

出　　处：《计算机系统应用》2014年第10期132-137,共6页Computer Systems & Applications

基　　金：国家自然科学基金(51106022);上海市教委科研创新项目(12ZZ068);教育部新世纪优秀人才支持计划项目(NCET-10-0321);中央高校基本科研业务费专项基金(11D10715)

摘　　要：随着暖体假人、燃烧假人、三维扫描仪等服装设备的应用,服装工程的大样本实验产生了大量数据,采用传统的数据分析方法未能发挥实验中大样本量的优势.本研究利用Clementine软件,选取了合适的分析方法,对防火服装的形变等有关数据进行了数据挖掘的尝试.首先通过变量重要性分析,研究了热收缩的重要影响因素,然后根据决策树与神经网络的变量重要性排序,提取了影响热收缩的关键因素,并进一步通过对热收缩及关键影响因素的聚类分析,探索了防火服不同部位区域的热防护特点.研究发现,热流量及衣下空气层是影响热收缩形变的关键因素,手臂及腿部对应服装部位需重点防护,数据挖掘技术是探索服装舒适性与功能机制与特点的有效工具.With the application of the thermal manikin and fire manikin, mass data are produced during the experiment research in the clothing engineering area. The advantages of the larger samples can not be revealed using the conventional analysis method. Thus, in the paper, by using the Clementine software, data mining method is used to explore the data produced by flash fire experiment. The decision tree method and the neural net method are used to determine the key influence factors of the thermal shrinkage, which are then used in Kohonen cluster to divide protective clothing into different parts. Research shows that heat flux is the most important factor to the degree of shrinkage and the arm and leg are the key parts to be protected. It suggested that data mining is an effective tool to explore the character and function of the protective clothing.

关 键 词：数据挖掘 闪火 防火服 热收缩 热流量 衣下空气层 

分 类 号：TS941.731.3[轻工技术与工程—服装设计与工程]