文献类型：期刊文章

作　　者：张宏伟[1] 葛志强[1] 袁小锋[1] 宋执环[1] 叶凌箭[1,2]

机构地区：[1]浙江大学工业过程控制研究所工业控制国家重点实验室,杭州310027 [2]浙江大学宁波理工学院,宁波315100

出　　处：《Transactions of Nonferrous Metals Society of China》

基　　金：Project(2011BAE23B05)supported by National Key Technology R&D Program of China;Project(61004134)supported by the National Natural Science Foundation of China;Project(LQ13F030007)supported by Zhejiang Provincial Natural Science Foundation of China

年　　份：2014

卷　　号：24

期　　号：8

起止页码：2665-2676

语　　种：中文

收录情况：AJ、CAS、CSA、CSA-PROQEUST、CSCD、CSCD2013_2014、EBSCO、EI、IC、INSPEC、JST、SCI、SCI-EXPANDED(收录号：WOS:000342138600031)、SCIE、SCOPUS、WOS、ZGKJHX、普通刊

摘　　要：A vision-based color analysis system was developed for rapid estimation of copper content in the secondary copper smelting process. Firstly, cross section images of secondary copper samples were captured by the designed vision system. After the preprocessing and segmenting procedures, the images were selected according to their grayscale standard deviations of pixels and percentages of edge pixels in the luminance component. The selected images were then used to extract the information of the improved color vector angles, from which the copper content estimation model was developed based on the least squares support vector regression (LSSVR) method. For comparison, three additional LSSVR models, namely, only with sample selection, only with improved color vector angle, without sample selection or improved color vector angle, were developed. In addition, two exponential models, namely, with sample selection, without sample selection, were developed. Experimental results indicate that the proposed method is more effective for improving the copper content estimation accuracy, particularly when the sample size is small.

关 键 词：secondary copper  copper content estimation  sample selection  color vector angle  least squares support vector regression  

分 类 号：TP391.41] TF811[计算机类]