上海交通大学学报(自然版) ›› 2011, Vol. 45 ›› Issue (08): 1211-1215.

• 一般工业技术 • 上一篇    下一篇

基于彩色图像特征的铜成分软测量模型

张宏伟,宋执环   

  1. (浙江大学 工业控制研究所, 杭州 310027)
  • 收稿日期:2011-03-14 出版日期:2011-08-30 发布日期:2011-08-30
  • 基金资助:

    国家高技术研究发展计划(863)项目(2009AA04Z154)

A Copper Compositions Soft Sensor Using Color Vision and LSSVR

 ZHANG  Hong-Wei, SONG  Zhi-Huan   

  1. (State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China)
  • Received:2011-03-14 Online:2011-08-30 Published:2011-08-30

摘要:  针对再生铜熔炼过程中铜成分离线检测时滞大的问题,提出了一种基于颜色特征的再生铜成分参数准在线估计方法.首先获取现场取样并固化再生铜样品的彩色图像,然后使用RGB颜色空间、色调和颜色向量角分别量化再生铜颜色特征,最后利用最小二乘支持向量回归(LSSVR)对铜成分参数建立回归模型,进而实现铜成分参数估计.通过对再生铜样品的对比分析,该软测量模型能够准确快速地估计出测试样本的铜成分,既解决了铜成分参数离线检测时滞大的问题,又满足了工程要求的精度,验证了利用彩色图像特征构建铜成分软测量模型的方法是可行、有效的.

关键词: 铜成分, 颜色特征, 最小二乘支持向量回归, 软测量

Abstract: This paper presented a new copper compositions soft sensor using color images of copper and LSSVR(least square support vector regression) modeling, as opposed to its delayed measurement by hardware measuring device. The soft sensor is based on a color vision system which includes computer and a color camera. First, a highperformance color chargecoupled device camera captures color from copper samples and transmits images to the computer. Then RGB, hue and color vector angle are used to characterize copper samples’ color for the comparative purpose. Three different LSSVR based soft sensor were developed using RGB, hue and color vector angle respectively. Finally, the real time estimation obtained by the soft sensor achieves superior performance. The experiments on test copper samples show that the proposed method is feasible and efficient.

Key words: copper composition, color vision, least square support vector regression(LSSVR), soft sensor

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