摘要
针对表征矿物浮选工况的泡沫图像特征冗余性大的问题,提出了一种无监督约简的浮选泡沫图像特征选择方法.该方法首先定义敏感性指数,并基于敏感性指数约简得到与工况相关的敏感图像特征集;然后针对敏感图像特征之间的自相关性,提出基于粗糙集属性重要度的敏感图像特征集约简方法;最后将该方法应用于金锑浮选过程,并利用工业现场数据进行测试,证明了该方法的有效性,为基于机器视觉的浮选过程监控创造了条件.
Considering the redundancy of froth image characters that reflect the status of the flotation process, an unsupervised reduction method is proposed for selecting froth image characters for flotation. First, a sensitivity index is defined, and sensitive characters are set. These characters, which are strongly related to the status of the flotation process, are obtained through a reduction method based on the sensitivity index. Then, for solving the correlation problem that exists in sensitive image characters, a reduction method for sets of sensitive image characters is developed using rough set attribute importance. Finally, application to the flotation of gold and antimony and tests based on industrial local data demonstrate the method＇s effectiveness, which improves machine-vision-based process monitoring of froth flotation.
出处
《信息与控制》
CSCD
北大核心
2014年第3期314-317,333,共5页
Information and Control
基金
国家863计划资助项目(2009AA04Z124)
国家自然科学基金资助项目(61134006)
国家杰出青年科学基金资助项目(61025015)
关键词
敏感性指数
特征约简
泡沫浮选
机器视觉
属性重要度
sensitivity index
characters reduction
froth flotation
machine vision
attribute importance
