| 引用本文: | 曹文艳,王然风,樊民强,付翔,王宇龙.MRMR和SSGMM联合分类模型的煤泥浮选系统药况图像识别[J].控制理论与应用,2021,38(12):2045~2058.[点击复制] |
| CAO Wen-yan,WANG Ran-feng,FAN Min-qiang,FU Xiang,WANG Yu-long.Recognition of reagent dosage condition image for coal flotation system based on joint classification model of MRMR and SSGMM[J].Control Theory and Technology,2021,38(12):2045~2058.[点击复制] |
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| MRMR和SSGMM联合分类模型的煤泥浮选系统药况图像识别 |
| Recognition of reagent dosage condition image for coal flotation system based on joint classification model of MRMR and SSGMM |
| 摘要点击 1270 全文点击 340 投稿时间：2020-08-04 修订日期：2021-10-14 |
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| DOI编号 10.7641/CTA.2021.00503 |
| 2021,38(12):2045-2058 |
| 中文关键词 煤泥浮选泡沫 加药状况 机器视觉 图像特征提取和选择 半监督学习 联合分类模型 |
| 英文关键词 coal flotation froth reagent dosage condition machine vision feature extraction and selection semisupervised learning joint classification model |
| 基金项目 |
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| 中文摘要 |
| 为解决煤泥浮选过程依靠工人肉眼识别泡沫特征来调节药剂用量, 造成药剂浪费, 产品质量不合格的问题,
提出一种MRMR和SSGMM联合分类模型的药况图像识别方法. 针对泡沫图像的形态、纹理、颜色特征与泡沫类别
具有不同程度的相关性. 将精煤灰分作为泡沫的类别信息, 利用最大相关最小冗余(MRMR)算法筛选最优特征; 针
对传统的高斯混合模型(GMM)在聚类时, 存在结果需人为判断实现分类的问题, 通过引入少量已知加药状况下的
泡沫图像特征样本对其改进, 构建半监督高斯混合模型(SSGMM)泡沫图像聚类器. 将优选的且具有少量先验标签
信息的多维泡沫图像特征融合到SSGMM聚类模型中, 利用少量的标记样本引导聚类, 并将其标签信息映射给聚类
结果实现自动分类. 实验表明, 这种联合分类模型提高了泡沫识别的准确性, 为药剂用量的准确控制与精煤产品质
量提供了关键技术支持. |
| 英文摘要 |
| In order to solve the problem that the coal flotation process depends on the naked eyes of the workers to
identify the froth features to adjust the dosage of reagent which results in the waste of reagents and the unqualified product,
an recognition method of reagent dosage condition image based on joint classification model of MRMR and SSGMM
is proposed. With respect to the different degrees of correlations between the morphology, texture and color features of
the froth image and the froth classification, the ash content of clean coal is taken as the classification information, the
optimal froth image features are screened out by maximal-relevance-minimal-redundancy (MRMR) algorithm; aiming at
the problem that the results need to be judged artificially to realize the classification when the clustering of traditional
Gaussian mixture model (GMM), it is improved by introducing a small number of froth image feature samples under
the condition of known reagent dosage, a semi-supervised Gaussian mixture model (SSGMM) cluster is constructed. The
optimal multi-dimensional froth image features with a small amount of prior label information are integrated into the
SSGMM clustering model, the clustering is guided by labeling samples, and their label information is mapped to the
clustering result, so that the automatic classification is realized. The experimental results show that the accuracy of froth
recognition has been improved by this kind of joint classification model, and the key technical support has been provided
for the accurate control of the dosage of reagent and the quality of clean coal products. |
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