ABSTRACT

To detect coal content in gangue, a novel approach based on image analysis and particle swarm optimization–support vector machine (PSO-SVM) was presented. First, 15 features that included four sizes and 11 density parameters were extracted from coal and gangue regions of sample pictures, respectively. For the size parameters, the values of each feature are summed up by class, while for the density parameters, the average operation was implemented. Then, the values of coal features were divided by that of gangue features to obtain final features. Using the feature selection method based on the Pearson correlation coefficient, we identified six features that best demonstrated that a consideration of the interaction between size and density parameters can achieve better prediction results. Finally, the coal content in the gangue model was determined using SVM optimized by PSO. The experiment was repeated three times, and the average relative errors were 10.0%, 9.8%, and 9.5%.

摘要

为了检测矸石中的煤含量，提出了一种基于图像分析和粒子群优化-支持向量机（PSO-SVM）的新方法。首先，分别从样本图片的煤和矸石区域中提取了包括四种尺寸和 11 个密度参数的 15 个特征。对于尺寸参数，每个特征的值按类别相加，而对于密度参数，则进行平均操作。然后，煤特征的值除以矸石特征的值，得到最终的特征。使用基于 Pearson 相关系数的特征选择方法，我们确定了六个特征，这些特征最能证明考虑尺寸和密度参数之间的相互作用可以获得更好的预测结果。最后，矸石模型中的煤含量采用 PSO 优化的 SVM 确定。实验重复3次，平均相对误差为10.0%、9.8%、9.5%。