摘要
充分利用烧结机尾断面的火焰图像所蕴含的有效信息,利用随机森林算法对烧结状态进行短期预测,该算法在工程上具有可行性。为改善随机森林中重要性较低的属性对分类结果的影响,提出了一种基于概率决策的随机森林改进算法,实现对烧结机尾断面火焰状态的短期预测。首先,对300张烧结断面火焰图像进行统一预处理,将获得的10个图像几何特征作为输入量;其次,对提取到的10个图像几何特征进行K均值聚类和模糊C均值聚类,根据聚类结果的准确率赋予叶子节点处各个类别出现的概率;最后,实验验证了优化的随机森林算法能提高对烧结状态分类的准确性。
For the effective use of the valid information contained in the flame image of the tail section of the sintering machine,the random forest algorithm is used to predict the sintering state in a short time.The algorithm is feasible in engineering.To improve the influence of lowimportance properties in random forests on classification results,we propose a random forest improvement algorithm using probability decision,making to realize the shortterm prediction of the flame state of the sintering machine tail section.First,300 sintered section flame images were uniformly preprocessed,and geometric features of 10 images were given as input.Second,Kmean and fuzzy Cmean clusterings were performed on the geometric features of the 10 extracted images.Finally,the probability of each category appearing at the leaf node was given according to the accuracy of the clustering results.The experiment proves that the proposed optimized random forest algorithm improves the accuracy for sintering state classification.
作者
王福斌
王蕊
武晨
Wang Fubin;Wang Rui;Wu Chen(College of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,Hebei,China;Tang Steel International Engineering Technology Co.,Ltd.,Tangshan 063000,Hebei,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第18期372-378,共7页
Laser & Optoelectronics Progress
基金
高端钢铁联合研究基金(F2019209323)。
关键词
火焰图像
K均值分割
几何特征
随机森林
flame image
Kmean segmentation
geometric feature
random forest
