计量学报2024，Vol.45Issue(9)：1273-1280,8.DOI:10.3969/j.issn.1000-1158.2024.09.04
基于MCMC算法的粗集料粒径分布推断研究
Coarse Aggregate Particle Size Distribution of Inference Based on MCMC Algorithm Research
摘要
Abstract
Machine vision is used to collect data on the coarse aggregate dropped from the vibrating plate,and a'pseudo-three-dimensional'image of the dynamic aggregate is obtained.Since the image information cannot accurately express the aggregate.Therefore,the idea of Bayesian statistical inference is introduced to infer the particle size distribution of aggregates.The equivalent Feret short diameter is selected as the image feature,but the error between the Feret short diameter and the actual particle size of the aggregate is large when the particle size is large,so the equivalent elliptical short diameter is added as the second feature.In order to obtain accurate posterior distribution and efficient engineering computing capabilities,the Markov-Monte Carlo(MCMC)algorithm is used,thus breaking through the problem of insufficient high-dimensional calculations of traditional Bayesian statistical inference,and thus obtaining the aggregate Posterior distribution of particle size distribution.Experimental results show that the particle size distribution measurement error of this method for qualified aggregates is maintained within±2.5％,and the error for unqualified aggregates is maintained within±3.5％.关键词
几何量计量/粗集料/粒径分布/视觉跟踪/贝叶斯推断/MCMC算法/级配检测Key words
geometrial metrology/coarse aggregate/particle size distribution/visual tracking/Bayesian inference/MCMC algorithm/grading test分类
通用工业技术引用本文复制引用
童欣,陆艺,李静伟,范伟军..基于MCMC算法的粗集料粒径分布推断研究[J].计量学报,2024,45(9):1273-1280,8.基金项目
国家自然科学基金(52075511) （52075511）
