Coarse Aggregate Particle Size Distribution of Inference Based on MCMC Algorithm Research
TONG Xin1，LU Yi1，LI Jingwei2,FAN Weijun1
1. College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou, Zhejiang 310018, China
2. Hangzhou Wolei Intelligent Technology Co,Ltd, Hangzhou, Zhejiang 310018, China
Abstract：Machine vision is used to collect data on the coarse aggregate dropped from the vibrating plate,and a ‘pseudothreedimensional’ 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 MarkovMonte Carlo (MCMC) algorithm is used,thus breaking through the problem of insufficient highdimensional 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%.
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2024, Vol. 45 
