基于MCMC算法的粗集料粒径分布推断研究

doi:10.3969/j.issn.1000-1158.2024.09.04

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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%.

Key words： geometrial metrology coarse aggregate particle size distribution； visual tracking Bayesian inference MCMC algorithm grading test

Received: 12 October 2023 Published: 26 September 2024

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TB92

Fund:The State Quality Inspection Administration Public Welfare Scientific Research Project

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	Articles by authors
	TONG Xin
	LU Yi
	LI Jingwei
	FAN Weijun

Cite this article:

TONG Xin,LU Yi,LI Jingwei, et al. Coarse Aggregate Particle Size Distribution of Inference Based on MCMC Algorithm Research[J]. Acta Metrologica Sinica, 2024, 45(9): 1273-1280.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2024.09.04 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2024/V45/I9/1273

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