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| Coarse aggregate particle size distribution of inference based on MCMC algorithm research |
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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%.
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Received: 12 October 2023
Published: 26 September 2024
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| Fund:The State Quality Inspection Administration Public Welfare Scientific Research Project |
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Corresponding Authors:
Yi LU
E-mail: luyi9798paper@163.com
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2024, Vol. 45 
