Composition Analysis and Identification of Ancient Glass Products
DOI:
https://doi.org/10.54097/hset.v21i.3157Keywords:
Chi-square test, ARMA model, K-means clustering algorithm.Abstract
In this paper, aiming at the problem of composition analysis and identification of ancient glass products, a correlation analysis model based on chi square test is established to solve the relationship between the surface weathering of cultural relics and glass types, patterns and colors; ARMA model is established to solve the statistical law of chemical composition content with or without weathering and the problem of predicting chemical composition content before weathering; A classification model based on K-means++clustering is established to solve the problem of classification rules; An evaluation model based on principal component analysis is established to solve the problem of type identification of glass relics.
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