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Study on Composition Analysis and Identification of Ancient Glass Products

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DOI: 10.23977/analc.2022.010108 | Downloads: 16 | Views: 832

Author(s)

Shengqiang Yao 1, Yang Nie 1, Wei Zhu 1, Xiaojing Yang 2

Affiliation(s)

1 School of International Business, Zhejiang Yuexiu University, Shaoxing, 312069, China
2 School of Information Technology, Hebei University of Economics and Business, Shijiazhuang, 050061, China

Corresponding Author

Shengqiang Yao

ABSTRACT

Affected by the weathering process, the internal elements in ancient glass will be exchanged with the external environmental elements, resulting in a change in the proportion of chemical components, but there will be a certain correlation between the exchange of elements. In this paper, a mathematical model for component analysis and prediction using the K-means clustering model and BP neural network is established.

KEYWORDS

Grey correlation, k-means clustering, contour, chi-square tes

CITE THIS PAPER

Shengqiang Yao, Yang Nie, Wei Zhu, Xiaojing Yang, Study on Composition Analysis and Identification of Ancient Glass Products. Analytical Chemistry: A Journal (2022) Vol. 1: 61-67. DOI: http://dx.doi.org/10.23977/analc.2022.010108.

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