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  4. Vol 7, Issue 6, 2023

Composition analysis of ancient glass products based on logistic regression and principal component analysis

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DOI: 10.23977/acss.2023.070612 | Downloads: 10 | Views: 366

Author(s)

Tianyu Zheng 1, Ziyi Gong 1, Yutong Chen 2, Qianyun Ma 3

Affiliation(s)

1 School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China
2 School of Science, Wuhan University of Science and Technology, Wuhan, China
3 School of Foreign Languages, Wuhan University of Science and Technology, Wuhan, China

Corresponding Author

Tianyu Zheng

ABSTRACT

In order to analyze and study the composition of ancient glass products, this paper preprocessed the data and carried out statistical analysis of charts to qualitatively analyze the relationship between weathering and color, type and pattern of cultural relics, and established a Logistic regression model for quantitative analysis. There are four types of cultural relics according to the type of cultural relics and whether they are weathered. Through the evaluation model of principal component analysis, the statistical law of weathering and chemical composition content of excavated glass cultural relics is calculated according to the corresponding comprehensive score range of each kind. A multiple linear regression model was established to predict the pre-weathering component content. The correlation and difference between the chemical components of different kinds of glass relics were analyzed. The correlation coefficients of high potassium glass and lead barium glass were analyzed respectively, and the correlation coefficient heat maps were drawn. Determine the relationship between the chemical components and the differences between different chemical components.

KEYWORDS

Logistic regression model, Principal component analysis, Multiple linear regression model, Correlation coefficient

CITE THIS PAPER

Tianyu Zheng, Ziyi Gong, Yutong Chen, Qianyun Ma, Composition analysis of ancient glass products based on logistic regression and principal component analysis. Advances in Computer, Signals and Systems (2023) Vol. 7: 92-101. DOI: http://dx.doi.org/10.23977/acss.2023.070612.

REFERENCES

[1] Li Yingli. Research on Glassware unearthed in China from the 10th to the 12th century [D]. Liaoning: Liaoning Normal University, 2021. 
[2] Scientific Platform Serving for Statistics Professional 2021. SPSSPRO. (Version 1. 0. 11) [Online Application Software]. Retrieved from https://www.spsspro.com. 1-2. 
[3] Gu Liangliang, Zhou Jing. Some Problems on the Research of Ancient Chinese Glass Art [J]. Art and Design, 2021, 2(8):111-114. 
[4] Liang Beichen, Dai Jingmin. Application of Partial least squares Method in System Fault diagnosis [J]. Journal of Harbin Institute of Technology, 20, 52(3):156-164. DOI:10. 11918/201805149. 1-2. 
[5] He Xiaoqun. Multivariate statistical analysis. Beijing: China Renmin University Press, 2012. 1-2. 
[6] Gan Fuxi, Zhao Hongxia, Li Qinghui, et al. Science and technology analysis and research of the Warring States Period glass unearthed in Hubei Province [J]. Jianghan Archaeology, 2010(2):108-116, 151, 152.

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