Author(s)

Xiaochuan Ai ¹

Affiliation(s)

¹ Computer Science and Engineering, Xi'an Technological University, Xi'an, Shaanxi, 710021, China

Corresponding Author

Xiaochuan Ai

ABSTRACT

Antique glass products are highly susceptible to environmental influences and weathering, and their chemical composition ratios are prone to change. Given this, this article is based on integrating known data processing and mathematical methods such as comprehensive evaluation and mean analysis to establish a multiple linear regression model to explore the changes in surface chemical composition. According to the clustering analysis method, accurately classify subcategories and explore the rationality and sensitivity of the classification results. Finally, use Euclidean distance to determine the unknown category of cultural relics to be tested. The results show that: (1) For lead barium glass, Na₂O and Al₂O₃ have a protective effect on weathered cultural relics, and the SiO₂ content decreases after weathering, while the PbO, BaO, P₂O₅, and CaO content increases; For high potassium glass, the content of SrO, SnO, and SO₂ is almost zero, and the content of Na₂O remains unchanged before and after weathering. The content of SiO₂ increases while the content of other elements decreases. (2) The model successfully subdivided the glass subclass into four categories: low SiO₂, BaO-PbO-CuO, high PBO high BaO-SO₂, low BaO high PbO-SiO₂, and high SiO₂-PbO low BaO-Al₂O₃. Three types of high potassium glass: high SiO₂, low SiO₂, SiO₂-CaO-Al₂O₃.

KEYWORDS

Glass artifacts; Weathering; Comprehensive evaluation; Multiple linear regression; System clustering method; Euclidean distance

CITE THIS PAPER

Xiaochuan Ai. Study on Composition Analysis and Species Identification of Glass Relics Based on the Multiple Linear Regression Model. Advances in Computer, Signals and Systems (2023) Vol. 7: 55-63. DOI: http://dx.doi.org/10.23977/acss.2023.070408.

REFERENCES

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