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  4. Vol 4, Issue 3, 2023

Composition Analysis and Identification of Ancient Glassware Based on Multimodal Analysis

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DOI: 10.23977/autml.2023.040309 | Downloads: 3 | Views: 251

Author(s)

Jianguo Deng 1, Jiahang Liang 1, Ruiqing Yang 1, Yuan Wang 1, Zhenxing Zhang 2

Affiliation(s)

1 College of Artificial Intelligence, Tianjin University of Science and Technology, Tianjin, 300222, China
2 College of Sciences, Tianjin University of Science and Technology, Tianjin, 300222, China

Corresponding Author

Jianguo Deng

ABSTRACT

Based on the data of cultural relics, this paper constructs the models of Logistics regression and stepwise regression, and discusses the relationship between the changes of chemical composition before and after weathering and the factors of glass relics. First of all, the relationship between weathering and glass type, pattern and color are analyzed by Logistics regression model and chi-square test. The results show that the relationship between weathering and glass type is strong, the relationship between weathering and grain decoration is weak, and the relationship between weathering and color is weak. Then, this study established a stepwise regression model and cross-verified to reveal the statistical law of weathering chemical composition. Six independent variables of weathering, lead oxide, sodium oxide, potassium oxide, barium oxide and alumina were retained by stepwise regression analysis. Finally, the component content prediction model before weathering is established by using the mean method to predict the corresponding compound content of the weathering point before weathering under the same glass type. In addition, with the help of the multiple regression equation, the cultural relic data are substituted into the regression equation to predict the classification results, and the sensitivity is analyzed by variance test. A judgment index, bad degree, is defined to judge the sensitivity. The prediction results show that 6 of the 8 cultural relics are less bad, the sensitivity of the prediction results is weak, and the classification results are better.

KEYWORDS

Glass, Logistics regression model, Chi-square test, Stepwise regression model, Severity

CITE THIS PAPER

Jianguo Deng, Jiahang Liang, Ruiqing Yang, Yuan Wang, Zhenxing Zhang, Composition Analysis and Identification of Ancient Glassware Based on Multimodal Analysis. Automation and Machine Learning (2023) Vol. 4: 71-79. DOI: http://dx.doi.org/10.23977/autml.2023.040309.

REFERENCES

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[2] Mingxu F,Xiaoqin Z,Jinzhou S. Experimental investigation on cutting force and machining parameters optimization in in-situ laser-assisted machining of glass–ceramic[J]. Optics and Laser Technology,2024,169.
[3] Yongliu Z,Yuan L,Hui S, et al. Improving the adhesion of diamond-like carbon films on glass substrate by chemical etching and oxygen plasma treatment[J]. Vacuum,2023,218.
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