Issue |
E3S Web Conf.
Volume 478, 2024
6th International Conference on Green Energy and Sustainable Development (GESD 2023)
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Article Number | 01027 | |
Number of page(s) | 4 | |
DOI | https://doi.org/10.1051/e3sconf/202447801027 | |
Published online | 16 January 2024 |
Analysis and research on the chemical composition of ancient Chinese glass
1 School of Computer Science, Guangdong University of Technology, Guangzhou, 510006, China
2 School of Foreign Language, Guangdong University of Technology, Guangzhou, 510006, China
* Corresponding author: Ming Fang
The purpose of this study is to investigate the classification rules for lead-barium and highpotassium glass. First, we noticed that the glass types differed considerably for the five components. After analyzing the data and establishing the independent T-test model, We developed the Logistic regression classification model using these components as independent factors and the glass types as dependent variables, and we derived the classification function for the glass types. Second, high-potassium glass, was divided into two subclasses based on calcium and aluminum content using the K-means++ model and associated literature on the chemical composition of ancient Chinese glass [1][2]. These were grass-ash high-potassium glass and nitrate high-potassium glass, as well as two types of lead-barium glass: lead-sodium glass and lead-calcium glass. Finally, the cluster center coordinate values were perturbed by -10% and 10%, verifying the classification of each sample under different perturbations, and the resulting subclass divisions remained unchanged, confirming the low sensitivity of the classification results.
Key words: logistic regression / K-means++ model / sensitivity analysis
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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