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- W4387148521 abstract "With the continuous development of cultural relics and archaeological techniques in China, the composition analysis and identification of ancient glass products have gradually become the focus of discussion. In this paper, we use principal component analysis to analyze the classification pattern of two types of glass, and analyze the main factors that affect the classification of high potassium glass and lead-barium glass, respectively. The chemical composition of each class was analyzed by subclass analysis using spectral cluster analysis, and lead-barium glass could be divided into four subclasses, and high-potassium glass could be divided into three subclasses. And the reasonableness and sensitivity of the algorithm were tested by calculating the composite correlation coefficient and class averaging method, and the reasonableness of the algorithm was obtained to be stronger and less sensitive. Then, based on the previous paper, we added weathering and non-weathering as classification conditions, and used principal component analysis to classify the unknown category of glass artifacts into two categories: high-potassium glass and lead-barium glass, and then further classified them according to the subcategory classification method in the previous paper. The sensitivity and rationality of this classification algorithm were finally tested by the class averaging method." @default.
- W4387148521 created "2023-09-29" @default.
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- W4387148521 date "2023-08-11" @default.
- W4387148521 modified "2023-09-30" @default.
- W4387148521 title "Composition Analysis and Identification of Ancient Glass Products Based on Spectral Clustering Algorithm" @default.
- W4387148521 doi "https://doi.org/10.1109/icipca59209.2023.10257722" @default.
- W4387148521 hasPublicationYear "2023" @default.
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