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Academic Journal of Computing & Information Science, 2022, 5(13); doi: 10.25236/AJCIS.2022.051306.

Composition identification of ancient glass products based on cluster analysis

Author(s)

Zhenghu Pang

Corresponding Author:
Zhenghu Pang
Affiliation(s)

Institute Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, China, 233030

Abstract

The Silk Road was a passage for the exchange of Chinese and Western cultures in ancient times, and glass was a valuable physical evidence of early trade exchanges. The main raw material of glass is quartz sand, and the main chemical composition is silicon dioxide (SiO2). Due to the high melting point of pure quartz sand, in order to reduce the melting temperature, it is necessary to add flux during refining. Since different materials can cause glass products to have different properties, the classification of glass products is worth studying. In this paper, a composition analysis and identification model of glass products is developed. First of all, the relationship between surface weathering and glass type, ornamentation and color of glass cultural relics is studied, and after the chemical composition content is counted, the chemical composition content before weathering is predicted according to the weathering point detection data.

Keywords

Glass composition identification, K-means, Systematic clustering

Cite This Paper

Zhenghu Pang. Composition identification of ancient glass products based on cluster analysis. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 13: 38-43. https://doi.org/10.25236/AJCIS.2022.051306.

References

[1] Li Aiying. Prediction of particulate matter concentration in Urumqi based on RF-KMEANS-LIBSVM [J].Environmental Protection Science, 2022, 48(04):118-124.

[2] Gu Lijuan, Si Shoukui, Sun Huijing, Dong Chao. Fuzzy Clustering Analysis Applied to Artillery Precision Strike Effectiveness Assessment [J].Ordnance Automation, 2015, 34(12):1-3.

[3] Qin Risheng, Guan Hua, He Xin, Duan Ruimin. Clustering Analysis of Actual Daily Load Curve Based on Improved PSO-Kmeans Algorithm [J].Electrotechnical Techniques, 2022(11):1-6.

[4] Zhu Yifei. Application of kmeans-MOTE algorithm of clustering fusion in personal credit risk assessment [D].Shanghai Normal University, 2022.

[5] Chen Chenpeng, Zhao Xin, Bi Guihong, Chen Shilong, Xie Xu. Short-term wind speed prediction based on Kmeans-VMD-LSTM[J].Motor and Control Applications, 2021, 48(12)

[6] Li Zipeng, Wang Decai, Zhang Yu, Tan Guohui, Sun Hua, Qiao Mu, Mei Shuqi, Peng Xianwen. Principal component analysis and cluster analysis of slaughtering and body size traits of selenium black pigs[J].Genetic Breeding, 2022, 58(08)

[7] Zhang Minjuan, Hu Minmin, Bian Zhenhua. Identification of Chinese herbal ingredients and quantitative analysis of simferlin in regulating spleen and suppressing pancreas combination [J]. Jiangsu Medicine, 2017, 43(13):912-914.

[8] Wang Rongshen, Zhang Shoujun, Tao Huimin, Liu Dongmei, Li Wanzhong. Study on the identification of chemical constituents of Aconite seeds [J]. Guangzhou Chemical Industry, 2016, 44(02):93-95.

[9] Liu Mengtian. New research progress on the identification method of animal-derived ingredients in meat foods [J]. Science and Technology Perspectives, 2015, (25):157-158.

[10] Guo Zhongkun, Wang Kezhou, Ji Guoxia, Ji Chuanliang. Research progress on the composition, identification methods and pharmacological effects of Colla Corii Asini [J]. Journal of Liaoning University of Traditional Chinese Medicine, 2015, 17(04):71-74.