Study on Composition Analysis and Identification of Ancient Glass Products
DOI:
https://doi.org/10.54097/hset.v29i.4842Keywords:
Grey relational analysis, Paired sample t-test, Ancient glass.Abstract
The Silk Road, as a channel of cultural exchange between China and the West in ancient times, promoted the development and dissemination of glass technology and culture in ancient China. The analysis of the chemical factors of ancient glass is of great help to the study of the economy. The main research content of this paper is to analyze the chemical content of ancient glass products. To analyze the correlation of different types of chemical components, first of all, this paper uses SPSS to carry out grey correlation analysis. According to the grey correlation degree, we can screen out the elements with the greatest degree of correlation between each chemical component and it. Then, the paired samples t-test was used to analyze the differences in chemical composition association between different categories. At the significance level a = 0.05, it was found that except for SO2, there were significant differences in the chemical composition of other elements among different categories.
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References
Fu Weizhong, Chu Liuping. Research on the Evaluation of Manufacturing High Quality Development from the Perspective of Yangtze River Delta Integration -- TOPSIS Evaluation Model based on the improved CRITIC- entropy weight method combined weight [J]. Industrial technology and economics, 2020, 39(09): 145-152.
L Syafaah, D Arisnanda, M Irfan, R H Hendaryati. Correlation of risk factors for coronary heart disease in type-2 diabetes mellitus using the chi-square method [J]. IOP Conference Series Materials Science and Engineering, 2020, 821(1).
li xingqi, gao xiaohong. Dimensionless method of effectiveness evaluation [J]. Journal of statistics and decision, 2021 ((15): 24 to 28, DOI:10.13546/j.carol carroll nki tjyjc. 2021.15.005.
Shen Ying. T test and F Application of inspection in Trend Analysis of chemical test quality control data [J]. Chinese journal of inspection and quarantine, 2019, 29(02): 105-107.
he xiuli. Multivariate Linear model and Ridge regression analysis [D]. Huazhong University of Science and Technology, 2005.
Yan Chun, Li Yaqi, Sun Haitang. Research on Auto Insurance Fraud Identification based on Ant Colony Optimization Random Forest Model [J]. Insurance research, 2017(6): 114-127. The DOI:10.13497/j.carol carroll nki is. 2017.06.011.
Deng Yurui, xu-dong cheng, Tang Fang, zhou yong. Risk control of Rice mildew in storage based on multiple linear regression analysis and random forest algorithm [J]. Journal of University of Science and Technology of China, 202, 52(01): 47-54+72+55.
Tian Bing, Huang Shan, Sun Ye, Yan Yujie, Jin Kangkang. Rockburst Prediction based on SOM neural network clustering and gray TOPSIS evaluation method [J]. China mining, 2021, 30(01): 188-192.
guo wenjuan. K-means clustering algorithm based on Optimized initial clustering Center [J]. Wind technology, 2022(4): 63-65. The DOI: 10.19392/j.carol carroll nki. 1671-7341.202204021.
Wu Huijie. Study on Application and Optimization Method of random machine forest Algorithm [D]. Jiangnan great learning, 2021. DOI:10.27169/, dc nki. Gwqgu. 2021.001718.
Zhang Weifeng. Statistical analysis of Spearman's simple correlation coefficient and Gini Gamma correlation coefficient [D]. wide East university of technology, 2020. DOI:10.27029/, dc nki. Ggdgu. 2020.001333.
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