Potassium Glasses Research Articles

Fisher Linear Discriminant and Systematic Cluster Analysis based on analysis and identification of the composition of ancient glass products

The Silk Road was a channel of cultural exchange between China and the West in ancient times, of which glass was a valuable physical evidence of early trade exchanges. Therefore, it is important to analyze the ancient glass products for the research related to ancient times. In this paper, in order to get the classification law of high potassium and lead-barium glass, firstly, the discriminant relationship function was obtained by using Fisher Linear Discriminant Analysis. Secondly, the data were downscaled by Principal Component Analysis to get five principal components, and then Fisher Linear Discriminant Analysis was used to get the discriminant function, and the results all passed the cross-check, and the model accuracy was 95.29% and 91.43%, respectively. The subclasses were classified by Systematic Clustering Model and Elbow Method, and it was concluded that the high potassium glass was mainly divided into two subclasses based on whether the silica content was greater than 80%; the lead-barium glass was mainly divided into four subclasses based on the content of three different chemical components: silica, barium oxide and phosphorus pentoxide. And through the reasonableness and sensitivity analysis, this paper got that different chemical compositions have different size influence on the classification of glass.

An archaeometric contribution to the interpretation of blue-green glass beads from Iron age Central Italy

Several types of (mostly) blue-green glass beads from Iron-Age archaeological sites in Central Italy were studied using a range of spectroscopic techniques: portable X-Ray Fluorescence spectrometry, Fibre Optics Reflectance Spectroscopy, Scanning Electron Microscopy coupled with Energy Dispersive X-ray Spectrometry, micro-Raman spectroscopy and Laser Ablation Inductively Coupled Plasma Mass Spectrometry. Complementary information was gathered from each technique and discussed in the frame of the archaeological typology of the objects. The systematic evaluation of the results allowed us to draw some conclusions on the raw materials employed for primary production and to highlight some provenance indicators in the glass. Some of the beads found in the Iron Age (IA) contexts were preliminarily attributed to the Final Bronze Age (FBA) production based on their typology, and the compositional data obtained in this work confirmed that they were low magnesium high potassium (LMHK) glass, typical of FBA in the Italian peninsula. Other beads were assigned to low magnesium glass (LMG) or high magnesium glass (HMG), thus giving further information on the fluxing agents employed in the Early Iron Age (EIA) and beyond. Colour variations among the beads reflected their chemical composition, with different bead typologies coloured in a specific way. In some instances, it was possible to establish different origins for the colouring raw materials. The provenance of the samples was difficult to place, but the chemical evidence suggested a subdivision within the raw glass used to produce the beads: for one set of samples, a local origin of the glass could be hypothesised, whereas several production sites in the Near East were suggested for most of the beads considered in this study. Some preliminary clues for the local working of imported glass were also highlighted for one typological group.

Research and Analysis of Glass Weathering Based on Ergodic K-Prototype Algorithm

When making glass products, adding fluxes with different chemical compositions can form different glass types. Affected by the burial environment, the composition proportion of ancient glass products will change over time, thus affecting the correct judgment of their types. Based on decision tree, traversal K-prototype algorithm, grey correlation analysis and other methods, this paper studies and analyzes the classification and identification related to glass weathering and the statistics of chemical composition laws, the relationship between the surface weathering of glass relics and the glass type, decoration and color is analyzed. In this paper, after data pretreatment, the surface weathering and the other three indicators are preliminarily observed through contingency analysis, and then the correlation between the variables is further clarified by using the chi-square test. It is found that the surface weathering is significantly related to the overall glass type, and lead barium glass is more prone to surface weathering than high potassium glass; There is no obvious correlation between surface weathering and decoration and color. Then, according to the glass types, the glass relics are divided into four glass types: lead-barium weathering/non-weathering, high-potassium weathering/non-weathering, and the statistical analysis is established after the pretreatment of the chemical composition of these four types. Then, combined with the independence t-test, we can further obtain the specific element differences, and give a ranking table of the degree of influence. Finally, based on the above statistical exploration, the model of chemical composition law of glass relics before weathering is built according to the mechanism analysis of the four composition laws of high-potassium glass Si and its chemical composition that significantly affects the weathering, the chemical composition that significantly affects the weathering of lead-barium glass, and the chemical composition that has no effect on the weathering of glass. According to the test data of weathering point, the prediction table of chemical composition content before weathering is given. The interval test of the model results shows that the failure rate of high potassium prediction is high. The revised model of chemical composition law of glass relics before weathering is obtained by analyzing and considering the modification of the value rule of high potassium Si.

Glass Classification and Identification Based on Random Forest Model and Systematic Clustering

A large exchange of material elements occurs during glass weathering, which will affect the identification and judgment of ancient glass. To this end, the data is first used to give the basis for dividing lead barium and high potassium glass, and the difference between the chemical components of each type of glass can be compared to obtain a conclusion. Since the final number of subclasses is unknown, this paper uses systematic cluster analysis to obtain the number of subclasses and classification results at the same time. In order to identify the glass types of unknown cultural relics, this paper uses the existing data to establish a random forest model and construct a method and standard for dividing glass types.

Class classification of glass products based on K-means clustering

The main raw material of glass is quartz sand, due to the high melting point of pure quartz sand, to reduce the melting temperature, it is necessary to add fluxes during refining. Different fluxes are added, and their main chemical composition is also different. To accurately classify high potassium glass and lead-barium glass, this paper establishes a K-means clustering model to classify glass types. High potassium glasses were classified into three subclasses by the content of iron oxide and barium oxide and lead barium glasses were classified into three subclasses by the content of magnesium oxide and barium oxide. Sensitivity analysis was performed on the model and the results remained significantly correlated.

Analysis of the composition of ancient glass objects based on cluster analysis and Bayesian discriminant methods

The aim of this study is to investigate the open problem of classification laws and results. First, a comprehensive research method of quantitative analysis-Bayesian discrimination is used to reasonably and accurately investigate the classification laws of high potassium glass and lead-barium glass, obtaining the Bayesian discriminant for high potassium glass ????1 = 2.909????1 - 0.117????2 + 4.487 ????3 + 2.432????4 + 1.097????5 + 3.352????6 + 3.403????7 + 6.303????8 + 2.669????9 + 0.897????10 + 1.882????11 - 4.543????12 + 5.002????14 - 150.381 with the lead-barium glass Bayesian discriminant ????2 = 2.447????1 - 1.195????2 + 42.807????3 + 2.117????4 + 1.0971.534????5 + 3.221????6 + 3.299????7 + 4.830????8 + 2.513????9 + 1.226????10 + 1.590????11 - 5.628????12 + 3.087????13-111.481. Then, using the clustering process-Bayesian discrimination method, the number of sample clusters was roughly determined using systematic clustering, and the data were categorized in detail using K-Means clustering, resulting in the possibility of classifying each of the high-potassium and lead-barium glasses into Three subclasses were identified, and Bayesian discriminant functions were derived for each subclass, and the data were tested to justify the subclasses.

An analytical identification model for ancient glass based on support vector machine and cluster analysis

Glass artifacts are easily weathered by environmental influences during burial, and the internal chemical composition is exchanged with the external environment, resulting in changes in its composition ratio. For archaeological interrogation, it is of great significance to study the chemical composition of glass before and after weathering and related issues. In this paper, a support vector machine model is used to reasonably separate the data points of high potassium glass and lead-barium glass, and a linear classification function is derived, i.e., the classification law of the two types of glass is obtained. Then, this paper performs R-type cluster analysis on the chemical composition indexes first, and reduces the dimensionality of several indexes; then performs Q-type cluster analysis on the sample detection points, and performs a one-way ANOVA test on the classification scheme of different numbers of subclasses obtained, and finally divides them into four subclasses. On this basis, the data of the unknown class of glass artifacts are substituted into the trained support vector machine model, and the type to which they belong can be determined by calculating the value of the judgment linear classification function.

Composition analysis and identification of ancient glass products based on K-Means clustering analysis algorithm

Ancient glass is a valuable physical evidence of early trade exchanges, and it is very susceptible to weathering caused by the environmental impact of burial. Therefore, the establishment of a glass cultural relics identification model and the analysis of the chemical composition of glass cultural relics are extremely important for the classification and protection of cultural relics in our country. In this paper, we first use differential analysis to find out the chemical composition with significant differences before and after weathering, and obtain the classification laws of high potassium glass and lead-barium glass. Then the 14 chemical components of high potassium glass and lead-barium glass were analyzed by K-Means clustering, and finally 8 subclasses were divided. After testing, the model has low sensitivity, strong stability and good robustness. On this basis, the improved PYH-Topsis solution distance method is used to construct a judgment matrix, analyze the chemical composition of glass cultural relics of unknown categories, and identify the types to which they belong.

Composition Analysis and Identification of Ancient Glass Objects Using Regression and Clustering Algorithms

Among the artifacts excavated today, many are glass-products. Archaeologists have classified archaeological artifacts into two main types, high potassium glass and lead-barium glass, based on the chemical composition of the glass artifacts and other testing methods. For the protection of cultural relics, the chemical composition of the artifacts needs to be further analyzed and sub-categorized in order to achieve different protection measures for different categories. In this paper, firstly, based on the data, we analyze the classification pattern of high potassium glass and lead-barium glass, use logistic regression, test the significance of the results, and evaluate and analyze the regression results with the statistical distribution of the data. Then for different categories select suitable chemical indicators for subclass classification, suitable chemical indicators imply their relatively large amount of information, so use the entropy weight method to process chemical composition indicators to derive their information entropy weights, select chemical indicators according to the weights, and finally perform clustering classification according to the selected indicators. Finally, for the analysis of the rationality and sensitivity of the classification results, different classification algorithms may lead to different classification results, so the rationality and sensitivity can only be analyzed by comparing and analyzing the classification results based on different clustering algorithms for the selected chemical composition indicators.

Based on the CART Decision Tree Model of Prediction and Classification of Ancient Glass-Related Properties

Weathering occurs when glass interacts with the atmosphere. Ancient glass is susceptible to the influence of the burial environment. In the process of glass weathering, its composition ratio will be changed due to a large number of internal elements and environmental elements exchange. In this paper, Fisher's exact test and frequency analysis are used to test the glass pattern, type, color, and surface weathering respectively, and the conclusion is that there are significant differences between glass type and surface weathering, while there are no significant differences between pattern, color, and surface weathering. Then, the CART decision tree algorithm was used to classify the glass into weathered and unweathered, and a binary tree was established with 14 chemical components as characteristics. The average chemical composition of all the sampling points in the sibling nodes of the node where the weathering point is located was used as the predictive value of the chemical composition before the weathering point. Finally, the correlation analysis of the chemical constituents of high potassium glass and lead barium glass was carried out by hierarchical clustering method. In a high-potassium glass, silica is one category, and other chemical components are another category. In a lead-barium glass, silica and lead oxide are one category, and other chemical components are another category. Therefore, it can be considered that the chemical composition relationship of lead-barium glass is more complex.

Composition analysis and identification of ancient glass products based on gray correlation

The study of ancient glass, as an important branch of scientific and technological archaeology, is an important physical material for exploring the economic, technological and cultural exchanges between China and foreign countries on the Silk Road. It is of great significance for the study of the development of ancient societies and cultural exchanges between China and foreign countries. This paper focuses on the composition analysis and identification of ancient glass products, based on data mining and software analysis, giving the weathering pattern of glass surface, predicting the chemical composition content before weathering, as well as cluster analysis and correlation analysis of different categories of glass and their chemical composition. We also concluded that the composition of silica, potassium oxide and barium oxide in high potassium glass artifacts changed to a great extent before and after weathering, while the composition of silica and lead oxide in lead-barium glass artifacts changed to a great extent, and finally, we constructed a multiple linear regression model for prediction by the data of each chemical composition at the weathering point, and conducted F-test on the model to prove that the prediction models of silica, potassium oxide, barium oxide and lead oxide had a good performance. The F-test of the model proved the reasonableness of the prediction models for silica, potassium oxide, barium oxide, and lead oxide. It can be concluded that the glass type has an influence on the chemical composition of glass, so the glass can be divided into two categories of glass: high potassium and lead-barium, and then the chemical composition of these two categories of glass were analyzed separately by gray correlation analysis to obtain the top five correlation ranking of the chemical composition of each category of glass, and then the differences in the chemical composition of these two categories of glass were compared.

Fisher's linear discriminant analysis model for inscribing the effect of chemical composition on cultural heritage

In this study, the relationship between weathering and glass type, decoration and colour was first analysed using Goodman and Kruskal's tau-y coefficients, and a chi-squared test was performed, which showed that weathering was most influenced by glass type, and was influenced by glass decoration but very little by glass colour. A Fisher linear discriminant analysis model was then established, and the eight chemical components with the greatest correlation to artefact type were calculated using SPSS software under the conditions of the two glass types respectively, and optimised using a grey correlation algorithm with specified thresholds for screening components, resulting in four final elements affecting high potassium glass as potassium oxide, magnesium oxide, iron oxide and copper oxide, and affecting lead and barium types as calcium oxide, aluminium oxide, lead oxide and barium oxide, aluminium oxide, lead oxide, barium oxide and strontium oxide.

Component Analysis Model of Glass Relics Based on Logistic Regression and Fisher Linear Discrimination

In recent years, with the attention paid by various countries to the exploitation and protection of cultural relics and the continuous development of industrialization, people have gradually realized the necessity of analyzing the weathering of ancient glass relics. In this paper, the physical characteristics, chemical composition and content of sample cultural relics are analyzed and their types are identified by using multiple algorithms such as Logistic regression model, Fisher linear discrimination, Fisher’s exact test, etc. The study of this issue can provide guiding suggestions for the analysis and classification of ancient cultural materials, and help archaeologists carry out a series of work such as cultural relics protection. The results show that the "type" factor has the greatest relationship with weathering, followed by "color" and "decoration". The regression equation between the sample and weathering chemical composition is established, and it is concluded that "type" and "SnO2" have a large negative correlation with weathering, "MgO" and "SO2" have a large positive correlation, and other variables have a weak correlation. Finally, Fisher linear discriminant method is used to identify the category of unknown glass relics. By estimating the training group and testing the test group, it is found that the algorithm can correctly classify 100.0% of the original grouped cases. We can identify the unknown cultural relics and find that A1, A5, A6 and A7 cultural relics belong to high potassium glass, and the rest belong to lead barium glass. In order to test the accuracy of the model in identifying the category of glass relics, Fisher's exact test algorithm was used. It was found that the classification results were more sensitive to silica in the fluctuation range of - 50% to - 25%, and less sensitive to other chemical components, it is stable.

Composition Analysis of Glass Based on Weighted Mean and Correlation Analysis

Due to weathering and other external influences, the surface and chemical composition of ancient cultural relics will be corroded to a certain extent. This paper analyzes the chemical composition of two main kinds of ancient glass, high potassium glass and lead barium glass, classifies the cultural relic samples according to the type and weathering degree, and studies the chemical composition content rule of weathered samples respectively through descriptive statistics. The weighted average was used to predict the chemical constituents before weathering, and the correlation analysis of the main constituents was carried out. The composition analysis of ancient cultural relics in this paper lays a foundation for further effective measures to protect and restore cultural relics.

Analysis and identification of glass products based on fisher linear discriminant analysis and logistic regression for dichotomous classification

This paper focuses on establishing multiple linear regression models, correlation analysis, fisher linear discriminant analysis, logistic regression to deal with dichotomous classification, sensitivity analysis, systematic clustering and gray correlation analysis to solve the problem of compositional analysis and identification of ancient glass products. The subclasses of each category of glass are divided, and the division results are derived, so that the rationality and sensitivity of the classification results can be analyzed, and finally the classification laws of high potassium glass and lead-barium glass are derived. Analysis The chemical composition of unknown glass artifacts, predict their glass types, and perform sensitivity analysis on the predicted results.

Classification model of glass relics based on decision tree algorithm

In order to assist in the compositional analysis and identification of glass artefacts, to clarify the correlations and differences between the individual chemical components, and to achieve the aim of analyzing the patterns of classification of glass artefacts, a classification model was developed in this paper. The data obtained from the chemical components are first processed accordingly, and then a multiple linear regression model is established to investigate the relationship between each chemical component variable and the variable of weathering, and a few chemical components that are significantly affected by weathering are selected as the basis, and a decision tree model based on a particle swarm algorithm is constructed to investigate the classification pattern and subclasses. The final result is that glass products can be classified into high potassium glass types and lead-barium glass types according to whether the chemical composition PbO content is greater than 6.078. The K-Means classification model was also used to classify three subclasses of high-potassium glass products and five subclasses of lead-barium glass products according to the content of the relevant chemical components.

Composition Analysis and Identification Scheme of Ancient Glass Products Based on K-Means Algorithm

Chinese ancient glass mainly includes high potassium glass and lead barium glass. In the process of weathering, its chemical composition is easy to change, affecting the judgment of its category. The data of this study are based on the chemical composition ratio of classified glass relics obtained by preprocessing. Firstly, a random forest model is constructed based on the Gini coefficient to obtain the contribution of lead oxide to the classification. The decision tree in the random forest is extracted, and the optimal classification rule is obtained by using the majority voting mechanism : when ω ( PbO ) is 10 %, it is lead barium glass. Secondly, the K-means clustering algorithm is divided into three sub-categories. The characteristic quantity with large standard deviation is used as the basis for classification, and the classification results are obtained : high potassium glass sub-categories include low calcium type, medium calcium aluminum type and high calcium aluminum type ; lead barium glass subclass includes low barium type, medium lead barium type, high lead barium type. For the identification of unknown types, the Euclidean distance between the unknown category and the sub-category centroid is calculated, and the minimum distance is taken to determine the sub-category it belongs to.

Glass Classification and Identification based on K-means++ Clustering Analysis and BP Neural Network Method

The weathering caused by the burial environment will cause the ancient glass to exchange material with the environment, and the weathering layer produced by the material exchange will have an impact on the class judgment of ancient glass. In this paper, we analyze the classification rules of high potassium glass and lead-barium glass; select the appropriate chemical composition for subclass division and establish the classification model of glass. After this, a binary classification (high potassium/lead-barium) model is established by BP neural network training, and the subclassified main components of each type of glass derived from the classification model are used as adjustment variables to analyze the chemical composition of unknown categories of glass artifacts and identify the types to which they belong by controlling a single variable.

Glass Classification and Identification based on Decision Tree and Random Forest Classification Models

Glass artifacts are weathered due to age and improper preservation, etc. This paper analyzes the classification laws of high potassium glass and lead-barium glass based on the data; for each category, suitable chemical compositions are selected to classify them into subcategories, and firstly, a decision tree classification model is used to analyze the classification laws of the two categories of glass by substituting the data set for machine learning, and specific classification methods and classification results are given. And the chemical composition of the unknown category of glass artifacts is analyzed, and the random forest classification model is used to predict the training set and test set by.

Analysis and Identification of the Composition of Ancient Glass Objects based on Statistical Research and Machine Learning Algorithms

In order to promote the study of ancient glass artifacts, this paper integrates the weathering principle and the nature of chemical elements in glass, and analyzes the color and chemical composition of glass using statistical and machine learning methods. First, the classification and regression tree featuring Gini index is applied to explore the classification criteria of high potassium glass and lead barium glass, and the accuracy of the model is tested by 10-fold cross-validation. Secondly, subclasses were classified for the collected sample data. In this paper, Two Step Cluster Algorithm is used to divide the high potassium glass into four subclasses and the lead barium glass into five subclasses based on the Euclidean distance between the samples. Finally, the Spearman’s rank correlation coefficients between chemical components are computed separately based on the proportion of each chemical component in glass to explore the correlation between them.