Application of Multidimensional Data Visualization by Means of Self-Organizing Kohonen Maps to Evaluate Classification Possibilities of Various Coal Types / Zastosowanie Wizualizacji Wielowymiarowych Danych Za Pomocą Sieci Kohonena Do Oceny Możliwości Klasyfikacji Różnych Typów Węgla

2015 ◽  
Vol 60 (1) ◽  
pp. 39-50 ◽  
Author(s):  
Dariusz Jamróz ◽  
Tomasz Niedoba
Keyword(s):  
Data Visualization ◽  
Multidimensional Data ◽  
Material Quality ◽  
Kohonen Maps ◽  
Multidimensional Data Visualization ◽  
Visualization Techniques ◽  
Modern Tool ◽  
Coal Type

Abstract Multidimensional data visualization methods are a modern tool allowing to classify some analysed objects. In the case of grained materials e.g. coal, many characteristics have an influence on the material quality. The paper presents the possibility of applying visualization techniques for coal type identification and determination of significant differences between various types of coal. To achieve this purpose, the method of Kohonen maps was applied by means of which three types of coal - 31, 34.2 and 35 (according to Polish classification of coal types) were investigated. It was stated that the applied methodology allows to identify certain coal types efficiently and can be used as a qualitative criterion for grained materials.

Application of Relevance Maps in Multidimensional Classification of Coal Types / Zastosowanie Map Odniesienia W Wielowymiarowej Klasyfikacji Typów Węgla

2015 ◽  
Vol 60 (1) ◽  
pp. 93-106 ◽  
Author(s):  
Tomasz Niedoba
Keyword(s):  
Particle Density ◽  
Multidimensional Data ◽  
Density Fraction ◽  
Density Fractions ◽  
Multidimensional Data Visualization ◽  
Visualization Techniques ◽  
Modern Tool ◽  
Coal Type

Abstract Multidimensional data visualization methods are a modern tool allowing to classify some analyzed objects. In the case of grained materials e.g. coal, many characteristics have an influence on the material quality. In case of coal, apart from most obvious features like particle size, particle density or ash contents there are many others which cause significant differences between considered types of material. The paper presents the possibility of applying visualization techniques for coal type identification and determination of significant differences between various types of coal. Author decided to apply relevance maps to achieve this purpose. Three types of coal - 31, 34.2 and 35 (according to Polish classification of coal types) were investigated, which were initially screened on sieves and then divided into density fractions. Then, each size-density fraction was chemically analyzed to obtain other characteristics. It was stated that the applied methodology allows to identify certain coal types efficiently and can be used as a qualitative criterion for grained materials. However, it was impossible to achieve such identification comparing all three types of coal together. The presented methodology is new way of analyzing data concerning widely understood mineral processing.

Application of Multidimensional Scaling to Classification of Various Types of Coal/Zastosowanie Skalowania Wielowymiarowego Do Klasyfikacji Różnych Typów Węgli

2014 ◽  
Vol 59 (2) ◽  
pp. 413-425 ◽  
Author(s):  
Dariusz Jamróz
Keyword(s):  
Multidimensional Scaling ◽  
Particle Density ◽  
Multidimensional Data ◽  
Graphical Methods ◽  
Scaling Method ◽  
Density Fractions ◽  
Multidimensional Data Visualization ◽  
Visualization Techniques

Abstract Visualization of multidimensional data is a new way of statistical analysis of so-called statistical graphical methods. These methods allow to classify some analyzed objects, including their various features. Facing grained materials problems, like coal or ores many characteristics have an influence on the quality of product. In case of coal, many features must be taken into consideration to determine quality of the material. Apart from most obvious characteristics like particle size, particle density or ash contents there are many others which cause significant differences between considered types of material. In the paper the application of Multidimensional Scaling Method is presented which is one of the multidimensional data visualization techniques. To this purpose, sampling of three types of coal was performed, which were 31, 34.2 and 35 (according to Polish classification of coal types). First, the material was screened on sieves and then divided into density fractions. Next step was to analyze chemically the obtained particle and size fractions of researched coal. Then, the Multidimensional Scaling Method was applied to visualize the investigated set of data. It was proved that the applied methodology allows to identify certain coal types efficiently and can be used as a qualitative criterion for grained materials. However, it was impossible to achieve such identification comparing all three types of coal together. The Multidimensional Scaling Method is new technique of data analysis concerning widely understood mineral processing.

scholarly journals Multidimensional data visualization

2002 ◽  
Vol 34 (2) ◽  
pp. 158-162 ◽  
Author(s):  
Matthew J. Pastizzo ◽  
Robert F. Erbacher ◽  
Laurie B. Feldman
Keyword(s):  
Data Visualization ◽  
Multidimensional Data ◽  
Multidimensional Data Visualization

Employing Multidimensional Data Visualization Tools to Assess the Impact of Constraint Uncertainties on Complex Design Problems

2017 ◽  
Author(s):  
Gary M. Stump ◽  
Simon W. Miller ◽  
Michael A. Yukish ◽  
Christopher M. Farrell
Keyword(s):  
Data Visualization ◽  
Decision Makers ◽  
Multidimensional Data ◽  
Design Problems ◽  
Design Constraints ◽  
Performance Space ◽  
Multidimensional Data Visualization ◽  
Complex Design ◽  
Visualization Tools ◽  
The Impact

A potential source of uncertainty within multi-objective design problems can be the exact value of the underlying design constraints. This uncertainty will affect the resulting performance of the selected system commensurate with the level of risk that decision-makers are willing to accept. This research focuses on developing visualization tools that allow decision-makers to specify uncertainty distributions on design constraints and to visualize their effects in the performance space using multidimensional data visualization methods to solve problems with high orders of computational complexity. These visual tools will be demonstrated using an example portfolio design scenario in which the goal of the design problem is to maximize the performance of a portfolio with an uncertain budget constraint.

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