Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The significance of data visualization in modern research is growing steadily. In mineral processing scientists have to face many problems with understanding data and finding essential variables from a large amount of data registered for material or process. Hence it is necessary to apply visualization of such data, especially when a set of data is multi-parameter and very complex. This paper puts forward a proposal to introduce the autoassociative neural networks for visualization of data concerning three various types of hard coal. Apart from theoretical discussion of the method, the empirical applications of the method are presented. The results revealed that it is a useful tool for a researcher facing a complicated set of data which allows for its proper classification. The optimal neural network parameters to successfully separate the analyzed three types of coal were found out for the analyzed example.
Rocznik
Tom
Strony
719--734
Opis fizyczny
Bibliogr. 36 poz., rys.
Twórcy
autor
- AGH University of Science and Technology, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, Department of Applied Computer Science, al. Mickiewicza 30, 30-059 Krakow, Poland
Bibliografia
- 1. Ahmed H.A.M., Drzymala J.: Two-dimensional fractal linearization of distribution curves, Physicochemical Problems of Mineral Processing, vol. 39, 129–139, 2005.
- 2. Aldrich C., Visualization of transformed multivariate data sets with autoassociative neural networks. Pattern Recognition Letters, Volume: 19, Issue: 8, June, 1998, 749–764.
- 3. Asimov D., The Grand Tour: A Tool for Viewing Multidimensional Data, SIAM Journal of Scientific and Statistical Computing, 128–143, vol. 6, No, 1985.
- 4. Assa J., Cohen-Or D., Milo T., RMAP: a system for visualizing data in multidimensional relevance space, Visual Computer, vol.15, no.5, 217–34. Publisher: Springer-Verlag, Germany, 1999.
- 5. Brozek M., Surowiak A., Argument of Separation at Upgrading in the Jig, Archives of Mining Sciences, vol. 55, 21–40, 2010.
- 6. Brozek M., Surowiak A., Effect of Particle Shape on Jig Separation Efficiency, Physicochemical Problems of Mineral Processing, vol. 41, 397–413, 2007.
- 7. Brozek M., Surowiak A., The Dependence of Distribution of Settling Velocity of Spherical Particles on the Distribution of Particle Sizes and Densities, Physicochemical Problems of Mineral Processing, vol. 39, 199–210, 2005.
- 8. Cleveland W.S., McGill R., The many faces of a scatterplot, Journal of the American Statistical Association, vol.79, 807–822. 1984.
- 9. Drzymala J., Mineral processing: foundations of theory and practice of minerallurgy, Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław, 2007.
- 10. Drzymala J., Basics of minerallurgy, Oficyna Wydawnicza Politechniki Wrocławskiej, 2009. [in Polish]
- 11. Gawenda T., Saramak D., Tumidajski T., Regression models of rock materials crushing in jaw crushers, Scientific Issues of Civil Engineering and Environmental Engineering Faculty of Koszalin University of Science and Technology, series: Environmental Engineering, no 22, 659–670, 2005. [in Polish]
- 12. Hotelling H., Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology, vol. 24, 417–441, and 498–520, 1933.
- 13. Inselberg A., Parallel Coordinates: VISUAL Multidimensional Geometry and its Applications, Springer, 2009.
- 14. Jain A.K., Mao J., Artificial neural network for non-linear projection of multivariate data. In: Proc. IEEE Internat. Joint Conf. On Neural Networks, Baltimore, MD, vol. 3, 335–340, 1992.
- 15. Jamróz D., Niedoba T., Application of Observational Tunnels Method to Select Set of Features Sufficient to Identify a Type of Coal, Physicochemical Problems of Mineral Processing, vol 50(1), 185–202, 2014.
- 16. Jamroz D., Visualization of objects in multidimensional spaces, Doctoral Thesis, AGH, Kraków, 2001. [in Polish]
- 17. Jamroz D., Multidimensional labyrinth - multidimensional virtual reality. [In:] Cyran K., Kozielski S., Peters J., Stanczyk U., Wakulicz-Deja A. (eds.), Man-Machine, Interactions, AISC, Heidelberg, Springer-Verlag, vol. 59, 445–450, 2009.
- 18. Jamroz D., Application of Multidimensional Data Visualization in Creation of Pattern Recognition Systems, In: Gruca A., Czachórski T., Kozielski S. (eds.), Man-Machine, Interactions 3, AISC, Switzerland, Springer International Publishing, vol. 242, 443–450, 2014a.
- 19. Jamroz D., Application of multidimensional data visualization by means of multidimensional scaling to qualitative classification of various types of coal, Archives of Mining Sciences, paper in printing, 2014b.
- 20. Jolliffe I.T. Principal Component Analysis, Series: Springer Series in Statistics, 2nd ed., Springer, NY, 2002.
- 21. Kohonen T., Self Organization and Associative Memory, Springer-Verlag, 1989.
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- 23. Lyman G. J., Application of Line-Length Related Interpolation Methods to Problems in Coal Preparation – III: Two dimensional Washability Data Interpolation, Coal Preparation, vol. 13, 179–195, 1993.
- 24. Niedoba T., Jamroz D., Visualization of multidimensional data in purpose of qualitative classification of various types of coal, Archives of Mining Sciences, vol. 58, iss. 4, 1317–1333, 2013.
- 25. Niedoba T., Multidimensional distributions of grained materials characteristics by means of non-parametric approximation of marginal statistical density function, AGH Journal of Mining and Geoengineering, iss. 4, 235–244, 2009 [in Polish].
- 26. Niedoba T., Multidimensional characteristics of random variables in description of grained materials and their separation processes, Wydawnictwo Instytutu Gospodarki Surowcami Mineralnymi i Energią PAN, Kraków, 2013a [in Polish].
- 27. Niedoba T., Statistical analysis of the relationship between particle size and particle density of raw coal, Physicochemical Problems of Mineral Processing, vol. 49, iss. 1, 175–188, 2013b.
- 28. Niedoba T., Multi-parameter data visualization by means of principal component analysis (PCA) in qualitative evaluation of various coal types, Physicochemical Problems of Mineral Processing, vol. 50, iss. 2, 575–589, 2014.
- 29. Niedoba T., Surowiak A., Type of coal and multidimensional description of its composition with density and ash contents taken into consideration, in Proceedings of the XXVI International Mineral Processing Congress, vol. 1, 3844–3854, 2012.
- 30. Niedoba T., Three-dimensional distribution of grained materials characteristics, in Proceedings of the XIV Balkan Mineral Processing Congress, Tuzla, Bosnia and Herzegovina, vol. 1, 57–59, 2011.
- 31. Olejnik T., Surowiak A., Gawenda T., Niedoba T., Tumidajski T., Multidimensional coal characteristics as basis for evaluation and adjustment of its beneficiation technology, AGH Journal of Mining and Geoengineering, vol. 34, iss. 4/1, 207–216, 2010. [in Polish]
- 32. Saramak D., Mathematical models of particle size distribution in simulation analysis of High-pressure grinding rolls operation, Physicochemical Problems of Mineral Processing, vol. 49(1), 495–512, 2013.
- 33. Saramak D., Technological Issues Of High-Pressure Grinding Rolls Operation In Ore Comminution Processes, Archives of Mining Sciences, vol. 56, no 3, 517–526, 2011.
- 34. Snopkowski R., Napieraj A., Method Of The Production Cycle Duration Time Modeling Within Hard Coal Longwall Faces, Archives of Mining Sciences, vol. 57, no. 1, 121–138, 2012.
- 35. Tumidajski T., Saramak D.: Methods and models of mathematical statistics in mineral processing, Wydawnictwo AGH, Kraków, 2009. [in Polish].
- 36. Tumidajski T.: Stochastic analysis of grained materials properties and their separation processes, Wydawnictwo AGH, Kraków, 1997. [in Polish].
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6e42b387-4d72-4d66-84e8-f8f9e492f3f5