Application of Multi-Parameter Data Visualization by Means of Multidimensional Scaling to Evaluate Possibility of Coal Gasification

Jamroz, Dariusz; Niedoba, Tomasz; Surowiak, Agnieszka; Tumidajski, T.; Roman, Szostek; Gajer, M.

doi:10.1515/amsc-2017-0034

Cited by 11 publications

(11 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figures 19 and 20 present views obtained using multidimensional scaling [12,[21][22][23]. Figure 19 shows that the curve separating areas occupied by samples with different susceptibility to fluidal gasification has two inflection points.…”

Section: Discussionmentioning

confidence: 99%

“…Real seven-dimensional data describing coal samples in terms of their susceptibility to fluidal gasification was used for the first time in the paper for the analysis of the effectiveness of methods utilizing neural networks. However, they were already utilized for the evaluation of other visualization methods' effectiveness [11][12][13][14]. Methods analyzed in the paper participated in the ranking of various methods of qualitative analysis of multidimensional data through its visualization which was developed in previous papers.…”

Section: Introductionmentioning

confidence: 99%

“…The PCA method [11,[16][17][18][19][20] constitutes a projection onto two eigenvectors corresponding to two eigenvalues of the dataset covariance matrix largest in terms of module. Multidimensional scaling [12,[21][22][23] constitutes such a representation that distance between each two images of points in the two-dimensional output space representing the screen is as close as possible to the distance between points in the input space corresponding to them. In the method of relevance maps [14,24,25], special points representing axes of the coordinate system are additionally used.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The experimental study of the effectiveness of Kohonen maps and autoassociative neural networks in the qualitative analysis of multidimensional data by the example of real data describing coal susceptibility to fluidal gasification

Jamroz

2020

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

The qualitative analysis of multidimensional data using their visualization allows to observe some characteristics of data in a way which is the most natural for a human, through the sense of sight. Thanks to such an approach, some characteristics of the analyzed data are simply visible. This allows to avoid using often complex algorithms allowing to examine specific data properties. Visualization of multidimensional data consists in using the representation transforming a multidimensional space into a two-dimensional space representing a computer screen. The important information which can be obtained in this way is the possibility to separate points belonging to different classes in the multidimensional space. Such information can be directly obtained if images of points belonging to different classes occupy other areas of the picture presenting these data. The paper presents the effectiveness of the qualitative analysis of multidimensional data conducted in this way through their visualization with the application of Kohonen maps and autoassociative neural networks. The obtained results were compared with results obtained using the perspective-based observational tunnels method, PCA, multidimensional scaling and relevance maps. Effectiveness tests of the above methods were performed using real sevendimensional data describing coal samples in terms of their susceptibility to fluidal gasification. The methods' effectiveness was compared using the criterion for the readability of the multidimensional visualization results, introduced in earlier papers. Keywords Multidimensional visualization Á Multidimensional data analysis Á Data mining Á Self-organized neural network Á Autoassociative neural network Á Kohonen maps & Dariusz Jamróz

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The experimental study of the effectiveness of Kohonen maps and autoassociative neural networks in the qualitative analysis of multidimensional data by the example of real data describing coal susceptibility to fluidal gasification

Jamroz

2020

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

show abstract

“…Visualization of multidimensional spaces is also used during the analysis of multidimensional data sets [5,[7][8][9][10][11][12][13][14]17]. However, such visualization concerns objects of a completely different type-sets of points located in multidimensional spaces; therefore, we only mention it here.…”

Section: Related Studiesmentioning

confidence: 99%

Multidimensional virtual reality-MVR method: a new method of visualization of multidimensional worlds

Jamroz

2019

Vis Comput

Self Cite

View full text Add to dashboard Cite

The paper presents a new, original method of multidimensional worlds' visualization. It allows to present views of any dimension objects out of which it is possible to construct even the most complicated multidimensional virtual world on a computer screen. Due to this, it is possible to observe multidimensional worlds modeled in this way, analyze mutual relations between multidimensional objects, move between them and, most importantly, verify whether human brain is able to adapt to the perception of more than three-dimensional space. This paper presents example interior views of four-dimensional and fivedimensional labyrinths. It also presents results of the research performed on 97 IT students at the AGH University of Science and Technology. Students in total made 357 attempts to leave virtual four-dimensional and five-dimensional labyrinths, each having three difficulty levels. The method presented in this paper is sufficiently general to allow observation of objects in an n-dimensional space for any n ≥ 3. Simultaneously, it is the natural extension of our reality perception because using this method for n = 3 we obtain views known to us from our human experience from the three-dimensional space.

show abstract

“…The application of various methods to analyze possibilities of recognition of various coal properties becomes more and more interesting issue. Earlier, other visualization methods were applied, including observational tunnels method (Jamróz & Niedoba, 2013;Niedoba & Jamróz, 2014;Jamróz, 2014aJamróz, , 2014bJamróz, , 2014cNiedoba, 2014). Application of relevance maps method to this purpose is new way of approaching to this topic.…”

Section: Introductionmentioning

confidence: 99%

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

Niedoba¹

2015

Archives of Mining Sciences

Self Cite

View full text Add to dashboard Cite

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.Keywords: relevance maps, multidimensional data visualization, coal, identification of data, pattern recognition Surowce mineralne, które podlegają wzbogacaniu w celu ich lepszego wykorzystania mogą być (charakteryzują się) charakteryzowane wieloma wskaźnikami opisującymi ich, interesujące przeróbkarza, cechy. Podstawowymi cechami są wielkość ziaren oraz ich gęstość, które decydują o przebiegu rozdziału zbiorów ziaren (nadaw) i efektach takiego rozdziału. Rozdział prowadzi się z reguły, w celu uzyskania produktów o zróżnicowanych wartościach średnich wybranej cechy, która zwykle charakteryzowana jest zawartością określonego składnika surowca wyznaczoną na drodze analiz chemicznych. Takie podejście do surowca mineralnego prowadzi do potraktowania go jako wielowymiarowego wektora X = [X 1 ,..., X n ]. Zasadniczym problemem jest także wybór jednostki populacji generalnej (ziarno, jednostka objętości lub masy), co może decydować o kierunkach charakteryzowania wielowymiarowych powiązań cech wektora X. Takimi kierunkami charakteryzowania mogą być:-wielowymiarowe rozkłady wektora losowego X wraz ze wszystkimi konsekwencjami metody (Lyman, 1993;Niedoba, 2009;Olejnik et al., 2010; Niedoba i Surowiak, 2012 94-wielowymiarowe równania regresji wraz z analizą macierzy współczynników korelacji liniowej oraz korelacji cząstkowej (Niedoba, 2013b); -analiza czynnikowa (Tumidajski, 1997; Tumidajski and Saramak, 2009); -metody wielowymiarowej wizualizacji danych.W artykule zastosowano nowoczesną metodę wizualizacji wielowymiarowych danych -metodę tzw. map odniesienia (z ang. relevance maps). Aby zastosować ww. metodę przeprowadzono doświadczenia na trzech typach węgla, pobranych z trzech kopalni węgla kamiennego, zlokalizowanych w Górno-śląskim Okręgu Przemys...

show abstract

Application of Multi-Parameter Data Visualization by Means of Multidimensional Scaling to Evaluate Possibility of Coal Gasification

Cited by 11 publications

References 6 publications

The experimental study of the effectiveness of Kohonen maps and autoassociative neural networks in the qualitative analysis of multidimensional data by the example of real data describing coal susceptibility to fluidal gasification

The experimental study of the effectiveness of Kohonen maps and autoassociative neural networks in the qualitative analysis of multidimensional data by the example of real data describing coal susceptibility to fluidal gasification

Multidimensional virtual reality-MVR method: a new method of visualization of multidimensional worlds

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

Contact Info

Product

Resources

About