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Wyświetlanie 1-10 z 466
Tytuł:
Strength and Deformation of Sand-Tire Rubber Mixtures (STRM): An Experimental Study
Autorzy:
Al-Rkaby, Alaa H. J.
Tematy:
tire rubber
shear strength
deformation
major principal strains
minor principal strains
intermediate principal strains
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Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Powiązania:
https://bibliotekanauki.pl/articles/178850.pdf  Link otwiera się w nowym oknie
Opis:
Waste material such as used tires is increasing every year, which poses environmental problems. However, such material has been used in several geotechnical applications as alternative lightweight backfill in highway embankments and/or behind retaining walls, providing environmental, economic and technical benefits. These applications require knowledge of engineering properties of soil-tire rubber mixtures. The present study aims to show the possibility of tire rubber usage in sand by evaluating the shear strength and deformability of sand mixed with granulated rubber, in weight percentages between 0 and 50%. The tire rubber content was found to influence the stress-strain and deformation behavior of the mixtures. The shear strength of sand mixed with 10% or 20% tire rubber was higher than that measured for sand only. However, the trend for TRC = 30–50% was different. Samples with a rubber content of 30-50% exhibited a rapid decrease in the stress ratio compared with that of sand. The major principal strain at maximum stress ratio was found to increase with increasing tire rubber content. However, it was observed that the lateral strains (minor and intermediate principal strains) of samples reduced significantly with the addition of tire rubber to the sand.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Principal Component Analysis versus Factor Analysis
Autorzy:
Gniazdowski, Zenon
Tematy:
principal component analysis
factor analysis
number of principal components
number of factors
determining number of principal components
determining number of factors
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Wydawca:
Warszawska Wyższa Szkoła Informatyki
Powiązania:
https://bibliotekanauki.pl/articles/1790041.pdf  Link otwiera się w nowym oknie
Opis:
The article discusses selected problems related to both principal component analysis (PCA) and factor analysis (FA). In particular, both types of analysis were compared. A vector interpretation for both PCA and FA has also been proposed. The problem of determining the number of principal components in PCA and factors in FA was discussed in detail. A new criterion for determining the number of factors and principal components is discussed, which will allow to present most of the variance of each of the analyzed primary variables. An efficient algorithm for determining the number of factors in FA, which complies with this criterion, was also proposed. This algorithm was adapted to find the number of principal components in PCA. It was also proposed to modify the PCA algorithm using a new method of determining the number of principal components. The obtained results were discussed.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A comparative analysis of the principal component method and parallel analysis in working with official statistical data
Autorzy:
Holubova, Halyna
Tematy:
principal components
principal component analysis
factor analysis
Kaiser criterion
рarallel analysis
simulation
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Wydawca:
Główny Urząd Statystyczny
Powiązania:
https://bibliotekanauki.pl/articles/10559806.pdf  Link otwiera się w nowym oknie
Opis:
The dynamic development of the digitized society generates large-scale information data flows. Therefore, data need to be compressed in a way allowing its content to remain complete and informative. In order for the above to be achieved, it is advisable to use the principal component method whose main task is to reduce the dimension of multidimensional space with a minimal loss of information. The article describes the basic conceptual approaches to the definition of principle components. Moreover, the methodological principles of selecting the main components are presented. Among the many ways to select principle components, the easiest way is selecting the first k-number of components with the largest eigenvalues or to determine the percentage of the total variance explained by each component. Many statistical data packages often use the Kaiser method for this purpose. However, this method fails to take into account the fact that when dealing with random data (noise), it is possible to identify components with eigenvalues greater than one, or in other words, to select redundant components. We conclude that when selecting the main components, the classical mechanisms should be used with caution. The Parallel analysis method uses multiple data simulations to overcome the problem of random errors. This method assumes that the components of real data must have greater eigenvalues than the parallel components derived from simulated data which have the same sample size and design, variance and number of variables. A comparative analysis of the eigenvalues was performed by means of two methods: the Kaiser criterion and the parallel Horn analysis on the example of several data sets. The study shows that the method of parallel analysis produces more valid results with actual data sets. We believe that the main advantage of Parallel analysis is its ability to model the process of selecting the required number of main components by determining the point at which they cannot be distinguished from those generated by simulated noise.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Principal Intuitionistic Fuzzy Ideals and Filters on a Lattice
Autorzy:
Boudaoud, Sarra
Zedam, Lemnaouar
Milles, Soheyb
Tematy:
lattice
intuitionistic fuzzy set
principal intuitionistic fuzzy ideal
principal intuitionistic fuzzy filter
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Wydawca:
Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
Powiązania:
https://bibliotekanauki.pl/articles/55795352.pdf  Link otwiera się w nowym oknie
Opis:
In this paper, we generalize the notion of principal ideal (resp. filter) on a lattice to the setting of intuitionistic fuzzy sets and investigate their various characterizations and properties. More specifically, we show that any principal intuitionistic fuzzy ideal (resp. filter) coincides with an intuitionistic fuzzy down-set (resp. up-set) generated by an intuitionistic fuzzy singleton. Afterwards, for a given intuitionistic fuzzy set, we introduce two intuitionistic fuzzy sets: its intuitionistic fuzzy down-set and up-set, and we investigate their interesting properties.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bounds for index of a modified graph
Autorzy:
Zhou, Bo
Tematy:
graph
eigenvalue
principal eigenvector
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Wydawca:
Uniwersytet Zielonogórski. Wydział Matematyki, Informatyki i Ekonometrii
Powiązania:
https://bibliotekanauki.pl/articles/744474.pdf  Link otwiera się w nowym oknie
Opis:
If a graph is connected then the largest eigenvalue (i.e., index) generally changes (decreases or increases) if some local modifications are performed. In this paper two types of modifications are considered:
(i) for a fixed vertex, t edges incident with it are deleted, while s new edges incident with it are inserted;
(ii) for two non-adjacent vertices, t edges incident with one vertex are deleted, while s new edges incident with the other vertex are inserted.
Within each case, we provide lower and upper bounds for the indices of the modified graphs, and then give some sufficient conditions for the index to decrease or increase when a graph is modified as above.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Principal Component Analysis versus Factor Analysis
Autorzy:
Gniazdowski, Zenon
Wydawca:
Warszawska Wyższa Szkoła Informatyki
Cytata wydawnicza:
Gniazdowski, Z. (2021). Principal Component Analysis versus Factor Analysis. Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki, 15(24).
Opis:
The article discusses selected problems related to both principal component analysis (PCA) and factor analysis (FA). In particular, both types of analysis were compared. A vector interpretation for both PCA and FA has also been proposed. The problem of determining the number of principal components in PCA and factors in FA was discussed in detail. A new criterion for determining the number of factors and principal components is discussed, which will allow to present most of the variance of each of the analyzed primary variables. An efficient algorithm for determining the number of factors in FA, which complies with this criterion, was also proposed. This algorithm was adapted to find the number of principal components in PCA. It was also proposed to modify the PCA algorithm using a new method of determining the number of principal components. The obtained results were discussed.
Dostawca treści:
Repozytorium Centrum Otwartej Nauki
Artykuł
Wyświetlanie 1-10 z 466

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