publication . Article . 2017

THE STUDY OF THE CHARACTERIZATION INDICES OF FABRICS BY PRINCIPAL COMPONENT ANALYSIS METHOD

HRISTIAN Liliana; OSTAFE Maria Magdalena; BORDEIANU Demetra Lacramioara; APOSTOL Laura Liliana;
Open Access English
  • Published: 01 May 2017 Journal: Annals of the University of Oradea: Fascicle of Textiles (issn: 1843-813X, eissn: 2457-4880, Copyright policy)
  • Publisher: Editura Universităţii din Oradea
Abstract
The paper was pursued to prioritize the worsted fabrics type, for the manufacture of outerwear products by characterization indeces of fabrics, using the mathematical model of Principal Component Analysis (PCA). There are a number of variables with a certain influence on the quality of fabrics, but some of these variables are more important than others, so it is useful to identify those variables to a better understanding the factors which can lead the improving of the fabrics quality. A solution to this problem can be the application of a method of factorial analysis, the so-called Principal Component Analysis, with the final goal of establishing and analyzing ...
Subjects
free text keywords: Principal component analysis, degreeof compactness, orosity, factorial axis, Manufactures, TS1-2301

[1] Abdi. H., & Williams, L.J., „Principal component analysis”, Wiley Interdisciplinary Reviews: Computational Statistics, vol. 2 (4), 2010, pp.433-459.

[2] Hristian, L., Sandu, A.V., Manea, L.M., Tulbure, E.A., Earar, K., „Analysis of the Principal Components on the Durability and Comfort Indices of the Fabrics Made of Core-coating Filament Yarns”, Journal of Chemistry, vol.66, no. 3, 2015, pp. 342-347.

[3] Hristian, L., Ostafe, M.M., Manea, L.R., and Leon, A.L., „The study about the improvement of the quality for the fabrics made of chenille yarn”, IOP Conf. Series: Materials Science and Engineering 145, 022014, 2016, pp.1-7.

[4] Qi, X.L., Meyer, T., Stanford, T.R., Constantinidis, C., Neural correlates of a decision variable before learning to perform a match/non-match task”, Journal of Neuroscience, vol. 32, 2012, pp. 6161-6169. [OpenAIRE]

[5] Manea, L.R., Bertea, A., Nechita, E., Popescu, C.V., Sandu, I., „Mathematical Model of the Electrospinning Process II. Effect of the technological parameters on the electrospun fibers diameter”, Rev. Chim. (Bucharest), 67, no. 8, 2016, pp. 1607-1614.

[6] Andrecut, M., „Parallel GPU Implementation of Iterative PCA Algorithms”, Journal of Computational Biology, vol. 16 (11), 2009, pp.1593-1599. [OpenAIRE]

[7] Manea, L.R., Bertea, A., Nechita, E., Popescu, C.V., Sandu, I., „Mathematical Model of the Electrospinning Process I. Effect of the distance between electrodes on the electrospun fibers diameter”, Rev. Chim. (Bucharest), 67, no. 7, 2016, pp. 1284-1289. [OpenAIRE]

[8] Hristian, L., Bordeianu, D.L., P. Iurea, Sandu, I., Earar, K., „Study of the Tensile Properties of Materials Destined to Manufacture Protective Clothing for Firemen”, Mat. Plat., vol. 51, no. 4, 2014, pp. 405-409. [OpenAIRE]

[9] Manea, L.R.,, Hristian, L., Ostafe, M.M., Apostol, L.L., Sandu, I., „Analysis of Characterization Indexes for Worsted Fabrics Type Using Correlation Method as a Statistical Tool”, Revista de chimie (Bucharest), vol. 67, no. 9, 2016, pp. 1758-1762 [OpenAIRE]

[10] Qi, X.L., Meyer, T., Stanford, T.R., Constantinidis, C., Neural correlates of a decision variable before learning to perform a match/non-match task”, Journal of Neuroscience, vol. 32, 2012, pp. 6161-6169.

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