How we can use multivariate statistical analysis to predict creasing of fabrics

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dc.contributor.authorZelová, Katarína
dc.contributor.authorFridrichová, Ludmila
dc.date.accessioned2015-10-26
dc.date.available2015-10-26
dc.date.issued2014
dc.description.abstractThe creasing of textiles was evaluated by means of the innovative method of measuring the angle of recovery. Our aim is to find statistically significant features contributing to the determination of creasing materials. For this purpose, to identify the inner structures of data, the method of PCA analysis was used a method with latent variables. By means of PCA analysis (method of principal components) the original nine characteristics can be reduced to two latent variables, i.e. principal components. The structure and links among the examined features are characterized by methods like: Scree Plot, Score and component loading, Scatrerplot and Dendrogram. © (2014) Trans Tech Publications, Switzerland.en
dc.formattext
dc.identifier.doi10.4028/www.scientific.net/AMM.543-547.1930
dc.identifier.isbn9783038350606
dc.identifier.issn1660-9336
dc.identifier.scopus2-s2.0-84898869068
dc.identifier.urihttps://dspace.tul.cz/handle/15240/13288
dc.language.isoen
dc.publisherTrans Tech Publications
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.relation.ispartofApplied Mechanics and Materialsen
dc.sourced-scopus
dc.subjectangle of recoveryen
dc.subjectcreasingen
dc.subjectlinear regressionen
dc.subjectpca analysisen
dc.titleHow we can use multivariate statistical analysis to predict creasing of fabricsen
dc.typeConference paper
local.citation.epage1933
local.citation.spage1930
local.event.edate2014-02-20
local.event.locationBeijing
local.event.sdate2014-02-19
local.event.titleInternational Conference on Vehicle and Mechanical Engineering and Information Technology, VMEIT 2014
local.facultyFaculty of Textile Engineering
local.relation.volume543-547
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