GENERALIZATION OF COLORING LINEAR TRANSFORMATION

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F18%3APU130577" target="_blank" >RIV/00216305:26110/18:PU130577 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.31490/tces-2018-0013" target="_blank" >http://dx.doi.org/10.31490/tces-2018-0013</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.31490/tces-2018-0013" target="_blank" >10.31490/tces-2018-0013</a>

Jazyk výsledku

angličtina

Název v původním jazyce

GENERALIZATION OF COLORING LINEAR TRANSFORMATION

Popis výsledku v původním jazyce

The paper is focused on the technique of linear transformation between correlated and uncorrelated Gaussian random vectors, which is more or less commonly used in the reliability analysis of structures. These linear transformations are frequently needed to transform uncorrelated random vectors into correlated vectors with a prescribed covariance matrix (coloring transformation), and also to perform an inverse (whitening) transformation, i.e. to decorrelate a random vector with a non-identity covariance matrix. Two well-known linear transformation techniques, namely Cholesky decomposition and eigendecomposition (also known as principal component analysis, or the orthogonal transformation of a covariance matrix), are shown to be special cases of the generalized linear transformation presented in the paper. The proposed generalized linear transformation is able to rotate the transformation randomly, which may be desired in order to remove unwanted directional bias. The conclusions presented herein may be useful for structural reliability analysis with correlated random variables or random fields.

Název v anglickém jazyce

GENERALIZATION OF COLORING LINEAR TRANSFORMATION

Popis výsledku anglicky

The paper is focused on the technique of linear transformation between correlated and uncorrelated Gaussian random vectors, which is more or less commonly used in the reliability analysis of structures. These linear transformations are frequently needed to transform uncorrelated random vectors into correlated vectors with a prescribed covariance matrix (coloring transformation), and also to perform an inverse (whitening) transformation, i.e. to decorrelate a random vector with a non-identity covariance matrix. Two well-known linear transformation techniques, namely Cholesky decomposition and eigendecomposition (also known as principal component analysis, or the orthogonal transformation of a covariance matrix), are shown to be special cases of the generalized linear transformation presented in the paper. The proposed generalized linear transformation is able to rotate the transformation randomly, which may be desired in order to remove unwanted directional bias. The conclusions presented herein may be useful for structural reliability analysis with correlated random variables or random fields.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

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OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

<a href="/cs/project/LO1408" target="_blank" >LO1408: AdMaS UP - Pokročilé stavební materiály, konstrukce a technologie</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Transactions of the VŠB – Technical University of Ostrava, Civil Engineering Series

ISSN

1804-4824

e-ISSN

—

Svazek periodika

18

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

5

Strana od-do

31-35

Kód UT WoS článku

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EID výsledku v databázi Scopus

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