1999
DOI: 10.1006/jsvi.1999.2305
|View full text |Cite
|
Sign up to set email alerts
|

Using SVD to Detect Damage in Structures With Different Operational Conditions

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
33
0

Year Published

2001
2001
2018
2018

Publication Types

Select...
6
2
2

Relationship

0
10

Authors

Journals

citations
Cited by 71 publications
(33 citation statements)
references
References 5 publications
(2 reference statements)
0
33
0
Order By: Relevance
“…It is essentially an orthogonal transformation. If a matrix has a linearly correlated row or column, it can be changed into a diagonal matrix by multiplying the orthogonal matrix on the left with that on the right [21]. After the change, we can obtain some singular values whose number reflects the independent row (column) vector number of the original matrix.…”
Section: Svd Integrated Into Visual Attentionmentioning
confidence: 99%
“…It is essentially an orthogonal transformation. If a matrix has a linearly correlated row or column, it can be changed into a diagonal matrix by multiplying the orthogonal matrix on the left with that on the right [21]. After the change, we can obtain some singular values whose number reflects the independent row (column) vector number of the original matrix.…”
Section: Svd Integrated Into Visual Attentionmentioning
confidence: 99%
“…Conventional approaches include regression analysis [17], novelty detection [12], missing data analysis [8,9], singular value decomposition [19,22], and a support vector machine [15]. Another approach evaluating the environmental effects statistically with measured signals only without using an analytical model includes principal component analysis(PCA) [25], factor analysis [5,7] and neural network(NN) [6].…”
Section: Elimination Of Environmental Effectsmentioning
confidence: 99%
“…In general, POD has been used to obtain approximate, low-dimensional descriptions of turbulent fluid flow (Holmes et al, 1996), structural vibrations (Cusumano et al, 1994;Feeny and Kappagantu, 1998), and damage detection (Ruotolo and Surace, 1999), and is a very useful technique in signal processing, pattern recognition, and image compression (Chatterjee, 2000). Generally speaking, SVD is used to decompose a set of data, like a matrix, into orthonormal basis functions whereby an approximation of that data set can be reconstructed using a finite number of these basis functions.…”
Section: Singular Value Decompositionmentioning
confidence: 99%