Eigenvalues and Eigenvectors

2008 ◽  
pp. 185-203
Author(s):  
Steven Roman
Keyword(s):  
Eigenvalues And Eigenvectors

Improved first-order approximation of eigenvalues and eigenvectors

AIAA Journal ◽  
1998 ◽  
Vol 36 ◽  
pp. 1721-1727
Author(s):  
Prasanth B. Nair ◽  
Andrew J. Keane ◽  
Robin S. Langley
Keyword(s):  
Order Approximation ◽  
Eigenvalues And Eigenvectors ◽  
First Order ◽  
First Order Approximation

scholarly journals Matching Point Clouds with STL Models by Using the Principle Component Analysis and a Decomposition into Geometric Primitives

2021 ◽  
Vol 11 (5) ◽  
pp. 2268
Author(s):  
Erika Straková ◽  
Dalibor Lukáš ◽  
Zdenko Bobovský ◽  
Tomáš Kot ◽  
Milan Mihola ◽  
...  
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Point Clouds ◽  
Component Analysis ◽  
Language Models ◽  
Computational Time ◽  
Dominant Eigenvalue ◽  
Eigenvalues And Eigenvectors ◽  
Correct Model ◽  
Geometric Primitives

While repairing industrial machines or vehicles, recognition of components is a critical and time-consuming task for a human. In this paper, we propose to automatize this task. We start with a Principal Component Analysis (PCA), which fits the scanned point cloud with an ellipsoid by computing the eigenvalues and eigenvectors of a 3-by-3 covariant matrix. In case there is a dominant eigenvalue, the point cloud is decomposed into two clusters to which the PCA is applied recursively. In case the matching is not unique, we continue to distinguish among several candidates. We decompose the point cloud into planar and cylindrical primitives and assign mutual features such as distance or angle to them. Finally, we refine the matching by comparing the matrices of mutual features of the primitives. This is a more computationally demanding but very robust method. We demonstrate the efficiency and robustness of the proposed methodology on a collection of 29 real scans and a database of 389 STL (Standard Triangle Language) models. As many as 27 scans are uniquely matched to their counterparts from the database, while in the remaining two cases, there is only one additional candidate besides the correct model. The overall computational time is about 10 min in MATLAB.

DVDSON: A subroutine to evaluate selected sets of eigenvalues and eigenvectors of large symmetric matrices

1986 ◽  
Vol 10 (4) ◽  
pp. 281-291 ◽  
Author(s):  
Gerardo Cisneros ◽  
Manuel Berrondo ◽  
Carlos F. Bunge
Keyword(s):  
Symmetric Matrices ◽  
Eigenvalues And Eigenvectors

A General Solution of Reynolds’ Equation for a Full Finite Journal Bearing

1967 ◽  
Vol 89 (2) ◽  
pp. 203-210 ◽  
Author(s):  
R. R. Donaldson
Keyword(s):  
Fourier Series ◽  
Series Solution ◽  
Reynolds Equation ◽  
Journal Bearing ◽  
Load Capacity ◽  
Separation Of Variables ◽  
Hill Equation ◽  
Eigenvalues And Eigenvectors ◽  
General Series ◽  
Bearing Load

Reynolds’ equation for a full finite journal bearing lubricated by an incompressible fluid is solved by separation of variables to yield a general series solution. A resulting Hill equation is solved by Fourier series methods, and accurate eigenvalues and eigenvectors are calculated with a digital computer. The finite Sommerfeld problem is solved as an example, and precise values for the bearing load capacity are presented. Comparisons are made with the methods and numerical results of other authors.

scholarly journals Using Pade´ Approximants and Curve-fitting to Approximate Eigenvalues and Eigenvectors for Large Design Changes

1996 ◽  
Vol 118 (1) ◽  
pp. 151-153 ◽  
Author(s):  
J. M. Vance ◽  
J. E. Bernard
Keyword(s):  
Finite Element ◽  
Natural Frequencies ◽  
Solution Process ◽  
Mode Shapes ◽  
Eigenvalues And Eigenvectors ◽  
Computational Burden ◽  
Design Change ◽  
Design Changes ◽  
Develop Software ◽  
Unique Method

Our overall goal is to develop software that facilitates the interactive participation of the designer in the optimization process. We are focusing this research on problems which use finite element solutions as part of the objective function. One challenge to implementing interactive participation in these types of problems is the high computational burden of computing a finite element solution for each design change. The research presented here focuses on a unique method to develop fast approximations for natural frequencies and mode shapes which can be used to avoid the time-consuming re-solution process and which will facilitate interactive design for systems with even large design changes.

Derivatives of Eigenvalues and Eigenvectors of Matrix Functions

1993 ◽  
Vol 14 (4) ◽  
pp. 903-926 ◽  
Author(s):  
Alan L. Andrew ◽  
K.-W. Eric Chu ◽  
Peter Lancaster
Keyword(s):  
Matrix Functions ◽  
Eigenvalues And Eigenvectors ◽  
Derivatives Of
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