A unified minimum variance spectrum-based approach for simultaneous identification of both harmonic and stationary random noise fields

1997 ◽  
Vol 45 (6) ◽  
pp. 1659-1663 ◽  
Author(s):  
A. Sexton ◽  
D. Lyon
Keyword(s):  
Random Noise ◽  
Minimum Variance ◽  
Simultaneous Identification ◽  
Variance Spectrum

Parameters Identification for Nonlinear Dynamic Systems Via Genetic Algorithm Optimization

2009 ◽  
Vol 4 (4) ◽  
Author(s):  
A. C. Gondhalekar ◽  
E. P. Petrov ◽  
M. Imregun
Keyword(s):  
Genetic Algorithm ◽  
Random Noise ◽  
Parametric Identification ◽  
Frequency Domain Method ◽  
Algorithm Optimization ◽  
Response Data ◽  
Nonlinear Force ◽  
Restoring Forces ◽  
Simultaneous Identification ◽  
Clearance Nonlinearity

This paper presents a frequency domain method for the location, characterization, and identification of localized nonlinearities in mechanical systems. The nonlinearities are determined by recovering nonlinear restoring forces, computed at each degree-of-freedom (DOF). Nonzero values of the nonlinear force indicate nonlinearity at the corresponding DOFs and the variation in the nonlinear force with frequency (force footprint) characterizes the type of nonlinearity. A library of nonlinear force footprints is obtained for various types of individual and combined nonlinearities. Once the location and the type of nonlinearity are determined, a genetic algorithm based optimization is used to extract the actual values of the nonlinear parameters. The method developed allows simultaneous identification of one or more types of nonlinearity at any given DOF. Parametric identification is possible even if the type of nonlinearity is not known in advance, a very useful feature when the type characterization is difficult. The proposed method is tested on simulated response data. Different combinations of localized cubic stiffness nonlinearity, clearance nonlinearity, and frictional nonlinearity are considered to explore the method’s capabilities. Finally, the response data are polluted with random noise to examine the performance of the method in the presence of measurement noise.

A method for direct measurement of specimen noise in HREM images

1993 ◽  
Vol 51 ◽  
pp. 458-459
Author(s):  
Sidnei Paciornik ◽  
Roar Kilaas ◽  
Ulrich Dahmen ◽  
Michael Adrian O'Keefe
Keyword(s):  
Random Noise ◽  
Image Interpretation ◽  
Thin Foil ◽  
High Resolution Electron Microscopy ◽  
Black Dot ◽  
Atomic Column ◽  
Amorphous Oxide ◽  
Resolution Electron ◽  
Imaging Conditions ◽  
Image Simulations

High resolution electron microscopy (HREM) is a primary tool for studying the atomic structure of defects in crystals. However, the quantitative analysis of defect structures is often seriously limited by specimen noise due to contamination or oxide layers on the surfaces of a thin foil.For simple monatomic structures such as fcc or bcc metals observed in directions where the crystal projects into well-separated atomic columns, HREM image interpretation is relatively simple: under weak phase object, Scherzer imaging conditions, each atomic column is imaged as a black dot. Variations in intensity and position of individual image dots can be due to variations in composition or location of atomic columns. Unfortunately, both types of variation may also arise from random noise superimposed on the periodic image due to an amorphous oxide or contamination film on the surfaces of the thin foil. For example, image simulations have shown that a layer of amorphous oxide (random noise) on the surfaces of a thin foil of perfect crystalline Si can lead to significant shifts in image intensities and centroid positions for individual atomic columns.

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