Influence of bandwidth on some nonlinear transformations of a Gaussian random process

1968 ◽  
Vol 14 (1) ◽  
pp. 88-93 ◽  
Author(s):  
R. Langseth ◽  
R. Lambert
Keyword(s):  
Random Process ◽  
Gaussian Random Process ◽  
Nonlinear Transformations

scholarly journals On Spectrum Estimation of a Clipped Gaussian Random Process

1972 ◽  
Vol 12 (2) ◽  
pp. 11-15
Author(s):  
V. G. Alekseyev
Keyword(s):  
Random Process ◽  
Spectrum Estimation ◽  
Gaussian Random Process

The abstracts (in two languages) can be found in the pdf file of the article. Original author name(s) and title in Russian and Lithuanian: В. Алексеев. Об оценке спектра квантованного по уровню гауссовского случайного процесса V. Aleksejevas. Apie atsitiktinio Gauso proceso spektro, kvantuoto pagal lygmenį, įvertinimą H

A Mathematical Basis for the Random Decrement Vibration Signature Analysis Technique

1982 ◽  
Vol 104 (2) ◽  
pp. 307-313 ◽  
Author(s):  
J. K. Vandiver ◽  
A. B. Dunwoody ◽  
R. B. Campbell ◽  
M. F. Cook
Keyword(s):  
Random Process ◽  
Linear Time ◽  
Signature Analysis ◽  
Gaussian Random Process ◽  
Mathematical Basis ◽  
Linear Time Invariant ◽  
Random Decrement Technique ◽  
Random Decrement ◽  
Vibration Signature ◽  
Linear Time Invariant System

The mathematical basis for the Random Decrement Technique of vibration signature analysis is established. The general relationship between the autocorrelation function of a random process and the Randomdec signature is derived. For the particular case of a linear time invariant system excited by a zero-mean, stationary, Gaussian random process, a Randomdec signature of the output is shown to be proportional to the auto-correlation of the output. Example Randomdec signatures are computed from acceleration response time histories from an offshore platform.

Identification of Non-Gaussian Stochastic System

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Sung-man Park ◽  
O-shin Kwon ◽  
Jin-sung Kim ◽  
Jong-bok Lee ◽  
Hoon Heo
Keyword(s):  
Random Process ◽  
Random Noise ◽  
Stochastic Simulations ◽  
Kolmogorov Equation ◽  
Gaussian Random Process ◽  
System Parameters ◽  
Analysis Technique ◽  
Gaussian Analysis ◽  
Gaussian System ◽  
Non Gaussian

This paper proposes a method to identify non-Gaussian random noise in an unknown system through the use of a modified system identification (ID) technique in the stochastic domain, which is based on a recently developed Gaussian system ID. The non-Gaussian random process is approximated via an equivalent Gaussian approach. A modified Fokker–Planck–Kolmogorov equation based on a non-Gaussian analysis technique is adopted to utilize an effective Gaussian random process that represents an implied non-Gaussian random process. When a system under non-Gaussian random noise reveals stationary moment output, the system parameters can be extracted via symbolic computation. Monte Carlo stochastic simulations are conducted to reveal some approximate results, which are close to the actual values of the system parameters.

scholarly journals A Novel Combination Co-Kriging Model Based on Gaussian Random Process

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Huan Xie ◽  
Wei Zeng ◽  
Hong Song ◽  
Wen Sun ◽  
Tao Ren
Keyword(s):  
Random Process ◽  
Prediction Accuracy ◽  
Engineering Application ◽  
Kriging Model ◽  
Gaussian Random Process ◽  
Computation Cost ◽  
New Model ◽  
Correlation Models ◽  
Sample Data ◽  
Engineering Problems

Co-Kriging (CK) modeling provides an efficient way to predict responses of complicated engineering problems based on a set of sample data obtained by methods with varying degree of accuracy and computation cost. In this work, the Gaussian random process (GRP) is introduced to construct a novel combination CK model (CK-GRP) to improve the prediction accuracy of the conventional CK model, in which all the sample information provided by different correlation models is well utilized. The features of the new model are demonstrated and evaluated for a numerical case and an engineering application. It is shown that the CK-GRP model proposed in this work is effective and can be used to improve the prediction accuracy and robustness of the CK model.

scholarly journals Modelling the Gravitational Effects of Random Underground Density Variations

2019 ◽  
Vol 52 (6) ◽  
pp. 759-781
Author(s):  
Kevin Ridley
Keyword(s):  
Random Process ◽  
Vertical Component ◽  
Vertical Gradient ◽  
Fractal Model ◽  
Small Scale ◽  
Underground Structures ◽  
Gaussian Random Process ◽  
Tunnel Detection ◽  
Fractal Properties ◽  
Gravity Variations

Abstract A mathematical model for small-scale spatial variations in gravity above the Earth’s surface is presented. Gravity variations are treated as a Gaussian random process arising from underground density variations which are assumed to be a Gaussian random process. Expressions for two-point spatial statistics are calculated for both the vertical component of gravity and the vertical gradient of the vertical component. Results are given for two models of density variations: a delta-correlated model and a fractal model. The effect of an outer scale in the fractal model is investigated. It is shown how the results can be used to numerically generate realisations of gravity variations with fractal properties. Such numerical modelling could be useful for investigating the feasibility of using gravity surveys to locate and characterise underground structures; this is explored through the simple example of a tunnel detection scenario.

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