Simulation of strongly non-Gaussian stochastic vector processes using translation process theory

2011 ◽  
pp. 2727-2734
Author(s):  
M Shields ◽  
G Deodatis
Keyword(s):  
Process Theory ◽  
Translation Process ◽  
Non Gaussian ◽  
Stochastic Vector

A Simulation Method for Non-Gaussian Rough Surfaces Using Fast Fourier Transform and Translation Process Theory

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Yuechang Wang ◽  
Ying Liu ◽  
Gaolong Zhang ◽  
Yuming Wang
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Rough Surface ◽  
Tribological Behavior ◽  
Rough Surfaces ◽  
Simulation Method ◽  
Process Theory ◽  
Asperity Contact ◽  
Translation Process ◽  
Non Gaussian

The simulated rough surface with desired parameters is widely used as an input for the numerical simulation of tribological behavior such as the asperity contact, lubrication, and wear. In this study, a simulation method for generating non-Gaussian rough surfaces with desired autocorrelation function (ACF) and spatial statistical parameters, including skewness (Ssk) and kurtosis (Sku), was developed by combining the fast Fourier transform (FFT), translation process theory, and Johnson translator system. The proposed method was verified by several numerical examples and proved to be faster and more accurate than the previous methods used for the simulation of non-Gaussian rough surfaces. It is convenient to simulate the non-Gaussian rough surfaces with various types of ACFs and large autocorrelation lengths. The significance of this study is to provide an efficient and accurate method of non-Gaussian rough surfaces generation to numerically simulate the tribological behavior with desired rough surface parameters.

Estimating extremes of combined two Gaussian and non-Gaussian response processes

2014 ◽  
Vol 14 (03) ◽  
pp. 1350076 ◽  
Author(s):  
K. Gong ◽  
X. Z. Chen
Keyword(s):  
Gaussian Process ◽  
Gaussian Processes ◽  
New Combination ◽  
Parameter Study ◽  
Stochastic Dynamic ◽  
Translation Process ◽  
Combination Rules ◽  
Dynamic Loadings ◽  
Non Gaussian ◽  
Vector Valued

Assessment of structural performance under stochastic dynamic loadings requires estimation of the extremes of stochastic response components and the resultant responses as their linear and nonlinear combinations. This paper addresses the evaluations and combination rules for the extremes of scalar and vectorial resultant responses from two response components that may show non-Gaussian characteristics. The non-Gaussian response process is modeled as a translation process from an underlying Gaussian process. The mean crossing rates and extreme value distributions of resultant responses are calculated following the theory for vector-valued Gaussian processes. An extensive parameter study is conducted concerning the influence of statistical moments of non-Gaussian response components on the extremes of resultant responses. It is revealed that the existing combination rules developed for Gaussian processes are not applicable to the case of non-Gaussian process. New combination rules are suggested that permit predictions of the extremes of resultant responses directly from the extremes of response components.

Estimation of extremes of non-Gaussian wind pressure on building roof: Sampling error in moment-based translation process model with no monotonic limit

2019 ◽  
Vol 23 (4) ◽  
pp. 810-826 ◽  
Author(s):  
Fengbo Wu ◽  
Min Liu ◽  
Qingshan Yang ◽  
Liuliu Peng
Keyword(s):  
Hermite Polynomial ◽  
Sampling Error ◽  
Polynomial Model ◽  
Wind Pressure ◽  
Transformation Model ◽  
Sampling Errors ◽  
Translation Process ◽  
Building Roof ◽  
Wind Pressures ◽  
Non Gaussian

Estimation of extremes of non-Gaussian wind pressure on building roof is necessary for cladding design. When limited length of non-Gaussian wind pressure is used for calculation, the estimated extreme involves sampling error. The moment-based Hermite polynomial model is extensively applied for estimation of extreme wind pressure due to the straightforwardness and accuracy, however, Hermite polynomial model has a monotonic limit resulting in a restricted application region of skewness and kurtosis combination. However, another two moment-based translation process models with no monotonic limit including Johnson transformation model and piecewise Hermite polynomial model have attracted some attention as these two models can be applied to a broader region of skewness and kurtosis combination. The sampling error in estimation of extremes of non-Gaussian wind pressure on building roof by Hermite polynomial model is proposed in the literature recently. Nevertheless, the sampling errors in Johnson transformation model and piecewise Hermite polynomial model have not been addressed. In this study, sampling errors in estimation of extremes of non-Gaussian wind pressures by Johnson transformation model are investigated. Formulations for estimating sampling errors of newly defined statistical moments and subsequent extremes in piecewise Hermite polynomial model are presented. The performance of sampling errors in Hermite polynomial model, Johnson transformation model, and piecewise Hermite polynomial model are finally compared with each other. Based on very long wind pressures from wind tunnel tests, it is shown that the sampling error of minimum wind pressure (suction) in Hermite polynomial model is generally the smallest compared to Johnson transformation model and piecewise Hermite polynomial model, while that of maximum wind pressure in piecewise Hermite polynomial model seems to be the smallest.

A challenge for predictive coding: Representational or experiential diversity?

2020 ◽  
Vol 43 ◽  
Author(s):  
Martina G. Vilas ◽  
Lucia Melloni
Keyword(s):  
Brain Function ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Process Theory

Abstract To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.

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