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 A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 149
Author(s):  
Yaohui Li ◽  
Jingfang Shen ◽  
Ziliang Cai ◽  
Yizhong Wu ◽  
Shuting Wang
Keyword(s):  
Global Optimization ◽  
Pareto Frontier ◽  
Kriging Model ◽  
Optimization Method ◽  
Function Evaluation ◽  
Sampling Point ◽  
Constrained Global Optimization ◽  
Multi Objective ◽  
Sample Data ◽  
Sampling Points

The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while improving the accuracy of convergence and reducing the number of expensive evaluations has been a wide concern. For this reason, a kriging-assisted multi-objective constrained global optimization (KMCGO) method has been proposed. The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle. Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective optimization. Finally, the sample data from the Pareto frontier set is further screened to obtain more promising and valuable sampling points. The test results of five benchmark functions, four design problems, and a fuel economy simulation optimization prove the effectiveness of the proposed algorithm.

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

scholarly journals Effectiveness of Predicting The Spatial Distributions of Target Contaminants of a Coking Plant Based On Their Related Pollutants

2021 ◽  
Author(s):  
Pengwei Qiao ◽  
Donglin Lai ◽  
Sucai Yang ◽  
Qianyun Zhao ◽  
Hengqin Wang
Keyword(s):  
Spatial Distribution ◽  
Prediction Accuracy ◽  
Reduction Rate ◽  
Auxiliary Variables ◽  
Relevant Research ◽  
Sample Data ◽  
Coking Plant ◽  
A Site ◽  
Mean Square Errors ◽  
The Relationship

Abstract The prediction accuracy of the spatial distribution of soil pollutants at a site is relatively low. Related pollutants can be used as auxiliary variables to improve the prediction accuracy. However, little relevant research has been conducted on site soil pollution. To analyze the prediction accuracy of target pollutants combined with auxiliary pollutants, Cu, toluene, and phenanthrene were selected as the target pollutants for this study. Based on geostatistical analysis and spatial analysis, the following results were obtained. (1) The reduction rate of the root mean square errors (RMSEs) for Cu, toluene, and phenanthrene with multivariable cokriging were 68.4%, 81.6%, and 81.2%, respectively, which are proportional to the correlation coefficient of the relationship between the auxiliary pollutants and the target pollutants. (2) The predicted results for Cu, phenanthrene, and toluene and their corresponding related pollutants are more accurate than the results obtained not using the related pollutants. (3) In the interpolation process, the RMSEs for Cu, toluene, and phenanthrene with multivariable cokriging basically increase as the neighborhood sample data increases, and then they become stable. (4) When 84, 61, and 34 sample points were removed, the RMSEs for Cu, toluene, and phenanthrene, respectively with multivariable cokriging were close to the RMSEs of the target pollutants based on the total samples. The results are of great significance to improving the prediction accuracy of the spatial distribution of soil pollutants at coking plant sites.

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.

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