The Experts below are selected from a list of 445365 Experts worldwide ranked by ideXlab platform
David Lamb - One of the best experts on this subject based on the ideXlab platform.
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reliability based design optimization with Confidence Level for non gaussian distributions using bootstrap method
Journal of Mechanical Design, 2011Co-Authors: Yoojeong Noh, Kyung K. Choi, Ikjin Lee, David Gorsich, David LambAbstract:Abstract : For reliability-based design optimization (RBDO), generating an input statistical model with Confidence Level has been recently proposed to offset inaccurate estimation of the input statistical model with Gaussian distributions. For this, the Confidence intervals for the mean and standard deviation are calculated using Gaussian distributions of the input random variables. However, if the input random variables are non-Gaussian, use of Gaussian distributions of the input variables will provide inaccurate Confidence intervals, and thus, yield an undesirable Confidence Level of the reliability-based optimum design meeting the target reliability . In this paper, an RBDO method using a bootstrap method, which accurately calculates the Confidence intervals for the input parameters for non-Gaussian distributions, is proposed to obtain a desirable Confidence Level of the output performance for non-Gaussian distributions. The proposed method is examined by testing a numerical example and M1A1 Abrams tank roadarm problem.
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reliability based design optimization with Confidence Level under input model uncertainty due to limited test data
Structural and Multidisciplinary Optimization, 2011Co-Authors: Yoojeong Noh, Kyung K. Choi, Ikjin Lee, David Gorsich, David LambAbstract:For obtaining a correct reliability-based optimum design, the input statistical model, which includes marginal and joint distributions of input random variables, needs to be accurately estimated. However, in most engineering applications, only limited data on input variables are available due to expensive testing costs. The input statistical model estimated from the insufficient data will be inaccurate, which leads to an unreliable optimum design. In this paper, reliability-based design optimization (RBDO) with the Confidence Level for input normal random variables is proposed to offset the inaccurate estimation of the input statistical model by using adjusted standard deviation and correlation coefficient that include the effect of inaccurate estimation of mean, standard deviation, and correlation coefficient.
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Reliability-Based Design Optimization With Confidence Level Under Input Model Uncertainty
Volume 5: 35th Design Automation Conference Parts A and B, 2009Co-Authors: Yoojeong Noh, Kyung K. Choi, Ikjin Lee, David Gorsich, David LambAbstract:For obtaining correct reliability-based optimum design, an input model needs to be accurately estimated in identification of marginal and joint distribution types and quantification of their parameters. However, in most industrial applications, only limited data on input variables is available due to expensive experimental testing costs. The input model generated from the insufficient data might be inaccurate, which will lead to incorrect optimum design. In this paper, reliability-based design optimization (RBDO) with the Confidence Level is proposed to offset the inaccurate estimation of the input model due to limited data by using an upper bound of Confidence interval of the standard deviation. Using the upper bound of the Confidence interval of the standard deviation, the Confidence Level of the input model can be assessed to obtain the Confidence Level of the output performance, i.e. a desired probability of failure, through the simulation-based design. For RBDO, the estimated input model with the associated Confidence Level is integrated with the most probable point (MPP)-based dimension reduction method (DRM), which improves accuracy over the first order reliability method (FORM). A mathematical example and a fatigue problem are used to illustrate how the input model with Confidence Level yields a reliable optimum design by comparing it with the input model obtained using the estimated parameters.Copyright © 2009 by ASME
Xubin Pan - One of the best experts on this subject based on the ideXlab platform.
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Calculation of sampling size for non-zero tolerance Level
Global Ecology and Conservation, 2020Co-Authors: Xubin PanAbstract:Abstract The tolerance Level of 0 and the Confidence Level of 0.95 are widely applied in current investigation and sampling strategies for the crime, food, environment, and biodiversity fields. Although some researchers recommend the non-zero tolerance Level, few relevant sampling plans have been proposed. In this report, I used the binomial distribution as an example to show the estimate of sampling size, considering different detection efficacies, proportion of concerned units, and Confidence Level. The results indicate that the required sampling size based on non-zero tolerance is larger than that based on zero tolerance when other parameters are the same. High detection efficacy and high proportion of concerned units can decrease the sampling size. Especially, large sampling size can not only increase the Confidence Level, but also decrease the proportion of concerned regulated units with effective policy measurement.
David B Rorabacher - One of the best experts on this subject based on the ideXlab platform.
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statistical treatment for rejection of deviant values critical values of dixon s q parameter and related subrange ratios at the 95 Confidence Level
Analytical Chemistry, 1991Co-Authors: David B RorabacherAbstract:Critical values at the 95% Confidence Level for the two-tailed Q test, and related tests based upon subrange ratios, for the statistical rejection of outlying data have been interpolated by applying cubic regression analysis to the values originally published by Dixon. Corrections to errors in Dixon's original tables are also included. It is recommended that the newly generated 95% critical values be adopted by analytical chemists as the general standard for the rejection of outlier values
Ph J Vial - One of the best experts on this subject based on the ideXlab platform.
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Confidence Level solutions for stochastic programming
Automatica, 2008Co-Authors: Yu Nesterov, Ph J VialAbstract:We propose an alternative approach to stochastic programming based on Monte-Carlo sampling and stochastic gradient optimization. The procedure is by essence probabilistic and the computed solution is a random variable. We propose a solution concept in which the probability that the random algorithm produces a solution with an expected objective value departing from the optimal one by more than @e is small enough. We derive complexity bounds on the number of iterations of this process. We show that by repeating the basic process on independent samples, one can significantly reduce the number of iterations.
Yoojeong Noh - One of the best experts on this subject based on the ideXlab platform.
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reliability based design optimization with Confidence Level for non gaussian distributions using bootstrap method
Journal of Mechanical Design, 2011Co-Authors: Yoojeong Noh, Kyung K. Choi, Ikjin Lee, David Gorsich, David LambAbstract:Abstract : For reliability-based design optimization (RBDO), generating an input statistical model with Confidence Level has been recently proposed to offset inaccurate estimation of the input statistical model with Gaussian distributions. For this, the Confidence intervals for the mean and standard deviation are calculated using Gaussian distributions of the input random variables. However, if the input random variables are non-Gaussian, use of Gaussian distributions of the input variables will provide inaccurate Confidence intervals, and thus, yield an undesirable Confidence Level of the reliability-based optimum design meeting the target reliability . In this paper, an RBDO method using a bootstrap method, which accurately calculates the Confidence intervals for the input parameters for non-Gaussian distributions, is proposed to obtain a desirable Confidence Level of the output performance for non-Gaussian distributions. The proposed method is examined by testing a numerical example and M1A1 Abrams tank roadarm problem.
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reliability based design optimization with Confidence Level under input model uncertainty due to limited test data
Structural and Multidisciplinary Optimization, 2011Co-Authors: Yoojeong Noh, Kyung K. Choi, Ikjin Lee, David Gorsich, David LambAbstract:For obtaining a correct reliability-based optimum design, the input statistical model, which includes marginal and joint distributions of input random variables, needs to be accurately estimated. However, in most engineering applications, only limited data on input variables are available due to expensive testing costs. The input statistical model estimated from the insufficient data will be inaccurate, which leads to an unreliable optimum design. In this paper, reliability-based design optimization (RBDO) with the Confidence Level for input normal random variables is proposed to offset the inaccurate estimation of the input statistical model by using adjusted standard deviation and correlation coefficient that include the effect of inaccurate estimation of mean, standard deviation, and correlation coefficient.
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Reliability-Based Design Optimization With Confidence Level Under Input Model Uncertainty
Volume 5: 35th Design Automation Conference Parts A and B, 2009Co-Authors: Yoojeong Noh, Kyung K. Choi, Ikjin Lee, David Gorsich, David LambAbstract:For obtaining correct reliability-based optimum design, an input model needs to be accurately estimated in identification of marginal and joint distribution types and quantification of their parameters. However, in most industrial applications, only limited data on input variables is available due to expensive experimental testing costs. The input model generated from the insufficient data might be inaccurate, which will lead to incorrect optimum design. In this paper, reliability-based design optimization (RBDO) with the Confidence Level is proposed to offset the inaccurate estimation of the input model due to limited data by using an upper bound of Confidence interval of the standard deviation. Using the upper bound of the Confidence interval of the standard deviation, the Confidence Level of the input model can be assessed to obtain the Confidence Level of the output performance, i.e. a desired probability of failure, through the simulation-based design. For RBDO, the estimated input model with the associated Confidence Level is integrated with the most probable point (MPP)-based dimension reduction method (DRM), which improves accuracy over the first order reliability method (FORM). A mathematical example and a fatigue problem are used to illustrate how the input model with Confidence Level yields a reliable optimum design by comparing it with the input model obtained using the estimated parameters.Copyright © 2009 by ASME
