Author
Chih-Young Lin
Bio: Chih-Young Lin is an academic researcher from National Central University. The author has contributed to research in topics: Iterative method & Monte Carlo method. The author has an hindex of 1, co-authored 1 publications receiving 98 citations.
Papers
More filters
TL;DR: It can be concluded that the tolerance-allocation model combined with a tolerance-cost relationship can provide a very practical and useful approach for design engineers.
101 citations
Cited by
More filters
Book•
01 Jan 2014
TL;DR: In this article, statistical models in engineering are used to evaluate the performance of statistical models for software engineering problems in the field of software engineering, including software engineering and software engineering..
Abstract: Statistical models in engineering , Statistical models in engineering , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
386 citations
TL;DR: This paper reviews the standard definitions of verification and validation in the context of engineering design and progresses to provide a coherent analysis and classification of these activities from preliminary design, to design in the digital domain and the physical verification and validate of products and processes.
239 citations
TL;DR: In this paper, a reliability-based robust design optimization method is developed using DRM and compared to PMI and PDM for accuracy and efficiency, and the numerical results show that DRM is effective when the number of random variables is small, whereas PMI is more effective when a relatively large number of variables is relatively large.
183 citations
TL;DR: In this article, the authors proposed an inverse reliability analysis method that can be used to obtain accurate probability of failure calculation without requiring the second-order sensitivities for reliability-based design optimization (RBDO) of nonlinear and multi-dimensional systems.
181 citations
TL;DR: In this article, the eigenvector dimension reduction (EDR) method was proposed for probability analysis that makes a significant improvement based on univariate dimension reduction method for estimating statistical moments of mildly nonlinear system responses in engineering applications.
Abstract: This paper presents the eigenvector dimension reduction (EDR) method for probability analysis that makes a significant improvement based on univariate dimension reduction (DR) method. It has been acknowledged that the DR method is accurate and efficient for assessing statistical moments of mildly nonlinear system responses in engineering applications. However, the recent investigation on the DR method has found difficulties of instability and inaccuracy for highly nonlinear system responses while maintaining reasonable efficiency. The EDR method integrates the DR method with three new technical components: (1) eigenvector sampling, (2) one-dimensional response approximation, and (3) a stabilized Pearson system. First, 2N+1 and 4N+1 eigenvector sampling schemes are proposed to resolve correlated and asymmetric random input variables. The eigenvector samples are chosen along the eigenvectors of the covariance matrix of random parameters. Second, the stepwise moving least squares (SMLS) method is proposed to accurately construct approximate system responses along the eigenvectors with the response values at the eigenvector samples. Then, statistical moments of the responses are estimated through recursive numerical integrations. Third, the stabilized Pearson system is proposed to predict probability density functions (PDFs) of the responses while eliminating singular behavior of the original Pearson system. Results for some numerical and engineering examples indicate that the EDR method is a very accurate, efficient, and stable probability analysis method in estimating PDFs, component reliabilities, and qualities of system responses.
172 citations