• 제목/요약/키워드: Random Distribution

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Reliability-based stochastic finite element using the explicit probability density function

  • Rezan Chobdarian;Azad Yazdani;Hooshang Dabbagh;Mohammad-Rashid Salimi
    • Structural Engineering and Mechanics
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    • 제86권3호
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    • pp.349-359
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    • 2023
  • This paper presents a technique for determining the optimal number of elements in stochastic finite element analysis based on reliability analysis. Using the change-of-variable perturbation stochastic finite element approach, the probability density function of the dynamic responses of stochastic structures is explicitly determined. This method combines the perturbation stochastic finite element method with the change-of-variable technique into a united model. To further examine the relationships between the random fields, discretization of the random field parameters, such as the variance function and the scale of fluctuation, is also performed. Accordingly, the reliability index is calculated based on the explicit probability density function of responses with Gaussian or non-Gaussian random fields in any number of elements corresponding to the random field discretization. The numerical examples illustrate the effectiveness of the proposed method for a one-dimensional cantilever reinforced concrete column and a two-dimensional steel plate shear wall. The benefit of this method is that the probability density function of responses can be obtained explicitly without the use simulation techniques. Any type of random variable with any statistical distribution can be incorporated into the calculations, regardless of the restrictions imposed by the type of statistical distribution of random variables. Consequently, this method can be utilized as a suitable guideline for the efficient implementation of stochastic finite element analysis of structures, regardless of the statistical distribution of random variables.

Reliability-based Structural Design Optimization Considering Probability Model Uncertainties - Part 1: Design Method (확률모델 불확실성을 고려한 구조물의 신뢰도 기반 최적설계 - 제1편: 설계 방법)

  • Ok, Seung-Yong;Park, Wonsuk
    • Journal of the Korean Society of Safety
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    • 제27권5호
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    • pp.148-157
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    • 2012
  • Reliability-based design optimization (RBDO) problem is usually formulated as an optimization problem to minimize an objective function subjected to probabilistic constraint functions which may include deterministic design variables as well as random variables. The challenging task is that, because the probability models of the random variables are often assumed based on limited data, there exists a possibility of selecting inappropriate distribution models and/or model parameters for the random variables, which can often lead to disastrous consequences. In order to select the most appropriate distribution model from the limited observation data as well as model parameters, this study takes into account a set of possible candidate models for the random variables. The suitability of each model is then investigated by employing performance and risk functions. In this regard, this study enables structural design optimization and fitness assessment of the distribution models of the random variables at the same time. As the first paper of a two-part series, this paper describes a new design method considering probability model uncertainties. The robust performance of the proposed method is presented in Part 2. To demonstrate the effectiveness of the proposed method, an example of ten-bar truss structure is considered. The numerical results show that the proposed method can provide the optimal design variables while guaranteeing the most desirable distribution models for the random variables even in case the limited data are only available.

Control of Intensity Distribution Profile of Laser Beam using Beam Shaping Mask with Random Array Slits (빔셰이퍼 마스크를 이용한 레이저 빔의 강도 분포 제어)

  • Oh, Jae-Yong;Park, Deog-Su;Shin, Bo-Sung
    • Laser Solutions
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    • 제15권2호
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    • pp.11-14
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    • 2012
  • In this paper, we have made a proposal concerning the beam shaping mask(BSM) using random-array slits to control intensity distribution profile of laser beam and demonstrated its proprieties experimentally. When a lot of slits are set out irregularly, diffraction patterns of light does not appear but granularity patterns as a bundle of fibers appear. Intensity distribution profile is controlled by densities distribution of circular slits arrayed randomly because the number of slits and its area means amount of light energy through BSM. Namely as the number of slits in high intensity area is increased and that in low intensity area decreased, amount of light energy is same over all local parts. So gaussian intensity distribution could be changed to flat-top.

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Bayes' Excuse for the Introduction of Prior Uniform Distribution (베이즈의 사전균등분포의 도입에 대한 변명)

  • PARK, Sun-Yong
    • Journal for History of Mathematics
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    • 제35권6호
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    • pp.149-170
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    • 2022
  • This study discusses in terms of historical genesis whether it is reasonable for Bayes to introduce a prior uniform distribution. In this study, we try to analyze the way he dealt with postulates, lemmas, and propositions in Bayes' essay and to understand its characteristics. The results of the study show that Bayes used random variables for two parameters and that the two random variables were converted to each other through cumulative distribution. Furthermore, it is revealed that the introduction of prior uniform distribution can be justified by this way.

Reliability-Based Design of Shallow Foundations Considering The Probability Distribution Types of Random Variables (확률변수의 분포특성을 고려한 얕은기초 신뢰성 설계)

  • Kim, Chang-Dong;Kim, Soo-Il;Lee, Jun-Hwan;Kim, Byung-Il
    • Journal of the Korean Geotechnical Society
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    • 제24권1호
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    • pp.119-130
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    • 2008
  • Uncertainties in physical and engineering parameters for the design of shallow foundations arise from various aspects such as inherent variability and measurement error. This paper aims at investigating and reducing uncertainty from deterministic method by using the reliability-based design of shallow foundations accounting for the variation of various design parameters. A probability distribution type and statistics of random variables such as unit weight, cohesion, infernal friction angle and Young's modulus in geotechnical engineering are suggested to calculate the ultimate bearing capacities and immediate settlements of foundations. Reliability index and probability of failure are estimated based on the distribution types of random variables. Widths of foundation are calculated at target reliability index and probability of failure. It is found that application and analysis of the best-fit distribution type for each random variables are more effective than adoption of the normal distribution type in optimizing the reliability-based design of shallow foundations.

Estimation of Random Coefficient AR(1) Model for Panel Data

  • Son, Young-Sook
    • Journal of the Korean Statistical Society
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    • 제25권4호
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    • pp.529-544
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    • 1996
  • This paper deals with the problem of estimating the autoregressive random coefficient of a first-order random coefficient autoregressive time series model applied to panel data of time series. The autoregressive random coefficients across individual units are assumed to be a random sample from a truncated normal distribution with the space (-1, 1) for stationarity. The estimates of random coefficients are obtained by an empirical Bayes procedure using the estimates of model parameters. Also, a Monte Carlo study is conducted to support the estimation procedure proposed in this paper. Finally, we apply our results to the economic panel data in Liu and Tiao(1980).

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On the Comparison of Particle Swarm Optimization Algorithm Performance using Beta Probability Distribution (베타 확률분포를 이용한 입자 떼 최적화 알고리즘의 성능 비교)

  • Lee, ByungSeok;Lee, Joon Hwa;Heo, Moon-Beom
    • Journal of Institute of Control, Robotics and Systems
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    • 제20권8호
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    • pp.854-867
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    • 2014
  • This paper deals with the performance comparison of a PSO algorithm inspired in the process of simulating the behavior pattern of the organisms. The PSO algorithm finds the optimal solution (fitness value) of the objective function based on a stochastic process. Generally, the stochastic process, a random function, is used with the expression related to the velocity included in the PSO algorithm. In this case, the random function of the normal distribution (Gaussian) or uniform distribution are mainly used as the random function in a PSO algorithm. However, in this paper, because the probability distribution which is various with 2 shape parameters can be expressed, the performance comparison of a PSO algorithm using the beta probability distribution function, that is a random function which has a high degree of freedom, is introduced. For performance comparison, 3 functions (Rastrigin, Rosenbrock, Schwefel) were selected among the benchmark Set. And the convergence property was compared and analyzed using PSO-FIW to find the optimal solution.