• 한국자동차공학회논문집
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• 제6권4호
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• pp.76-85
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• 1998

Recently, Genetic Algorithm(GA) is widely adopted into a search procedure for structural optimization, which is a stochastic direct search strategy that mimics the process of genetic evolution. This methods consist of three genetics operations maned selection, crossover and mutation. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GA, being zero-order method, is very simple. So, they can be easily applicable to wide area of design optimization problems. Also, owing to multi-point search procedure, they have higher probability of converge to global optimum compared to traditional techniques which take one-point search method. In this study, a method of finding the optimum values of suspension parameters is proposed by using the GA. And vehicle is modelled as planar vehicle having 5 degree-of-freedom. The generalized coordinates are vertical motion of passenger seat, sprung mass and front and rear unsprung mass and rotate(pitch) motion of sprung mass. For rapid converge and precluding local optimum, share function which distribute chromosomes over design bound is introduced. Elitist survival model, remainder stochastic sampling without replacement method, multi-point crossover method are adopted. In the sight of the improvement of ride comfort, good result can be obtained in 5-D.O.F. vehicle model by using GA.

• Nuclear Engineering and Technology
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• 제37권3호
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• pp.245-258
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• 2005

This paper addresses two types of uncertainty: stochastic uncertainty and subjective uncertainty in probabilistic accident consequence assessments. The off-site consequence assessment code OSCAAR has been applied to uncertainty and sensitivity analyses on the individual risks of early fatality and latent cancer fatality in the population outside the plant boundary due to a severe accident. A new stratified meteorological sampling scheme was successfully implemented into the trajectory model for atmospheric dispersion and the statistical variability of the probability distributions of the consequence was examined. A total of 65 uncertain input parameters was considered and 128 runs of OSCAAR with 144 meteorological sequences were performed in the parameter uncertainty analysis. The study provided the range of uncertainty for the expected values of individual risks of early and latent cancer fatality close to the site. In the sensitivity analyses, the correlation/regression measures were useful for identifying those input parameters whose uncertainty makes an important contribution to the overall uncertainty for the consequence. This could provide valuable insights into areas for further research aiming at reducing the uncertainties.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.849-855
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• 2021

The purpose of this study is to analyze and compare Islamic commercial banks and Islamic banking units with the stochastic frontier analysis (SFA) method during 2014-2018. The data in research using Islamic commercial banks and Islamic banking units. There are 10 Islamic commercial banks and 5 Islamic banking units that meet the criteria of purposive sampling. The calculation of efficiency level using the SFA method with the function of production shows that Islamic commercial banks and Islamic banking units always experience an increase in efficiency every year with the average level of efficiency of Islamic commercial banks being 0.43994, while the average rate of efficiency of Islamic banking units is slightly higher at 0.47654. This shows that Islamic banking units are slightly more optimal in generating total financing in the period 2010-2014. The test results using Independent Sample T-Test can be concluded that there is no difference in the efficiency value between Islamic commercial banks and Islamic banking units. Operating costs are not significant and have a positive effect on the total financing; total assets have a significant effect and a positive impact on total financing; labor costs are not significant and have a negative effect on total financing.

• 한국해안·해양공학회논문집
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• 제31권3호
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• pp.158-169
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• 2019

추계학적 확률과정의 하나인 감마 확률과정을 이용하여 구조물의 잔류유효수명을 확률론적으로 예측할 수 있는 수학적 모형을 수립하였다. 수립된 모형은 과거부터 현재 시점까지 관측된 피해자료와 관련된 표본의 불확실성과 장래 시간 진행에 따른 누적피해의 불확실성을 올바로 고려할 수 있다. 또한 최소자승법과 모멘트법을 함께 사용하여 경사제의 재령, 운용환경 그리고 피해이력을 고려할 수 있는 모수 추정법을 제시하였다. 먼저 현재 재령의 단일 피해 자료를 갖는 임의의 조건에서 모수에 대한 민감도 분석을 수행하여, 잔류유효수명과 관련된 여러가지 거동 특성들을 분석하였다. 또한 잔류유효수명 예측모형을 경사제에 적용하였다. 경사제 피복재의 피해 이력에 대한 실험자료를 이용하여 감마 확률과정의 모수를 추정하였는데 실험자료와 매우 잘 일치하였다. 해석 결과에 의하면 현재 시점으로부터 상당히 오랜 시간이 경과하면 파괴한계를 초과할 확률이 일정한 값으로 수렴해야 하는 제약 조건을 잘 만족하였다. 한편 기대 잔류유효수명은 피해 이력의 거동 특성에 따라 각기 다르게 산정되었다. 특히 피해의 변동계수가 크면 추계학적으로 산정된 기대 잔류유효수명은 결정론적 회기모형의 해석 결과와 큰 차이를 보인다. 이는 해석과정에 포함된 불확실성의 영향으로 판단된다. 변동계수가 크면 파괴한계에 도달하는 시간의 분포가 넓게 퍼지기 때문이다. 따라서 본 연구에서 수립된 추계학적 잔류유효수명 예측모형은 현재 재령에서 경사제의 피해에 대한 확률적 평가를 수행할 수 있을 뿐만 아니라 장래 시간의 진행에 따른 누적피해의 불확실성을 올바로 고려할 수 있다.

• 한국농공학회지
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• 제22권3호
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• 한국임상약학회지
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• 제23권2호
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• pp.137-141
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• 2013

Purpose: This study compared the performance of new NONMEM estimation methods using a population analysis dataset collected from a clinical study that consisted of 40 individuals and 567 observations after a single oral dose of glimepiride. Method: The NONMEM 7.2 estimation methods tested were first-order conditional estimation with interaction (FOCEI), importance sampling (IMP), importance sampling assisted by mode a posteriori (IMPMAP), iterative two stage (ITS), stochastic approximation expectation-maximization (SAEM), and Markov chain Monte Carlo Bayesian (BAYES) using a two-compartment open model. Results: The parameters estimated by IMP, IMPMAP, ITS, SAEM, and BAYES were similar to those estimated using FOCEI, and the objective function value (OFV) for diagnosing the model criteria was significantly decreased in FOCEI, IMPMAP, SAEM, and BAYES in comparison with IMP. Parameter precision in terms of the estimated standard error was estimated precisely with FOCEI, IMP, IMPMAP, and BAYES. The run time for the model analysis was shortest with BAYES. Conclusion: In conclusion, the new estimation methods in NONMEM 7.2 performed similarly in terms of parameter estimation, but the results in terms of parameter precision and model run times using BAYES were most suitable for analyzing this dataset.

• 한국전산유체공학회지
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• 제17권4호
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• pp.32-40
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• 2012

Efficient Global Optimization (EGO) method is a global optimization technique which can select the next sample point automatically by infill sampling criteria (ISC) and search for the global minimum with less samples than what the conventional global optimization method needs. ISC function consists of the predictor and mean square error (MSE) provided from the kriging model which is a stochastic metamodel. Also the constrained EGO method can minimize the objective function dealing with the constraints under EGO concept. In this study the constrained EGO method applied to the RAE2822 airfoil shape design formulated with the constraint. But the noisy CFD data caused the kriging model to fail to depict the true function. The distorted kriging model would make the EGO deviate from the correct search. This distortion of kriging model can be handled with the interpolation(p=free) kriging model. With the interpolation(p=free) kriging model, however, the search of EGO solution was stalled in the narrow feasible region without the chance to update the objective and constraint functions. Then the accuracy of EGO solution was not good enough. So the three-step search method was proposed to obtain the accurate global minimum as well as prevent from the distortion of kriging model for the noisy constrained CFD problem.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1993년도 가을 학술발표회논문집
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• pp.248-255
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• 1993

Recently, due to the increasing attention to the structural safety under uncertain environments, many researches on the structural reliability analysis have been peformed. Some useful methods are available to evaluate performance reliability of structures with explicit limit states. However, for large structures, in which structural behaviors can be analyzed with finite element models and the limit states are only expressed implicitly, Monte-Carlo simulation method has been mainly used. However, Monte-Carlo simulation method spends too much computational time on repetitive structural analysis. Many alternative methods are suggested to reduce the computational work required in Monte-Carlo simulation. Response surface method is widely used to improve the efficiency of structural reliability analysis. Response surface method is based on the concept of approximating simple polynomial function of basic random variables for the limit state which is not easily expressed in explicit forms of design random variables. The response surface method has simple algorithm. However, the accuracy of results highly depends on how properly the stochastic characteristics of the original limit state has been represented by approximated function, In this study, an improved response surface method is proposed in which the sampling points for creating response surface are modified to represent the failure surface more adequately and the combined use of a linear response surface function and Rackwitz-Fiessler method has been employed. The method is found to be more effective and efficient than previous response surface methods. In addition more consistent convergence is achieved, Accuracy of the proposed method has been investigated through example.

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.

• Nuclear Engineering and Technology
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• 제56권9호
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• pp.3626-3643
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• 2024

One of the issues in the development of the sodium-cooled fast reactor (SFR) using transuranic (TRU) metallic fuel is the absence of criticality benchmark experiment that faithfully mocks up the nuclear characteristics of the target design for validation of the reactor core design code and its uncertainty quantification (UQ). This study aims to quantify the criticality uncertainty of a typical TRU burner with metallic fuel by using the standard upper safety limit (USL) estimation framework based on the similarity analysis of existing benchmark experiments but elaborated in two aspects:1) application of two-sided rather than one-sided tolerance interval and 2) inclusion of additional uncertainty to account for fission products and minor actinides not included in the benchmark experiments. To conduct the similarity analysis and evaluate the nuclear-data induced uncertainty, existing, well-verified computing codes were integrated, including the nuclear data sampling code SANDY, the nuclear data processing code NJOY, and the continuous-energy Monte Carlo code McCARD. Finally, using the SFR benchmark database comprising both publicly available and proprietary benchmark experiments, the criticality uncertainty of the TRU core model with metallic fuel was evaluated.