• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.429-430
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• 2009

The flexural strength of reinforced concrete members is strongly governed by mechanical properties of reinforcing bars, especially by yield strength, which have many uncertainties. The correct choice of probabilistic models for yield strength of reinforcement is an essential step to assure the safety and reliability of members. In this paper, a probabilistic model of yield strength of reinforcing bars is proposed based on literature and own experimental data.

• Economic and Environmental Geology
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• v.28 no.4
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• pp.433-438
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• 1995

The most popular minimization method is based on the least-squares criterion, which uses the $L_2$ norm to quantify the misfit between observed and synthetic data. The solution of the least-squares problem is the maximum likelihood point of a probability density containing data with Gaussian uncertainties. The distribution of errors in the geophysical data is, however, seldom Gaussian. Using the $L_2$ norm, large and sparsely distributed errors adversely affect the solution, and the estimated model parameters may even be completely unphysical. On the other hand, the least-absolute-deviation optimization, which is based on the $L_1$ norm, has much more robust statistical properties in the presence of noise. The solution of the $L_1$ problem is the maximum likelihood point of a probability density containing data with longer-tailed errors than the Gaussian distribution. Thus, the $L_1$ norm gives more reliable estimates when a small number of large errors contaminate the data. The effect of outliers is further reduced by M-fitting method with Cauchy error criterion, which can be performed by iteratively reweighted least-squares method.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.262-267
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• 2001

In this study, the Reliability of degraded steam turbine blade was evaluated using the limited fatigue data. The statistical estimation of limited fatigue data implies that some unknown uncertainties which may be involved in fatigue reliability analysis. Therefore, an appropriate distribution in the fatigue strength was determined by the characteristic distribution - linear correlation coefficient, fatigue physics, error parameter. 3-parameter Weibull distribution is the most appropriate distribution to assume for infinite region. The load applied on the blade is mainly tensile. The maximum Von-Mises stress is 219.4 MPa at the steady state service condition. The failure probability($F_p$) derived from the strength-stress interference model using Monte carlo simulation under variable service condition is 0.25% at the 99.99% confidence level.

• Computational Structural Engineering
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• v.7 no.2
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• pp.131-138
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• 1994

Derivatives of eigenvalues and eigenvectors for statistical properties is presented. Dynamic response of random system including uncertainties for the design variable is calculated with the first order perturbation method to original governing equation. In optimal design methods, there is fundamental requirement for design gradients. A method for calculating the sensitivity coefficients is developed using the governing equation and first order perturbed equation.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.362-365
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• 2011

본 논문에서는 통계적인 방법을 이용하여 점탄성 제진재인 합성고무의 물성에 대한 변동성을 평가하는 방법을 제안하고 측정데이터를 이용하여 합성고무에 대한 평가를 수행하였다. 고무 물성의 불확실성 인자로는 외기 온도의 변화와 실험 데이터의 오차 및 점탄성 제진모델의 오차를 고려하였다. 고무는 분수차 미분 모델로 표현되었고 온도의 영향은 비선형 이동계수모델을 도입하여 복소계수로 나타내어 동강성과 감쇠를 표현하였다. 이러한 물성모델을 바탕으로 고무에 대한 물성 실험데이터와 물성계수의 확률밀도함수 사이에 정의된 우도함수를 최대화하는 통계적 보정방법을 이용하여 물성모델의 물질계수들에 대한 변동성을 추정하였다.

• Analytical Science and Technology
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• v.15 no.6
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• pp.580-583
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• 2002

New approach is developed employing the overall uncertainty obtained from multiple measurements to evaluate the statistical significance for the difference between a given reference value and its measured value determined in a lab. The overall uncertainty is determined by separate combinations of the uncertainties arising from systematic and random effects. It is shown that the uncertainty term in regular t-test can be underestimated by n measurements.

• Journal of Public Diplomacy
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• v.2 no.1
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• pp.28-42
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• 2022

The aim of this article is to rethink the role of international trade as a public diplomacy tool by considering the uncertainties that stem from political tensions. The main contribution made in this article is theoretical rather than statistical. However, we analyze trade and public opinion data to study the relationship between both factors. Using Latinobarometer, a cross-sectional survey that collects public opinion data from Latin America, this article analyses public opinion toward the United States and China. One of the main takeaways from this study is that, despite its potential to showcase political stability, public diplomacy undervalues 'unintended consequences' of international trade relations. This article takes up international trade as an unintended, but arguably effective, resource to be developed for the practice of public diplomacy. Findings presented in this article do not claim causation between trade and opinion, something that can be explored by further research, but rather introduce new questions for further research on the public diplomacy of trade relations.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1990.04a
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• pp.272-288
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• 1990

The uncertainty quantification process in probabilistic Risk Assessment usually involves a specification of the uncertainty in the input data and the propagation of this uncertainty to the final risk results. The distributional sensitivity analysis is to study the impact of the various assumptions made during the quantification of input parameter uncertainties on the final output uncertainty. The uncertainty importance of input parameters, in this case, should reflect the degree of changes in the whole output distribution and not just in a point estimate value. A measure of the uncertainty importance is proposed in the present paper. The measure is called the distributional sensitivity measure(DSM) and explicitly derived from the definition of the Kullback's discrimination information. The DSM is applied to three typical discrimination information. The DSM is applied to three typical cases of input distributional changes: 1) Uncertainty is completely eliminated, 2) Uncertainty range is increased by a factor of 10, and 3) Type of distribution is changed. For all three cases of application, the DSM-based importance ranking agrees very well with the observed changes of output distribution while other statistical parameters are shown to be insensitive.

• Journal of Astronomy and Space Sciences
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• v.40 no.2
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• pp.67-77
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• 2023

The Titius-Bode's relation has been historically successful in predicting the location of Ceres in the solar system, while its physical basis remains hidden. In this study, we attempt to answer the question of whether the Titius-Bode's relation is universally valid for exoplanetary systems with plural exoplanets. For this purpose, we statistically study the distribution of the ratio of the orbiting periods of two planets in 32 exoplanetary systems hosted by a single star. We only consider the period ratios derived from exoplanets orbiting a single star since celestial objects under investigation are kept as simple as possible and free from uncertainties such as the mass of the host star. We find that the distribution of period ratios of two exoplanets appears inconsistent with that derived from the Titius-Bode's relation using the χ² test. We also found that the distance distribution in exoplanetary systems unlikely follows the uniform distribution or the Poisson's distribution. It is noted, however, that more rigorous statistical tests should be carried out to reach a more certain conclusion.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.2
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• pp.163-168
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• 2011

Robust optimization or reliability-based design optimization are some of the methodologies that are employed to take into account the uncertainties of a system at the design stage. For applying such methodologies to solve industrial problems, accurate and efficient methods for estimating statistical moments and failure probability are required, and further, the results of sensitivity analysis, which is needed for searching direction during the optimization process, should also be accurate. The aim of this study is to employ the function approximation moment method into the sensitivity analysis formulation, which is expressed as an integral form, to verify the accuracy of the sensitivity results, and to solve a typical problem of reliability-based design optimization. These results are compared with those of other moment methods, and the feasibility of the function approximation moment method is verified. The sensitivity analysis formula with integral form is the efficient formulation for evaluating sensitivity because any additional function calculation is not needed provided the failure probability or statistical moments are calculated.