• Computational Structural Engineering
• /
• v.28 no.3
• /
• pp.13-17
• /
• 2015

본 기사에서는 모델링불확실성(modeling uncertainty)에 따른 신뢰성 해석결과의 가변성(variability)을 가능성 분포함수(possibility distribution function)를 구성하여 해결하는 방법을 AISC(1998), AIJ(1985), CSA(1994)에서 제안된 3개의 최대 D/t 계산식을 예로 들어 소개하였다. 확신정도가 측정된 신뢰성지수 들을 얻을 수 있으며, 확신정도를 고려한 신뢰성지수의 결정이 가능하게 된다. 다양한 형태의 불확실성에 대하여 그 형태에 맞는 적합한 불확실성 모델링을 사용하는 것도 중요하지만, 확률적 표현에 익숙한 우리의 인지구조를 고려하여 기존의 신뢰성 해석에 어떻게 다양한 불확실성 모델링 방법을 접목시킬 것인지에 대한 연구도 중요할 것이다.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
• /
• 2011.11a
• /
• pp.243-247
• /
• 2011

본 논문에서는 원자력발전소의 화재 안전성평가에서 제기되는 화재모델링 불확실성 분석 방법을 검토하고 논의하였다. 원자력발전소의 성능기반 화재 안전성평가에 대해서는 NUREG-1934를, 확률론적 화재 안전성 평가에 대해서는 NUREG/CR-6850를 중심으로 화재 모델링 불확실성 분석 방법을 소개하고 몬테칼로 시뮬레이션을 이용한 불확실성 분석 방법에 대해 논의하였다.

• 대한공업교육학회지
• /
• v.34 no.2
• /
• pp.396-410
• /
• 2009

The goal of this work is to present an advanced method of an estimation of the Modeling Uncertainties coming up in industrial rigid robot's manipulator and actuators. First, with the given physical robot model, the motion equation was derived. Considering a fictitious model, a new extended motion equation is developed. Based on this extended model, an observer and observer bank are designed for the estimation of modeling uncertainties which are involving the effects of gravity, friction, mass unbalance, and Coriolis which show the nonlinear characteristics in operation states.

• Fire Science and Engineering
• /
• v.26 no.2
• /
• pp.40-52
• /
• 2012

This paper presents the uncertainty analysis results of fire modeling input parameters for motor control center in switchgear room of nuclear power plants. FDS (Fire Dynamics simulator) 5.5 was used to simulate the fire scenario and Latin Hyper Cube Monte Carlo simulations were employed to generate random samples for FDS input parameters. The uncertainty analysis results of input parameters are compared with those of the model uncertainty analysis and sensitivity analysis approaches of NUREG-1934. The study results show that the input parameter uncertainty analysis approach may lead to more conservative results than the uncertainty analysis and sensitivity analysis methods of NUREG-1934.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
• /
• 2011.04a
• /
• pp.53-60
• /
• 2011

본 논문에서는 원자력발전소의 스위치기어실 화재로 인한 케이블 손상시간과 케이블 온도를 평가하기 위해 화재모델링 입력모수 불확실성 분석을 수행하였다. 화재모델링은 FDS 5.5를 사용하였으며 입력모수 샘플링은 Wilks 식에 따라 93회를 수행하였다. 단순히 입력모수 평균 값을 사용한 화재모델링 분석결과는 화재모델링 불확실성 분석결과보다, 케이블 손상시간은 최대 1.6배 늦게, 케이블 온도는 최대 0.45배 낮게 평가될 수 있는 것으로 나타났다.

• Journal of the Korean Society for information Management
• /
• v.36 no.1
• /
• pp.191-213
• /
• 2019

The process of obtaining scientific knowledge is conducted through research. Researchers deal with the uncertainty of science and establish certainty of scientific knowledge. In other words, in order to obtain scientific knowledge, uncertainty is an essential step that must be performed. The existing studies were predominantly performed through a hedging study of linguistic approaches and constructed corpus with uncertainty word manually in computational linguistics. They have only been able to identify characteristics of uncertainty in a particular research field based on the simple frequency. Therefore, in this study, we examine pattern of scientific knowledge based on uncertainty word according to the passage of time in biomedical literature where biomedical claims in sentences play an important role. For this purpose, biomedical propositions are analyzed based on semantic predications provided by UMLS and DMR topic modeling which is useful method to identify patterns in disciplines is applied to understand the trend of entity based topic with uncertainty. As time goes by, the development of research has been confirmed that uncertainty in scientific knowledge is moving toward a decreasing pattern.

• Smart Media Journal
• /
• v.11 no.9
• /
• pp.9-20
• /
• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.16 no.4
• /
• pp.401-406
• /
• 2003

This paper deals with stress analysis of single-lap adhesive joints which have uncertain material properties. Basically, material properties have a certain amount of scatter and such uncertainties can affect the performance of joints. In this paper, the convex modeling is introduced to consider such uncertainties in calculating peel and shear stress of adhesive joints and the results are compared with those from the Monte Carlo simulation. Numerical results show that stresses increase when uncertainties considered, which indicates that such uncertainties should not be ignored for estimation of structural safety. Also, the results obtained by the convex modeling and the Monte Carlo simulation show good agreement, which demonstrates the effectiveness of convex modeling.

• Journal of Korean Society of Environmental Engineers
• /
• v.22 no.3
• /
• pp.535-546
• /
• 2000

Juam lake is a major water resource for the industrial and agricultural activities as well as the resident life of Kwangju and Chonnam area. However, the water quality of the lake is getting worse due to a large quantity of pollutant inflowing to the lake. As a preliminary step in making the countermeasure to achieve the water quality goal of the lake. it is necessary to understand how the water quality of the lake will be in future. Several computer programs can be used to predict the water quality of lake. Each of these programs requires a number of input data such as hydrological and meteorological data. and the quantity of the pollutant inflowed. but some or most of the input data contain uncertainty. which eventually results in the uncertainty of prediction value (future level of water quality). Generally. the uncetainty stems from the lack of information available. the randomness of future situation. and the incomplete knowledge of expert. Thus. the purpose of this study is to present a method for representing the degree of the uncertainty contained in input data by applying fuzzy theory and incorporating it directly into the water quality modeling process. By using the method. the prediction on the future water quality level of Juam lake can be made that is more appropriate and realistic than the one made without taking uncertainty in account.

• 한국신재생에너지학회:학술대회논문집
• /
• 2009.11a
• /
• pp.427-430
• /
• 2009

풍력 터빈 블레이드용 익형의 경우 운용 조건에서 높은 양항비를 가지도록 설계되나 풍속, 풍향의 변동에 의해 운용조건에 변화가 발생할 경우 성능의 저하가 발생할 수 있다. 따라서 운용조건의 변동이 발생하더라도 공력 성능이 크게 변하지 않는 익형이 요구된다. 본 연구에서는 이러한 운용조건의 불확실성을 고려하여 풍력 터빈 블레이드용 익형의 신뢰성 기반 강건 최적 설계를 수행하였다. 익형 설계를 위해서 여러 익형 형상 변수들을 고려할 수 있는 익형 모델링 함수를 정의하였고 기저형상으로는 NREL에서 개발한 S809 익형을 사용하였다.