• Korean Journal of Construction Engineering and Management
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• v.17 no.2
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• pp.70-79
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• 2016

The current remodeling cost estimation process is not only dependent on the historical data of new building construction, but it also has a poor linkage with risk-based estimation approach. As such, there is a high risk of falling short of initial budget. To overcome this, a risk-based estimation approach is necessary by providing a probabilistic estimation in consideration of the potential risk factors in conducting the remodeling projects. In addition, the decision-making process should be linked with the risk-based estimation results in stead of intuitive and/or experience-based estimation. This study provides a probabilistic estimation process for residential remodeling projects by developing a detailed methodology in which a step-by-step approach can be achieved. The new proposed estimation approach can help in decision-making for remodeling projects in terms of whether to proceed or not, by effectively reflecting the potential risk factors in the early stage of the project. In addition, the study can enhance the reliability of the estimation results by developing a sustainable estimation process model where a risk-based evaluation can be accomplished by setting up the cost-risk relationship database structure.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2001.04a
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• pp.81-84
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• 2001

고준위 방사성 폐기물 영구처분 안전성을 평가하기 위하여 입력 자료로 처분장 주변 각 암 반에서의 지하수 유동 속도 및 유동 시간이 요구된다. 이러한 유동 속도와 시간은 대부분의 경우 단일 값이 요구되지만 고준위 방사성 처분의 경우 지하 매질의 불확실성을 고려하기 위하여 확률론적 분석이 요구된다. 지하수 유동 속도 및 시간이 확률밀도함수로 표시되기 위해서는 기존의 방법에서는 수리 해석의 입력 인자 값들을 변화시키면서 반복적인 계산을 수행하는 방법이 사용되었다. 그러나 이러한 방법론의 한계를 극복하기 위해 최근 섭동 이론을 이용한 adjoint 방법론이 사용되고 있는 바 이를 이용하여 가상 처분장에서의 지하수 유동 속도와 시간을 확률론적으로 해석하였다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.13 no.1
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• pp.147-158
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• 2000

For the large scale reinforced concrete cooling tower shells, the shape imperfection can be introduced due not only to mistakes in the process of construction but also to the long term behavior of concrete. The shape imperfection evokes the additional responses such as displacements and stresses in addition to the design values. In this study, the statistical behavior of the RC cooling tower shell due to the shape imperfection is investigated using the Monte Carlo simulation. The radius of cooling tower and the shell thickness are adopted as the parameters which cause the shape imperfection. The shape imperfection is modeled as a stochastic field rather than the local one of axisymmetric or bulge type of imperfection. The randomness in the radius is shown to be more affecting the structural responses than the randomness in the shell thickness. In addition to the geometrical randomness, the effect of randomness in the modulus of elasticity on the structural response is also investigated and compared with that of the geometrical ones.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.464-469
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• 2009

This paper discusses a robust observer-based output-feedback stabilization of an uncertain Takagi-Sugeno (T-S) fuzzy system in a network. In the networked control system, the input delay occurs inevitably and it is expressed by the Markovian stochastic process. To design robust sampled-data observer-based output-feedback controller, we discretize the T-S fuzzy system and represent as a jump system. Stochastic robust stabilization condition is formulated in terms of linear matrix inequalities.

• Economic and Environmental Geology
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• v.50 no.3
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• pp.239-250
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• 2017

Physically based landslide susceptibility analysis has been recognized as an effective analysis method because it can consider the mechanism of landslide occurrence. The physically based analysis used the slope geometry and geotechnical properties of slope materials as input. However, when the physically based approach is adopted in regional scale area, the uncertainties were involved in the analysis procedure due to spatial variation and complex geological conditions, which causes inaccurate analysis results. Therefore, probabilistic method have been used to quantify these uncertainties. However, the uncertainties caused by lack of information are not dealt with the probabilistic analysis. Therefore, fuzzy set theory was adopted in this study because the fuzzy set theory is more effective to deal with uncertainties caused by lack of information. In addition, the vertex method and Monte Carlo simulation are coupled with the fuzzy approach. The proposed approach was used to evaluate the landslide susceptibility for a regional study area. In order to compare the analysis results of the proposed approach, Monte Carlo simulation as the probabilistic analysis and the deterministic analysis are used to analyze the landslide susceptibility for same study area. We found that Fuzzy Monte Carlo simulation showed the better prediction accuracy than the probabilistic analysis and the deterministic analysis.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.02a
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• pp.1-22
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• 2004

현재 댐 위험도 분석 등의 사용되는 Monte Carlo Simulation 방법을 이용하는데 있어서 가장 어려운 문제이면서 약점으로 지적되어 온 것이 수리 수문학적 불확실성 변수들에 대해서 정확한 확률분포를 정의할 수 없다는 것이다. 즉 기존 매개변수적 방법들은 모집단 분포의 형태를 가정하고, 분포의 매개변수에 관한 통계적 분석을 하는 방법이다. 조사대상이 되는 모집단 분포에 관한 정보가 부족하기 때문에 어떤 가정이 곤란하거나 부정확한 가정을 전제로 하지 않고, 관측자료로부터 통계량을 분석하여 통계적 추론을 하는 방법이 필요하게 되며, 이를 비매개변수적 방법(nonparametric methods)이라고 한다. (중략)

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.3
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• pp.251-259
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• 2004

The prediction accuracy of prestress plays an important role in the quality of maintenance and the decision on rehabilitation of infrastructure such as prestressed concrete bridges. In this paper, the Bayesian statistical method that uses in-situ measurement data for reducing the uncertainties or updating long-term prediction of prestress is presented. For Bayesian analysis, prior probability distribution is developed to represent the uncertainties of creep and shrinkage of concrete and likelihood function is derived and used with data acquired in site. Posterior probability distribution is then obtained by combining prior distribution and likelihood function. The numerical results of this study indicate that more accurate long-term prediction of prestress forces due to creep and shrink age is possible.

• Smart Media Journal
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• v.11 no.1
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• pp.38-45
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• 2022

A lot of research is being going until this day in order to obtain stable profit in the stock market. Trading algorithms are widely used, accounting for over 80% of the trading volume of the US stock market. Despite a lot of research, there is no trading algorithm that always shows good performance. In other words, there is no guarantee that an algorithm that performed well in the past will perform well in the future. The reason is that there are many factors that affect the stock price and there are uncertainties about the future. Therefore, in this paper, we propose a model using TimeGAN that predicts future returns well and selects algorithms that are expected to have high returns based on past records of the returns of algorithms. We use TimeGAN becasue it is probabilistic, whereas LSTM method predicts future time series data is deterministic. The advantage of TimeGAN probabilistic prediction is that it can reflect uncertainty about the future. As an experimental result, the method proposed in this paper achieves a high return with little volatility and shows superior results compared to many comparison algorithms.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.70-70
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• 2011

기후변화 연구는 불확실한 미래를 전망하는 과정이므로 '불확실성'은 모든 기후변화 영향평가의 키워드임에 분명하다. 하지만 불확실성 평가를 위해 IPCC에서 제공되고 있는 수많은 GCM 시나리오를 모두 활용하기에는 많은 시간과 노력이 필요하기 때문에 이를 효율적으로 수행할 수 있는 방법이 필요하다 본 연구에서는 시나리오 저감(scenario reduction)방법을 이용하여, 수많은 GCM 시나리오 대신 몇 개의 대표적 GCM 시나리오로도 충분히 불확실성을 유지할 수 있는 시나리오 저감(scenario reduction)방법을 수립하고 제시하였다. IPCC 기후시나리오 중 20C3M과 A & B 배출시나리오를 바탕으로 생산되는 71개의 GCM 시나리오를 다운로드 받아 월평균 기온과 강수량에 대하여 한반도를 대상으로 분석하였다. 비교결과, 기온 전망은 실측과 비슷한 경향성을 보였으나 강수량은 홍수기를 모의하지 못하는 것으로 나타났다. 시나리오 저감방법은 시나리오 분류(scenario cluster)방법과 시나리오 선정(scenario selection) 방법으로 구성된다. 시나리오 분류방법에서는 k-mean방법을 이용하여 5개의 cluster로 나누었으며, 시나리오 선정방법에서는 GCM 시나리오 선정기법을 조사 분석하여 연구방향과 목적에 따라 GCM 시나리오 선정기법을 선택할 수 있는 표를 제시하고, 이 중 시나리오의 확률밀도함수를 이용하는 PDF method를 적용하였다. 본 연구에서는 불확실성 정량화를 위해 maximum entropy를 이용하였다. 또한 시나리오 저감방법이 불확실성을 유지하는지 비교하기 위해 PDF method를 이용하여 정확성이 높은 순으로 5개의 GCM 시나리오를 선정(best 시나리오)하여 불확실성을 정량화하였다. GCM 시나리오의 분산을 이용하여 maximum entropy를 산정한 결과, 20C3M 배출시나리오에서는 모든 시나리오의 entropy는 3.08, 시나리오 저감방법은 2.75, best 시나리오는 2.28이었으며, 이는 시나리오 저감방법은 모든 시나리오의 89.3%의 불확실성을 설명하고 있으나 best 시나리오는 74.0%밖에 설명하지 못한다는 것을 나타낸다. A & B 배출시나리오에서도 시나리오 저감 방법을 사용한 GCM 시나리오가 best 시나리오보다 모든 시나리오의 불확실성을 더 잘 설명하는 것으로 나타났다. 이와 같이 수많은 GCM 시나리오를 사용하는 것보다 몇 개의 대표 시나리오를 이용하여 기후 변화 불확실성을 유지하면서 미래전망을 할 수 있다면, 매우 효율적으로 기후변화 연구를 수행할 수 있을 것으로 사료된다.