• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.27-30
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• 2012

One of the most fundamental functions required for an intelligent agent is to estimate its current position based upon uncertain sensor data. In this paper, we explain the implementation of a robot localization system using Particle filters, which are the most effective one of the probabilistic localization methods, and then present the result of experiments for evaluating the performance of our system. Through conducting experiments to compare the effect of the noise-free model with that of the noisy state transition model considering inherent errors of robot actions, we show that it can help improve the performance of the Particle filter localization to apply a state transition model closely approximating the uncertainty of real robot actions.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.87-106
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• 2022

Uncertainties in the energy market are increasing due to technology developments (shale revolution), trade wars, COVID-19, and the Russia-Ukraine war. Especially, since 2020, the risk of international trade in the energy market has increased significantly due to changes in the supply chain of transportation and due to prolonged demand reduction because of COVID-19 and the Russian-Ukraine war. Considering these points, this study analyzed connectedness between energy price, tanker index, and uncertainty to understand the connectedness between international trade in the energy market. Main results are summarized as follows. First, as a result of analyzing stable period and unstable period of the energy price model using the MS-VAR model, it was confirmed that both the crude oil market model and the natural gas market model had a higher probability of maintaining stable period than unstable period, increasing volatility by specific events. Second, looking at the results of the analysis of the connectedness between stable period and unstable period of the energy market, it was confirmed that in the case of total connectedness, connectedness between variables was increased in the unstable period compared to the stable period. In the case of the energy market stable period, considering the degree of connectedness, it was confirmed that the effect of the tanker freight index, which represents the demand-side factor, was significant. Third, unstable period of the natural gas market model increases rapidly compared to the crude oil market model, indicating that the volatility spillover effect of the natural gas market is greater when uncertainties affecting energy prices increase compared to the crude oil market.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.605-613
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• 2007

In order to overcome the difficulties of quantitative risk analysis such as complexity of model, we propose a systematic methodology for risk quantification of railway system which consists of 6 steps: The identification of risk factors, the determination of major scenarios for each risk factor by using event tree, the development of supplementary fault trees for evaluating branch probabilities, the evaluation of event probabilities, the quantification of risk, and the analysis in consideration of accident situation. In this study, in order to address the feasibility of the propose methodology, this framework is applied to the prototype risk model of nation-wide railway level crossings. And the quantification result based on the data of 2005 in Korea will also be presented.

• Journal of the Korean Geotechnical Society
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• v.22 no.6
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• pp.51-61
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• 2006

An efficient and accurate hybrid reliability analysis method is proposed in this paper to quantify the risk of an axially loaded single pile considering pile-soil interaction behavior and uncertainties in various design variables. The proposed method intelligently integrates the concepts of the response surface method, the finite difference method, the first-order reliability method, and the iterative linear interpolation scheme. The load transfer method is incorporated into the finite difference method for the deterministic analysis of a single pile-soil system. The uncertainties associated with load conditions, material and section properties of a pile and soil properties are explicitly considered. The risk corresponding to both serviceability limit state and strength limit state of the pile and soil is estimated. Applicability, accuracy and efficiency of the proposed method in the safety assessment of a realistic pile-soil system subjected to axial loads are verified by comparing it with the results of the Monte Carlo simulation technique.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.158-158
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• 2012

기후시나리오는 시 공간 해상도가 낮아 결과를 직접적으로 활용하기에는 한계가 있다. 따라서 국내외적으로 지역기후모형(RCM)을 통해 고해상도 기후시나리오를 생산하여 각 분야의 영향평가 시 활용하고 있다. 그럼에도 불구하고 기후모형이 갖는 한계로 인하여 시나리오는 관측자료에 비해 과소모의되는 경향이 발생하기 때문에 이를 고려할 수 있는 편의보정 과정이 필요하다. 하지만 국내 외적으로 여러 편의보정기법이 존재하며, 편의보정기법 선정에 따라 최종 평가 결과에 영향을 미칠 수 있다. 특히 수문 분야에서 활용하기 위해 기후시나리오 중 가장 중요한 요건은 일단 관측치의 월 및 계절별 변동성이 잘 반영되는 가이며, 두 번째는 극한 사상(high, low)을 얼마나 잘 모의하여 홍수와 가뭄을 평가하는데 용이한 가이다. 따라서 본 연구에서는 기존 편의보정 기법의 불확실성을 평가하고, 이를 통해 수문학적 활용을 위한 고해상도 기후시나리오의 편의 보정 기법을 제안 및 적용성 평가를 수행하고자 한다. 기존 편의보정기법의 적용성을 평가하기 위해 Change factor method, Quantile mapping, Weather Generator 등을 이용하였다. 이를 위해 역학적으로 상세화된 기후시나리오와 기상청 관할의 기상관측소의 최고기온, 최저기온, 평균기온, 강수량 등의 기후 자료를 수집하였다. 평가를 위해 선정한 관측소 지점은 1951년부터 강수 및 기온 자료가 존재하는 기상청 관할 기상관측소를 토대로, 지역적인 평가를 위해 최종적으로 서울, 강릉, 대구, 부산, 목포, 광주, 전주, 울산, 추풍령을 선정하였다. 이 중 1956~1980년을 과거기간으로 1981~2005년를 미래기간으로 가정하고, 편의 보정 기법 적용하여 기온과 강수량의 통계적 특성을 비교 분석하였으며 평가결과, 편의보정 기법의 따른 한계점들을 도출하였다. 한계점들을 개선하기 위해 본 연구에서 제안한 편의 보정기법은 강수량을 크게 3단계(극한 호우사상, 강수일수, 평균 표준편차 보정)로 나누어 편의보정을 실시하는 것으로 극한 호우사상을 위해서는 연최대치 계열을 이용한 회귀식을 이용하여 보정하였고, 비초과확률을 이용하여 RCM 결과값의 강수일수를 보정하였다. 최종적으로 나머지 강수시나리오에 대해서 평균과 표준편차를 보정하여 최종시나리오를 생산 및 적용성을 평가하였다. 평가 결과, 기존 편의보정기법의 단점을 극복할 수 있었으며, 이를 통해 향후 수문학 분야에 적용하여 신뢰성 있는 기후변화 영향평가를 수행될 수 있을 것이다. 제안한 편의보정 기법 및 평가 결과에 대한 자세한 내용은 발표 시 제시하고자 한다.

• The Journal of Engineering Geology
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• v.20 no.2
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• pp.155-167
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• 2010

In analysis of slope stability, deterministic analysis which yields a factor of safety has been used until recently. However, probability of failure is considered as a more efficient method because it deals with the uncertainty and variability of rock mass. In both methods, a factor of safety or a probability of failure is calculated for a slope although characteristics of rock mass, such as characteristics of joints, weathering degree of rock and so on, are not uniform throughout the slope. In this paper, we divided a model slope into several zones depending on conditions of rock mass and joints, and probabilities of failure in each zone are calculated and compared with that calculated in whole slope. The persistence of joint was also used as a parameter in calculation of probability of failure. A rock slope located in Hongcheon, Gangwondo was selected and the probability of failure using zoning and persistence as parameter was calculated to confirm the applicability of model analysis.

• Journal of the Korean Geotechnical Society
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• v.28 no.12
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• pp.65-76
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• 2012

In this study, probabilistic analysis of seepage through a two-layered soil foundation was performed. The hydraulic conductivity of soil shows significant spatial variations in different layers because of stratification; further, it varies on a smaller scale within each individual layer. Therefore, the deterministic seepage analysis method was extended to develop a probabilistic approach that accounts for the uncertainties and spatial variation of the hydraulic conductivity in a layered soil profile. Two-dimensional random fields were generated on the basis of the Karhunen-Lo$\grave{e}$ve expansion in a manner consistent with a specified marginal distribution function and an autocorrelation function for each layer. A Monte Carlo simulation was then used to determine the statistical response based on the random fields. A series of analyses were performed to verify the application potential of the proposed method and to study the effects of uncertainty due to the spatial heterogeneity on the seepage behavior of two-layered soil foundation beneath water retaining structure. The results showed that the probabilistic framework can be used to efficiently consider the various flow patterns caused by the spatial variability of the hydraulic conductivity in seepage assessment for a layered soil foundation.

• Korean Journal of Construction Engineering and Management
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• v.2 no.2 s.6
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• pp.90-97
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• 2001

The paper considers non-deterministic methods of analysing the risk exposure in a cost estimate. The method(referred to as the 'Monte Carlo simulation' method) interprets cost data indirectly, to generate a probability distribution for total costs from the deficient elemental experience cost distribution. The Monte Carlo method is popular method for incorporating uncertainty relative to parameter values in risk assessment modelling. Non-deterministic methods, they are here presented as possibly effective foundation on which to risk management in cost estimating. The objectives of this research is to develop a computerized algorithms to forecast the probabilistic total construction cost and the elemental work cost at the planning stage.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.5
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• pp.381-389
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• 2017

The kernel density was determined based on sampling points obtained in a Markov chain simulation and was assumed to be an important sampling function. A Kriging metamodel was constructed in more detail in the vicinity of a limit state. The failure probability was calculated based on importance sampling, which was performed for the Kriging metamodel. A pre-existing method was modified to obtain more sampling points for a kernel density in the vicinity of a limit state. A stable numerical method was proposed to find a parameter of the kernel density. To assess the completeness of the Kriging metamodel, the possibility of changes in the calculated failure probability due to the uncertainty of the Kriging metamodel was calculated.

• 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.