• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.2
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• pp.39-60
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• 1995

In this paper, an integration of stochastic dynamic programming (SDP), integer goal programming (IGP) and analytic hierarchy process (AHP) is proposed to handle multiobjective-multicriteria sequential decision making problems under uncertainty inherent in R & D investment planning. SDP has its capability to handle problems which are sequential and stochastic. In the SDP model, the probabilities of the funding levels in any time period are generated using a subjective model which employs functional relationships among interrelated parameters, scenarios of future budget availability and subjective inputs elicited from a group of decision makers. The SDP model primarily yields an optimal investment planning policy considering the possibility that actual funding received may be less than anticipated one and thus the projects being selected under the anticipated budget would be interrupted. IGP is used to handle the multiobjective issues such as tradoff between economic benefit and technology accumulation level. Other managerial concerns related to the determination of the optimal project portifolio within each stage of the SDP model. including project selection, project scheduling and annual budget allocation are also determined by the IGP. AHP is proposed for generating scenario-based transformation probabilities under budgetary uncertainty and for quantifying the environmental risk to be considered.

• Korean Management Science Review
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• v.31 no.3
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• pp.95-111
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• 2014

It is essential to carry out an economic analysis on public water supply projects so that policy makers and water enterprises are aware of the actual value of the project. However, many popular approaches based on discounted cash flow analysis do not capture the uncertainties inherent in cash flow. In order to analyze the economic values of the water supply project of local governments, we utilize real option model, which considers uncertainty in future water price behavior and captures the value of real life flexibility. The real option model is designed to incorporate the option to expand and abandon, and it is applied to a local government case. Furthermore, we assess the project by exploring Luehrman's option space to accommodate the more efficient decision making. The results show that substantial amount of potential value is included in the public water supply service, and the overall value is greater than the value obtained from the discounted cash flow model.

• Smart Structures and Systems
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• v.22 no.4
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• pp.441-457
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• 2018

Characteristic parameter values of seismic isolators deviate from their nominal design values due to uncertainties and/or errors in their material properties and element dimensions, etc. Deviations may increase over service life due to environmental effects and service conditions. For accurate evaluation of the seismic safety level, all such effects, which would result in deviations in the structural response, need to be taken into account. In this study, the sensitivity of the probability of failure of the structures equipped with nonlinear base isolation systems to the uncertainties in various isolation system characteristic parameters is investigated in terms of various isolation system and superstructure response parameters in the context of a realistic three-dimensional base-isolated building model via Monte Carlo Simulations. The inherent record-to-record variability nature of the earthquake ground motions is also taken into account by carrying out analyses for a large number of ground motion records which are classified as those with and without forward-directivity effects. Two levels of nominal isolation periods each with three different levels of uncertainty are considered. Comparative plots of cumulative distribution functions and related statistical evaluation presented here portray the potential extent of the deviation of the structural response parameters resulting from the uncertainties and the uncertainty levels considered, which is expected to be useful for practicing engineers in evaluating isolator test results for their projects.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.1
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• pp.63-73
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• 2010

This paper presents the optimal design method of an overlapped ultrasonic sensor ring for reduced positional uncertainty, and its application to the obstacle detection with improved resolution. Basically, it is assumed that a set of ultrasonic sensors are installed to form a circle at regular intervals with their beams overlapped. First, exploiting the overlapped beam pattern, the positional uncertainty inherent to an ultrasonic sensor is shown to be significantly reduced. Second, for an ideal ultrasonic sensor ring of zero radius, the effective beam width is defined to represent the positional uncertainty, and the optimal number of ultrasonic sensors required for minimal effective beam width is obtained. Third, for an actual ultrasonic sensor ring of nonzero radius, the design index is defined to represent the degree of positional uncertainty, and an optimal design of an overlapped ultrasonic sensor ring consisting of commercial ultrasonic sensors with low directivity is given. Fourth, given measured distances from ultrasonic sensors, the geometric method is described to compute the obstacle position with reference to the center of a mobile robot. Finally, through experiments using our overlapped ultrasonic sensor ring prototype, the validity and performance of the proposed method is demonstrated.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• Journal of Soil and Groundwater Environment
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• v.17 no.6
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• pp.69-81
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• 2012

Diverse geostatistical tools such as kriging have been used to estimate the volume and spatial coverage of contaminated soil needed for remediation. However, many approaches frequently yield estimation errors, due to inherent geostatistical uncertainties. Such errors may yield over- or under-estimation of the amounts of polluted soils, which cause an over-estimation of remediation cost as well as an incomplete clean-up of a contaminated land. Therefore, it is very important to use a better estimation tool considering uncertainties arising from incomplete field investigation (i.e., contamination survey) and mathematical spatial estimation. In the current work, as better estimation tools we propose stochastic simulation approaches which allow the remediation volume to be assessed more accurately along with uncertainty estimation. To test the efficiency of proposed methods, heavy metals (esp., Pb) contaminated soil of a shooting range area was selected. In addition, we suggest a quantitative method to delineate the confident interval of estimated volume (and spatial extent) of polluted soil based on the spatial aspect of uncertainty. The methods proposed in this work can improve a better decision making on soil remediation.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.473-476
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• 2003

This paper presents a systematic risk assessment procedure with uncertainty modeling for general construction projects. Since the approach is able to effectively deal with all the related construction risks in terms of the assumed probability with conditional probability concept that systematically incorporate expert's experiences and subjective judgement, the proposed methods with uncertainty modeling is able to apply to all the construction projects inherent in lots of uncertain risk events. The fuzzy set theory is adopted to enhance risk assessment to effectively handle the vague and dynamic phenomenon of an event Therefore, the fuzzy-based risk assessment is very useful, for those countries, such as Korea, where objective probabilistic data for risk assessment is extremely rare, and thus the utilization of subjective judgmental data based on expert's experiences is inevitable.

• Journal of Korea Water Resources Association
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• v.50 no.2
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• pp.121-128
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• 2017

In this study, robustness index and uncertainty analysis were proposed to quantify the risk inherent in the process of climate change vulnerability assessment. The water supply vulnerability for six metropolitan cities (Busan, Daegu, Incheon, Gwangju, Daejeon, and Ulsan), except for Seoul, were prioritized using TOPSIS, a kind of multi-criteria decision making method. The robustness index was used to analyze the possibility of rank reversal and the uncertainty analysis was introduced to derive the minimum changed weights of the criteria that determine the rank reversal between any paired cities. As a result, Incheon and Daegu were found to be very vulnerable and Daegu and Busan were derived to be very sensitive. Although Daegu was relatively vulnerable against the other cities, it can be largely improved by developing and performing various climate change adaptation measures because it is more sensitive. This study can be used as a preliminary assessment for establishing and planning climate change adaptation measure.

• Earthquakes and Structures
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• v.10 no.1
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• pp.141-161
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• 2016

In seismic fragility and risk analysis, the definition of structural limit state (LS) capacities is of crucial importance. Traditionally, LS capacities are defined according to design code provisions or using deterministic pushover analysis without considering the inherent randomness of structural parameters. To assess the effects of structural randomness on LS capacities, ten structural parameters that include material strengths and gravity loads are considered as random variables, and a probabilistic pushover method based on a correlation-controlled Latin hypercube sampling technique is used to estimate the uncertainties in LS capacities for four typical reinforced concrete frame buildings. A series of ten LSs are identified from the pushover curves based on the design-code-given thresholds and the available damage-controlled criteria. The obtained LS capacities are further represented by a lognormal model with the median $m_C$ and the dispersion ${\beta}_C$. The results show that structural uncertainties have limited influence on $m_C$ for the LSs other than that near collapse. The commonly used assumption of ${\beta}_C$ between 0.25 and 0.30 overestimates the uncertainties in LS capacities for each individual building, but they are suitable for a building group with moderate damages. A low uncertainty as ${\beta}_C=0.1{\sim}0.15$ is adequate for the LSs associated with slight damages of structures, while a large uncertainty as ${\beta}_C=0.40{\sim}0.45$ is suggested for the LSs near collapse.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.122-123
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• 2018

Recently, CM at Risk delivery system(CM@R) that could solve the problems of Design Bid Build delivery(DBB) system has been emerging. In the CM@R delivery system, the contractor negotiates for a maximum guaranteed price(GMP) with the client at the design stage, and the contractor carries out the construction within the GMP. In CM @ R, the construction company with expertise in construction participates from the design stage to reflects the construction know-how in the design. On the other hand, the modification design frequently occurs due to the change of the construction cost when negotiating the GMP. In addition, uncertainties are inherent in the GMP calculation because the calculation is based on unfinished drawings and documents. This study proposes a probabilistic GMP estimation method applying MCS to the BIM - based cost prediction model, in order to extract the accurate quantity information when estimating the GMP and to cope with the change of the construction cost inherent in uncertainty.