• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.12 s.255
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• pp.1139-1146
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• 2006

Light Oil Flow Standard System(LOFSS) in Korea Research Institute of Standards and Science(KRISS) was designed for oil flowmeter calibration. In order to extend the flow range from 120 $m^3/h$ to 200 $m^3/h$, the build-up technique was applied with two positive displacement flowmeters as master flowmeter. The master flowmeters were calibrated against with LOFSS, which has 0.04 % uncertainty of flow quantity determination, then the test flowmeter is calibrated against two master flowmeters. For uncertainty analysis, the repeatability of master flowmeters, the variation of the fluid density and the pipe volume due to temperature change were scrutinized. The contribution of each uncertainty factors to the calibrator and the correlation of each factors were discussed. For investigating the feasibility of uncertainty analysis, a turbine flowmeter as a transfer package was tested with LOFSS and two reference flowmeter. The hypothesis test for both results was coincide with a 95 % significant level. This means that the uncertainty analysis procedure of the calibrator is reasonable and the extension of flow range with master meters was carry out successfully.

• Journal of Climate Change Research
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• v.7 no.3
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• pp.325-334
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• 2016

We projected the distribution of Hedera rhombea, an evergreen broad-leaved climbing plant, under current climate conditions and predicted its future distributions under global warming. Inaddition, weexplained model uncertainty by employing 9 single Species Distribution model (SDM)s to model the distribution of Hedera rhombea. 9 single SDMs were constructed with 736 presence/absence data and 3 temperature and 3 precipitation data. Uncertainty of each SDM was assessed with TSS (Ture Skill Statistics) and AUC (the Area under the curve) value of ROC (receiver operating characteristic) analyses. To reduce model uncertainty, we combined 9 single SDMs weighted by TSS and resulted in an ensemble forecast, a TSS weighted ensemble. We predicted future distributions of Hedera rhombea under future climate conditions for the period of 2050 (2040~2060), which were estimated with HadGEM2-AO. RF (Random Forest), GBM (Generalized Boosted Model) and TSS weighted ensemble model showed higher prediction accuracies (AUC > 0.95, TSS > 0.80) than other SDMs. Based on the projections of TSS weighted ensemble, potential habitats under current climate conditions showed a discrepancy with actual habitats, especially in the northern distribution limit. The observed northern boundary of Hedera rhombea is Ulsan in the eastern Korean Peninsula, but the projected limit was eastern coast of Gangwon province. Geomorphological conditions and the dispersal limitations mediated by birds, the lack of bird habitats at eastern coast of Gangwon Province, account for such discrepancy. In general, potential habitats of Hedera rhombea expanded under future climate conditions, but the extent of expansions depend on RCP scenarios. Potential Habitat of Hedera rhombea expanded into Jeolla-inland area under RCP 4.5, and into Chungnam and Wonsan under RCP 8.5. Our results would be fundamental information for understanding the potential effects of climate change on the distribution of Hedera rhombea.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.49 no.4
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• pp.219-225
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• 2000

This paper presents the shape optimization considering the uncertainty of design variable to find robust optimal solution that has insensitive performance to its change of design variable. Stochastic finite element method (SFEM) is used to treat input data as stochastic variables. It is method that the potential values are series form for the expectation and small variation. Using correlation function of their variables, the statistics of output obtained form the input data distributed. From this, design considering uncertainty of design variables.

• Environmental and Resource Economics Review
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• v.26 no.3
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• pp.359-398
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• 2017

Uncertainty is central to energy and climate policy. A growing number of literature show that almost all components of energy and climate models are, to some extent, uncertain and that the effect of uncertainty on the model outputs, in turn policy recommendations, is significantly large. Most existing energy and climate-economy models developed and used in Korea, however, do not take uncertainty into account explicitly. Rather, many models conduct a deterministic analysis or do a simple (limited) sensitivity analysis. In order to help social planners to make more robust decisions (across various plausible situations) on energy and climate change issues, an uncertainty analysis should be conducted. As a first step, this paper reviews the theory of decision making under uncertainty and the method for addressing uncertainty of existing probabilistic energy and climate-economy models. In addition, the paper proposes a strategy to apply an uncertainty analysis to energy and climate-economy models used in Korea. Applying the uncertainty analysis techniques, this paper revises the FUND model and investigates the impacts of climate change in Korea.

• Korean Management Science Review
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• v.29 no.3
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• pp.67-80
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• 2012

This study analyzes the relationship among environment uncertainty, local infrastructure, flexible-open firm culture, operations performance and marketing performance focus on SMEs. This research has revealed that the relation among firm size, firm type, firm culture, operations performance and marketing performance as well. The findings show that firm has its culture which is preparing environment uncertainty and local infrastructure influence on forming firm culture. Change-oriented and leaning-oriented firm cultures affect operations performance and marketing performance. In conclusion, this study suggests implication and limitations for further research.

• Atmosphere
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• v.22 no.2
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• pp.149-161
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• 2012

Going by the research results of the past, of all the uncertainties resulting from the research on climate change, the uncertainty caused by the climate change scenario has the highest degree of uncertainty. Therefore, depending upon what kind of climate change scenario one adopts, the projection of the water resources in the future will differ significantly. As a matter of principle, it is highly recommended to utilize all the GCM scenarios offered by the IPCC. However, this could be considered to be an impractical alternative if a decision has to be made at an action officer's level. Hence, as an alternative, it is deemed necessary to select several scenarios so as to express the possible number of cases to the maximum extent possible. The objective standards in selecting the climate change scenarios have not been properly established and the scenarios have been selected, either at random or subject to the researcher's discretion. In this research, a new scenario selection process, in which it is possible to have the effect of having utilized all the possible scenarios, with using only a few principal scenarios and maintaining some of the uncertainties, has been suggested. In this research, the use of cluster analysis and the selection of a representative scenario in each cluster have efficiently reduced the number of climate change scenarios. In the cluster analysis method, the K-means clustering method, which takes advantage of the statistical features of scenarios has been employed; in the selection of a representative scenario in each cluster, the selection method was analyzed and reviewed and the PDF method was used to select the best scenarios with the closest simulation accuracy and the principal scenarios that is suggested by this research. In the selection of the best scenarios, it has been shown that the GCM scenario which demonstrated high level of simulation accuracy in the past need not necessarily demonstrate the similarly high level of simulation accuracy in the future and various GCM scenarios were selected for the principal scenarios. Secondly, the "Maximum entropy" which can quantify the uncertainties of the climate change scenario has been used to both quantify and compare the uncertainties associated with all the scenarios, best scenarios and the principal scenarios. Comparison has shown that the principal scenarios do maintain and are able to better explain the uncertainties of all the scenarios than the best scenarios. Therefore, through the scenario selection process, it has been proven that the principal scenarios have the effect of having utilized all the scenarios and retaining the uncertainties associated with the climate change to the maximum extent possible, while reducing the number of scenarios at the same time. Lastly, the climate change scenario most suitable for the climate on the Korean peninsula has been suggested. Through the scenario selection process, of all the scenarios found in the 4th IPCC report, principal climate change scenarios, which are suitable for the Korean peninsula and maintain most of the uncertainties, have been suggested. Therefore, it is assessed that the use of the scenario most suitable for the future projection of water resources on the Korean peninsula will be able to provide the projection of the water resources management that maintains more than 70~80% level of uncertainties of all the scenarios.

• Journal of Korea Water Resources Association
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• v.56 no.1
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• pp.35-43
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• 2023

Future runoff analysis is influenced by climate change scenarios and hydrologic model parameters, with uncertainties. In this study, the uncertainty of future runoff analysis according to the shared socioeconomic pathway (SSP) scenario and hydrologic model parameters was analyzed. Among the SSP scenarios, the SSP2-4.5 and SSP5-8.5 scenarios were used, and the soil and water assessment tool (SWAT) model was used as the hydrologic model. For the parameters of the SWAT model, a total of 11 parameter were optimized to the observed runoff data using SWAT-CUP. Then, uncertainty analysis of future estimated runoff compared to the observed runoff was performed using jensen-shannon divergence (JS-D), which can calculate the difference in distribution. As a result, uncertainty of future runoff was analyzed to be larger in SSP5-8.5 than in SSP2-4.5, and larger in the far future (2061-2100) than in the near future (2021-2060). In this study, the uncertainty of future runoff using future climate data according to the parameters of the hydrologic model is as follows. Uncertainty was greatly analyzed when parameters used observed runoff data in years with low flow rates compared to average years. In addition, the uncertainty of future runoff estimation was analyzed to be greater for the parameters of the period in which the change in runoff compared to the average year was greater.

• Journal of Korea Water Resources Association
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• v.50 no.2
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• pp.99-109
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• 2017

This research adopted a Robust Decision Making framework to re-evaluate four alternative strategies proposed by the Comprehensive Flood Management Plan for the Yeongsan River Basin report (MLTM, 2005) considering uncertainties of future floods under condition of climate change. To reflect the uncertainties, multiple sets of future flood scenarios were used with three uncertainty factors: the change in rainfall intensity based on the RCP climate change scenarios and the changes in the temporal and the spatial flood distributions. With combinations of these factors, 216 plausible flood scenario sets were generated and the performances of the four alternatives under different future states were evaluated. From the results, the most robust alternative among the strategies was identified. Moreover, the key factors which made the tested alternatives poor were discovered through assessment of the uncertainty factors. This information can provide detailed insights to decision makers and can be utilized to overcome alternatives' potential vulnerabilities by modifying the strategy to be more robust.

• Journal of the Korean Society of Safety
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• v.31 no.1
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• pp.25-32
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• 2016

A sensitivity analysis of FDS(Fire Dynamics Simulator) results for the input uncertainty of heat release rate (Q) which might be the most influencing parameter to fire behaviors was performed. The calculated results were compared with experimental data obtained by the OECD/NEA PRISME project. The sensitivity of FDS results with the change in Q was also compared with the empirical correlations suggested in previous literature. As a result, the change in the specified Q led to the different dependence of major quantities such as temperature and species concentrations for the over- and under-ventilated fire conditions, respectively. It was also found that the sensitivity of major quantities to uncertain value of Q showed the significant difference in results obtained using the previous empirical correlations.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.208-208
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• 2022

Recently, the frequency of abnormal weather due to complex factors such as global warming is increasing frequently. From the past rainfall patterns, it is evident that climate change is causing irregular rainfall patterns. This phenomenon causes difficulty in predicting rainfall and makes it difficult to prevent and cope with natural disasters, casuing human and property damages. Therefore, accurate rainfall estimation and rainfall occurrence time prediction could be one of the ways to prevent and mitigate damage caused by flood and drought disasters. However, rainfall prediction has a lot of uncertainty, so it is necessary to understand and reduce this uncertainty. In addition, when accurate rainfall prediction is applied to the rainfall-runoff model, the accuracy of the runoff prediction can be improved. In this regard, this study aims to increase the reliability of rainfall prediction by analyzing the uncertainty of the Korean rainfall ensemble prediction data and the outflow analysis model using the Limited Area ENsemble (LENS) and the Grid based Rainfall-runoff Model (GRM) models. First, the possibility of improving rainfall prediction ability is reviewed using the QM (Quantile Mapping) technique among the bias correction techniques. Then, the GRM parameter calibration was performed twice, and the likelihood-parameter applicability evaluation and uncertainty analysis were performed using R², NSE, PBIAS, and Log-normal. The rainfall prediction data were applied to the rainfall-runoff model and evaluated before and after calibration. It is expected that more reliable flood prediction will be possible by reducing uncertainty in rainfall ensemble data when applying to the runoff model in selecting behavioral models for user uncertainty analysis. Also, it can be used as a basis of flood prediction research by integrating other parameters such as geological characteristics and rainfall events.