• Journal of Korea Water Resources Association
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• v.51 no.spc
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• pp.1067-1078
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• 2018

Water resources projection typically consists of several stages including emission scenarios, global circulation models (GCMs), downscaling techniques, and hydrological models, and each stage is a source of total uncertainty in water resources projection. Several studies proposed methods to quantify the relative contribution of each stage to total uncertainty, and we call such analysis uncertainty decomposition. Uncertainty decomposition enables us to investigate the stages yielding large uncertainties and to establish the uncertainty reduction plan that reflects them. Interactions between stages is one of the important issues to be considered in uncertainty decomposition. This study suggests a new uncertainty decomposition method considering interaction effect. The proposed method has an advantage of decomposing the total uncertainty to the uncertainty from each stage considering both the main and interactions effects. We apply the proposed method to streamflow projection for Chungju Dam basin. The results show that the uncertainties from the main effects are larger than the uncertainties from interaction effects in both summer and winter. Using the proposed uncertainty decomposition method, we show that the GCM stage is the largest source of the total uncertainty in summer and the downscaling technique stage is the one in winter among the following four stages: emission scenarios, GCMs, downscaling techniques, and hydrological models.

• Journal of the Korean earth science society
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• v.31 no.4
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• pp.301-312
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• 2010

Geochemical data have been regarded as one of the important environmental variables in the environmental management. Since they are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations, but also to assess the uncertainty attached to the prediction for further analysis. The main objective of this paper is to exemplify how indicator geostatistics can be effectively applied to geochemical data processing for providing decision-supporting information as well as spatial distribution of the geochemical data. A whole geostatistical analysis framework, which includes probabilistic uncertainty modeling, classification and risk analysis, was illustrated through a case study of cadmium mapping. A conditional cumulative distribution function (ccdf) was first modeled by indicator kriging, and then e-type estimates and conditional variance were computed for spatial distribution of cadmium and quantitative uncertainty measures, respectively. Two different classification criteria such as a probability thresholding and an attribute thresholding were applied to delineate contaminated and safe areas. Finally, additional sampling locations were extracted from the coefficient of variation that accounts for both the conditional variance and the difference between attribute values and thresholding values. It is suggested that the indicator geostatistical framework illustrated in this study be a useful tool for analyzing any environmental variables including geochemical data for decision-making in the presence of uncertainty.

• Journal of Korea Multimedia Society
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• v.20 no.12
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• pp.1874-1889
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• 2017

The paper aims to analyze structure of I/Q data observed from radar and reliably estimate rainfall through quality control of I/Q data that can quantify uncertainty of I/Q data occurring due to resultant errors. Radar rainfall data have strong uncertainty due to various factors influencing quality. In order to reduce this uncertainty, previously enumerated errors in quality need to be eliminated. However, errors cannot be completely eliminated in some cases as seen in random errors, so uncertainty is necessarily involved in radar rainfall data. Multi-Lag Method, one of I/Q data quality control methods, was applied to estimate precipitation with regard to I/Q data of rainfall radar in Mt. Sobaek.

• Korea Trade Review
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• v.44 no.3
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• pp.25-42
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• 2019

This research examined the mediation effect of Workforce Agility (WA) on the relationship between environmental uncertainty and operational performance. We manipulated the control variables that are known to be affected by employment flexibility. Employment flexibility is caused by idiosyncratic characteristics of Korean port system. The analysis was tested by Baron & Kenny's method. The result indicates that each path of the proposed model is significant. Furthermore, the mediation effect was checked with the Sobel Test. The research revealed that environment uncertainty poses an indirect effect on operational performance. Both supply/demand uncertainty and technological uncertainty affected operational performance through the mediation effect of WA. Most of the distribution centers located in Busan Newport Distripark are operated in a bimodal labor (human resource) system which includes both permanent employees (workers) and temporary employees (workers). This empirical research provides theoretical and managerial implications by suggesting ways to increase efficiency in distribution center operation through WA enhancement, and to improve the unloading labor system.

• Journal of Navigation and Port Research
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• v.40 no.3
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• pp.129-138
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• 2016

Shipping companies involved in sea transportation service are exposed to uncertainty due to its volatile business environment. It is crucial that shipping companies take effective strategies to cope with uncertain business environments and to achieve high business performance. Using Porter's generic strategy, this study investigates the relationships between environmental uncertainty and competitive strategies, and the impact of competitive strategies on firm's business performance. To test hypotheses, OLS regressions and Tobit analysis were conducted on Korea shipping companies who provide global sea transportation service. The findings of this study can be summarized as follows. First, variation of environmental uncertainty affects cost-leadership strategy and differentiation strategy. Diversity of environmental uncertainty affects differentiation strategy, but not cost-leadership strategy. Second, firms attain business performance by using the cost-leadership strategy. That is, shipping companies can achieve high business performance by reducing their operating costs in the entire process of transportation service.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.6
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• pp.922-928
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• 2006

An absolute absorption cross section of benzene was measured with a spectrometer system including a mono-chrometer and a grating in the wavelength region of $240{\sim}280nm$ under the atmospheric pressure and room temperature in the laboratory. A certificated reference benzene gas ($98{\mu}mol/mol$ in $N_2$) was used to measure its absorption cross section. A 710 mm cell with a quartz window and a 150 W Xe arc lamp were employed. The magnitude of absorption cross section of $1.41{\times}10^{-18}cm^2$ was lower than that of the reference spectra ($2.5{\times}10^{-18}cm^2$) of high resolution spectrometer, Total measurement uncertainty was estimated to be 4.0%.

• Journal of Environmental Science International
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• v.28 no.1
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• pp.19-35
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• 2019

This study investigates the performance of four Bayesian methods, Random Walk Metropolis (RWM), Hit-And-Run Metropolis (HARM), Adaptive Mixture Metropolis (AMM), and Population Monte Carlo (PMC), for estimating the parameters and uncertainties of probability rainfall distribution, and the results are compared with those of conventional parameter estimation methods; namely, the Method Of Moment (MOM), Maximum Likelihood Method (MLM), and Probability Weighted Method (PWM). As a result, Bayesian methods yield similar or slightly better results in parameter estimations compared with conventional methods. In particular, PMC can reduce parameter uncertainty greatly compared with RWM, HARM, and AMM methods although the Bayesian methods produce similar results in parameter estimations. Overall, the Bayesian methods produce better accuracy for scale parameters compared with the conventional methods and this characteristic improves the accuracy of probability rainfall. Therefore, Bayesian methods can be effective tools for estimating the parameters and uncertainties of probability rainfall distribution in hydrological practices, flood risk assessment, and decision-making support.

• Knowledge Management Research
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• v.17 no.3
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• pp.117-136
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• 2016

External knowledge search is critical to expand the firm's knowledge base, increasing the innovation performance. However, prior literature has paid less scholarly attention on the boundary condition of the search activity. In particular, the few literatures on the contingency factors are either conceptual or focus on the firm characteristics such as resources and capability. In this regard, this study argues the negative moderating effect of knowledge protection and environmental uncertainty of a firm on the positive relationship between external knowledge search and innovation performance, because these contingency factors hamper the reciprocity and the mutual trust between the firm and its external partner that provides knowledge needed in the innovation process. The empirical analysis is based on the sample drawn from Korean Innovation Survey, provided by STEPI in Korea. The sample consists of 1,637 respondent firms that experienced product innovation during the survey period. We could find statistically supporting results for the negative moderating effect on the positive relationship between external knowledge search and innovation performance. This study extends the academic debate on the boundary conditions of external knowledge search and provides managerial implications for successful product innovation.

• Journal of the Korean GEO-environmental Society
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• v.16 no.5
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• pp.13-25
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• 2015

This paper proposes an extended model evaluation method that considers not only the model performance but also the model structure and parameter uncertainties in hydrologic modeling. A simple reservoir model (SFM) and distributed kinematic wave models (KWMSS1 and KWMSS2 using topography from 250-m, 500-m, and 1-km digital elevation models) were developed and assessed by three evaluative criteria for model performance, model structural stability, and parameter identifiability. All the models provided acceptable performance in terms of a global response, but the simpler SFM and KWMSS1 could not accurately represent the local behaviors of hydrographs. Moreover, SFM and KWMSS1 were structurally unstable; their performance was sensitive to the applied objective functions. On the other hand, the most sophisticated model, KWMSS2, performed well, satisfying both global and local behaviors. KMSS2 also showed good structural stability, reproducing hydrographs regardless of the applied objective functions; however, superior parameter identifiability was not guaranteed. A number of parameter sets could result in indistinguishable hydrographs. This result indicates that while making hydrologic models complex increases its performance accuracy and reduces its structural uncertainty, the model is likely to suffer from parameter uncertainty.

• Wind and Structures
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• v.21 no.5
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• pp.461-487
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• 2015

In recent years, the Graphics Processing Unit (GPU) has become a competitive computing technology in comparison with the standard Central Processing Unit (CPU) technology due to reduced unit cost, energy and computing time. This paper describes the derivation and implementation of GPU-based algorithms for the analysis of wind loading uncertainty on high-rise systems, in line with the research field of probability-based wind engineering. The study begins by presenting an application of the GPU technology to basic linear algebra problems to demonstrate advantages and limitations. Subsequently, Monte-Carlo integration and synthetic generation of wind turbulence are examined. Finally, the GPU architecture is used for the dynamic analysis of three high-rise structural systems under uncertain wind loads. In the first example the fragility analysis of a single degree-of-freedom structure is illustrated. Since fragility analysis employs sampling-based Monte Carlo simulation, it is feasible to distribute the evaluation of different random parameters among different GPU threads and to compute the results in parallel. In the second case the fragility analysis is carried out on a continuum structure, i.e., a tall building, in which double integration is required to evaluate the generalized turbulent wind load and the dynamic response in the frequency domain. The third example examines the computation of the generalized coupled wind load and response on a tall building in both along-wind and cross-wind directions. It is concluded that the GPU can perform computational tasks on average 10 times faster than the CPU.