• The Journal of Industrial Distribution & Business
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• v.10 no.1
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• pp.51-58
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• 2019

Purpose - Manufacturers in uncertain environments need to depend on governance mechanisms to reduce the inherent risk in these environments. However, few studies have examined which governance mechanisms a given manufacturers will develop in uncertain environments for managing the relationships with its vertical partner. This study explores how different governance mechanisms function under uncertain environmental circumstances. We also try to investigate the contextual effect of interfirm benevolence as moderator. Research design, data, and methodology - This research provide the conceptual framework of interfirm benevolence on which this research's propositions are predicted. The theoretical background for environmental uncertainty, governance mechanisms and interfirm benevolence will be discussed. Results - The expected results are as follows. Manufacturers in an uncertain environments rely on different governance mechanisms under conditions of high and low interfirm benevolence. In terms of role of interfirm benevolence, interfirm benevolence provides a better understanding of how governance mechanisms can develop in an uncertain supply markets. Conclusions - This research suggests several theoretical and practical implications between channel partners, particularly, this research offers that interfirm benevolence is a crucial competitive factor under environmental uncertainty situation. In future studies, it is necessary to investigate the effect of each governance mechanism structure on performance in an uncertain environment and various level of interfirm benevolence.

• Journal of Korea Water Resources Association
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• v.50 no.8
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• pp.521-535
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• 2017

Acoustic Doppler Current Profilers (ADCPs) have been widely utilized for assessing streamflow discharge, yet few comprehensive studies were conducted to evaluate discharge uncertainty in consideration of individual uncertainty components. It could be mostly because it was not easy to determine which uncertainty framework can be appropriate to rigorously analyze streamflow discharge driven by ADCPs. In this regard, considerable efforts have been made by scientific and engineering societies to develop a standardized theoretical framework for uncertainty analysis in hydrometry. One of the well-established UA methodology based on sound statistical and engineering concepts is Guide to the Expression of Uncertainty Measurement (GUM) adopted widely by various scientific and research communities. This research fundamentally adapted the GUM framework to assess individual uncertainty components of ADCP discharge measurements, and subsequently provided results of a customized experiment in a controllable real-scale artificial river channel. We focused particularly upon sensitivities of uncertainty components in the GUM framework driven by ADCPs direct measurements such as depths, edge distance, submerged depth, velocity gap, sampling time, repeatability, bed roughness and so on. Section-by-Section method for ADCP discharge measurement was applied for uncertainty analysis for this study. All of measurements were carefully compared with data using other instrumentations such as ADV to evaluate individual uncertainty components.

• Korean Journal of Food Science and Technology
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• v.49 no.6
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• pp.591-598
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• 2017

This study aimed to evaluate the measurement uncertainty for the quantitative determination of chlorite and chlorate in ready-to-eat fresh-cut vegetables using ion chromatography with a hydroxide-selective column. One gram of the homogenized sample in deionized water was sonicated and centrifuged at 8,500 rpm. The supernatant was purified by passing it through a Sep-Pak tC18 cartridge, followed by chromatographic determination using a Dionex IonPac AS27 column. The linearity of the calibration curves, recovery, repeatability, and reproducibility of the method were satisfactory. The method detection limit was estimated to be approximately 0.5 mg/kg. Each uncertainty component was evaluated separately, and the combined and expanded uncertainty values were calculated at the 95% confidence level. The measured concentrations for 3 mg/kg of chlorite and chlorate standard materials were $3.18{\pm}0.32$ and $3.10{\pm}0.42mg/kg$, respectively. These results confirmed the reliability of the developed method for measuring the two chlorine-based oxyanions in fresh-cut vegetables.

• Journal of Korean Society on Water Environment
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• v.36 no.1
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• pp.14-28
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• 2020

Future climate change may affect the hydro-thermal and biogeochemical characteristics of dam reservoirs, the most important water resources in Korea. Thus, scientific projection of the impact of climate change on the reservoir environment, factoring uncertainties, is crucial for sustainable water use. The purpose of this study was to predict the future water temperature and stratification structure of the Soyanggang Reservoir in response to a total of 42 scenarios, combining two climate scenarios, seven GCM models, one surface runoff model, and three wind scenarios of hydrodynamic model, and to quantify the uncertainty of each modeling step and scenario. Although there are differences depending on the scenarios, the annual reservoir water temperature tended to rise steadily. In the RCP 4.5 and 8.5 scenarios, the upper water temperature is expected to rise by 0.029 ℃ (±0.012)/year and 0.048 ℃ (±0.014)/year, respectively. These rise rates are correspond to 88.1 % and 85.7 % of the air temperature rise rate. Meanwhile, the lower water temperature is expected to rise by 0.016 ℃ (±0.009)/year and 0.027 ℃ (±0.010)/year, respectively, which is approximately 48.6 % and 46.3 % of the air temperature rise rate. Additionally, as the water temperatures rises, the stratification strength of the reservoir is expected to be stronger, and the number of days when the temperature difference between the upper and lower layers exceeds 5 ℃ increases in the future. As a result of uncertainty quantification, the uncertainty of the GCM models showed the highest contribution with 55.8 %, followed by 30.8 % RCP scenario, and 12.8 % W2 model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.311-322
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• 1999

Major problems involved in an electrical utility expansion planning within a time horizon are how to efficiently deal with objectives considering multiple factors and uncertainty. But justification factors in study these days have considered only quantitative factors except qualitative factors. Therefore, the purpose of this paper is to develop a new model for economic evaluation of nuclear power plants through the scoring model with the quantitative and qualitative factors under uncertainty. The quantitative factors use a levelized generation cost method considering time value of money. Especially, the environmental, risk, and safety factors in this paper have been also explained for the rational economic justification of the qualitative factors under uncertainty. This paper not only proposes a new approach method using the scoring model in evaluating economy of the nuclear power plant in the long term, but also provides the more efficient decision making criterion for nuclear power plants under uncertainty.

• Environmental and Resource Economics Review
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• v.27 no.4
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• pp.639-666
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• 2018

This paper analyses the investment options of groundwater development project under water price uncertainty. The optimal investment threshold price which trigger the investment are calibrated base on monopolistic real options model. Stochastic dynamic model is set to reflect the uncertainty of water price which follows the GBM (Geometric Brownian Motion) process. Our finding from non-cooperative investment decision model is that uncertainty of water price could deter the groundwater investment by considering the existence of option values. For policy markers, it is easy to manage 'charges for utilization of groundwater' rather than 'performance guarantee ratio' when managing groundwater investment with pricing policy. And it is necessary to make comprehensive and well-designed policies considering the characteristics of regional groundwater reservoir and groundwater developers.

• Journal of Environmental Science International
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• v.3 no.4
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• pp.349-356
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• 1994

The purpose of this study is to develop a linear reservoir model with Kalman filter using Kalman filter theory which removes a physical uncertainty of :ainfall-runoff process. A linear reservoir model, which is the basic model of Kalman filter, is used to calculate runoff from rainfall in river basin. A linear reservoir model with Kalman filter is composed of a state-space model using a system model and a observation model. The state-vector of system model in linear. The average value of the ordinate of IUH for a linear reservoir model with Kalman filter is used as the initial value of state-vector. A .linear reservoir model with Kalman filter shows better results than those by linear reserevoir model, and decreases a physical uncertainty of rainfall-runoff process in river basin.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.04a
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• pp.316-319
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• 2005

Contaminated area should be identified for designing polluted groundwater cleanup plan. A methodology was suggested to identify a contaminant plume distribution geostatistically. James & Gorelick (1994) suggested a methodology to evaluate data worth as expected reducing remediation cost. In this study, their methodology was modified to evaluate data worth as expected reducing uncertainty of the contaminant plume distribution. In suggested methodology, the source identification model by Mahar & Datta (2001) using a forward solute transport model is integrated. Suggested methodology was assessed by two simple example problems and its result represented reducing uncertainties of contaminant plume distribution successfully.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.122-129
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• 2021

For the Simultaneous Localization and Mapping (SLAM) problem, a different path results in different SLAM results. Usually, SLAM follows a trail of input data. Active SLAM, which determines where to sense for the next step, can suggest a better path for a better SLAM result during the data acquisition step. In this paper, we will use reinforcement learning to find where to perceive. By assigning entire target area coverage to a goal and uncertainty as a negative reward, the reinforcement learning network finds an optimal path to minimize trajectory uncertainty and maximize map coverage. However, most active SLAM researches are performed in indoor or aerial environments where robots can move in every direction. In the urban environment, vehicles only can move following road structure and traffic rules. Graph structure can efficiently express road environment, considering crossroads and streets as nodes and edges, respectively. In this paper, we propose a novel method to find optimal SLAM path using graph structure and reinforcement learning technique.

• Journal of Korea Water Resources Association
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• v.49 no.8
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• pp.657-671
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• 2016

It is expected that water resources will be changed spatially and temporally due to the global climate change. The quantitative assessment of change in water availability and appropriate water resources management measures are needed for corresponding adaptation. However, there are large uncertainties in climate change impact assessment on water resources. For this reason, development of technology to evaluate the uncertainties quantitatively is required. The objectives of this study are to develop the climate change uncertainty assessment method and to apply it. The 5 RCMs (HadGEM3-RA, RegCM4, MM5, WRF, and RSM), 5 statistical post-processing methods (SPP) and 2 hydrological models (HYM) were applied for evaluation. The results of the uncertainty analysis showed that the RCM was the largest sources of uncertainty in Spring, Summer, Autumn (29.3~68.9%), the hydrological model was the largest source of uncertainty in Winter (46.5%). This method can be possible to analyze the changes in the total uncertainty according to the specific RCM, SPP, HYM model. And then it is expected to provide the method to reduce the total uncertainty.