• Journal of Korean Society on Water Environment
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• v.31 no.3
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• pp.241-252
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• 2015

Runoff behaviors by five bias correction methods were analyzed, which were Change Factor methods using past observed and estimated data by the estimation scenario with average annual calibration factor (CF_Y) or with average monthly calibration factor (CF_M), Quantile Mapping methods using past observed and estimated data considering cumulative distribution function for entire estimated data period (QM_E) or for dry and rainy season (QM_P), and Integrated method of CF_M+QM_E(CQ). The peak flow by CF_M and QM_P were twice as large as the measured peak flow, it was concluded that QM_P method has large uncertainty in monthly runoff estimation since the maximum precipitation by QM_P provided much difference to the other methods. The CQ method provided the precipitation amount, distribution, and frequency of the smallest differences to the observed data, compared to the other four methods. And the CQ method provided the rainfall-runoff behavior corresponding to the carbon dioxide emission scenario of SRES A1B. Climate change scenario with bias correction still contained uncertainty in accurate climate data generation. Therefore it is required to consider the trend of observed precipitation and the characteristics of bias correction methods so that the generated precipitation can be used properly in water resource management plan establishment.

• Journal of Climate Change Research
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• v.7 no.4
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• pp.513-520
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• 2016

Korea's energy consumption has been constantly increasing. Final energy consumption was increased by an annual average of 2.9% compared to 2010. The consumption of all energy sources except for its oil was increased during the same time. While electric demand has increased coal consumption increased rapidly. Therefore, calorfic value and carbon emission factor development can improve the quality of Korea's greenhouse gas inventory. Calorific value is the amount of heat generated while burning coal. Caloric value is one of the most important factors in the development of carbon emission factors. Calorific value is used as the basis for the analysis of the various energy statistics. This study has calculated the other bituminous coal and coking coal's calorfic value by the data received from domestic coal-fired power plants and steel manufacturer. Calorofic value's trend analysis period is the year of 2010~2014. Through analyzing the carbon content it was calculated the carbon emission factor. The bituminous coal and coking coal's uncertainty analysis was performed using a Monte Carlo simulation.

• Journal of Climate Change Research
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• v.8 no.2
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• pp.145-151
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• 2017

Korea's energy consumption has been constantly increasing. Final energy consumption was increased by anannual average of 2.9% compared to 2010. The consumption of all energy sources except for its oil was increased during the same time. While electric demand has increased coal consumption increased rapidly. Therefore, calorfic value and carbon emission factor development can improve the quality of Korea's greenhouse gas inventory. Calorific value is the amount of heat generated while burning coal. Caloric value is one of the most important factors in the development of carbon emission factors. Calorific value is used as the basis for the analysis of the various energy statistics. This study has calculated the sub-bituminous coal's calorfic value by the data received from domestic coal-fired power plants. Calorofic value's trend analysis period is the year of 2011~2015. Through analyzing the carbon content it was calculated the carbon emission factor. The sub-bituminous coal's uncertainty analysis was performed using a Monte Carlo simulation.

• KIEAE Journal
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• v.15 no.6
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• pp.5-10
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• 2015

Purpose: To evaluate the economic performance of grid-connected photovoltaic system in residential house, household electricity bill policy of Korea Electric Power Corporation (KEPCO) must be applied precisely, and market tendency and uncertainty of system also need to be considered. In this study, to evaluate the economic feasibility of PV system, we measured PV power generation and electricity consumption of six of Green home in Daejeon through web based remote monitoring system. Method: We applied Monte-Carlo simulation based on life cycle cost analysis, to reflect an uncertainty of main factor in economic feasibility evaluation of photovoltaic system. Result: First, with deterministic analysis, the difference of NPV of cumulative financial savings among households varied from -3,310 ~ 24,170 thousand won, portraying notably big range. Also the possibility of getting the same result was 50% when applying uncertainty. Second, the higher electricity consumption is, the more economic feasibility of photovoltaic system increases because KEPCO uses progressive taxation in household electricity bill policy. Third, The contribution to variance of electricity price increases in NPV varied from 98.5% to 99.9%. While the inflation rate and annual degradation contributed very little to none.

• Journal of Sensor Science and Technology
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• v.20 no.5
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• pp.329-335
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• 2011

We demonstrated that high-stability thermistors can be calibrated with an uncertainty less than 1 mK, if the error due to the heat conduction is minimized. We first investigated the effect of the self-heating of typical thermistor probes to see how accurate we need to determine the effect of self-heating. We, then, calibrated thermistors and fitted the results using various modeling equations. We found out that the heat conduction is an important factor in achieving the calibration uncertainty under 1 mK for thermistors when the diameter of the probe is as thick as 10 mm. Therefore, we controlled the room temperature within $0.5^{\circ}C$ to minimize the heat conduction error during the calibration. The calibration with an uncertainty below 1 mK was possible when the stabilization time for each calibration was long enough to obtain a good thermal equilibrium.

• 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.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.195-203
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• 2016

In order to overcome the influence of system stability and accuracy caused by uncertainty, estimation errors and external disturbances in Eight-Rotor MAV, L2 gain control method was proposed based on interval type II fuzzy neural network identification here. In this control strategy, interval type II fuzzy neural network is used to estimate the uncertainty and non-linearity factor of the dynamic system, the adaptive variable structure controller is applied to compensate the estimation errors of interval type II fuzzy neural network, and at last, L2 gain control method is employed to suppress the effect produced by external disturbance on system, which is expected to possess robustness for the uncertainty and non-linearity. Finally, the validity of the L2 gain control method based on interval type II fuzzy neural network identifier applied to the Eight-Rotor MAV attitude system has been verified by three prototy experiments.

• The KSFM Journal of Fluid Machinery
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• v.7 no.5 s.26
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• pp.29-35
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• 2004

The turbine efficiency is an important factor in power plant, and accurate evaluation of steam turbine performance is the key issue in turbo machinery industry. The difficulty of evaluating the steam turbine performance due to its high steam temperature and pressure environment makes the most steam turbine tests to be replaced by air similarity test. This paper presents how to decide the similarity conditions of the steam turbine test and describes its limitations and assumptions. The test facility was developed and arranged to conduct an air similarity turbine performance test with various inlet pressure, temperature and mass flow rate. The eddy-current type dynamometer measures the turbine-generated shaft power and controls the rotating speed. Pressure ratio of turbine can be controled by back pressure control valve. To verify its test results, uncertainty analysis was performed and relative uncertainty of turbine efficiency was obtained.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.2941-2959
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• 2022

The fire brigade non-suppression probability model is a major factor that should be considered in evaluating fire-induced risk through fire probabilistic risk assessment (PRA), and also uncertainty is a critical consideration in support of risk-informed performance-based (RIPB) fire protection decision-making. This study developed an optimal integrated probabilistic fire brigade non-suppression model considering uncertainty of parameters based on the Bayesian Markov Chain Monte Carlo (MCMC) approach on electrical fire which is one of the most risk significant contributors. The result shows that the log-normal probability model with a location parameter (µ) of 2.063 and a scale parameter (σ) of 1.879 is best fitting to the actual fire experience data. It gives optimal model adequacy performance with Bayesian information criterion (BIC) of -1601.766, residual sum of squares (RSS) of 2.51E-04, and mean squared error (MSE) of 2.08E-06. This optimal log-normal model shows the better performance of the model adequacy than the exponential probability model suggested in the current fire PRA methodology, with a decrease of 17.3% in BIC, 85.3% in RSS, and 85.3% in MSE. The outcomes of this study are expected to contribute to the improvement and securement of fire PRA realism in the support of decision-making for RIPB fire protection programs.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.10 no.1
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• pp.51-55
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• 2014

Based on the results of Probabilistic Safety Assessment(PSA) for a Nuclear Power Plant (NPP), Common Cause Failure(CCF) events have been recognized as one of the main contributors to the risk. Also, the CCF data and estimation method used in domestic PSA models have been pointed out as an issue with respect to the quality. The existing method of MGL and non-staggered testing even widely used were considered conservative in estimating the safety and had a limited capability in uncertainty analyses. Therefore, this paper presents the CCF estimation using a new generic data source and Alpha factor method. The analyses showed that Alpha factor and staggered method are effective in estimating the CCF contribution and risk insights of reference plant. This method will be a common bases for the optimization of new design for the construction plants as well as for the updating of safety assessment on the operating nuclear power plants.