• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.729-734
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• 2004

The e-Logistics means the virtual business activity and service architecture among the logistics companies based on the Internet technology. The management of vehicles' location in most conventional vehicle tracking system has some critical defects when it deals with data which are continuously changed. It means the conventional vehicle tracking system based on the conventional database is unable eventually to cope with the environment that should manage the frequently changed location of vehicles. The important things in the evaluation of the vehicle tracking system is to determine the threshold of cost of database ,update period and communication period between vehicles and the system. In other words, the difference between the reallocation of vehicle and the data in database can evaluate the overall performance of vehicle tracking systems. Most of the previous works considers only the information that is valid at the current time, and is hard to manage efficiently the past and future information. To overcome this problem, the efforts on moving objects management system(MOMS) and uncertainty processing have been started from a few years ago. In this paper, we propose an uncertainty processing model and system implementation of moving object that tracks the location of the vehicles. We adopted both linear-interpolation method and trigonometric function to chase up the location of vehicles for the past time as well as future time, respectively. We also explain the comprehensive examples of MOMS and uncertainty processing in parcel application that is one of major application of e-Logistics domain.

• Journal of the Korean Solar Energy Society
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• v.30 no.4
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• pp.79-85
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• 2010

The uncertainty in WindCube LIDAR measurements, which are specific to wind profiling at less than 200m above ground levelin wind resource assessments, was analyzed focusing on the error caused by its volume sampling principle. A two-month SODAR measurement campaign conducted in an urban environment was adopted as the reference wind profile assuming that various atmospheric boundary layer shapes had been captured. The measurement error of LIDAR at a height z was defined as the difference in the wind speeds between the SODAR reference data, which was assumed to be a virtually true value, and the numerically averaged wind speed for a sampling volume height interval of $z{\pm}12.5m$. The pattern of uncertainty in the measurement was found to have a maximum in the lower part of the atmospheric boundary layer and decreased with increasing height. It was also found that the relative standard deviations of the wind speed error ratios were 6.98, 2.70 and 1.12% at the heights of 50, 100 and 150m above ground level, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6C
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• pp.492-496
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• 2012

This paper presents a decoding technique for speech recognition using uncertainty information from feature compensation method to improve the speech recognition performance in the low SNR condition. Traditional feature compensation algorithms have difficulty in estimating clean feature parameters in adverse environment. Those algorithms focus on the point estimation of desired features. The point estimation of feature compensation method degrades speech recognition performance when incorrectly estimated features enter into the decoder of speech recognition. In this paper, we apply the uncertainty information from well-known feature compensation method, such as IMM, to the recognition engine. Applied technique shows better performance in the Aurora-2 DB.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.156-156
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• 2018

The subbasin spatial scale can affect a hydrological simulation result. The objective of this study was to investigate an appropriate subbasin spatial scale for reproducing the different flow phases with the Soil and Water Assessment Tool (SWAT). Moreover, this study addressed the total hydrologic model uncertainty using the Generalized Likelihood Uncertainty Estimation (GLUE) method. The hydrologic modelling uncertainty analysis revealed that the courser subbasin spatial scale provided a relatively better coverage of most of the observations by the 95PPU. On the other hand, the finer subbasin spatial scale produced the best single simulation output closer to the observation. Moreover, most of the observed high flows were enveloped by the 95PPU while this did not happen for the low flows. The overall average performance improvement through an appropriate subbasin spatial scale for reproducing the different flow phases in the Yongdam and Gilgelabay watersheds were found to be 36% and 53%, respectively. It is, therefore, a worth that to put more effort in reproducing the different flow phases by investigating an appropriate subbasin spatial scale to improve our understanding about the frequency and magnitude of the different flow phases.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4412-4421
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• 2022

The pressure measurement in the high-temperature liquid metal system, such as Sodium-cooled Fast Reactor(SFR), is important and yet it is very challenging due to its nature. The measuring pressure is relatively at low range and the applied temperature varies in wide range. Moreover, the pressure transfer material in impulse line needs to considered the high temperature condition. The conventional diaphragm-based approach cannot be used for it is impossible to remove the effect of thermal expansion. In this paper, the Fiber Bragg Grating(FBG) sensor-based pressure measuring concept is suggested that it is free of problems induced by the thermal expansion. To verify this concept, a prototype was fabricated and tested in an appropriate conditions. The uncertainty analysis result of the experiment is also included. The final result of this study clearly showed that the FBG-based pressure transmitter system is applicable to the extreme environment, such as SFR and any other high-temperature liquid metal system and the measurement uncertainty is within reasonable range.

• Journal of Korean Society on Water Environment
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• v.28 no.4
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• pp.595-600
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• 2012

This study is to assess the application of SWAT-CUP(Soil and Water Assessment Tool-Calibration Uncertainty Programs) and to extend daily flow data from 8-day interval flow data which has been measured by Korean Ministry of Environment(MOE). Model sensitivity analysis and calibration were performed with sequential uncertainty fitting(SUIF-2), which is one of the programs interfaced with SWAT, in the package SWAT-CUP. The most sensitive parameters were SOL_K.sol, CH_N2.rte, CN2.mgt, SOL_BD.sol, ALPHA_BF.gw, ALPHA_BNK.rte, SOL_AWC.sol, CH_K2.rte, SFTMP.bsn, GW_DELAY.gw. Following the sensitivity analysis, SWAT-CUP calibration was carried out using 8-day interval flow data from January 2008 to December 2010. The results were then assessed based on the visual agreement and simulated flow plots and the performance statistics generated $R^2$ and NSE which are 0.71 and 0.61 respectively. Results of these statistics indicated that there was a good agreement between the observed and simulated flow. To extend daily flow data from 8-day interval flow data, parameters, which were estimated by SWAT-CUP, re-entered for SWAT model. As a result, the observed flow data were found to reflect the trend of simulated flow data. From these results, it is thought that this method could be used to provide daily flow data using 8-day interval flow data.

• Korean Journal of Ecology and Environment
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• v.40 no.2
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• pp.193-200
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• 2007

The objective of this study was to estimate uncertainty of online TOC analyzer in water quality monitoring system. A procedure for the estimation of measurement uncertainty of total organic compounds (TOC) based on the ISO approach is presented. It is based on a mathematical model that involves 4 input parameters (standardization, sensitivity, solute effect and representativeness). In this study, a major problem in estimating the uncertainty of online TOC analyzer was the solute effect. It was strongly depends on organic materials. So homogeneity of the sample is the most important consideration. Modified concentration and combined standard uncertainty was $4.71{\pm}0.36$ mg $L^{-1}$ by model modified in this study.

• Journal of the Korean Society of Safety
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• v.29 no.6
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• pp.166-171
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• 2014

Nuclear power plants are equipped with the reactor trip system (RTS) and the engineered safety features actuation system (ESFAS) to improve safety on the normal operation. In the event of the design basis accident (DBA), a various of post accident monitor(PAM)systems support to provide important details (e.g. Containment pressure, temperature and pressure of reactor cooling system and core exit temperature) to determine action of main control room (MCR). Operator should be immediately activated for the accident mitigation with the information. Especially, core exit temperature is a critical parameter because the operating mode converts from normal mode to emergency mode when the temperature of core exit reaches $649^{\circ}C$. In this study, uncertainty which was caused by exterior environment, characteristic of thermocouple/connector and accuracy of calibrator/indicator was evaluated in accordance with ANSI-ISA 67.04. The square root of the sum of square (SRSS) methodology for combining uncertainty terms that are random and independent was used in the synthesis. Every uncertainty that may exist in the hardware which is used to measure the core exit temperature was conservatively applied and the associative relation between the elements of uncertainty was considered simultaneously. As a result of uncertainty evaluation, the channel statistical allowance (CSA) of single channel of core exit temperature was +1.042%Span. The range of uncertainty, -0.35%Span ($-4.05^{\circ}C$) ~ +2.08%Span($24.25^{\circ}C$), was obtained as the operating criteria of core exit temperature.

• Journal of the Korean Solar Energy Society
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• v.31 no.1
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• pp.51-58
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• 2011

The energy makes the basic element which improves the quality of life with motive power of industry and life. However, using the fossil fuel resources was restricted through it's abuse and exhaustion, and that cause a global warming resultingly. According to the reason, the world increased the interest that are stability and use of new and renewable energy which is clean energy with environment. Therefore, the property data of new and renewable is needed for developing and supplying the energy. In other words, the data of new and renewable energy becomes the standards for supply and evaluation of new and renewable energy with development of industry and technology. Also, the necessity came to the fore as the reference and standards of new and renewable energy data. Therefore, in this study, we evaluate and collect the solar radiation data as the new and renewable data and process the collected data through the standards for valuation. We evaluate uncertainty with standards which are NREL, WMO, and GUM. Whereby the data becomes reference standards data and gains the credibility. For the reliability data, we correct the measuring instrument with correction period. Using the DQMS and SERI QC, we efficiently manage and evaluate the solar radiation data. As a result, we evaluate uncertainty as 1,120 case about 16 area. we achieve credibility of data from evaluated solar radiation data and provide an accurate information to user. The annual average of horizontal radiation presents between 1,484 and 4,577, then the uncertainty evaluates from 163 to 453. The error of uncertainty presents smaller than the measurement values. So, we judge a credibility of data by expression of reliability quantitatively. In additional, the reference standards data which is possible to approach anywhere will be used for the supporting related industry and policy making.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.