• Journal of Korean Society for Atmospheric Environment
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• v.34 no.4
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• pp.610-615
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• 2018

A filter-based optical technique is one of the representative ways for the measurement and quantification of black carbon (BC). Since the filter-based technique adopts a simple principle, it is easy to put into practical use and instrumental products have already been commercialized. In this study, however, the absorption coefficients of BC after the correction process was estimated to be approximately 3 times lower than those before the correction process. In addition, the difference between before and after corrections was also evident for the trend of increasing and decreasing absorption coefficient. When BC concentration is low, uncertainty may increase regardless of corrections due to the artifacts of filter. In this sense, techniques without using a filter are required, and uncertainties will be minimized if these techniques are used to further complement the filter-based black carbon measurements. Finally, this study is believed to help understand the uncertainty and correction of filter-based black carbon measurements.

• Proceedings of the KSR Conference
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• 2001.05a
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• pp.459-465
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• 2001

The Prefabricated Vertical Drain(PVD) method is most widely used technique to accelerate the consolidation process and to strengthen the weak clayey soil in situ. Uncertainty in the consolidation process via the Prefabricated Vertical Drain(PVD), and the effects of uncertainty on the design of PVDs, are investigated in this paper, Among the effect factors, the coefficient of horizontal(radial) consolidation, C$\sub$h/, is the most important and sensitivity analysis of the degree of consolidation with respect to the other effect factors are carried out. For the reliable determination of uncertain quantities, the laboratory and in-situ tests are carried out. Henceforth, probability analysis that take the uncertainty into account are executed and reliable design method is provided in practice.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.34-44
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• 1997

This study presents the recursive constraint bounding(RCB) method to reduce the cycle time in internal cylindrical plunge grinding process. This method can cope with process noise as well as modeling bias. The main features of RCB method are its utilization of measurements at the end of each cycle and its use of monotonicity analysis for determining the extremes of bias and noise. This method is investigated in simulation and evaluated by experiment in internal cylindrical plunge grinding operation. The results from simulation and experiment show that it is effective in reducing cycle time in spite of modeling uncertainty in the forms of process noise and modeling bias.

• Industrial Engineering and Management Systems
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• v.12 no.1
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• pp.2-8
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• 2013

Uncertainty theory is a branch of mathematics based on normolity, duality, subadditivity and product axioms. Uncertain process is a sequence of uncertain variables indexed by time. Canonical Liu process is an uncertain process with stationary and independent increments. And the increments follow normal uncertainty distributions. Uncertain differential equation is a type of differential equation driven by the canonical Liu process. Stability analysis on uncertain differential equation is to investigate the qualitative properties, which is significant both in theory and application for uncertain differential equations. This paper aims to study stability properties of linear uncertain differential equations. First, the stability concepts are introduced. And then, several sufficient and necessary conditions of stability for linear uncertain differential equations are proposed. Besides, some examples are discussed.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.195-200
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• 2021

In recent years, Vietnam's economic growth rate has been attributed to the growth of many well-managed industries within Southeast Asia. Among them is the civil construction industry. Construction projects typically take a long time to complete and require a huge budget. Many socio-economic variables and factors affect total construction project costs due to market fluctuations. In recent years, crucial socioeconomic development indicators of construction reached a fairly high growth rate. Also, most infrastructure and construction projects have a high degree of complexity and uncertainty. This makes it challenging to predict the accurate project price. These challenges raise the need to recognize significant factors that influence the construction price index of civil buildings in Vietnam, both micro and macro. Therefore, this paper presents critical factors that affect the construction price index using the fuzzy extent analysis process in an uncertain environment. This proposed quantitative model is expected to reflect the uncertainty in the process of evaluating and ranking the influencing factors of the construction price index in Vietnam. The research results would also allow project stakeholders to be more informed of the factors affecting the construction price index in the context of Vietnam's civil construction industry. They also enable construction contractors to estimate project costs and bid rates better, enhancing their project and risk management performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.287-287
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• 2023

Surface water-groundwater interaction (SWGI) is an important hydrological process that influences both the quantity and quality of water resources. However, regional scale SWGI model calibration and uncertainty analysis have been a challenge because integrated models inherently carry a vast number of parameters, modeling assumptions, and inputs, potentially leaving little time and budget to explore questions related to model performance and forecasting. In this study, we have proposed the application of iterative ensemble smoother (IES) for uncertainty analysis and calibration of the widely used integrated surface-subsurface model, SWAT-MODFLOW. SWAT-MODFLOW integrates Soil and Water Assessment Tool (SWAT) and a three-dimensional finite difference model (MODFLOW). The model was calibrated using a parameter estimation tool (PEST). The major advantage of the employed IES is that the number of model runs required for the calibration of an ensemble is independent of the number of adjustable parameters. The pilot point approach was followed to calibrate the aquifer parameters, namely hydraulic conductivity, specific storage, and specific yield. The parameter estimation process for the SWAT model focused primarily on surface-related parameters. The uncertainties both in the streamflow and groundwater level were assessed. The work presented provides valuable insights for future endeavors in coupled surface-subsurface modeling, data collection, model development, and informed decision-making.

• Journal of Korean Society on Water Environment
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• v.29 no.5
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• pp.681-690
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• 2013

Watershed models have been increasingly used to support an integrated management of land and water, non-point source pollutants, and implement total daily maximum load policy. However, these models demand a great amount of input data, process parameters, a proper calibration, and sometimes result in significant uncertainty in the simulation results. For this reason, uncertainty analysis is necessary to minimize the risk in the use of the models for an important decision making. The objectives of this study were to evaluate three different uncertainty analysis algorithms (SUFI-2: Sequential Uncertainty Fitting-Ver.2, GLUE: Generalized Likelihood Uncertainty Estimation, ParaSol: Parameter Solution) that used to analyze the sensitivity of the SWAT(Soil and Water Assessment Tool) parameters and auto-calibration in a watershed, evaluate the uncertainties on the simulations of runoff and sediment load, and suggest alternatives to reduce the uncertainty. The results confirmed that the parameters which are most sensitive to runoff and sediment simulations were consistent in three algorithms although the order of importance is slightly different. In addition, there was no significant difference in the performance of auto-calibration results for runoff simulations. On the other hand, sediment calibration results showed less modeling efficiency compared to runoff simulations, which is probably due to the lack of measurement data. It is obvious that the parameter uncertainty in the sediment simulation is much grater than that in the runoff simulation. To decrease the uncertainty of SWAT simulations, it is recommended to estimate feasible ranges of model parameters, and obtain sufficient and reliable measurement data for the study site.

• Journal of Multimedia Information System
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• v.2 no.3
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• pp.255-262
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• 2015

The modern manufacturing systems and technologies produce products that are more accurate day by day. This can be reached mainly by improvement the manufacturing process with at the same time restricting more and more the quality specifications and reducing the uncertainty in part. The main objective an industry becomes to lower the part's variability, since the less variability - the better is product. One of the part of this task is measuring the object's uncertainty. The main purpose of this study is to understand the application of bootstrap method for uncertainty evaluation. Bootstrap method is a collection of sample re-use techniques designed to estimate standard errors and confidence intervals. In the case study a surface of an automobile engine block - (Top view side) is measured by Coordinate Measuring Machine (CMM) and analyzed for uncertainty using Geometric Least Squares in complex with bootstrap method. The designed experiment is composed by three similar measurements (the same features in unique reference system), but with different points (5, 10, 20) concentration at each level. Then each cloud of points was independently analyzed by means of non-linear Least Squares, after estimated results have been reported. A MatLAB software tool used to generate new samples using bootstrap function. The results of the designed experiment are summarized and show that the bootstrap method provides the possibility to evaluate the uncertainty without repeating the Coordinate Measuring Machine (CMM) measurements many times, i.e. potentially can reduce the measuring time.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.923-931
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• 1999

In this paper, a modeling and a robust time-delay control for the reclaimer are investigated. Supplying the same amount of a raw material throughout the reclamation process from the raw yard to a sinter plant is important to keep the quality of the molten steel uniform in blast furnaces. As the actual parameter values of the reclaimer are not available, the boom rotational dynamics are modeled as a second order differential equation with unknown coefficients. The unknown parameters in the nominal model are estimated using a recursive estimation method. Another important factor in the control design of the reclaimer is the large time-delay in output measurement. Assuming a multiplicative uncertainty, that accounts for both the unstructured uncertainty neglected in the modeling and the structured uncertainty contained in the parameter estimation, a robust Smith predictor is designed. A robust stability criterion for the multiplicative uncertainty is also derived. Following the work of Goodwin et al. [4], a quantifying procedure of the multiplicative uncertainty bound, through experiments , is described. Experimental and simulation results are provided.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.329-340
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• 2011

The probability concepts mainly used for rainfall or flood frequency analysis in water resources planning are the frequentist viewpoint that defines the probability as the limit of relative frequency, and the unknown parameters in probability model are considered as fixed constant numbers. Thus the probability is objective and the parameters have fixed values so that it is very difficult to specify probabilistically the uncertianty of these parameters. This study constructs the uncertainty evaluation model using Bayesian MCMC and Metropolis -Hastings algorithm for the uncertainty quantification of parameters of probability distribution in rainfall frequency analysis, and then from the application of Bayesian MCMC and Metropolis- Hastings algorithm, the statistical properties and uncertainty intervals of parameters of probability distribution can be quantified in the estimation of probability rainfall so that the basis for the framework configuration can be provided that can specify the uncertainty and risk in flood risk assessment and decision-making process.