• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1285-1289
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• 2004

Elman Discrete Recurrent Neural Networks Model(EDRNNM) was used to be a suitable short-term hydrological forecasting tool yielding a very high degree of flood stage forecasting accuracy at Musung station of Wi-stream one of IHP representative basins in South Korea. A relative new approach method has recurrent feedback nodes and virtual small memory in the structure. EDRNNM was trained by using two algorithms, namely, LMBP and RBP The model parameters, optimal connection weights and biases, were estimated during training procedure. They were applied to evaluate model validation. Sensitivity analysis test was also performed to account for the uncertainty of input nodes information. The sensitivity analysis approach could suggest a reduction of one from five initially chosen input nodes. Because the uncertainty of input nodes information always result in uncertainty in model results, it can help to reduce the uncertainty of EDRNNM application and management in small catchment.

• Journal of the Korea Society of Computer and Information
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• v.8 no.4
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• pp.165-175
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• 2003

The concept of measurement uncertainty has been recognised for many years since "Guide to the Expression of Uncertainty in Measurement" was published 1993 by ISO. This study firstly propose the mathematical model to evaluate uncertainty considering the dispersion of samples because the mathematical model of a measurement is an important to evaluate uncertainty, and it must contains every quantify which contribute significantly to uncertainty in the measurement result. Secondly the standard uncertainty of the result of a measurement, namely combined standard uncertainty is evaluated using the law of propagation of uncertainty, what is termed in GUM method. In GUM method, a measurand is usually approximated by a linear function of its variables by the transforming its input quantities. Furthermore central limit theorem is applied to the input quantity. However the mathematical model of a measurement is generally not always a linearity function, and a distribution function of input or output quantity is not necessarily normal distribution. Then, in some cases GUM method is not favorable to evaluate a measurement uncertainty. Therefore this study propose a new method and its algorithm which use the Monte-carlo simulation to evaluate a measurement uncertainty in both case of linearity or non-linearity function. function.

• Fire Science and Engineering
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• v.26 no.2
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• pp.40-52
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• 2012

This paper presents the uncertainty analysis results of fire modeling input parameters for motor control center in switchgear room of nuclear power plants. FDS (Fire Dynamics simulator) 5.5 was used to simulate the fire scenario and Latin Hyper Cube Monte Carlo simulations were employed to generate random samples for FDS input parameters. The uncertainty analysis results of input parameters are compared with those of the model uncertainty analysis and sensitivity analysis approaches of NUREG-1934. The study results show that the input parameter uncertainty analysis approach may lead to more conservative results than the uncertainty analysis and sensitivity analysis methods of NUREG-1934.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.324-328
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• 2005

This study is to figure out uncertainty relationship between input data and calibrated parameter on unsteady hydraulic routing model. The uncertainty would be present to model results as a variant water surface profile along the channel. Firstly, Manning's n is calibrated through the model with assumed uncertainty on input hydrograph. Then, spatially distributed n-values sets based on the calibrated n values are used to get water profile of each n-values set. The results show that ${\pm}0.002$ of error in Manning's n cause ${\pm}30cm$ of maximum water surface differences at the Sumjin river.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.566-573
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• 2005

Quality function deployment (QFD) is a useful tool for ensuring quality throughout each stage of the product development and production process. Since the focus of QFD is placed on the early stage, the uncertainty in the input information of QFD is inevitable. If the uncertainty is neglected, the QFD analysis results are likely to be misleading. This paper classifies the sources of uncertainty in QFD, and proposes a new approach to model and analyze the effects of uncertainty in QFD.

• Water Engineering Research
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• v.3 no.4
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• pp.215-234
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• 2002

Uncertainty in dam breach flood routing results was analyzed in order to provide the basis fer the investigation of their effects on the flood damage assessments and dam safety risk assessments. The Monte Carlo simulation based on Latin Hypercube Sampling technique was used to generate random values for two uncertain input parameters (i.e., dam breach parameters and Manning's n roughness coefficients) of a dam breach flood routing analysis model. The flood routing results without considering the uncertainty in two input parameters were compared with those with considering the uncertainty. This paper showed that dam breach flood routing results heavily depend on the two uncertain input parameters. This study indicated that the flood damage assessments in the downstream areas can be critical if uncertainty in dam breach flood routing results are considered in a reasonable manner.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.4
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• pp.327-334
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• 2002

The work presents sensitivity and uncertainty analysis of 2-compartment model for the evaluation of indoor radon pollution in a house. Effort on the development of such model is directed towards the prediction of the generation and transfer of radon in indoor air released from groundwater. The model is used to estimate a quantitative daily human exposure through inhalation of such radon based on exposure scenarios. However, prediction from the model has uncertainty propagated from uncertainties in model parameters. In order to assess how model predictions are affected by the uncertainties of model inputs, the study performs a quantitative uncertainty analysis in conjunction with the developed model. An importance analysis is performed to rank input parameters with respect to their contribution to model prediction based on the uncertainty analysis. The results obtained from this study would be used to the evaluation of human risk by inhalation associated with the indoor pollution by radon released from groundwater.

• Earthquakes and Structures
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• v.8 no.4
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• pp.915-934
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• 2015

Within the context of Structural Health Monitoring (SHM), it is often the case that structural systems are described by uncertainty, both with respect to their parameters and the characteristics of the input loads. For the purposes of system identification, efficient modeling procedures are of the essence for a fast and reliable computation of structural response while taking these uncertainties into account. In this work, a reduced order metamodeling framework is introduced for the challenging case of nonlinear structural systems subjected to earthquake excitation. The introduced metamodeling method is based on Nonlinear AutoRegressive models with eXogenous input (NARX), able to describe nonlinear dynamics, which are moreover characterized by random parameters utilized for the description of the uncertainty propagation. These random parameters, which include characteristics of the input excitation, are expanded onto a suitably defined finite-dimensional Polynomial Chaos (PC) basis and thus the resulting representation is fully described through a small number of deterministic coefficients of projection. The effectiveness of the proposed PC-NARX method is illustrated through its implementation on the metamodeling of a five-storey shear frame model paradigm for response in the region of plasticity, i.e., outside the commonly addressed linear elastic region. The added contribution of the introduced scheme is the ability of the proposed methodology to incorporate uncertainty into the simulation. The results demonstrate the efficiency of the proposed methodology for accurate prediction and simulation of the numerical model dynamics with a vast reduction of the required computational toll.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.123-123
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• 2020

The present study is aimed to correcting radar-based mean areal precipitation forecasts to improve urban flood predictions and uncertainty analysis of water levels contributed at each stage in the process. For this reason, a long short-term memory (LSTM) network is used to reproduce three-hour mean areal precipitation (MAP) forecasts from the quantitative precipitation forecasts (QPFs) of the McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation (MAPLE). The Gangnam urban catchment located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 24 heavy rainfall events, 22 grid points from the MAPLE system and the observed MAP values estimated from five ground rain gauges of KMA Automatic Weather System. The corrected MAP forecasts were input into the developed coupled 1D/2D model to predict water levels and relevant inundation areas. The results indicate the viability of the proposed framework for generating three-hour MAP forecasts and urban flooding predictions. For the analysis uncertainty contributions of the source related to the process, the Bayesian Markov Chain Monte Carlo (MCMC) using delayed rejection and adaptive metropolis algorithm is applied. For this purpose, the uncertainty contributions of the stages such as QPE input, QPF MAP source LSTM-corrected source, and MAP input and the coupled model is discussed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.140-146
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• 2016

This paper proposes a control method of the Segway with unknown control coefficient and input saturation. To design a simple controller for the Segway with the model uncertainty, the prescribed performance function is used. Furthermore, an auxiliary variable is introduced to deal with unknown time-varying control coefficient and input saturation problem. Due to the auxiliary variable, function approximators are not used in this paper although all model uncertainties are unknown. Thus, the controller can be simple. From the Lyapunov stability theory, it is proved that all errors of the proposed control system remain within the prescribed performance bounds. Finally, the simulation results are presented to demonstrate the performance of the proposed scheme.