• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 1995.11a
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• pp.75-81
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• 1995

DYLAM and its related applications are reviewed in detail and found to have many favourable characteristics. Concerning human factor analysis, the study demonstrates that DYLAM methodology represents an appropriate tool to study man-machine behaviour provided that DYLAM is used to model machine behaviour and an appropriate operator interface human factor model is included. A hybrid model which is a synthesis of the DYLAM model, a system thermodynamic simulation model and a neural network predicative model, is implemented and used to analyse dynamically the CANDU pressurizer system.

• 한국연소학회:학술대회논문집
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• 2015.12a
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• pp.325-326
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• 2015

Combustion of coke grains in a pellet used to be modeled using the shrinking core model in the previous indurator simulations. This leads to the discussions about its propriety due to the fundamental assumptions of the model inconsistent with the particle characteristics. The current study presents the grain model as an improvemen, and the differently used reaction models are compared. In addition, the simulations assuming changed particle conditions are conducted to display the effects of using the grain model.

• Journal of Korean Institute of Industrial Engineers
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• v.6 no.1
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• pp.27-38
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• 1980

This paper presents the validation of a stochastic model of muscle fatigue during static muscle contractions. Forty four laboratory experiments, covering eleven test conditions for two trained subjects, were run in order to estimate fatigue and recovery rates, based on EMG observations. The validation of the model was made by comparing the model predictions to the experimental fatigue time. The validation study supports that the stochastic model of muscle fatigue accurately represents the underlying fatigue process. The study also provides support that the fatigue model can be used as a monitor of individual muscle capabilities.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.05b
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• pp.407-412
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• 1997

The predictive results between a dynamic food-chain model (DYNACON) and an equilibrium model (NRC model) were compared to show the physical validity of DYNACON. Although the mathematical formulations and transport processes of radionuclides in the environment are different between two models, the comparative study shows good agreement for deposition events that occur during the growing season of plants.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.3
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• pp.44-50
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• 2004

The purpose of this study is to improve the prediction ability of the atomization and vaporization processes of GDI spray under high-pressure and high-temperature conditions. Several models have been introduced and compared. The atomization process was modeled using hybrid breakup model that is composed of Conical Sheet Disintegration (CSD) model and Aerodynamically Progressed TAB(APTAB) model. The vaporization process was modeled using Spalding model, modified Spalding model and Abramzon ＆ Sirignano model. Exciplex fluorescence method was used for comparing the calculated with the experimental results. The experiment and calculation were performed at the ambient pressure of 0.5 MPa and 1.0 MPa and the ambient temperature of 473k. Comparison of caldulated and experimental spray characteristics was carried out and Abramzon ＆ Sirignano model and modified Spalding model had the better prediction ability for vaporization process than Spalding model.

• 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.

• International Journal of Highway Engineering
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• v.16 no.3
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• pp.101-107
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• 2014

PURPOSES : This study deals with the pedestrian accidents in case of Cheongju. The goals are to develop the pedestrian accident model. METHODS : To analyze the accident, count data models, truncated count data models and Tobit regression models are utilized in this study. The dependent variable is the number of accident. Independent variables are traffic volume, intersection geometric structure and the transportation facility. RESULTS : The main results are as follows. First, Tobit model was judged to be more appropriate model than other models. Also, these models were analyzed to be statistically significant. Second, such the main variables related to accidents as traffic volume, pedestrian volume, number of Entry/exit, number of crosswalk and bus stop were adopted in the above model. CONCLUSIONS : The optimal model for pedestrian accidents is evaluated to be Tobit model.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.8
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• pp.2030-2038
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• 1995

In this study, three turbulent flame propagation models are compared using experimentally measured data of a 4 valves/cylinder spark-ignition engine. First two conventional models are B.K model and GESIM combustion model. The burning rates calculated from the two models are compared with the burning rates calculated from measured pressure data using the one-zone heat release analysis. GESIM combustion model predicts burning rates closer to the data acquired from the experiment in wide operating ranges than B-K model does. The third model is refined based on GESIM combustion model by including the effect of flame stretch, turbulent length scale band pass filter and a variable that considers flame size and the area of flame contacting the cylinder wall surface. The refined combustion model predicts burning rates closer to experimental results than GESIM combustion model does. Also, the refined combustion model predicts flame radius close to the experimental result measured by using optical fiber technique.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.271-281
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• 2005

Zokian(1993) and Li and Li(1996) developed TARCH(Threshold ARCH) model, considering the asymmetries in volatility. The models are based on Engle(1982)'s ARCH model and Bollerslev(1986)'s GARCH model. However, two TARCH models can be expressed a common model through Box Cox Power transformation, which was used by Higgins and Bera(1992) for developing NARCH(nonlinear ARCH) model. This article shows the PTARCH(Power transformation TARCH) model is necessary in some condition, and it checks the fact that PTARCH model has better performance comparing estimates and RMSE(Root Mean Square Error) with those of Zakoian's TARCH model and Li and Li's TARCH model. PTARCH model would give contribution in asymmetric study as well as heteroscedastic study.

• Journal of Environmental Science International
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• v.24 no.8
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• pp.1023-1036
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• 2015

A reliable streamflow forecasting is essential for flood disaster prevention, reservoir operation, water supply and water resources management. This study proposes a hybrid model for river stage forecasting and investigates its accuracy. The proposed model is the wavelet packet-based artificial neural network(WPANN). Wavelet packet transform(WPT) module in WPANN model is employed to decompose an input time series into approximation and detail components. The decomposed time series are then used as inputs of artificial neural network(ANN) module in WPANN model. Based on model performance indexes, WPANN models are found to produce better efficiency than ANN model. WPANN-sym10 model yields the best performance among all other models. It is found that WPT improves the accuracy of ANN model. The results obtained from this study indicate that the conjunction of WPT and ANN can improve the efficiency of ANN model and can be a potential tool for forecasting river stage more accurately.