• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.622-625
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• 2004

This paper presents multi-source spatial data integration models based on probability theory for landslide hazard assessment. Four probabilistic models such as empirical likelihood ratio estimation, logistic regression, generalized additive and predictive discriminant models are proposed and applied. The models proposed here are theoretically based on statistical relationships between landslide occurrences and input spatial data sets. Those models especially have the advantage of direct use of continuous data without any information loss. A case study from the Gangneung area, Korea was carried out to quantitatively assess those four models and to discuss operational issues.

• The Korean Journal of Applied Statistics
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• v.2 no.2
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• pp.37-45
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• 1989

Switching regression models are commonly used for the statistical analysis of the disequilibrium models. In this paper wer show how switching regression models can be classified by the sample separation criterion and how they are related to the disequilibrium models. The problems in the estimation of the disequilibrium models ar discussed for the ones with both known sample separation and unknown sample separation.

• Proceedings of the KSME Conference
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• 2000.11b
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• pp.410-415
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• 2000

Slip factor is defined as an empirical factor being multiplied to theoretical energy transfer for the estimation of real work input of a centrifugal compressor. Researchers have tried to develop a simple empirical model, for a century, to predict a slip factor. However most these models were developed on the condition of design point assuming inviscid flow. So these models often fail to predict a correct slip factor at off-design condition. In this study, we summarized various slip factor models and compared these models with experimental and numerical data at off-design condition. As a result of this study, Wiesner's and Paeng and Chung's models are applicable for radial impeller, but all the models are not suitable for backswept impeller. Finally, the essential avenues for future study is discussed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.741-744
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• 1996

Artifical Neural Network(ANN) models were used for forecasting interest rate as a new methodology, which has proven itself successful in financial domain. This research intended to construct ANN models which can maximize the performance of prediction, regarding Corporate Bond Yield (CBY) as interest rate. Synergistic Market Analysis (SMA) was applied to the construction of models [Freedman et al.]. In this aspect, while the models which consist of only time series data for corporate bond yield were devloped, the other models generated through conjunction and reorganization of fundamental variables and market variables were developed. Every model was constructed to predict 1,6, and 12 months after and we obtained 9 ANN models for interest rate forecasting. Multi-layer perceptron networks using backpropagation algorithm showed good performance in the prediction for 1 and 6 months after.

• Journal of Mechanical Science and Technology
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• v.18 no.1
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• pp.153-166
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• 2004

This paper presents a comparative study of a fully coupled, upwind, compressible Navier-Stokes code with three two-equation models and the Baldwin-Lomax algebraic model in predicting transonic/supersonic flow. The k-$\varepsilon$ turbulence model of Abe performed well in predicting the pressure distributions and the velocity profiles near the flow separation over the axisymmetric bump, even though there were some discrepancies with the experimental data in the shear-stress distributions. Additionally, it is noted that this model has y$\^$*/ in damping functions instead of y$\^$＋/. The turbulence model of Abe and Wilcox showed better agreements in skin friction coefficient distribution with the experimental data than the other models did for a supersonic compression ramp problem. Wilcox's model seems to be more reliable than the other models in terms of numerical stability. The two-equation models revealed that the redevelopment of the boundary layer was somewhat slow downstream of the reattachment portion.

• Journal of Korean Society for Atmospheric Environment
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• v.11 no.1
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• pp.15-28
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• 1995

Over the last two decades, air quality models have been developed and used in the United States. Many of these models ate distributed as parts of UNAMAP. These models have been used as useful tools for urban air quality management in Korea. However, when we use these models, many problems could be occured because of their different topographical and meteorological conditions. In the present study it is described that the characteristics about developement of the local scale diffusion models for application to Korea. Also for evaluation purpose of the models, input data requirements such as air pollutant concentrations, emission inventory, meteorological data, and field experiment data, were discussed.

• Transactions of Materials Processing
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• v.11 no.5
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• pp.394-404
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• 2002

The drawbead model for a three-dimensional a finite element analysis of sheet metal forming processes is developed. The mathematical models of the basic drawbeads like circular drawbead, stepped drawbead, and squared drawbaed are first derived using the bending theory, belt-pulley equation, and Coulomb friction law. Next, the experiments for finding the drawing characteristics of the drawbead are performed. Based on mathematical models and drawing test results, expert models of basic drawbeads are then developed employing a linear multiple regression method. For the expert models of combined drawbeads such as the double circular drawbead, double stepped drawbead, circular-and-stepped drawbead, etc., those of the basic drawbeads are summed. Finally, in order to verify the expert models developed, the drawing characteristics calculated by the expert models of the double circular drawbead and circular-and-stepped drawbead are compared with those obtained from the experiments. The predictions by expert models agree well with the measurements by experiments.

• Toxicological Research
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• v.30 no.1
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• pp.1-5
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• 2014

Xenograft models of human cancer play an important role in the screening and evaluation of candidates for new anticancer agents. The models, which are derived from human tumor cell lines and are classified according to the transplant site, such as ectopic xenograft and orthotopic xenograft, are still utilized to evaluate therapeutic efficacy and toxicity. The metastasis model is modified for the evaluation and prediction of cancer progression. Recently, animal models are made from patient-derived tumor tissue. The patient-derived tumor xenograft models with physiological characters similar to those of patients have been established for personalized medicine. In the discovery of anticancer drugs, standard animal models save time and money and provide evidence to support clinical trials. The current strategy for using xenograft models as an informative tool is introduced.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.73-76
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• 2020

The physics-based compact gate leakage current noise models in nanoscale MOSFETs are developed in such a way that the models incorporate important physical effects and are suitable for circuit simulators, including QM (quantum-mechanical) effects. An emphasis on the trap-related parameters of noise models is laid to make the models adaptable to the variations in different process technologies and to make its parameters easily extractable from measured data. With the help of an accurate and generally applicable compact noise models, the compact noise models are successfully implemented into BSIM (Berkeley Short-channel IGFET Model) format. It is shown that the noise models have good agreement with measurements over the frequency, gate-source and drain-source bias ranges.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.22-29
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• 2008

In order to improve the efficiency of a main transformer in a tilting train, the optimal operation of a cooling system is necessary. For the development of optimal control algorithms of a cooling system, mathematical models of a main transformer cooling system were developed. These include dynamic models of a main transformer, an oil pump, an oil cooler, a blower, and a pipe. Control algorithms for a blower and an oil pump were selected in order to identify the effectiveness of dynamic models. A simulation program was developed by using the developed dynamic models and the selected control algorithms. Simulation results showed good predictions of dynamic behaviors of a main transformer cooling system. Therefore, dynamic models, which were developed in this study, may be effectively used to develop control algorithms of a main transformer cooling system.