• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 추계정기학술대회:지능형기술과 CRM
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• pp.427-436
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• 2000

The prediction of stock price index is a very difficult problem because of the complexity of the stock market data it data. It has been studied by a number of researchers since they strong1y affect other economic and financial parameters. The movement of stock price index has a series of change points due to the strategies of institutional investors. This study presents a two-stage forecasting model of stock price index using change-point detection and artificial neural networks. The basic concept of this proposed model is to obtain Intervals divided by change points, to identify them as change-point groups, and to use them in stock price index forecasting. First, the proposed model tries to detect successive change points in stock price index. Then, the model forecasts the change-point group with the backpropagation neural network (BPN). Fina1ly, the model forecasts the output with BPN. This study then examines the predictability of the integrated neural network model for stock price index forecasting using change-point detection.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.349-357
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• 2005

We suggest a functional technique with loess smoothing for estimating the change-point when there is one change-point in the mean model. The proposed change-point estimator is consistent. Simulation study shows a good performance of the proposed change-point estimator in comparison with other parametric or nonparametric change-point estimators.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2000년도 춘계정기학술대회 e-Business를 위한 지능형 정보기술 / 한국지능정보시스템학회
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• pp.235-241
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• 2000

This article suggests integrated neural network models for the interest rate forecasting using change point detection. The basic concept of proposed model is to obtain intervals divided by change point, to identify them as change-point groups, and to involve them in interest rate forecasting. the proposed models consist of three stages. The first stage is to detect successive change points in interest rate dataset. The second stage is to forecast change-point group with data mining classifiers. The final stage is to forecast the desired output with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. This article is then to examine the predictability of integrated neural network models for interest rate forecasting using change-point detection.

• 한국펄프종이공학회:학술대회논문집
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• 한국펄프종이공학회 2004년도 춘계학술발표논문집
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• pp.107-114
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• 2004

In this work the Model algorithmic control method is applied to control the grade change operations in paper mills. The neural network model for the grade change operations is identified first model is then extracted from the neural model. Results of simulations for MAC control of grade change operations are compared with plant operation data response. From the comparison, we can see that the proposed MAC method exhibits faster response for the grade change of paper and achieves stable steady-state.

• 대한간호학회지
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• 제32권5호
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• pp.609-622
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• 2002

The purpose of this study was to investigate the relationships among variables of transtheoretical model for exercise in the elderly. Method: A hypothetical model explaining the stage of change was constructed based on a transtheoretical model. Empirical data for testing the hypothetical model was collected from 246 old adults over 65 years old in a community settings in Seoul, Korea in June and July, 2000. Data were analyzed by descriptive statistics and correlational analysis using pc-SAS program. The Linear Structural Modeling(LISREL) 8.0 program was used to find the best fit model which explain causal relationship of variables. Result: The fitness of modified model to the data was X2= 96.75(df=49, p=.00005), GFI= .98, NFI=.99, AGFI=.95, NNFI=.99. The the predictable variables of stage of change explained 64% of stage of change for exercise Conclusion: Results are consistent with the studies of application of the transtheoretical model, which has been used to understand how people change health behaviors. The findings of this study give useful informations to construct exercise intervention program for the elderly about relationships among variables influencing to the stage of change of exercise.

• 한국펄프종이공학회:학술대회논문집
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• 한국펄프종이공학회 2003년도 추계학술발표논문집
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• pp.291-305
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• 2003

In this work we developed the closed-loop model of a paper machine during grade change with the intention to provide a reliable dynamic model to be used in the model-based grade change control scheme. During the grade change, chemical and physical characteristics of paper process change with time. It is very difficult to represent these characteristics on-line by using physical process models. In this work, the wet circulation part and the drying section were considered as a single process and closed-loop identification technique was used to develop the grade change model. Comparison of the results of numerical simulations with plant operation data demonstrates the effectiveness of the model identified.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2010년 춘계학술발표회 논문집
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• pp.383-385
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• 2010

This study analyzed the change of flowout and suspend solid in Andong and Imha basin according to the climate change to develop evaluation index about turbid water occurrence possibility and to support the countermeasures for turbid water management using GIS-based Soil and Water Assessment Tools (SWAT). MIROC3.2 hires model values of A1B climate change scenario that were supplied by Intergovernmental Panel on Climate Change (IPCC) were applied to future climage change data. Precipitation and temperature were corrected by applying the output value of 20th Century Climate Coupled Model (20C3M) based on past climate data during 1977 and 2006 and downscaled with Change Factor (CF) method. And future climate change scenarios were classified as three periods (2020s, 2050s, 2080s) and the change of flowout and suspended solid according to the climate change were estimated by coupling modeled value with SWAT model.

• 한국항만학회지
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• 제15권1호
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• pp.47-56
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• 2001

A numerical model for practical use based on the 1-line theory is presented to simulate shoreline changes due to construction of offshore structures. The shoreline change model calculates the longshore sediment transport rate using breaking waves. Before the shoreline change model execution, a wave model, adopting the modified Boussinesq equation including the breaking parameters and bottom friction term, was used to provide the longshore distribution of the breaking waves. The contents of present model are outlined first. Then to examine the characteristics of this model, the effects of the parameters contained in this model are clarified through the calculations of shoreline changes for simple cases. Finally, as the guides for practical application of this model, several comments are made on the parameters used in the model, such as transport parameter, average beach slope, breaking height variation alongshore, depth of closure, etc. with the presentation of typical examples of 3-dimensional movable bed experimental results for application of this model. Here, beach change behind the offshore structures is represented by the movement of the shoreline position. Analysis gives that the transport parameters should be taken as site specific parameters in terms of time scale for the shoreline change and adjusted to achieve the best agreement between the calculated and the observed near the structures.

• Journal of the Korean Statistical Society
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• 제28권3호
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• pp.359-370
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• 1999

We considered a change-point hazard rate model generalizing constant hazard rate model. This type of model is very popular in the sense that the Weibull and exponential distributions formulating survival time data are the special cases of it. Maximum likelihood estimation and the asymptotic properties such as the consistency and its limiting distribution of the change-point estimator were discussed. A parametric bootstrap method for finding confidence intervals of the unknown change-point was also suggested and the proposed method is explained through a practical example.

• Communications for Statistical Applications and Methods
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• 제13권3호
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• pp.491-501
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• 2006

We consider a multiple change point model with autoregressive conditional heteroscedasticity (ARCH). The model assumes that all or the part of the parameters in the ARCH equation change over time. The occurrence of the change points is modelled as the discrete time Markov process with unknown transition probabilities. The model is estimated by Markov chain Monte Carlo methods based on the approach of Chib (1998). Simulation is performed using a variant of perfect sampling algorithm to achieve the accuracy and efficiency. We apply the proposed model to the simulated data for verifying the usefulness of the model.