• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.37-39
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• 2000

The purpose of this paper is to present a neural network model based on change-point detection for the prediction of the U.S. Treasury Securities. Interest rates have been studied by a number of researchers since they strongly affect other economic and financial parameters. Contrary to other chaotic financial data, the movement of interest rates has a series of change points due to the monetary policy of the U.S. government. 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 interest rates forecasting. The proposed model consists of three stages. The first stage is to detect successive change points in the interest rates dataset. The second stage is to forecast the change-point group with the backpropagation neural network (BPN). The final stage is to forecast the output with BPN. This study then examines the predictability of the integrated neural network model for interest rates forecasting using change-point detection.

• Journal of Climate Change Research
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• v.6 no.2
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• pp.121-131
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• 2015

As the temperature has changed by climate change, changes in its own characteristic values of the chemical substance or the movement and distribution of chemicals take place in accordance with the changes of hydrological and meteorological phenomena. Depending on the impact of climate change on the chemical behavior, it is necessary to understand and predict quantitative changes in the dynamics of the environment of pollutants due to climate change in order to predict in advance the occurrence of environmental disasters, and minimize the impact on the life and the environment after the incident. In this study, we have analysed and compared chemical fate models validated by previous studies in terms of model configuration, application size and input/output factors. The potential models applicable to municipal and industrial areas were selected on the basis of characteristic of each model, availability of input parameters and consideration for climate change, identified the problems, and then presented an approach to improve applicability.

• The Korean Journal of Applied Statistics
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• v.17 no.2
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• pp.281-301
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• 2004

In this paper we consider the change point problem in a sequence of univariate normal observations. We want to know whether there is any change point or not. In case a change point exists, we will identify its change type. Namely, it can be a mean change, a variance change, or both the mean and variance change. The intrinsic Bayes factors of Berger and Pericchi (1996, 1998) are used to find the type of optimal change model. The Gibbs sampling including the Metropolis-Hastings algorithm is used to estimate all the parameters in the change model. These methods are checked via simulation and applied to the winter average temperature data in Seoul.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.543-556
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• 2001

This study suggests integrated neural network modes for he stock price index forecasting using change-point detection. The basic concept of this proposed model is to obtain significant intervals occurred by change points, identify them as change-point groups, and reflect them in stock price index forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in stock price index dataset. The second phase is to forecast change-point group with various data mining classifiers. The final phase is to forecast the stock price index with backpropagation neural networks. The proposed model is applied to the stock price index forecasting. This study then examines the predictability of integrated neural network models and compares the performance of data mining classifiers.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.889-898
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• 2017

In this research multiple change-points estimation for South Korean electricity generation data is considered. We analyze the South Korean electricity data via deterministically trending dynamic time series model with multiple structural changes in trends in a Bayesian approach. The number of change-points and the timing are unknown. The goal is to find the best model with the appropriate number of change-points and the length of the segments. A genetic algorithm is implemented to solve this optimization problem with a variable dimension of parameters. We estimate the structural change-points for South Korean electricity generation data and Nile River flow data additionally.

• Journal of Climate Change Research
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• v.8 no.4
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• pp.287-295
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• 2017

This paper provides a mathematical approach for estimating flood risks due to the effects of climate change by developing a one dimensional (1D) hydraulic model for the mountainous river reaches located close to the Yeongwol thermal power plant. Input data for the model, including topographical data and river discharges measured every 10 minutes from July $1^{st}$ to September $30^{th}$, 2013, were imported to a 1D hydraulic model. Climate change scenarios were estimated by referencing the climate change adaptation strategies of the government and historical information about the extreme flood event in 2006. The down stream boundary was determined as the friction slope, which is 0.001. The roughness coefficient of the main channels was determined to be 0.036. The results show the effectiveness of the riverbed widening strategy through the six flooding scenarios to reduce flood depth and flow velocity that impact on the power plant. In addition, the impact of upper Namhan River flow is more significant than Dong River.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.539-556
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• 2019

This paper derive a method to solve change point regression problems via a process for obtaining consequential results using properties of a difference-based intercept estimator first introduced by Park and Kim (Communications in Statistics - Theory Methods, 2019) for outlier detection in multiple linear regression models. We describe the statistical properties of the difference-based regression model in a piecewise simple linear regression model and then propose an efficient algorithm for change point detection. We illustrate the merits of our proposed method in the light of comparison with several existing methods under simulation studies and real data analysis. This methodology is quite valuable, "no matter what regression lines" and "no matter what the number of change points".

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.5
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• pp.54-66
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• 2002

A grid-based hydrological model, CELTHYM, capable of estimating base flow and surface runoff using only readily available data, was used to assess hydrologic impacts caused by land use change on Little Eagle Creek (LEC) in Central Indiana. Using time periods when land use data are available, the model was calibrated with two years of observed stream flow data, 1983-1984, and verified by comparison of model predictions with observed stream flow data for 1972-1974 and 1990-1992. Stream flow data were separated into direct runoff and base flow using HYSEP (USGS) to estimate the impacts of urbanization on each hydrologic component. Analysis of the ratio between direct runoff and total runoff from simulation results, and the change in these ratios with land use change, shows that the ratio of direct runoff increases proportionally with increasing urban area. The ratio of direct runoff also varies with annual rainfall, with dry year ratios larger than those for wet years shows that urbanization might be more harmful during dry years than abundant rainfall years in terms of water yield and water quality management.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.37 no.1 s.109
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• pp.61-66
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• 2005

The grade change operations In paper mills exhibit inherent nonlinear dynamic characteristics. For this reason, the conventional model predictive control techniques based on linear process models are not adequate for the grade change operations. In this paper, a bilinear model for the nonlinear grade change processes was presented first and optimal input variables were calculated by using one-step-ahead predictive control method. Numerical simulations showed that the control performance lied within acceptable range and that the bilinear model predictive control scheme was highly promising control strategy for the grade change operations.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.37 no.1 s.109
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• pp.67-72
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• 2005

In the grade change operations inputs and outputs are highly correlated and application of conventional linear feedback control methods such as PID schemes might lead to poor control performance. In this study the neural networks model for the grade change operation is trained by using bilinear terms which can represent non-linear characteristics of grade change operations. The inverse model of the grade change operation is obtained from training and the optimal input variables are computed from the trained neural networks as well. The proposed bilinear inverse model predictive control scheme was found out to showlittle discrepancy between simulated outputs and setpoints.