• Communications for Statistical Applications and Methods
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• v.28 no.5
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• pp.511-520
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• 2021

Over the last decade, Bitcoin has attracted a great deal of public interest and Bitcoin market has grown rapidly. One of the main characteristics of the market is that it often undergoes some events or incidents that cause outlying observations. To obtain reliable results in the statistical analysis of Bitcoin data, these outlying observations need to be carefully treated. In this study, we are interested in change point analysis for Bitcoin return series having such outlying observations. Since these outlying observations can affect change point analysis undesirably, we use a robust test for parameter change to locate change points. We report some significant change points that are not detected by the existing tests and demonstrate that the model allowing for parameter changes is better fitted to the data. Finally, we show that the model with parameter change can improve the forecasting performance of Value-at-Risk.

• Journal of Korean Society of Rural Planning
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• v.16 no.2
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• pp.47-55
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• 2010

Land cover and land use change data are important in many studies including climate change and hydrological studies. Although the various theories and models have been developed, it is difficult to identify the driving factors of the land use change because land use change is related to policy options and natural and socio-economic conditions. This study is to attempt to simulate the land cover change using the CLUE model based on a statistical analysis of land-use change. CLUE model has dynamic modeling tools from the competition among land use change in between driving force and land use, so that this model depends on statistical relations between land use change and driving factors. In this study, Yongin, Icheon and Anseong were selected for the study areas, and binary logistic regression and factor analysis were performed verifying with ROC curve. Land cover probability map was also prepared to compare with the land cover data and higher probability areas are well matched with the present land cover demonstrating CLUE model applicability.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate 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 network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.6
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• pp.731-738
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• 2010

Cavitation is described by formation and collapse of the bubbles in a liquid when the ambient pressure decreases. Formed bubbles grow and collapse by change of pressure, and when they collapse, shockwave by high pressure is generated. In general, bubble behavior can be described by Rayleigh-Plesset equation under adiabatic or isothermal condition and hence, phase shift by the pressure change in a bubble cannot be considered in the equation. In our study, a numerical model is developed from the mathematical model considering the phase shift from the previous study. In the developed numerical model, size of single spherical bubble is calculated by the change of mass calculated from the change of the ambient pressure in a liquid. The developed numerical model is verified by a case of liquid flow in a narrow channel.

• Journal of Korean Society for Quality Management
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• v.13 no.1
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• pp.20-25
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• 1985

There are several approaches for dealing with the structural change in regression model, but by introducing a concept of Spline, the structural change can be expressed more clearly. This makes it possible not only to know the location where the structural change happens and the total number, but also to derive posterior distribution from anterior-posterior distribution when the probability of the judgement anterior for entire combination was given to each model, by which, the model that has the highest posterior probability is the method which realizes the structural change. The purpose of this study is to find a peculiarity of the posterior probability on the occasion of anterior information acquired and of not acquired with Baysian approach.

• Journal of the Korean Society of Safety
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• v.18 no.3
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• pp.126-130
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• 2003

The term stress is currently used to cover a wide variety of phenomena, ranging from physical to social and cultural factors. The term has defined psychological stress as an imbalance between perceived or subjective demand and perceived response capability. The behavior science model has provided the accident proneness through the life change unit factors considering human behavior, life style, ideas, culture, and psychological state. Therefore, this paper presents the Korean life change unit model through statistical testing with the proposed life change unit factors on the unmarried workers living in the middle area. The proposed model can be simply used in real fields in order to minimize the industrial accidents.

• East Asian mathematical journal
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• v.35 no.3
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• pp.305-318
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• 2019

Over the past decades, the Lee-Carter model [1] has attracted much attention from various demography-related fields in order to project the future mortality rates. In the Lee-Carter model, the speed of mortality improvement is stochastically modeled by the so-called mortality index and is used to forecast the future mortality rates based on the time series analysis. However, the modeling is applied to long time series and thus an important structural change might exist, leading to potentially large long-term forecasting errors. Therefore, in this paper, we are interested in detecting the structural change of the Lee-Carter model and investigating the actuarial implications. For the purpose, we employ the tests proposed by Coelho and Nunes [2] and analyze the mortality data for six countries including Korea since 1970. Also, we calculate life expectancies and whole life insurance premiums by taking into account the structural change found in the Korean male mortality rates. Our empirical result shows that more caution needs to be paid to the Lee-Carter modeling and its actuarial applications.

• Smart Structures and Systems
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• v.23 no.2
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• pp.107-122
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• 2019

As one of the most important parameters in structural health monitoring, structural frequency has many advantages, such as convenient to be measured, high precision, and insensitive to noise. In addition, frequency-change-ratio based method had been validated to have the ability to identify the damage occurrence and location. However, building a precise enough finite elemental model (FEM) for the test structure is still a huge challenge for this frequency-change-ratio based damage detection technique. In order to overcome this disadvantage and extend the application for frequencies in structural health monitoring area, a novel method was developed in this paper by combining the cross-model cross-mode (CMCM) model updating algorithm with the frequency-change-ratio based method. At first, assuming the physical parameters, including the element mass and stiffness, of the test structure had been known with a certain value, then an initial to-be-updated model with these assumed parameters was constructed according to the typical mass and stiffness distribution characteristic of shear buildings. After that, this to-be-updated model was updated using CMCM algorithm by combining with the measured frequencies of the actual structure when no damage was introduced. Thus, this updated model was regarded as a representation of the FEM model of actual structure, because their modal information were almost the same. Finally, based on this updated model, the frequency-change-ratio based method can be further proceed to realize the damage detection and localization. In order to verify the effectiveness of the developed method, a four-level shear building was numerically simulated and two actual shear structures, including a three-level shear model and an eight-story frame, were experimentally test in laboratory, and all the test results demonstrate that the developed method can identify the structural damage occurrence and location effectively, even only very limited modal frequencies of the test structure were provided.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2004.04a
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• pp.97-106
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• 2004

The paper making process itself is a typical nonlinear process with complicated dynamics. In the application of advanced control-methods especially for the grade change operations the nonlinear process is linearized to give suitable linear models to be used in the control strategies. However, the use of the linear model is limited within short range containing steady-state operating conditions for grade change operation. In this paper a bilinear model for the nonlinear grade change processes is presented. We can see that the dynamic behavior for grade change operations can be effective analyzed by using multivariable bilinear model.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.153-166
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• 2003

This study presents the time-divided forecasting model to integrate evolutionary optimization algorithm and change point detection based on artificial neural networks (ANN) for the prediction of (Korea) stock price index. The genetic algorithm(GA) is introduced as an evolutionary optimization method in this study. The basic concept of the proposed model is to obtain intervals divided by change points, to identify them as optimal or near-optimal change point groups, and to use them in the forecasting of the stock price index. The proposed model consists of three phases. The first phase detects successive change points. The second phase detects the change-point groups with the GA. Finally, the third phase forecasts the output with ANN using the GA. This study examines the predictability of the proposed model for the prediction of stock price index.