• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.73-73
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• 2010

The purpose of this study is to analyze illustrations, contents, and experiment in 6 kinds of science textbook from the 9th grade covering the phase change of the Moon (on the phase change of the Moon in six 9th grade science textbook) and to suggest coherent and effective contents and frame of the science textbook. Hence, the researcher decided the study problem. The study problems are as follows; 'Are the illustrations in the science textbook presented to help understand the phase change of the Moon depending on the position of the observer?', 'Does the contents of the book clearly mention the phase change of the Moon?', 'Can students understand the phase change of the Moon through the experiments in the science textbook?', 'Do illustrations, contents, and experiment of the science textbook consistently explain phase change of the Moon?'. 10 persons (9graduate students including the researcher) took part in this study. All things unanimously agreed upon by all participants were reflected in the results. The results are as follows. First, the universe observer's view point is mixed with the earth observer's view in illustration of these science textbook regarding the phase change of the Moon. Moreover, illustrations of some textbooks are presented with such words as 'sunrise', 'midnight' and consequently contain too much contents. Second, the contents of the science textbook concerning the phase change of the Moon is not described clearly. In addition, they don't give clear and detailed explanations for the reason of the phase change of the Moon. Third, all of the textbooks, except one textbook, describe the experiment regarding the phase change of the Moon with the earth observer's view point but don't specifically mention that the view point is that of the earth observer's view point. Fourth, illustrations, contents, and experiments in the science textbook don't coherently explain the phase change of the Moon. In addition, it is confirmed through the process of the result analysis that the described contents in the science curriculum is not well constructed or logical.

• 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.

• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.11a
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• pp.417-426
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• 2000

This study suggests integrated neural network models for Interest rate forecasting using change-point detection, classifiers, and classification functions based on structural change. The proposed model is composed of three phases with tee-staged learning. The first phase is to detect successive and appropriate structural changes in interest rare dataset. The second phase is to forecast change-point group with classifiers (discriminant analysis, logistic regression, and backpropagation neural networks) and their. combined classification functions. The fecal phase is to forecast the interest rate with backpropagation neural networks. We propose some classification functions to overcome the problems of two-staged learning that cannot measure the performance of the first learning. Subsequently, we compare the structured models with a neural network model alone and, in addition, determine which of classifiers and classification functions can perform better. This article then examines the predictability of the proposed classification functions for interest rate forecasting using structural change.

• 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 Data and Information Science Society
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• v.14 no.3
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• pp.641-651
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• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported 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 prediction ability of hybrid models to reflect the structural change.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1989.06a
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• pp.111-113
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• 1989

This paper reports the optical characteristics of TeS$_{5}$ Ge$_{5}$ thin film. In phase diagram, TeS$_{5}$ Ge$_{5}$ has the eutetic point with the loweat melting point. Therfore, TeS$_{5}$ Ge$_{5}$ thin film will be melted by Diode Laser with low energy. TeS$_{5}$ Ge$_{5}$ thin films start to change the phase from amorphous to crystalline near 10$0^{\circ}C$, but perfectly change the phase at 28$0^{\circ}C$. As-deposit TeS$_{5}$ Ge$_{5}$ thin film start to change the phase to crystalline in enviroment og 66$^{\circ}C$ 80%RH.circ}C$ 80%RH.

• Journal of the Korean Society of Earth Science Education
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• v.8 no.1
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• pp.56-65
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• 2015

The purpose of this study is to investigate how elementary school student understands or students understand the illustrations on the phase change of the Moon in science textbook and to find out how textbook illustration helps students form the conception of the phase change of the Moon. To identify this purpose, we selected illustrations on the phase change of the Moon in the science textbook revised in 2007 and 2009 revised science textbook. For this study we selected and interviewed 20 students in the fifth grade. We integrated all data collected through interviews and created a transcription and a protocol and then, confirmed scientific conceptions related to the phase change of the Moon in students' illustration reading. The result are as followings: First, students read more scientific conceptions related to the phase change of the Moon in illustration of the 2009 revised science textbook which is presented with the universal observer's view point and the earth observer's view point. Second, students who find meaning in the various elements of the illustration and interpret with the integration of the various elements, get a lot of relevant information from illustration. All students have no differences recognizing the elements presented in illustration. But there are differences of contents of illustration reading depending on how students interpret the illustrations with integration of the various elements and if students cannot figure out the four scientific concepts needed to understand the phase change of the Moon, they ignore the information provided by illustration or analysis in their own way according to information provided by illustration. So misconception appears in this process.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.355-365
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• 2000

The interferometric correlation, or coherence, is calculated to measure the variance of the interferometric phase and amplitude within the neighbourhood of any location within the image at a result of SAR (Synthetic Aperture Radar) interferometric process which utilizes the phase information of the images. The coherence contains additional information that is useful for detecting point targets which change their location in an area of interest (AOI). In this research, a RGB colour composite image was generated with a intensity image (master image), a intensity change image as a difference between master image and slave image, and a coherence image generated as a part of SAR interferometric processing. We developed a technique performing detection of a point target movement using SAR interferometry and applied it to suitable tandem pair images of ERS-1 and ERS-2 as test data. The possibility of change detection of a point target in the AOI could be identified with the technique proposed in this research.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.6
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• pp.962-971
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• 2004

This paper has dealt with thermo-physical properties of microencapsulated phase change material slurry as a latent heat storage material having a low melting point. The measured results of the thermo-physical properties of the test microencapsulated phase change material slurry, those are, density, specific heat, thermal conductivity and viscosity, were discussed for the temperature region of solid and liquid phases of the dispersion material (paraffin). The measurements of these properties of microencapsulated phase change material slurry have been carried out by using a specific-gravity meter, a water calorimeter, a differential scanning calorimeter(DSC), a transient hot wire method and rotating type viscometer, respectively. It was clarified that the additional properties law could be applied to the estimation of the density and specific heat of microencapsulated phase change material slurry and also the Euckens equation could be applied to the estimation of the thermal conductivity of this slurry.