• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.22-24
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• 2004

The land use has changed rapidly since 1960s in accordance with urbanization in Seoul Metropolitan Region. As a result, the urban microclimate has undergone changes as well. This study aims to recognize trend of the urban heat island change which is caused by land use change during urbanization in large city. Thermal data of Landsat TM images in 1987 and 1999 were for land surface temperature change detection in the study.

• Communications for Statistical Applications and Methods
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• 제6권2호
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• pp.467-478
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• 1999

This paper deals with the problem of change-point estimation where there is one level change in location with iid errors. A change-point estimator using rank average is proposed with the proof of its consistency. A comparison study of various change-point estimators is done by simulation on the mean the proportion and the variance when the errors are from the normal and the double exponential distributions.

• Journal of Advanced Marine Engineering and Technology
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• 제26권1호
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• pp.23-29
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• 2002

Solid-liquid phase change (i.e. melting or solidification) occurs in a number of situations of practical interest. Some common examples include the melting of edible oil, metallurgical process such as casting and welding, and materials science applications such as crystal growth. Therefore, due to the practical importance of the subject, there have been a large number of experimental and numerical studies of problems involving phase change during the past few decades. Also, this study presented the effective way to enhance phase change heat transfer.

• International Journal of Quality Innovation
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• 제2권1호
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• pp.30-49
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• 2001

This paper considers a recent environment in the manufacturing process in which data in large amounts can be obtained on-line in real-time. Under this environment an on-line real-time Statistical Process Control (SPC) scheme equipped with detection of a process change, change-point estimation, and recognition of the change pattern is proposed. The proposed SPC scheme is composed of a Cusum chart, filtering methods and Akaike Information Criterion (AIC). We examine the performance of this scheme by Monte Carlo simulation and show its usefulness.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.442-446
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• 2004

This paper propose scene change detection algorithm using coefficient of forward prediction macro-block, backward prediction macro-block, and intra-coded macro-block on getting motion estimation. Proposed method detect scene change with correlation according picture type forward two picture or forward and backward two picture on video sequences. Proposed algorithm is high accuracy and can detect all scene change on video, and detect to occur scene change on P, B, I-picture.

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2019년도 하계 학술발표초록집
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• pp.223-223
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• 2019

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2016년도 추계 학술발표논문집
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• pp.176-180
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• 2016

• 한국농림기상학회:학술대회논문집
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• 한국농림기상학회 2016년도 추계 학술발표논문집
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• pp.1-5
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• 2016

• 응용통계연구
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• 제24권6호
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• pp.1197-1211
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• 2011

The actual power performance of historical structural change tests are compared under various alternatives. The tests of interest are F, CUSUM, MOSUM, Moving Estimates and empirical distribution function tests with both recursive and ordinary least-squares residuals. Our comparison of the structural tests involves limiting distributions under the hypothesis, the ability to detect the alternative hypotheses under one or double structural change, and smooth change in parameters. Even though no version is uniformly superior to the other, the knowledge about the properties of those tests and connections between these tests can be used in practical structural change tests and in further research on other change tests.

• Industrial Engineering and Management Systems
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• 제8권3호
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• pp.139-147
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• 2009

Control charts are used to distinguish between chance and assignable causes in the variability of quality characteristics. When a control chart signals that an assignable cause is present, process engineers must initiate a search for the assignable cause of the process disturbance. Identifying the time of a process change could lead to simplifying the search for the assignable cause and less process down time, as well as help to reduce the probability of incorrectly identifying the assignable cause. The change point estimation by likelihood theory and the built-in change point estimation in a control chart have been discussed until now. In this article, we discuss two kinds of process change point estimation when the CUSUM ($\bar{x}$, s) control chart for monitoring process mean and variance simultaneously is operated. Throughout some numerical experiments about the performance of the change point estimation, the change point estimation techniques in the CUSUM ($\bar{x}$, s) control chart are considered.