• 정보기술응용연구
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• 제1권
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.8-12
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• 1999

Since the advent of high resolution satellite image, possibilities of applying various human interpretation mechanism to these images have increased. Also many studies about these possibilities in many fields such as computer vision, pattern recognition, artificial intellegence and remote sensing have been done. In this field of these studies, texture is defined as a kind of quantity related to spatial distribution of brightness and tone and also plays an important role for interpretation of images. Especially, methods of obtaining texture by statistical model have been studied intensively. Among these methods, texture measurement method based on cooccurrence matrix is highly estimated because it is easy to calculate texture features compared with other methods. In addition, these results in high classification accuracy when this is applied to satellite images and aerial photos. But in the existing studies using cooccurrence matrix, features have been chosen arbitrarily without considering feature variation. And not enough studies have been implemented for appropriate resolution selection in which cooccurrence matrix can extract texture. Therefore, this study reviews the concept of cooccurrence matrix as a texture measurement method, evaluates usefulness of several features obtained from cooccurrence matrix, and proposes appropriate resolution by investigating variance trend of several features.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.653-655
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• 2005

More than 70 percent of terrestrial territory of Korea is mountainous areas where degradation becomes serious year by year due to illegal tombs, expanding golf courses and stone mine development. We elaborate the potential usage of high resolution image for the monitoring of the phenomena. We made the classification of tombs and the statistical radiometric characteristics of graves were identified from this project. The graves could be classified to 4 groups from the field survey. As compared with grouping data after clustering and discriminant analysis, the two results coincided with each other. Object-oriented classification algorithm for feature extraction was theoretically researched in this project. And we did a pilot project, which was performed with mixed methods. That is, the conventional methods such as unsupervised and supervised classification were mixed up with the new method for feature extraction, object-oriented classification method. This methodology showed about $60\%$ classification accuracy for extracting tombs from satellite imagery. The extraction of tombs' geographical coordinates and graves themselves from satellite image was performed in this project. The stone mines and golf courses are extracted by NDVI and GVI. The accuracy of classification was around 89 percent. The location accuracy showed extraction of tombs from one-meter resolution image is cheaper and quicker way than GPS method. Finally we interviewed local government officers and made analyses on the current situation of mountainous area management and potential usage of KOMPSAT-II images. Based on the requirement analysis, we developed software, which is to management and monitoring system for mountainous area for local government.

• 정보처리학회논문지B
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• 제11B권1호
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• pp.53-62
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• 2004

인간과 컴퓨터간의 상호작용에 있어서 감정처리는 매우 중요한 부결이다. 특히, 비디오 정보처리에 있어서 사용자의 감정을 처리할 수 있다면 비디오 검색이나 요약본 추출 등 다양한 응용분야에 활용이 가능하다. 비디오 데이터로부터 이러한 감정 처리를 하기 위해서는 감정에 관련된 특징들을 표현하고, 검출하는 것이 필요하다. 쉽게 추출이 가능한 색상이나 모션 등의 저급 특징들로부터 고급 개념인 감정을 검출하는 것은 매우 어려운 일이지만, 감정에 관련된 여러 장면으로부터 LDA(Linear Discriminant Analysis)와 같은 통계적인 분석을 통해 감정에 관련된 특징들을 검출하는 것은 가능하다. 본 논문에서는 색상, 모션 및 셧 길이 정보로부터 감정과의 관련된 특징을 표현하고 검출하는 방법을 제안한다. 제안된 특징을 사용하여 감정 검출에 관련된 실험을 한 결과 바람직한 결과를 얻었다.

• 정보학연구
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• 제7권4호
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• pp.61-69
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• 2004

본 논문에서는 6.6kV급 고압전동기 모델 코일을 4가지의 권선의 인공적인 결함을 갖도록 제작하였다. 운전중 모의를 위해 모델 코일에 80pF의 에폭시-마이카 커플러를 설치하였으며, TGA를 사용하여 NQN, 부분방전 크기 및 부분방전 패턴 등을 측정하였다. 웨이블렛 변환기법을 이용한 고압전동기의 고정자권선의 결함에서 발생된 부분방전신호의 파형을 통하여 여러 가지의 통계적인 파라메터를 통하여 특징을 추출하였다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2002년도 하계학술대회 논문집 Vol.3 No.2
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• pp.894-899
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• 2002

In this papers, we proposed the new method in order to diagnosis partial discharge type of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion, skewness, kurtosis) about high frequency current signal per 3-electrode type (needle-plane electrode, IEC electrode and Void electrode.). Also. these coefficients are used to identify Signal of internal partial discharge in transformer. As a result. from compare of high frequency current signal amplitude and average value. we are obtained results of IEC electrode> Void electrode> Needle-Plane electrode. otherwise. In case of skewness and kurtosis, we are obtained results of Void electrode> IEC electrode > Needle-Plane electrode. As Improved method in order to diagnosis partial discharge type of transformers, we use neural network.

• 한국정보처리학회논문지
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• 제7권11호
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• pp.3592-3602
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• 2000

영상 처리는 크게 영상에 내재된 특성값을 얻어내는 영상분석과, 동일한 성질의 영상을 분류하는 영상분류의 두단계로 이루어진다. 본 논문에서는 텍스쳐에 내재된 일반적인 속성인 거침과 부드러움의 특성 추출을 통해서 영상에 포함된 다양한 텍스쳐를 자동적으로 인식하고 분류하는 방법을 제안한다. 특성추출은 텍스쳐 영상이 지닌 그레이 레벨의 공간적인 의존성을 이용한 통계적 분석에 기반한 것으로 모멘트와 동차성의 조합을 통해서 일반적인 텍스쳐의 속성을 검출하기 때문에 텍스쳐의 구조형태에 크게 영향을 받지 않는 이점을 가지고 있다. 거친 텍스쳐일수록 강하게 반응하는 모멘트와 부드러운 텍스쳐일수록 강하게 반응하는 동차성의 차를 이용하기 때문에 보다 뚜렷한 텍스쳐 분할이 가능하다. 제안한 시스템의 성능 평가를 위해서 다양한 텍스쳐 영상에 제안한 방법을 적용하고, 성공적인 결과를 보인다.

• 천문학회보
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• 제35권2호
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• pp.34.1-34.1
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• 2010

Studies suggest that activities in luminous AGNs, residing mostly in early-type galaxies, are triggered by merging. However, the observational evidence for the connection between mergers and AGN activity is not clear yet because tracer of the past merging activities such as tidal tail, shell are often too faint. To see if we can reveal the merging features with a small telescope, we observed an AGN, MARK 478, at z=0.077 with long exposure time (7550 seconds) in V filter at Maidanak observatory. Our 2-D fitting analysis shows that the host galaxy of MARK 478 has the bulge to the total luminosity ratio of 0.3. And the residual image, after subtracting point spread function (PSF), bulge and disk components, shows that the host galaxy has an arm-like feature that could be a spiral arm or a feature from minor merging. We also show that the structural parameters obtained from our 2-D fitting match well with those derived from HST image. The promising result suggests that studies of low redshift AGN host galaxies are possible with data from a small telescope. In order to allow a statistical analysis, we hope to expand our sample size in future.

• 응용통계연구
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• 제22권3호
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• pp.479-488
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• 2009

비모수적 판별분류방법으로 널리 사용되는 ${\kappa}$-Nearest Neighbors Classification(KNNC) 방법은 자료의 국소적 특징을 고려하지 않고 전체 자료에 대해 고정된 이웃의 개수 ${\kappa}$를 사용하여 개체를 분류하는 방법이다. 본 연구에서는 KNNC의 대안으로 자료의 국소적 특징을 고려하는 Adaptive Nearest Neighbors Classificaion(ANNC) 방법을 제안하였다. 제안된 방법의 특징을 규명하기 위하여 실제 자료에 대한 분석을 통하여 제안된 방법의 응용 가능성을 제시하였으며, 나아가 모의실험을 통하여 기존의 방법과의 효율성을 비교하였다.

• International Journal of Safety
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• 제5권1호
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• pp.37-41
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• 2006

The major objective of the statistical analysis about industrial accidents is to determine the safety factors so that it is possible to prevent or decrease the number of future accidents by educating those who work in a given industrial field in safety management. So far, however, there exists no quantitative method for evaluating danger related to industrial accidents. Therefore, as a method for developing quantitative evaluation technique, this study presents feature analysis of industrial accidents in manufacturing field using QUEST algorithm. In order to analyze features of industrial accidents, a retrospective analysis was performed on 10,536 subjects (10,313 injured people, 223 deaths). The sample for this work was chosen from data related to manufacturing businesses during a three-year period ($2002{\sim}2004$) in Korea. This study used AnswerTree of SPSS and the analysis results enabled us to determine the most important variables that can affect injured people such as the occurrence type, the company size, and the time of occurrence. Also, it was found that the classification system adopted in the present study using QUEST algorithm is quite reliable.