• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1409-1411
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• 2003

The application of simulated KOMPSAT-2 imagery to monitor graveyards is to be developed. Positions calculated from image were compared with those obtained from Geographic Positioning System. With 24 checkpoints, the position of graveyards showed within 5-meter range. Unsupervised classification, supervised classification, and objected-orientation classification algorithms were used to extract the graveyard. Unsupervised classification with masking processes based on National topographic data gives the best result. The graveyards were categorized with four types in field studies while the two types of graveyards were shown in descriptive statistics. Cluster Analysis and discriminant analysis showed the consistency with two types of tombs. It was hard to get a specific spectral signature of graveyards, as they are covered with grasses at different levels and shaded from the surrounding trees. The slopes and aspects of location of graveyards did not make any difference in the spectral signatures. This study gives the basic spectral characteristics for further development of objected-oriented classification algorithms and plausibility of KOMPSAT-2 images for management of mountainous areas in the aspect of position accuracy and classification accuracy.

• 한국도서관정보학회지
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• 제36권2호
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• pp.165-178
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• 2005

본 연구는 대학도서관의 단행본에 대한 분류검색 환경을 조사${\cdot}$분석함으로써, 그 실태를 파악하기 위한 것이다. 조사내용은 분류검색 제공여부, 접근방법, 검색수준 등에 중점을 두었다. 데이터 수집은 계통추출법에 의해 표집된 100개 도서관 중, 조사기간 동안 URL 연결이 가능한 97개 도서관을 대상으로 이루어졌다. 그 결과, 97개 도서관 중, $92.8\%$가 분류검색을 제공하고 있었으나, 이중 $52.2\%$가 분류기호만을 통해, $47.8\%$가 분류기호와 분류 디렉터리를 통해 접근이 가능한 것으로 나타났다. 따라서, 분류검색을 활성화하기 위해서는 분류기호만을 통해 접근이 가능한 도서관에 대한 검색환경 개선이 시급한 것으로 파악되었다.

• 대한공간정보학회지
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• 제19권2호
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• pp.9-17
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• 2011

국내 여러 기관에서 토지피복분류체계, 토지이용현황분류체계 등 국토의 정확한 현황 파악을 위해 다양한 지형분류체계를 활용 중에 있다. 그러나 이러한 분류체계로 국토변화를 탐지하기에는 적용성이 떨어지며, 변화지역을 추출하기에도 적합하지 않다는 문제점을 가지고 있다. 본 연구에서는 국토에 대한 자연적, 인위적 변화요소들을 모두 효과적으로 나타낼 수 있는 표준 지형분류체계를 제안하고자 한다. 이를 위해 국내외 유사 지형분류체계에 대한 비교 분석을 수행하고, 이를 바탕으로 표준 지형분류 항목을 제안하였다. 자동 지형분류 적용 가능성을 평가하기 위하여 감독분류 기반의 자동 지형분류와 선행지식 기반의 자동 지형분류를 수행하여 정확도를 평가하였다.

• Journal of information and communication convergence engineering
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• 제14권1호
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• pp.45-50
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• 2016

Gait-based classification has gained much interest as a possible authentication method because it incorporate an intrinsic personal signature that is difficult to mimic. The study investigates machine learning techniques to mitigate the natural variations in gait among different subjects. We incorporated several machine learning algorithms into this study using the data mining package called Waikato Environment for Knowledge Analysis (WEKA). WEKA's convenient interface enabled us to apply various sets of machine learning algorithms to understand whether each algorithm can capture certain distinctive gait features. First, we defined 24 gait features by analyzing three-axis acceleration data, and then selectively used them for distinguishing subjects 10 years of age or younger from those aged 20 to 40. We also applied a machine learning voting scheme to improve the accuracy of the classification. The classification accuracy of the proposed system was about 81% on average.

• 한국비블리아학회지
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• 제11권2호
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• pp.5-23
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• 2000

본 연구는 지식과 분류 사이의 연관성을 고찰하였다. 분류표는 분류 대상의 지식을 표현하는 구조로 그 안의 포함되는 내용과 다양한 관계를 표현해 주는데 대표적인 4가지 분류학적 접근방식인 계층 구조, 나무 구조, 패러다임. 패싯 분석을 새로운 지식의 창조와 발견의 측면에서 장단점을 기술하였다. 이를 바탕으로 지식과 분류 과정이 서로 영향을 주고받는 방식을 확인하고, 미래의 보다나은 새로운 분류 방식의 필요성과 그 가능성을 제시하였다.

• 한국멀티미디어학회논문지
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• 제17권2호
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• pp.113-123
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• 2014

Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in recognition of alertness level. In this paper, EEG signals have been analyzed using wavelet transform as well as discrete wavelet transform and classification using statistical classifiers such as euclidean and mahalanobis distance classifiers and a promising method SVM (Support Vector Machine). As a result of simulation, the average values of accuracies for the Linear Discriminant Analysis (LDA)-Quadratic, k-Nearest Neighbors (k-NN)-Euclidean, and Linear SVM were 48%, 34.2%, and 86%, respectively. The experimental results show that SVM classification method offer the better performance for reliable classification of the EEG signal in comparison with the other classification methods.

• 한국경영과학회지
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• 제33권2호
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• pp.1-16
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• 2008

The purpose of this paper is to propose a new binary classification method for predicting corporate failure based on genetic algorithm, and to validate its prediction power through empirical analysis. Establishing virtual companies representing bankrupt companies and non-bankrupt ones respectively, the proposed method measures the similarity between the virtual companies and the subject for prediction, and classifies the subject into either bankrupt or non-bankrupt one. The values of the classification variables of the virtual companies and the weights of the variables are determined by the proper model to maximize the hit ratio of training data set using genetic algorithm. In order to test the validity of the proposed method, we compare its prediction accuracy with ones of other existing methods such as multi-discriminant analysis, logistic regression, decision tree, and artificial neural network, and it is shown that the binary classification method we propose in this paper can serve as a premising alternative to the existing methods for bankruptcy prediction.

• 대한음성학회지:말소리
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• 제66호
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• pp.61-72
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• 2008

This paper proposes a method to improve pathological and normal voice classification performance by combining multiple features such as auditory-based and higher-order features. Their performances are measured by Gaussian mixture models (GMMs) and linear discriminant analysis (LDA). The combination of multiple features proposed by the frame-based LDA method is shown to be an effective method for pathological and normal voice classification, with a 87.0% classification rate. This is a noticeable improvement of 17.72% compared to the MFCC-based GMM algorithm in terms of error reduction.

• 한국과학교육학회지
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• 제11권2호
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• pp.133-142
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• 1991

The purpose of this study is to develop a data base system for the information on research and development of science education. As the first step of this study and development, a classification system for the research and development materials was devised after discussing the process of science education and the research and development of science education. The classification system has nine main categories : 1. area, 2. subject, 3. behavior, 4. skill, 5. support, 6. type, 7. materials, 8. language, and 9. the others, each of which has one or two levels of subcategory. This classification system was revised and supplemented through the theoretical analysis by speci.diSts and the practical classification of master's theses and doctoral dissertations from the Department of Science Education, Seoul National University. But it still needs more revision and enlargement through the continuous application and analysis.

• 대한산업공학회지
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• 제32권2호
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• pp.133-140
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• 2006

Large signal type data sets are difficult to classify, especially if the data sets are non-stationary. In this paper, large signal type and non-stationary data sets are wavelet transformed so that distinct features of the data are extracted in wavelet domain rather than time domain. For the classification of the data, a few wavelet coefficients representing class properties are employed for statistical classification methods : Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Network etc. The application of our wavelet-based feature selection method to a mass spectrometry data set for ovarian cancer diagnosis resulted in 100% classification accuracy.