• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.4
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• pp.397-404
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• 2012

Jeju where concentrates on agriculture and tourism, conversion of outdoor culture into cultivation under structure happens actively for the purpose of increasing profit so continuous examination on house cultivation area is very important for this region. This paper is to suggest the effective image classification method using high resolution satellite image to detect the greenhouse. We carried out classification of greenhouse using the supervised classification and rule-based classification method about Formosat-2 images. Connecting result of two classification try to find accuracy improvement for greenhouse detection. Results about each classification method were calculated the accuracy by comparing with the result of visual detection. As a result, mahalanobis distance among the supervised methods was resulted in the highest detection. Also, it could be checked that detection accuracy was improved by tying with result of supervised method and result of rule-based classification. Therefore, it was expected that effective detection of greenhouse would be feasible if henceforward further study is performed in the process of connecting supervised classification and rule-based classification.

• Archives of design research
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• v.16 no.1
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• pp.5-14
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• 2003

As the digital technology has improved, the numbers of Hangul font users have increased and their individual needs and taste are diversified. Therefore new and various Hangul fonts out of traditional form are developed and used. But under the present font classification system, it is hard to compare and analyze these various fonts. And the present classification system is hard to be the font user's guide for proper use of various Hangul fonts. For the better use of Hangul font, to diversify the font classification system is needed. So we propose the development of these thru classification standards. First, structural classification based on the structural character of Hangul. Second, image classification based on the visual images of each font. And third, usage classification based on the fonts proper usage in various media. For the development of various typographically balanced fonts and for the suitable and effective use of the various font, we must try to build the font classification system based on the diversified classification standards and build Hangul font database based on this classification system. Through these studies, we can expect the development of good quality fonts and the better use of these fonts.

• The Korean Journal of Archival Studies
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• no.5
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• pp.209-242
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• 2002

Bibliographical Description for Revolutionary Historical Archives is created to describe records at the item level. It defines descriptive elements, punctuations, formats and methods. Descriptive elements are composed of 20 elements, each of which is either mandatory or optional. Mandatory elements are: repositories codes, documents codes, dates, creators, title, classification codes, and subject vocabularies. Abstracts were previously included in card cataloging and are removed in the computerized system. New elements, such as "uncontrolled vocabularies," "name of places," "personal names," "organizational structures" and "meetings," are added to allow keyword search. Considering that subject vocabulary searches are the most important in computerized systems, however, Guidelines for the Subject Indexing for Revolutionary Historical Archives as well as Subject Headings, as a result from the Guidelines, are created. The most extraordinary features in Chinese archival description are said to be the Guidelines for the Classification Indexing for Revolutionary Historical Archives and Materials as well as the Classification Scheme, both of which are created to allow subject search of records content. It is because Chinese practice of records management distinguishes the classification for arrangement from that for retrieval. Chinese archival description is, therefore, composed of bibliographic description rules, subject headings, and the classification scheme for retrieval.

• Journal of Korean Society of Rural Planning
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• v.11 no.2 s.27
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• pp.59-69
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• 2005

Because of increasing numbers of cars many highways are being constructed lively, and the noise of passing cars has influenced surrounding areas. In consideration of this, some alternatives and researches for soundproof facilities are proceeding, but aesthetic approach hasn't been considered. Therefore, this research is focused on soundproof effects for each types, effectual simulation methods, visual assessment and estimation between the landscape before simulation and the landscape after. Soundproof facilities are divided largely by the soundproof barrier, the soundproof mounding, the soundproof grove. The soundproof grove has three main function. First, leaves and branches absorbs sound vibrations. Second, leaves absorbs sound, and branches obstruct sounds. Third, by means of sounds of shaking leaves, forest can offset noises. This research was proceeded by means of classification of soundproof grove types and investigation of visual simulation methods. We made visual simulation for each types, and estimated the landscape for each types.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.767-776
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• 2008

Microarray technology is extensively being used in experimental molecular biology field. Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for the phenotype classification of many diseases. One of the two major problems in microarray data classification is that the number of genes exceeds the number of tissue samples. The other problem is that current methods generate classifiers that are accurate but difficult to interpret. Our paper addresses these two problems. We performed a direct integration of individual microarrays with same biological objectives by transforming an expression value into a rank value within a sample and generated rank-comparison decision rules with variable number of genes for cancer classification. Our classifier is an ensemble method which has k top scoring decision rules. Each rule contains a number of genes, a relationship among involved genes, and a class label. Current classifiers which are also ensemble methods consist of k top scoring decision rules. However these classifiers fix the number of genes in each rule as a pair or a triple. In this paper we generalized the number of genes involved in each rule. The number of genes in each rule is in the range of 2 to N respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Also our classifier is readily interpretable, accurate with small number of genes, and shed a possibility of the use in a clinical setting.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1657-1664
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• 2004

We propose a method that classifies images into two object types man-made and natural objects. A central object is extracted from each image by using central object extraction method[1] before classification. A central object in an images defined as a set of regions that lies around center of the image and has significant color distribution against its surrounding. We define three measures to classify the object images. The first measure is energy of edge direction histogram. The energy is calculated based on the direction of only non-circular edges. The second measure is an energy difference along directions in Gabor filter dictionary. Maximum and minimum energy along directions in Gabor filter dictionary are selected and the energy difference is computed as the ratio of the maximum to the minimum value. The last one is a shape of an object, which is also represented by Gabor filter dictionary. Gabor filter dictionary for the shape of an object differs from the one for the texture in an object in which the former is computed from a binarized object image. Each measure is combined by using majority rule tin which decisions are made by the majority. A test with 600 images shows a classification accuracy of 86%.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1541-1549
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• 1997

Many classification algorithms require that training examples contain only discrete values. In order to use these algorithms when some attributes have continuous numeric values, the numeric attributes must be converted into discrete ones. This paper describes a new way of discretizing numeric values using information theory. Our method is context-sensitive in the sense that it takes into account the value of the target attribute. The amount of information each interval gives to the target attribute is measured using Hellinger divergence, and the interval boundaries are decided so that each interval contains as equal amount of information as possible. In order to compare our discretization method with some current discretization methods, several popular classification data sets are selected for experiment. We use back propagation algorithm and ID3 as classification tools to compare the accuracy of our discretization method with that of other methods.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.404-406
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• 1996

The objective of this paper is to present a new feature-vector extraction method for the automatic detection and classification of power quality(PQ) disturbances, where FFT, DWT(Discrete Wavelet Transform), and data compression are utilized to extract an appropriate feature vector. In particular, the proposed classifier consists of three parts: i.e., (i) automatic detection of PQ disturbances, where the wavelet transform and signal power estimation method are utilized to detect each disturbance, (ii) feature vector extraction from the detected disturbance, and (iii) automatic classification, where Multi-Layer Perceptron(MLP) is used to classify each disturbance from the corresponding extracted feature vector. To demonstrate the performance and applicability of the proposed classification algorithm, some test results obtained by analyzing 7-class power quality disturbances generated by the EMTP are also provided.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.144-147
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• 1999

The objective of this study is to quantitatively evaluate the effects of various SAR speckle reduction methods for multisource land-cover classification by backpropagation neural network, especially over the coastal region. The land-cover classification using neural network has an advantage over conventional statistical approaches in that it is distribution-free and no prior knowledge of the statistical distributions of the classes is needed. The goal of multisource land-cover classification acquired by different sensors is to reduce the classification error, and consequently SAR can be utilized an complementary tool to optical sensors. SAR speckle is, however, an serious limiting factor when it is exploited for land-cover classification. In order to reduce this problem. we test various speckle methods including Frost, Median, Kuan and EPOS. Interpreting the weights about training pixel samples, the “Importance Value” of each SAR images that reduced speckle can be estimated based on its contribution to the classification. In this study, the “Importance Value” is used as a criterion of the effectiveness.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1584-1595
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• 2017

Each cultural community has developed a variety of fonts to express their own language and characters. Hangul has also diversified its font shapes through changing the composition ratio and look of the consonants and vowels. Rather, thanks to the variety of these fonts, a considerable amount of time and effort must be devoted to the selection of a specific font shape. This is related to the fact that the current Hangul service and classification system process the font only with its name or the name of the manufacturer. It means that there is no consensus about the font shape classification system for Hangul. In this study, we propose a shape property set that can be a basis for classifying Hangul fonts. The font shape property set was generated by performing statistical analysis with features which have been studied by the font design experts and was verified through questionnaire using representative fonts based on the classification scheme defined by the Hangul font design classification system standard. This study is meaningful in that it is a study on shape classification properties of K-means and PCA statistical techniques based on font data rather than design field study.