• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.479-488
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• 2009

This paper shows a reinforced construction scheme of training data for improvement of text classification by automatic search of boundary category. The documents laid on boundary area are usually misclassified as they are including multiple topics and features. which is the main factor that we focus on. In this paper, we propose an automated exploring methodology of optimal boundary category based on previous research. We consider the boundary area among target categories to new category to be required training, which are then added to the target category sementically. In experiments, we applied our method to complex documents by intentionally making errors in training process. The experimental results show that our system has high accuracy and reliability in noisy environment.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.545-557
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• 2001

In this paper we propose a hierarchical classification algorithm based on rough membership function which can reason a new object approximately. We use the fuzzy reasoning method that substitutes fuzzy membership value for linguistic uncertainty and reason approximately based on the composition of membership values of conditional sttributes Here we use the rough membership function instead of the fuzzy membership function It can reduce the process that the fuzzy algorithm using fuzzy membership function produces fuzzy rules In addition, we transform the information system to the understandable minimal decision information system In order to do we, study the discretization of continuous valued attributes and propose the discretization algorithm based on the rough membership function and the entropy of the information theory The test shows a good partition that produce the smaller decision system We experimented the IRIS data etc. using our proposed algorithm The experimental results with IRIS data shows 96%~98% rate of classification.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.113-129
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• 2021

Recently, traffic congestion in the city is continuously increasing due to the expansion of the living area centered in the metropolitan area and the concentration of population in large cities. New road construction has become impossible due to the increase in land prices in downtown areas and limited sites, and the importance of efficient data-based road operation is increasingly emerging. For efficient road operation, it is essential to classify appropriate scenarios according to changes in traffic conditions and to operate optimal signals for each scenario. In this study, the Dynamic Time Warping model for cluster analysis of time series data was applied to traffic volume and speed data collected at continuous intersections for optimal scenario classification. We propose a methodology for composing an optimal signal operation scenario by analyzing the characteristics of the scenarios for each data used for classification.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1239-1249
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• 2018

Optical remote sensing sensors provide visually more familiar images than radar images. However, it is difficult to discriminate sea ice types in optical images using spectral information based machine learning algorithms. This study addresses two topics. First, we propose a semantic segmentation which is a part of the state-of-the-art deep learning algorithms to identify ice types by learning hierarchical and spatial features of sea ice. Second, we propose a new approach by combining of semi-supervised and active learning to obtain accurate and meaningful labels from unlabeled or unseen images to improve the performance of supervised classification for multiple images. Therefore, we successfully added new labels from unlabeled data to automatically update the semantic segmentation model. This should be noted that an operational system to generate ice type products from optical remote sensing data may be possible in the near future.

• Journal of Contemporary Eastern Asia
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• v.18 no.1
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• pp.7-29
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• 2019

This study describes an approach for comparative bibliometric analysis of scientific publications related to (i) individual or several departments comprising a university, and (ii) broader integrated subject areas using multiple disciplinary schemes. It uses a custom dataset of scientific publications (ca. 15,000 articles and reviews, published during 2009-2013, and recorded in the Web of Science Core Collections) with author affiliations to the research departments, dedicated to science, technology, engineering, mathematics, and medicine (STEMM), of a comprehensive university. The dataset was subjected, at first, to the department level and discipline level analyses using the newly available KAKEN-L3 classification (based on MEXT/JSPS Grants-in-Aid system), hierarchical clustering, correspondence analysis to decipher the major departmental and disciplinary clusters, and visualization of the department-discipline relationships using two-dimensional stacked bar diagrams. The next step involved the creation of subsets covering integrated subject areas and a comparative analysis of departmental contributions to a specific area (medical, health and life science) using several disciplinary schemes: Essential Science Indicators (ESI) 22 research fields, SCOPUS 27 subject areas, OECD Frascati 38 subordinate research fields, and KAKEN-L3 66 subject categories. To illustrate the effective use of the science mapping techniques, the same subset for medical, health and life science area was subjected to network analyses for co-occurrences of keywords, bibliographic coupling of the publication sources, and co-citation of sources in the reference lists. The science mapping approach demonstrates the ways to extract information on the prolific research themes, the most frequently used journals for publishing research findings, and the knowledge base underlying the research activities covered by the publications concerned.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.20 no.3
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• pp.149-161
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• 2009

The purpose of this study is to investigate and analyse the revised contents of the history class in the Korean Decimal Classification(KDC), 5th edition, and then identify problems and propose the revised contents for the KDC, 6the edition. Major analysed areas are divided into four. First, geographic area table is discussed. It includes extension of the geographic area table, emphasis of hierarchical structure in the geographical area, revision of North Korean geographical names, extension of subgeographical structure of major nations in the world, and revision of nations in the central and west Asia. Second, Korean time period is extended. Third, the notes of entries of the Chinese and Japanese history areas are shorten. Fourth, the geographical and personal names are changed their native pronunciation, specially Chinese and Japanese. For the revision of the KDC, 6th edition, four areas are discussed: first, Korean geographic areas would be categorized by broaden area, second, the areas are arranged from the capital of the nation to others, third, foreign geographical names would be used their native names, and last, time period would be categorized by years.

• Journal of the Korean Society of Clothing and Textiles
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• v.20 no.6
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• pp.1062-1070
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• 1996

Concept of the comfort and fitness has become a major concern in the basic function of the ready-made clothes. Until now, ready-made clothes were not made by on the basis of the bodytype, but by the body size only. This research was performed to classify and characterize the bodytypes of Korean adult males. Sample size was 1290 subjects and their age range was from 19 to 54 years old. 15 variables from the photographic data of 1112 subjects were applied to analyse the bodytype of th\ulcorner lower part of trunk. Data were analyzed by the multivariate method, especially factor and cluster analysis. The groups forming a cluster can be subdivided into 5 sets by crosstabulation extracted by the hierarchical cluster analysis. 5 bodytypes classified by the photographic sources could be combined with the anthropcmetric data and were demonstrated with 5 silhouette. Type 2 and 3 in the lower part of trunk were dominant and were composed of the majority of 56.8% of the subjects. Bodytypes of Korean males were influenced by the degree of posture erectness and of curvature of the front side of the body in waist and abdomen.

• Korean Journal of Adult Nursing
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• v.28 no.3
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• pp.302-313
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• 2016

Purpose: The objective of this study was to identify factors related to self-care adherence in symptomatic patients with heart failure (HF). Methods: Using a cross-sectional design, a convenience sample 209 outpatient clinic patients were recruited at two medical centers. Between October 2011 and August 2012, data were collected using the structured questionnaire. Factors related to self-care adherence were examined using hierarchical multiple regression. Results: Mean age of participants was 67.71 years and a half of them (53.6%) were female. They showed relatively low self-care adherence with mean scores of $61.88{\pm}12.92$. Lower self-care adherence was reported in asking for low sodium items, weighing oneself, checking for ankle edema, and exercising for 30 minutes. The overall model significantly explained 23.9% of variance in self-care adherence. Among the predictors, education, New York Heart Association functional classification, and social support were statistically significant in influencing self-care adherence. The variable of negative emotional status such as anxiety and depression were not found to be significant. Conclusion: These findings demonstrate that social support could help self-care adherence among symptomatic patients with HF. Thus, programs targeting self-care adherence in this population should consider the strategies improving social support.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.1
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• pp.73.1-73.1
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• 2015

Isolated galaxies in low-density regions are significant in the sense that they are least affected by the hierarchical pattern of galaxy growth and interactions with perturbers at least for the last few Gyr. To form a comprehensive picture of the star formation history of isolated galaxies, we construct a catalog of isolated galaxies and their comparison sample in relatively denser environments. The galaxies are drawn from SDSS DR7 in the redshift range of 0.025 < z < 0.044. We performed visual inspection and classified their morphology following the Hubble classification scheme. We have investigated the color-magnitude diagram and found elliptical and unbarred spiral galaxies in isolated systems are relatively fainter and bluer than those in denser regions. For the spectroscopic study, we make use of the OSSY catalog (Oh et al. 2011). Our analysis on the absorption-line properties based on the comparison with stellar population models suggests that isolated elliptical galaxies are likely to be younger and metal poorer, while isolated Sc-type galaxies seem to have older luminosity-weighted ages, than their high-density counterpart. In addition, according to the BPT diagnostics, early-type galaxies among isolated galaxies are rather evenly classified into star forming, composite, Seyfert and LINER, whereas their comparisons are mainly populated in the LINER region. On the other hand, late-type galaxies do not show any prominent difference. We discuss the evolutionary histories of isolated galaxies in the context of the standard ${\Lambda}CDM$ cosmology.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.204-207
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• 2002

The current paper proposes a new multispectral image data compression algorithm that can efficiently reduce spatial and spectral redundancies by applying classified prediction, a Karhunen-Loeve transform (KLT), and the three-dimensional set partitioning in hierarchical trees (3-D SPIHT) algorithm In the wavelet transform (WT) domain. The classification is performed in the WT domain to exploit the interband classified dependency, while the resulting class information is used for the interband prediction. The residual image data on the prediction errors between the original image data and the predicted image data is decorrelated by a KLT. Finally, the 3D-SPIHT algorithm is used to encode the transformed coefficients listed in a descending order spatially and spectrally as a result of the WT and KLT. Simulation results showed that the reconstructed images after using the proposed algorithm exhibited a better quality and higher compression ratio than those using conventional algorithms.