• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.67-80
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• 2022

This study proposes a legal limit from the administrative and management standpoint of the city hall/county office/gu office, which is the cadastral authority, in relation to the discrepancy between the actual land use status and the cadastral study that has been continuously raised. And also, from the point of view of civil complaints such as landowners, this study tried to evaluate the practical problems of the current land category system from the point of view of civil complaints such as landowners and to derive a solution to these problems. Therefore, this study indicates how the category of land use is classified, and how land use is restricted by the laws of Registration & Management of public cadastre. Also, it shows the reasons why discrepancy between the land use fixed by the law and the current state of actual use of land occurs. Addtionally, This study suggests a plan to reorganize the Land Category system and it includes consolidation and subdivision of land. The study also describes a way to minimize the targets for conversion of land under control of Land Category System as well as to improve the law that protects the people's property rights.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.251-260
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• 2008

In internet being a repository of massive information, we easily may not find our desired information, this issue also exists in e-commerce which gets rapid growth. In most of e-commerce sites, the methods furnishing information have been made use of statistical analysis or simple process by category-oriented, but these can't represent diverse correlation among products information and also hardly reflect users' purchasing patterns precisely. In this thesis, we propose more efficient web mining ways for discovering interesting information using effective clustering in e-commerce, which get achieved more suitable relationship among products information using both sequential patterns and association rules in category-independent, and experiments show the efficiency of our proposed methods. And we propose search using effective clustering rapidly.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.345-352
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• 2006

The practical English-Korean machine translation system should be able to translate long sentences quickly and accurately. The intra-sentence segmentation method has been proposed and contributed to speeding up the syntactic analysis. This paper proposes the syntactic category prediction method using decision trees for getting accurate parsing results. In parsing with segmentation, the segment is separately parsed and combined to generate the sentence structure. The syntactic category prediction would facilitate to select more accurate analysis structures after the partial parsing. Thus, we could improve the parsing accuracy by the prediction. We construct features for predicting syntactic categories from the parsed corpus of Wall Street Journal and generate decision trees. In the experiments, we show the performance comparisons with the predictions by human-built rules, trigram probability and neural networks. Also, we present how much the category prediction would contribute to improving the translation quality.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.12
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• pp.1454-1465
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• 2011

Menswear body types are often labeled on garments (to indicate how the garments are designed to fit) with indicators of a size category such as regular, portly, and stout, athletic, or big and tall. A drop (relationships between the chest and waist girths) is related to the fit of a tailored suit. However, current standards are not designed for various drops or body types. There is not enough information of categorizing men's body shapes for the apparel sizing systems. In this article, a set of men's data from SizeUSA sizing survey was analyzed to investigate men's body shapes and drops. Factor analysis and a cluster analysis method were used to categorize men's body shapes. In the results, twenty-five variables were selected through the factor analysis and found four factors: girth factor, height factor, torso girth factor, and slope degree factor. According to the factor and cluster analysis, various body shapes were found: Slim Shape (SS - tall ectomorphy), Heavy Shape (HS - athletic, big & tall, endomorphy and mesomorphy), Slant Inverted Triangle Shape (SITS - regular, slight ectomorphy and slight mesomorphy weight range from normal to slightly overweight), Short Round Top Shape (SRTS - portly and stout, endomorphy). Body shapes were related to fitting categories. SS and HS were related to big & tall fitting category. SITS was related to regular. SRTS was related to portly and stout. Shape 1 (31%) and Shape 2 (26%) were related to current big & tall category. Shape 3 (34%) were related to regular. Shape 4 (9%) were in portly and stout category. ASTM D 6240 standard was the only available standard that presented a regular fitting category. Various drops were found within a same chest size group; however, this study revealed great variances of drops by body shape.

• Journal of Internet Computing and Services
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• v.7 no.2
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• pp.71-80
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• 2006

In web search engines, there are two main methods: directory searching and keyword searching. Keyword searching shows high recall rate but tends to come up with too many search results to find which users want to see the pages. Directory searching has also a difficulty to find the pages that users want in case of selecting improper category without knowing the exact category, that is, it shows high precision rates but low recall rates. We designed and implemented a new web search engine to resolve the problems of directory search method. It regards a category as a fuzzy set which contains keywords and calculate the degree of inclusion between categories. The merit of this method is to enhance the recall rate of directory searching by expanding subcategories on the basis of similarity.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.2
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• pp.89-96
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• 2015

Location-based social media data have high potential to be used in various area such as big data, location based services and so on. In this study, we applied a series of analysis methodology to figure out how the important keywords in location-based social media are spatially distributed by analyzing text information. For this purpose, we collected tweet data with geo-tag in Gangnam district and its environs in Seoul for a month of August 2013. From this tweet data, principle keywords are extracted. Among these, keywords of three categories such as food, entertainment and work and study are selected and classified by category. The spatial clustering is conducted to the tweet data which contains keywords in each category. Clusters of each category are compared with buildings and benchmark POIs in the same position. As a result of comparison, clusters of food category showed high consistency with commercial areas of large scale. Clusters of entertainment category corresponded with theaters and sports complex. Clusters of work and study showed high consistency with areas where private institutes and office buildings are concentrated.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2001.11a
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• pp.340-347
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• 2001

Security operating system applied to MAC(Mandatory Access Control) or to MLS(Multi Level Security) gives both subject and object both Security Level and value of Category, and it restrict access to object from subject. But it violates Security policy of system and could be a circulated course of illegal information. This is correctly IPC(Interprocess Communication)mechanism and Covert Channel. In this thesis, I tried to design and implementation as OS kernel in order not only to give confidence of information circulation in the Security system, but also to defend from Covert Channel by Storage and IPC mechanism used as a circulated course of illegal information. For removing a illegal information flow by IPC mechanism. I applied IPC mechanism to MLS Security policy, and I made Storage Covert Channel analyze system call Spec. and than distinguish Storage Covert Channel. By appling auditing and delaying, I dealt with making low bandwidth.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.28 no.2
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• pp.175-189
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• 2018

Objectives: In this study, we analyze statistics on industrial accidents caused by chemical skin contact and provide skin toxicity hazard information on the related domestic system and circulation volumes. Methods and Results: We analyzed occupational fatalities and skin diseases caused by chemical leaks and contact from 2007 to 2016(10 years) and surveyed data on occupational skin diseases using the 2014 work environment survey data. The NIOSH Skin Notation Profiles for 57 chemical substances, which are provided to prevent occupational skin diseases, were searched and hazard information on skin contact with chemical substances was classified. In order to identify skin toxicity information among domestically distributed and legally regulated substances and to investigate skin-toxic substances, MSDS basic data on 19,740 chemical substances provided on the homepage of Korea Occupational Safety & Health Agency were searched. Acute toxicity(dermal) category 1-4 substances totaled 1,020, and the number of chemical substances classified as category 1 and 2 substances were 135 and 137, respectively. In the chemical substances prescribed by the Ministry of Employment and Labor, 173 substances were classified into acute toxicity(dermal) categories 1-4, 58 of which correspond to category 1 or 2. Conclusions: Within the present range of industrial accidents, the proportion of skin diseases due to contact with chemicals is not high. However, there is always a risk of occupational skin diseases due to increasing chemicals and due to the use of new chemicals. It is hoped that this information will be used by workplace safety and health officials and health and safety experts to prevent acute toxity(dermal) due to chemical skin contact.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.193-215
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• 2016

In recent years, the proliferation of diverse social networking services has led users to use many mediums simultaneously depending on their individual purpose and taste. Besides, while collecting information about particular themes, they usually employ various mediums such as social networking services, Internet news, and blogs. However, in terms of management, each document circulated through diverse mediums is placed in different categories on the basis of each source's policy and standards, hindering any attempt to conduct research on a specific category across different kinds of sources. For example, documents containing content on "Application for a foreign travel" can be classified into "Information Technology," "Travel," or "Life and Culture" according to the peculiar standard of each source. Likewise, with different viewpoints of definition and levels of specification for each source, similar categories can be named and structured differently in accordance with each source. To overcome these limitations, this study proposes a plan for conducting category mapping between different sources with various mediums while maintaining the existing category system of the medium as it is. Specifically, by re-classifying individual documents from the viewpoint of diverse sources and storing the result of such a classification as extra attributes, this study proposes a logical layer by which users can search for a specific document from multiple heterogeneous sources with different category names as if they belong to the same source. Besides, by collecting 6,000 articles of news from two Internet news portals, experiments were conducted to compare accuracy among sources, supervised learning and semi-supervised learning, and homogeneous and heterogeneous learning data. It is particularly interesting that in some categories, classifying accuracy of semi-supervised learning using heterogeneous learning data proved to be higher than that of supervised learning and semi-supervised learning, which used homogeneous learning data. This study has the following significances. First, it proposes a logical plan for establishing a system to integrate and manage all the heterogeneous mediums in different classifying systems while maintaining the existing physical classifying system as it is. This study's results particularly exhibit very different classifying accuracies in accordance with the heterogeneity of learning data; this is expected to spur further studies for enhancing the performance of the proposed methodology through the analysis of characteristics by category. In addition, with an increasing demand for search, collection, and analysis of documents from diverse mediums, the scope of the Internet search is not restricted to one medium. However, since each medium has a different categorical structure and name, it is actually very difficult to search for a specific category insofar as encompassing heterogeneous mediums. The proposed methodology is also significant for presenting a plan that enquires into all the documents regarding the standards of the relevant sites' categorical classification when the users select the desired site, while maintaining the existing site's characteristics and structure as it is. This study's proposed methodology needs to be further complemented in the following aspects. First, though only an indirect comparison and evaluation was made on the performance of this proposed methodology, future studies would need to conduct more direct tests on its accuracy. That is, after re-classifying documents of the object source on the basis of the categorical system of the existing source, the extent to which the classification was accurate needs to be verified through evaluation by actual users. In addition, the accuracy in classification needs to be increased by making the methodology more sophisticated. Furthermore, an understanding is required that the characteristics of some categories that showed a rather higher classifying accuracy of heterogeneous semi-supervised learning than that of supervised learning might assist in obtaining heterogeneous documents from diverse mediums and seeking plans that enhance the accuracy of document classification through its usage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2683-2702
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• 2015

A Content-based Image Retrieval (CBIR) system employs visual features rather than manual annotation of images. The selection of optimal features used in classification of images plays a key role in its performance. Category proliferation problem has a huge impact on performance of systems using Fuzzy Artmap (FAM) classifier. The proposed CBIR system uses a modified version of FAM called Non-Proliferation Fuzzy Artmap (NPFAM). This is developed by introducing significant changes in the learning process and the modified algorithm is evaluated by extensive experiments. Results have proved that NPFAM classifier generates a more compact rule set and performs better than FAM classifier. Accordingly, the CBIR system with NPFAM classifier yields good retrieval.