• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.841-848
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• 2010

Conventional filters using email header and body information equally judge whether an incoming email is spam or not. However this is unrealistic in everyday life because each person has different criteria to judge what is spam or not. To resolve this problem, we consider user preference information as well as email category information derived from the email content. In this paper, we have developed a personalized anti-spam system using ontologies constructed from rules derived in a data mining process. The reason why traditional content-based filters are not applicable to the proposed experimental situation is described. In also, several experiments constructing classifiers to decide email category and comparing classification rule learners are performed. Especially, an ID3 decision tree algorithm improved the overall accuracy around 17% compared to a conventional SVM text miner on the decision of email category. Some discussions about the axioms generated from the experimental dataset are given too.

• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.121-124
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• 2000

In this paper, we propose a pen gesture recognition system for user interface in multimedia terminal which requires fast processing time and high recognition rate. It is realtime and interaction system between graphic and text module. Text editing in recognition system is performed by pen gesture in graphic module or direct editing in text module, and has all 14 editing functions. The pen gesture recognition is performed by searching classification features that extracted from input strokes at pen gesture model. The pen gesture model has been constructed by classification features, ie, cross number, direction change, direction code number, position relation, distance ratio information about defined 15 types. The proposed recognition system has obtained 98% correct recognition rate and 30msec average processing time in a recognition experiment.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1521-1527
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• 2014

This paper presents to design User eXperience(UX) of anthropomorphic animal characters search system (hereinafter, AACSS) for efficient user search. To this end, meta data were utilized herein to elevate the search efficiency of multimedia information and text information. Anthropomorphic animal characters require the human elements and the animal elements, thus this paper extracted the key elements of meta data as below; phenotypic element in animal system classification (Morphologic property elements, Ecological property elements, Behavioral property elements), emotion classification, which is the trait of personification and the Step of Anthropomorphic Animal Characters.

• Korean Journal of Construction Engineering and Management
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• v.9 no.1
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• pp.126-133
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• 2008

Current changing to the customer-oriented market naturally causes suppliers to meet an age of competition on the quality. In order to plan housing meeting this quality competitiveness era, I set up the type classification system of quality differentiation for the unit of apartment housing by executing differentiation cases of unit quality and type analysis of the object. The system is consist of 3 classification systems by quality element, user convenience element and product element as follows: First element is to classify quality element on the basis of plane and interior elements, architectural elements and second one is user convenience element relating facility to classify environment-oriented, safety, energy saving and convenience. The other one is the product element to classify furniture, installing product and convenient product. I believe that this classification system will be useful to determine any classification elements of product for product positioning and product planning in the stage of marketing planning of apartment housing in the future.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.601-607
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• 2012

Objective: The aim of this study is to classify between intentional and natural blinks in vision based eye tracking system. Through implementing the classification method, we expect that the great eye tracking method will be designed which will perform well both navigation and selection interactions. Background: Currently, eye tracking is widely used in order to increase immersion and interest of user by supporting natural user interface. Even though conventional eye tracking system is well focused on navigation interaction by tracking pupil movement, there is no breakthrough selection interaction method. Method: To determine classification threshold between intentional and natural blinks, we performed experiment by capturing eye images including intentional and natural blinks from 12 subjects. By analyzing successive eye images, two features such as eye closed duration and pupil size variation after eye open were collected. Then, the classification threshold was determined by performing SVM(Support Vector Machine) training. Results: Experimental results showed that the average detection accuracy of intentional blinks was 97.4% in wearable eye tracking system environments. Also, the detecting accuracy in non-wearable camera environment was 92.9% on the basis of the above used SVM classifier. Conclusion: By combining two features using SVM, we could implement the accurate selection interaction method in vision based eye tracking system. Application: The results of this research might help to improve efficiency and usability of vision based eye tracking method by supporting reliable selection interaction scheme.

• Korean Journal of Remote Sensing
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• v.17 no.3
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• pp.275-284
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• 2001

This study was carried out to evaluate high resolution satellite imagery of IKONOS for classifying the land cover, especially forest type. The IKONOS imagery of 11km$\times$11km size was taken on April 24, 2000 in Bong-pyoung Myun Pyungchang-Gun, Kangwon Province. Land cover classes were water, coniferous evergreen, Larix leptolepis, broad-leaved tree, bare land, farm land, grassland, sandy soil and asphalted area. Supervised classification method with algorithm of maximum likelihood was applied for classification. The terrestrial survey was also carried out to collect the reference data in this area. The accuracy of the classification was analyzed with the items of overall accuracy, producer's accuracy, user's accuracy and k for test area through the error matrix. In the accuracy analysis of the test area, overall accuracy was 94.3%, producer's accuracy was 77.0-99.9%, user's accuracy was 71.9-100% and k and 0.93. Classes of bare land, sandy soil and farm land were less clear than other classes, whereas classification result of IKONOS in forest area showed higher performance than that of other resolution(5-30m) satellite data.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.105-124
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• 2019

This study was to suggest the improvement and the application of KDC 6 for classifying children's books by the literature review and survey. First, it was suggested to shorten the classification numbers by the divisions and subdivisions and to expand the classification numbers by sub-subdivisions according to the library-specific classification policy, using the subject statistics of the children's books held by 20 public libraries affiliated in Seoul Metropolitan Office of Education and representative C libraries. Second, the knowledge picture books and the fairy tales were suggested to be classified according to its subject, and the fairy tales in each country were suggested to be classified by adding sub-subdivisions and genre subdivisions. Third, it was suggested to shelve by collection and location codes that were distinguished by the ages and the reading level for user, to prepare a standard guideline for shelving, and to implement the regular user education about the classification system. This study could contribute to the development of the KDC abridged version for children's books in the future.

• Convergence Security Journal
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• v.18 no.4
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• pp.19-25
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• 2018

In this paper, we propose an user-centered integrated lost-goods management system through a ranking system based on weight and a hierarchical image classification system based on Deep Learning. The proposed system consists of a hierarchical image classification system that automatically classifies images through deep learning, and a ranking system modules that listing the registered lost property information on the system in order of weight for the convenience of the query process.In the process of registration, various information such as category classification, brand, and related tags are automatically recognized by only one photograph, thereby minimizing the hassle of users in the registration process. And through the ranking systems, it has increased the efficiency of searching for lost items by exposing users frequently visited lost items on top. As a result of the experiment, the proposed system allows users to use the system easily and conveniently.

• Journal of Internet Computing and Services
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• v.20 no.2
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• pp.93-100
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• 2019

Recently, in various business fields, companies are concentrating on providing chatbot services to various environments by adding artificial intelligence to existing messenger platforms. Organizations in the field of job placement also require chatbot services to improve the quality of employment counseling services and to solve the problem of agent management. A text-based general chatbot classifies input user sentences into learned sentences and provides appropriate answers to users. Recently, user sentences inputted to chatbots are inputted as short texts due to the activation of social network services. Therefore, performance improvement of short text classification can contribute to improvement of chatbot service performance. In this paper, we propose T-EBOW (Translation-Extended Bag Of Words), which is a method to add translation information as well as concept information of existing researches in order to strengthen the short text classification for employment chatbot. The performance evaluation results of the T-EBOW applied to the machine learning classification model are superior to those of the conventional method.

• Journal of Korean Library and Information Science Society
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• v.15
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• pp.113-151
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• 1988

The enumerative classification schemes do not represent the tiny mass of knowledge embodied in a article in a periodical or in a chapter or a paragraph of a book. But today's information centers regard a tiny spot of knowledge embodied in a article as a class. we call this micro-thought. But the enumerative classification are manual systems, it cannot be a n.0, pplied to the automation of classification. The purpose of this study is to build a general principle for the automatic book-classification which can be put to use in library operation, and to present a methodology of the automatic classification for the library. The methodology used is essentially theoretical, Published works by and about Ranganathan, especially 6th edition of the CC were studied, analyzed. The principle of automatic book classification derived from the analysis of colon classification and facet combinations. The results of this study can be summarized as follows ; (1) This study confined the fields of library science and agriculture. (2) This study represent a general principles for the automatic book classification of library science and agriculture. (3) Program flowcharts are designed as a basis of system analysis and program procedure in library science and agriculture. (4) The principles of the automatic classification in library, science is different from that of agriculture. (5) Automatic book classification can be performed by the principle of faceted classification, by inputting the title and subject code into a computer. In addition, the expected value from the automatic book-classification is as follows (1) The prompt and accurate of classification is possible. (2) Though a book is classified in any library, it can have same classification number. (3) The user can retrieve the classification code of a book for which he or she wants to search through dialogue with the computer. (4) Since the concept coordination method is employed, a tiny mass of knowledge embodied in a article in a periodical or in a chapter or a paragraph of a book can be represented as a class. (5) By performing automatic book-classification, the automation of library operation can be completed.