• Archives of design research
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• v.15 no.2
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• pp.223-232
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• 2002

As the Internet becomes widespread as a significant tool of obtaining information, there is a growing demand for a system to efficiently organize and manage information on the Internet. Accordingly, research on the directory classification structure that directly affects the efficiency of a users information search is actively investigated in every field. The study intends to suggest an efficient classification structure by comparing and analyzing the directory classification structure of current design portal sites with the theory of literature classification structure, in order to increase the efficiency of search according to the directory classification structure of design sector.

• Archives of design research
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• v.17 no.4
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• pp.79-88
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• 2004

This study is aimed to grasp the organization of Internet information resources and to infer the characteristics of resource search engines so that criteria may be established to classify and evaluate Internet information resources. In addition, the author has compared and analyzed interior design classification systems of directory sites of each subject that provide classification system based on the Internet, foreign sites to be used to search for information, and domestic information-specialized sites in order to set up models of interior design classification systems of directories of each Web subject. The systems have been analyzed against such four measures as comprehensiveness of the subject scope, logicality of classification systems, preciseness of subject terms, and effectiveness of searches. Information of interior designs is mixed with that of related fields, and so its information search and classification are not organized systematically. The author has analyzed such a problem so as to present models of search engine classification systems for interior design information classification after considering both academic and practical aspects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1743-1758
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• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.

• Journal of the Korean Data and Information Science Society
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• v.20 no.4
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• pp.749-755
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• 2009

Support vector classification (SVC) provides more complete description of the lin-ear and nonlinear relationships between input vectors and classifiers. In this paper. we propose the sparse kernel classifier to solve the optimization problem of classification with a modified hinge loss function and absolute loss function, which provides the efficient computation and the sparsity. We also introduce the generalized cross validation function to select the hyper-parameters which affects the classification performance of the proposed method. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.

• Journal of information and communication convergence engineering
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• v.3 no.4
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• pp.209-212
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• 2005

This study proposes hybrid case-based reasoning and genetic algorithms model for customer classification. In this study, vertical and horizontal dimensions of the research data are reduced through integrated feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed model may improve the classification accuracy and outperform various optimization models of typical CBR system.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2009.05b
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• pp.253-257
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• 2009

General technique and management technology of agriculture have development in every year to ensure the competitiveness of agriculture. Accordingly, Interested in using information systems management technology is improving. For information system, the first system of rural buildings category should be established. Classification system is set up through each specific code. and it takes advantage of the information system is to achieve the computerization of agricultural society. Therefore, in this study construction information classification system, quantity of output category, got to the standard classification system architecture, apply to agricultural buildings to review the situation and saw a problem. The result, it is the complexity and broad scope, and it is set to inappropriate setting of the Category item.

• Journal of Information Management
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• v.36 no.4
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• pp.91-115
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• 2005

While the amount of information exchanged through internet has dramatically increased recently, certain inefficiencies still exist with regard to the storage, distribution, and retrieval of information. As a means of improving efficiency in accessing information, many search portals provide directory services to present organized guidance to information, based on the classification schemes. This study examines the classification activities practiced by the major search portals in Korea and makes some suggestions to improve the quality of directory services.

• Journal of Korean Society for Geospatial Information Science
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• v.15 no.3
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• pp.33-39
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• 2007

Hyperspectral remote sensing data contain plenty of information about objects, which makes object classification more precise. In this paper, we proposed a new spectral similarity measure, called Spectral Mutual Information (SMI) for hyperspectral image classification problem. It is derived from the concept of mutual information arising in information theory and can be used to measure the statistical dependency between spectra. SMI views each pixel spectrum as a random variable and classifies image by measuring the similarity between two spectra form analogy mutual information. The proposed SMI was tested to evaluate its effectiveness. The evaluation was done by comparing the results of preexisting classification method (SAM, SSV). The evaluation results showed the proposed approach has a good potential in the classification of hyperspectral images.

• Korean Institute of Interior Design Journal
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• v.24 no.1
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• pp.207-215
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• 2015

It seems obvious that the spatial information of existing architectural heritage will be re-structured utilizing BIM technology. In the future to be able to implement such task, a new system of classification of spatial information, which fit to the structural nature of architectural heritage is necessary. This paper intend to suggest the conceptual model that can be the base of establishing new classification system for architectural heritage. For this study we reviewed researches related to classification system of architectural heritage (CS-AH) and BIM based architectural heritage (BIM-AH), first. As a result, we found that CS-AH is focused on building elevation and type, and BIM-AH is biased on the Library and Parametric Modeling. Second, we figured out a relationship between the CS-AH and BIM-AH. From this analysis, we found that BIM-AH is biased on Library and Parametric since the building elevation and type was focused on CS-AH. This review suggests a potential of the 3D CS-AH to expand the range of research for BIM-AH. At last, we suggest the three concept of classification are: 1)horizontality-accumulation relationship, 2)structure-infill relationship, 3)segment-member relationship. These three concept, together as one system of classification, could provide useful framework of new classification system of spatial information for architectural heritage.

• Journal of Korean Library and Information Science Society
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• v.49 no.4
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• pp.265-294
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• 2018

Nowadays, due to an explosive increase in information, information filtering is very important to provide proper information for users. Users hardly obtain scholarly information from a huge amount of information in NDSL of KISTI, except for simple search. In this paper, we propose the service, PIN to solve this problem. Pin provides the word cloud including analyzed users' and others' interesting, co-occurence, and searched keywords, rather than the existing word cloud simply consisting of all keywords and so offers user-customized papers, reports, patents, and trends. In addition, PIN gives the paper classification in NDSL according to keyword matching based classification with the overlapping classification enabled-academic classification system for better search and access to solve this problem. In this paper, Keywords are extracted according to the classification from papers published in Korean journals in 2016 to design classification model and we verify this model.