• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.523-528
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• 2004

This study proposed an anisotropic diffusion restoration fer image classification. The anisotropic diffusion restoration uses a probabilistic model based on Markov random field, which represents geographical connectedness existing in many remotely sensed images, and restores them through an iterative diffusion processing. In every iteration, the bonding-strength coefficient associated with the spatial connectedness is adaptively estimated as a function of brightness gradient. This study made experiments on the satellite images remotely sensed on the Korean peninsula. The experimental results show that the proposed approach is also very effective on image classification in remote sensing.

• The Journal of the Acoustical Society of Korea
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• v.26 no.2E
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• pp.56-62
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• 2007

This paper presents the automatic music emotion classification on acoustic data. A three-level structure is developed. The low-level extracts the timbre and rhythm features. The middle-level estimates the indication functions that represent the emotion probability of a single analysis unit. The high-level predicts the emotion result based on the indication function values. Experiments are carried out on 695 homogeneous music pieces labeled with four emotions, including pleasant, calm, sad, and excited. Three machine learning methods, GMM, MLP, and SVM, are compared on the high-level. The best result of 90.16% is obtained by MLP method.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.2059-2063
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• 2004

Most power plants have operated many independent computerize systems for maintenance. Independence of systems have caused complexity of business process and inconvenience of computer system management. Because the equipment and material master data is not standardize and structurize, it is difficult to manage equipment maintenance history and material delivery. Especially equipment classification criterion is important for standardization of every maintenance information. It is necessary to integrate function of independent systems for business process simplification and rapid work flow. this paper provides equipment classification criterion design and system integration method with the case of live system development.

• International Journal of Contents
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• v.1 no.2
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• pp.26-30
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• 2005

According to the fast growth of information on the Internet, it is becoming increasingly difficult to find and organize useful information. To reduce information overload, it needs to exploit automatic text classification for handling enormous documents. Support Vector Machine (SVM) is a model that is calculated as a weighted sum of kernel function outputs. This paper describes a document classifier for web documents in the fields of Information Technology and uses SVM to learn a model, which is constructed from the training sets and its representative terms. The basic idea is to exploit the representative terms meaning distribution in coherent thematic texts of each category by simple statistics methods. Vector-space model is applied to represent documents in the categories by using feature selection scheme based on TFiDF. We apply a category factor which represents effects in category of any term to the feature selection. Experiments show the results of categorization and the correlation of vector length.

• Journal of Biomedical Engineering Research
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• v.29 no.4
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• pp.329-336
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• 2008

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.1
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• pp.47-63
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• 2012

The purpose of this study is to improve the faceted navigation function of next-generation library catalogs(NGC) using the tables in a classification scheme. Because the tables provide forms or contents of materials systematically, faceted navigation based on the tables can be as effective as faceted navigation based on language, publishing date, or class-level subject facet. Therefore, class numbers based on the Korean Decimal Classification (KDC) were examined and the characteristics of schedules and tables were analyzed. As a result, suggestions to improve faceted navigation was provided. Moreover, the method using the tables does not need additional resources to derive facets because the facet analysis process is always carried out in the classification process.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.2
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• pp.47-56
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• 2014

Internet traffic has rapidly increased due to the supplying wireless devices and the appearance of various applications and services. By increasing internet traffic rapidly, the need of Internet traffic classification becomes important for the effective use of network resource. However, the traffic classification scheme is not much studied comparing to the study for classification method. This paper proposes novel classification scheme for multilateral and hierarchical traffic identification. The proposed scheme can support multilateral identification with 4 classification criteria such as service, application, protocol, and function. In addition, the proposed scheme can support hierarchical analysis based on roll-up and drill-down operation. We prove the applicability and advantages of the proposed scheme by applying it to real campus network traffic.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.417-422
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• 1998

This paper presents a method of object classification from dynamic image based on fuzzy inference algorithm which is suitable for low speed such as, conveyor, uninhabited transportation. At first, by using feature parameters of moving object, fuzzy if - then rule that can be able to adapt the wide variety of surroundings is developed. Secondly, implication function for fuzzy inference are compared with respect the proposed algorithm. Simulation results are presented to testify the performance and applicability of the proposed system.

• The Korean Journal of Archival Studies
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• no.76
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• pp.119-157
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• 2023

This study attempted to propose a faceted search function to supplement metadata for existing presidential gifts. To this end, based on 3,574 presidential gifts provided online by the Presidential Archives, identified the characteristics of records extracted from the gift name, gift giver, gift country, gift date, and receipt process, specifications, and characteristics of the presidential gift. Based on this, study designed a facet-based classification of presidential gifts with 5 basic facets and 51 sub-facets and structured facets define each facet element and assign an arrangement order and symbol. This classification system can be expected to be utilized as a basis for building faceted navigation by applying it to a search system. Through the study, it was confirmed that it was necessary to develop a new classification system for presidential gifts, and it was proposed to apply facet classification as an alternative classification system for this purpose.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.842-845
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• 2004

The aim of this study is to compare the classification results for choosing the fuzzy membership function within fuzzy rules. There are various methods of extracting rules from training data in the process of fuzzy rules generation. Pattern distribution characteristics are considered to produce fuzzy rules. The accuracy of classification results are depended on not only considering the characteristics of fuzzy subspaces but also choosing the fuzzy membership functions. This paper shows how to produce various type of fuzzy rules from the partitioning the pattern spaces and results of land cover classification in satellite remote sensing images by adopting various fuzzy membership functions. The experiments of this study is applied to Landsat TM image and the results of classification are compared by fuzzy membership functions.