• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.10C
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• pp.981-990
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• 2006

In this paper, we introduce the concept of a subcarrier-based virtual multiple antennas (SV-MIMO) for OFDM cellular systems, where the multiple antenna techniques are performed on a set of subcarriers, not on the actual multiple antennas. The virtual multiple antenna system can support multiple users simultaneously as well as reduce inter-cell interference (ICI) form adjacent cells with a single antenna. Also, this technique is easily extended to multiple antenna environments. The virtual multiple antenna techniques can be divided into a virtual smart antenna technique and a virtual MIMO technique. Especially, this method effectively reduces ICI at cell boundary with frequency reuse factor equal to 1, and can support flexible resource allocation depending on the amount of interference. It is shown by simulation that the proposed method is superior to conventional method under the same condition of data transmission.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.193-206
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• 2008

Medical diagnosis can be considered a classification task which classifies disease types from patient's condition data represented by a set of pre-defined attributes. This study proposes a hybrid genetic algorithm based classification method to develop classifiers for multidimensional pattern classification problems related with medical decision making. The classification problem can be solved by identifying separation boundaries which distinguish the various classes in the data pattern. The proposed method fits a finite number of regional agents to the data pattern by combining genetic algorithms and local adaptive operations. The local adaptive operations of an agent include expansion, avoidance and relocation, one of which is performed according to the agent's fitness value. The classifier system has been tested with well-known medical data sets from the UCI machine learning database, showing superior performance to other methods such as the nearest neighbor, decision tree, and neural networks.

• Journal of The Korean Association of Information Education
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• v.24 no.4
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• pp.291-300
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• 2020

Although the need for international online courses and the number of online learners has been rapidly increasing, the online class evaluation has been mostly relying on the quantitative survey analysis. So a more objective evaluation method has to be developed to more accurately assess online course satisfaction. This study highlights the benefits of using big data analysis from the bulletin board messages of online learning system as a method to evaluate the online courses. In fact, automatic classification technology is recognized as an important technology among big data analysis techniques. Our team applied this technique to evaluate the online courses. From the delphi analysis results, suggested method was concluded that the evaluation items and classification results are suitable for online course evaluation and applicable in schools or institutions. This study has confirmed that the rapidly accumulating big data analysis technology can be successfully applied to the education sector with the least change. It also diagnosed a meaningful possibility to expand the big data analysis for further application.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.475-486
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• 2010

Contents Recommender System predicts user's preferences towards contents, and then recommends highly-predicted contents to user. Digital Identifier plays its part in identifying abstract works or digital contents in digital network environment. Digital Identifier could be effectively used in content-based filtering and collaborative filtering that are mainly used in Contents Recommender Systems. Therefore, this paper proposes an improvement of UCI metadata and resolution service for effective use of UCI in massive contents recommender systems. UCI metadata is expanded by adding elements such as abstract, keyword, genre, age, rate and review. Resolution service allows the operation systems to collect user preference for content by including input part of preference in a result page. This paper also designs and implements an improved UCI operation system and shows that the proposed improvement of UCI metadata and resolution service could be used for massive contents recommendation.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.383-390
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• 2015

It is appropriate to use the NETCONF to monitor and manage today's complex networks that are composed of variety links and heterogeneous equipment. Since the first standard of the NETCONF are launched, there have been several revisions, but disadvantages of each layer capabilities is still present and the most typical disadvantage is XML document processing efficiency of the Content layer. In this paper, we perform data modeling by constructing a sub-tree based on the dependencies between Content layer data, and suggest method of extract efficiently data from XML by applying the extended VTD-XML technique for the XPath query. We performs experiment to compare NETCONF in proposed method to NETCONF in previous studies and NETCONF standard. we validate superiority of improved NETCONF in the paper. As experimental results, we verify that improved NETCONF is better than the other two NETCONF each 4% and 10% in terms of query processing rate, and faster than each 3.9 seconds and 10.4 seconds in terms of query processing speed.

• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.717-726
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• 2003

In this paper, we analyze the associated rule based deductive algorithm which creates the rules automatically for intrusion detection from the vast packet data. Based on the result, we also suggest the deductive algorithm which creates the rules of intrusion pattern fast in order to apply the intrusion detection systems. The deductive algorithm proposed is designed suitable to the concept of clustering which classifies and deletes the large data. This algorithm has direct relation with the method of pattern generation and analyzing module of the intrusion detection system. This can also extend the appication range and increase the detection speed of exiting intrusion detection system as the rule database is constructed for the pattern management of the intrusion detection system. The proposed pattern generation technique of the deductive algorithm is used to the algorithm is used to the algorithm which can be changed by the supporting rate of the data created from the intrusion detection system. Fanally, we analyze the possibility of the speed improvement of the rule generation with the algorithm simulation.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.195-200
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• 2019

Nowadays as the use of mobile communication devices such as smart phones and tablets and the use of Computer is expanded, data is being collected exponentially on the Internet. In addition, due to the development of SNS, users can freely communicate with each other and share information in various fields, so various opinions are accumulated in the from of big data. Accordingly, big data analysis techniques are being used to find out the difference between the response of the general public and the response of the media. In this paper, we analyzed the public response in SNS about child allowance and childbirth grant and analyzed the response of the media. Therefore we gathered articles and comments of users which were posted on Twitter for a certain period of time and crawling the news articles and applied sentiment analysis. From these data, we compared the opinion of the public posted on SNS with the response of the media expressed in news articles. As a result, we found that there is a different response to some national policy between the public and the media.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.19-28
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• 2021

Nowadays, interest in health care is increasing due to Coronavirus (COVID-19), and a lot of people are doing home training as there are more difficulties in using fitness centers and public facilities that are used together. In this paper, we propose a personalized exercise recommendation algorithm using personalized propensity information to provide more accurate and meaningful exercise recommendation to home training users. Thus, we classify the data according to the criteria for obesity with a k-nearest neighbor algorithm using personal information that can represent individuals, such as eating habits information and physical conditions. Furthermore, we differentiate the exercise dataset by the level of exercise activities. Based on the neighborhood information of each dataset, we provide personalized exercise recommendations to users through a dimensionality reduction algorithm (SVD) among model-based collaborative filtering methods. Therefore, we can solve the problem of data sparsity and scalability of memory-based collaborative filtering recommendation techniques and we verify the accuracy and performance of the proposed algorithms.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.19-24
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• 2009

이 논문에서는 메타데이터 레지스트리 (MDR, Metadata Registry) 기반의 웹 온톨로지 생성모델을 제안한다. 메타데이터 레지스트리는 국제 표준(ISO/IEC 11179)으로서 데이터베이스간 상호운용성 향상을 위해 개발되었다. 그러나 데이터 표현과 상호운용성을 위한 컴퓨팅 환경의 변화는 메타데이터 레지스트리의 확장은 물론 메타데이터 레지스트리의 활용 방법의 변화를 요구한다. 이 논문에서의 웹 환경의 변화란 정적인 웹 환경에서 웹 2.0 혹은 시맨틱 웹 이라고 정의하는 차세대 웹 환경으로의 변화를 의미한다. 이러한 환경을 위해서 다양한 기술 개발과 적용 기법에 관한 연구가 필요하다. 특히 차세대 웹을 위해서는 지원에 대한 명확한 의미 정의 및 활용이 요구된다. 이는 웹 온톨로지 스키마를 구성하는 개념들에 대한 보다 일관성 있는 정의 및 사용이 필요하다. 이러한 문제가 해결되지 않을 경우, 또 다시 온톨로지를 구성하는 개념들 간 이질성 문제를 야기한다. 메타데이터 레지스트리는 다양한 표준화 된 개념들을 포함하며, 응용을 위한 데이터를 위한 의미 또한 이 개념들을 이용하여 정의한다. 따라서 이러한 표준 요소를 이용한 웹 온톨로지 스키마 정의 및 활용이 요구되며, 이 논문에서 이와 관련된 기본 개념, 요구 사항을 장의하고 전체적인 모델을 제안한다.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.217-223
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• 2022

Subspace clustering for online data streams requires a large amount of memory resources as all subsets of data dimensions must be examined. In order to track the continuous change of clusters for a data stream in a finite memory space, in this paper, we propose a grid-based subspace clustering algorithm that effectively uses memory resources. Given an n-dimensional data stream, the distribution information of data items in data space is monitored by a grid-cell list. When the frequency of data items in the grid-cell list of the first level is high and it becomes a unit grid-cell, the grid-cell list of the next level is created as a child node in order to find clusters of all possible subspaces from the grid-cell. In this way, a maximum n-level grid-cell subspace tree is constructed, and a k-dimensional subspace cluster can be found at the k^th level of the subspace grid-cell tree. Through experiments, it was confirmed that the proposed method uses computing resources more efficiently by expanding only the dense space while maintaining the same accuracy as the existing method.