• Journal of Korea Multimedia Society
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• v.7 no.1
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• pp.132-144
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• 2004

With the rapid development of internet technology, the number of internet users and the amount of multimedia information on the internet is ever increasing. Recently, the web sites, such as e-business sites and shopping mall sites, deal with lots of image information. As a result, it is required to support content- based image retrieval efficiently on such image data. This paper proposes an intelligent image retrieval system, which adopts XML, technology. To support object-based col)tent retrieval on product catalog images containing multiple objects, we describe a multi -level metadata structure which represents the local features, global features, and semantics of image data. To enable semantic-based and content-based retrieval on such image data, we design a XML-Schema for the proposed metadata and show how to represent such metadata using XML- documents. We also describe how to automatically transform the retrieval results into the forms suitable for the various user environments, such as web browser or mobile browser, using XSLT The proposed scheme can be easily implemented on any commercial platforms supporting XML technology. It can be utilized to enable efficient image metadata sharing between systems, and it will contribute in improving the retrieval correctness and the user's satisfaction on content-based e-catalog image retrieval.

• Journal of KIISE:Databases
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• v.32 no.5
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• pp.547-557
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• 2005

The purpose of selectivity estimation is to maintain the summary data in a very small memory space and to minimize the error of estimated value and query result. In case of estimating selectivity for large spatial data, the existing works need summary information which reflect spatial data distribution well to get the exact result for query. In order to get such summary information, they require a much memory space. Therefore In this paper, we propose a new technique cumulative density wavelet Histogram, called CDW Histogram, which gets a high accurate selectivity in small memory space. The proposed method is to utilize the sub-histograms created by CD histogram. The each sub-histograms are used to generate the wavelet summary information by applying the wavelet transform. This fact gives us good selectivity even if the memory sire is very small. The experimental results show that the proposed method simultaneously takes full advantage of their strong points - gets a good selectivity using the previous histogram in ($25\%\~50\%$) memory space and is superior to the existing selectivity estimation techniques. The proposed technique can be used to accurately quantify the selectivity of the spatial range query in databases which have very restrictive memory.

• Journal of KIISE:Databases
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• v.35 no.6
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• pp.481-494
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• 2008

In this paper we propose Fourier magnitudes based privacy preserving clustering on time-series data. The previous privacy-preserving method, called DFT coefficient method, has a critical problem in privacy-preservation itself since the original time-series data may be reconstructed from privacy-preserved data. In contrast, the proposed DFT magnitude method has an excellent characteristic that reconstructing the original data is almost impossible since it uses only DFT magnitudes except DFT phases. In this paper, we first explain why the reconstruction is easy in the DFT coefficient method, and why it is difficult in the DFT magnitude method. We then propose a notion of distance-order preservation which can be used both in estimating clustering accuracy and in selecting DFT magnitudes. Degree of distance-order preservation means how many time-series preserve their relative distance orders before and after privacy-preserving. Using this degree of distance-order preservation we present greedy strategies for selecting magnitudes in the DFT magnitude method. That is, those greedy strategies select DFT magnitudes to maximize the degree of distance-order preservation, and eventually we can achieve the relatively high clustering accuracy in the DFT magnitude method. Finally, we empirically show that the degree of distance-order preservation is an excellent measure that well reflects the clustering accuracy. In addition, experimental results show that our greedy strategies of the DFT magnitude method are comparable with the DFT coefficient method in the clustering accuracy. These results indicate that, compared with the DFT coefficient method, our DFT magnitude method provides the excellent degree of privacy-preservation as well as the comparable clustering accuracy.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.40-51
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• 2007

To improve academic achievement, the most students not only participate in regular classes but also take various extra programs such as private lessons, private institutes, and educational TV programs. In this paper, we propose a data mining approach to identify which studying methods or usual life patterns during a school vacation affect changes in the academic achievement. First, we derive various studying methods and life patterns that are thought to be affecting changes in the academic achievement during a school vacation. Second, we propose the method of transforming and analyzing data to apply them to decision trees and association rules, which are representative data mining techniques. Third, we construct decision trees and find association rules from the real survey data of middle school students. We have discovered four representative results from the decision trees. First, for students in the higher rank, there is a tendency that private institutes give a positive effect on the academic achievement. Second, for the most students, the Internet teaming sites nay give a negative effect on the achievement. Third, private lessons that have thought to be making a large impact to the achievement, however, do not make a positive effect on the achievement. Fourth, taking several studying methods in parallel nay give a negative effect on the achievement. In association rules, however, we cannot find any meaningful relationships between academic achievement and usual life patterns during a school vacation. We believe that our approach will be very helpful for teachers and parents to give a good direction both in preparing a studying plan and in selecting studying methods during a school vacation.

• Journal of KIISE:Software and Applications
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• v.36 no.2
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• pp.169-177
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• 2009

In this paper, we propose a new shot boundary detection method which is optimized for news video story parsing. This new news shot boundary detection method was designed to satisfy all the following requirements: 1) minimizing the incorrect data in data set for anchor shot detection by improving the recall ratio 2) detecting abrupt cuts and gradual transitions with one single algorithm so as to divide news video into shots with one scan of data set; 3) classifying shots into static or dynamic, therefore, reducing the search space for the subsequent stage of anchor shot detection. The proposed method, based on singular value decomposition with incremental clustering and mercer kernel, has additional desirable features. Applying singular value decomposition, the noise or trivial variations in the video sequence are removed. Therefore, the separability is improved. Mercer kernel improves the possibility of detection of shots which is not separable in input space by mapping data to high dimensional feature space. The experimental results illustrated the superiority of the proposed method with respect to recall criteria and search space reduction for anchor shot detection.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.503-517
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• 2007

In this paper we address the MBR-safe property of Piecewise Aggregation Approximation(PAA), and propose an of efficient subsequence matching method based on the MBR-safe PAA. A transformation is said to be MBR-safe if a low-dimensional MBR to which a high- dimensional MBR is transformed by the transformation contains every individual low-dimensional sequence to which a high-dimensional sequence is transformed. Using an MBR-safe transformation we can reduce the number of lower-dimensional transformations required in similar sequence matching, since it transforms a high-dimensional MBR itself to a low-dimensional MBR directly. Furthermore, PAA is known as an excellent lower-dimensional transformation single its computation is very simple, and its performance is superior to other transformations. Thus, to integrate these advantages of PAA and MBR-safeness, we first formally confirm the MBR-safe property of PAA, and then improve subsequence matching performance using the MBR-safe PAA. Contributions of the paper can be summarized as follows. First, we propose a PAA-based MBR-safe transformation, called mbrPAA, and formally prove the MBR-safeness of mbrPAA. Second, we propose an mbrPAA-based subsequence matching method, and formally prove its correctness of the proposed method. Third, we present the notion of entry reuse property, and by using the property, we propose an efficient method of constructing high-dimensional MBRs in subsequence matching. Fourth, we show the superiority of mbrPAA through extensive experiments. Experimental results show that, compared with the previous approach, our mbrPAA is 24.2 times faster in the low-dimensional MBR construction and improves subsequence matching performance by up to 65.9%.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.449-457
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• 2001

As XML has become an emerging standard for information exchange on the World Wide Web, it has gained attention in database communities to extract information from XML seen as a database model. XML queries are based on regular path queries, which find objects reachable by given regular expressions. To answer many kinds of user queries, it is necessary to evaluate queries that have multiple regular path expressions. However, previous work such as query rewriting and query optimization in the frame work of semistructured data has dealt with a single regular expression. For queries that have multiple regular expressions we suggest a two phase optimizing technique: 1. query rewriting using views by finding the mappings from the view's body to the query's body and 2. for rewritten queries, evaluating each query conjunct and combining them. We show that our rewriting algorithm is sound and our query evaluation technique is more efficient than the previous work on optimizing semistructured queries.

• The Journal of the Acoustical Society of Korea
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• v.16 no.4
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• pp.64-70
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• 1997

This paper is aimed to propose a new wavelet audio signal coding scheme which reduces the complexity of well-known MPEG(Moving Picture Expert Group)-Audio. The filters of MPEG0audio apply subband technique on the 16-bits PCM audio to aquire bitstream of subband sample using dynamic bit allocation. If we use the wavelet coefficients instead of subband samples and 6 bands which is less than 32 bands of MPEG-audio, the complexity can be reduced. A new audio signal compression algorithm in this paper is based on wavelet transform and the proposed algorithm is compared with MPEG-audio. At the bitrate of 256kbps, the proposed algorithm maintains the CD(Compact-disc) quality. We were able to reduce the about 40% of complexity at encoder and about 70% at decoder.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2081-2093
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• 1994

In this study, we design and implement 64 QAM demodulator which has 155 Mbps, first level of CCITT G707 SDH(Synchronous Digital Hierachy) for STM 1 signal transmission. Carrier recovery which effects the demodulator performance uses decision feedback carrier using 8 bits A/D converter. Also, PSF(Pulse Shaping Filter) is 7 order elliptic filter. Carrier recovery circuit is designed and implemented digital type which use high 3 bits of 8 bits conversion data as data and the order low bits as error data and hybrid type which use VCO and analog integrator. Therefore we obtain stable performance recovery.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.105-113
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• 1999

최근 데이터와 데이터베이스의 폭발적 증가에 따라 무한한 데이터 속에서 정보나 지식을 찾고자하는 지식채굴과정 (knowledge discovery process)에 대한 관심이 높아지고 있다. 특히 기업 내외부 데이터베이스 뿐만 아니라 데이터웨어하우스 (data warehouse)를 기반으로 하는 OLAP환경에서의 데이터와 인터넷을 통한 웹 (web)에서의 정보 등 정보원의 다양화와 첨단화에 따라 다양한 환경 하에서의 지식채굴과정이 요구되고 있다. 본 연구에서는 인터넷 상의 지식을 효과적으로 채굴하기 위한 지식채굴과정을 제안한다. 제안된 지식채굴과정은 명시지 (explicit knowledge)외에 암묵지 (tacit knowledge)를 지식채굴과정에 반영하기 위해 선행지식베이스 (prior knowledge base)와 선행지식관리시스템 (prior knowledge management system)을 이용한다. 선행지식관리시스템은 퍼지인식도(fuzzy cognitive map)를 이용하여 선행지식베이스를 구축하여 이를 통해 웹에서 찾고자 하는 유용한 정보를 정의하고 추출된 정보를 지식변환시스템 (knowledge transformation system)을 통해 통합적인 추론과정에 사용할 수 있는 형태로 변환한다. 제안된 연구모형의 유용성을 검증하기 위하여 재무자료에 선행지식을 제외한 자료와 선행지식을 포함한 자료를 사례기반추론 (case-based reasoning)을 이용하여 실험한 결과, 제안된 지식채굴과정이 유용한 것으로 나타났다.