• ETRI Journal
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• v.23 no.3
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• pp.111-120
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• 2001

For scalable multicast transport, one of the promising approaches is to employ a control tree known as acknowledgement (ACK) tree which can be used to convey information on reliability and session status from receivers to a root sender. The existing tree configuration has focused on a 'bottom-up' scheme in which ACK trees grow from leaf receivers toward a root sender. This paper proposes an alternative 'top-down' configuration where an ACK tree begins at the root sender and gradually expands by including non-tree nodes into the tree in a stepwise manner. The proposed scheme is simple and practical to implement along with multicast transport protocols. It is also employed as a tree configuration in the Enhanced Communications Transport Protocol, which has been standardized in the ITU-T and ISO/IEC JTC1. From experimental simulations, we see that the top-down scheme provides advantages over the existing bottom-up one in terms of the number of control messages required for tree configuration and the number of tree levels.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.844-847
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• 2010

FP-tree는 연관성 규칙 알고리즘 전체의 성능을 향상 시키며 DB 스캔을 단 2회로 줄였다. 하지만 빈발 항목과 모든 트랜잭션의 tree 정보를 메모리에 상주 시키면서 많은 메모리 공간을 요구했다. 그래서 나온 DRFP알고리즘은 메모리 요구 문제를 저장장치에 저장함으로 해결 하였으나 FP-tree와는 달리 시간 성능에서의 문제점을 가졌다. 그래서 우리는 이러한 문제점을 보완할 NRFP-tree(Nare disc-Resident Frequent pattern Tree)를 제안한다.

• The KIPS Transactions:PartD
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• v.18D no.3
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• pp.169-178
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• 2011

Recently, data sharing between enterprises or organizations is required matter for task cooperation. In this process, when the enterprise opens its database to the affiliates, it can be occurred to problem leaked sensitive information. To resolve this problem it is needed to hide sensitive information from the database. Previous research hiding sensitive information applied different heuristic algorithms to maintain quality of the database. But there have been few studies analyzing the effects on the items modified during the hiding process and trying to minimize the hided items. This paper suggests eFP-Tree(Extended Frequent Pattern Tree) based FP-Tree(Frequent Pattern Tree) to hide sensitive frequent itemsets. Node formation of eFP-Tree uses border to minimize impacts of non sensitive frequent itemsets in hiding process, by organizing all transaction, sensitive and border information differently to before. As a result to apply eFP-Tree to the example transaction database, the lost items were less than 10%, proving it is more effective than the existing algorithm and maintain the quality of database to the optimal.

• Spatial Information Research
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• v.19 no.4
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• pp.45-54
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• 2011

In this paper, we propose a hybrid index structure, called the SQMR-tree(Spatial Quad MR-tree) that can process spatial data efficiently by combining advantages of the MR-tree and the SQR-tree. The MR-tree is an extended R-tree using a mapping tree to access directly to leaf nodes of the R-tree and the SQR-tree is a combination of the SQ-tree(Spatial Quad-tree) which is an extended Quad-tree to process spatial objects with non-zero area and the R-tree which actually stores spatial objects and are associated with each leaf node of the SQ-tree. The SQMR-tree consists of the SQR-tree as the base structure and the mapping trees associated with each R-tree of the SQR-tree. Therefore, because spatial objects are distributedly inserted into several R-trees and only R-trees intersected with the query area are accessed to process spatial queries like the SQR-tree, the query processing cost of the SQMR-tree can be reduced. Moreover, the search performance of the SQMR-tree is improved by using the mapping trees to access directly to leaf nodes of the R-tree without tree traversal like the MR-tree. Finally, we proved superiority of the SQMR-tree through experiments.

• Journal of the Korea Society of Computer and Information
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• v.22 no.4
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• pp.33-39
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• 2017

In this paper, we expand the process of classification to an ensemble of fuzzy decision tree. For indoor space recognition, many research use Boosted Tree, consists of Adaboost and decision tree. The Boosted Tree extracts an optimal decision tree in stages. On each stage, Boosted Tree extracts the good decision tree by minimizing the weighted error of classification. This decision tree performs a hard decision. In most case, hard decision offer some error when they classify nearby a dividing point. Therefore, We suggest an ensemble of fuzzy decision tree, which offer some flexibility to the Boosted Tree algorithm as well as a high performance. In experimental results, we evaluate that the accuracy of suggested methods improved about 13% than the traditional one.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.749-751
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• 2003

R-Tree는 일반적으로 트리 노드의 크기를 디스크 페이지의 크기와 같게 함으로써 I/O 성능에 최적이 되도록 구현한다. 최근에는 CPU 캐시 성능을 최적화하는 R-Tree의 변형이 개발되었다. 이는 노드의 크기를 캐시 라인 크기의 수 배로 하고 MBR에 저장되는 키를 압축하여 노드 하나에 더 많은 엔트리를 저장함으로써 가능하였다. 그러나, 디스크 최적 R-Tree와 CPU 캐시 최적 R-Tree의 노드 크기 사이에는 수십-수백 바이트와 수-수십 킬로바이트라는 큰 차이가 있으므로, 디스크 최적 R-Tree는 캐시 성능이 나쁘고, CPU 캐시 최적 H-Tree는 나쁜 디스크 성능을 보이는 문제점을 가지고 있다. 이 논문에서는 CPU 캐시와 디스크에 모두 최적인 R-Tree. TR-Tree를 제안한다. 먼저, 디스크 페이지 안에 들어가는 페이지 내부 트리의 높이와 단말, 중간 노드의 크기를 결정하는 방법을 제시한다. 그리고, 이틀 이용하여 TR-Tree의 검색 연산에 필요한 캐시 미스 수를 최소화였고. TR-Tree의 검색 성능을 최적화하였다. 또한, 디스크 I/O 성능을 최적화하기 위해 메모리 노드들을 디스크 페이지에 잘 맞게 배치하였다. 여기에서 구현한 TR-Tree는 디스크 최적 R-Tree보다 삽입 연산에서 6에서 28배 정도 빨랐으며, 검색 연산에서는 1.28배에서 2배의 성능 향상을 보였다.

• 한국데이터정보과학회:학술대회논문집
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• 2005.04a
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• pp.164-173
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, fraud detection, data reduction and variable screening, category merging, etc. We analyze waste database united with local information using decision tree techniques for environmental information. We can use these decision tree outputs for environmental preservation and improvement.

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.19-29
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• 2004

Recently, studies have been performed to improve the cache performance of the R-Tree in main memory. A general mothed to improve the cache performance of the R-Tree is to reduce size of an entry so that a node can store more entries and fanout of it can increase. However, this method generally requites additional process to reduce information of entries and do not support incremental updates. In addition, the cache miss always occurs on moving between a parent node and a child node. To solve these problems efficiently, this paper proposes and evaluates the PR-Tree that is an extended R-Tree indexing method using prefetching in main memory. The PR-Tree can produce a wider node to optimize prefetching without additional modifications on the R-Tree. Moreover, the PR-Tree reduces cache miss rates that occur in moving between a parent node and a child node. In our simulation, the search performance, the update performance, and the node split performance of the PR-Tree improve up to 38%. 30%, and 67% respectively, compared with the original R-Tree.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.1
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• pp.86-93
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• 2017

In this paper, we propose a novel defect detection method using component tree representations of scanning electron microscopy (SEM) images. The component tree contains rich information about the topological structure of images such as the stiffness of intensity changes, area, and volume of the lobes. This information can be used effectively in detecting suspicious defect areas. A quasi-linear algorithm is available for constructing the component tree and computing these attributes. In this paper, we modify the original component tree algorithm to be suitable for our defect detection application. First, we exclude pixels that are near the ground level during the initial stage of component tree construction. Next, we detect significant lobes based on multiple attributes and edge information. Our experiments performed with actual SEM wafer images show promising results. For a $1000{\times}1000$ image, the proposed algorithm performed the whole process in 1.36 seconds.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.15-18
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• 2004

이동 객체를 효율적으로 처리하기 위해 여러 가지 색인 기법들이 제안되었다. 이들 중 3D R-tree와 같은 색인 기법은 시간과 공간을 동일한 차원으로 생각하고 있으나, 실제로 이 두 차원은 단위와 성격이 다르므로 분리해서 처리해야 한다. 특히 본 논문에서 고려하는 환경은 실내와 같은 한정된 공간이므로, 이런 환경에서는 시간과 공간이 같이 성장하는 것이 아니라 공간은 한정되어 있는 반면 시간 차원만이 성장한다. 따라서 R-tree와 1차원(시간차원)의 TB-tree 두 개의 색인을 유지하여, 공간정보와 고정된 객체는 R-tree에, 시간 정보와 이동 객체는 TB-tree에 저장하는 시공간 분리 트리(STS-tree : Separation of Time and Space tree)를 제안한다.