• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.796-798
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• 2003

RNA 분자의 이차구조는 그 분자의 생물학적 기능을 이해하는데 필요한 요소이다. 본 논문은 RNA 이차 구조의 loop과 helix를 그룹화 하여 하나의 객체 단위로 설계한 뒤 필요한 loop과 helix들의 연결구조를 부분 시각화 하는 알고리즘을 소개한다. 기존의 존재하는 시각화 알고리즘들은 정보손실에 해당하는 겹침에 의한 피해를 줄이기 위해 다양한 변형 방법을 이용하였으나 이번 알고리즘은 사용자에 의해 필요로 하는 정보만을 선택해 볼 수 있기에 정보 손실의 문제를 피할 수 있다.

• Spatial Information Research
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• v.21 no.3
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• pp.11-19
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• 2013

Compared with traditional video monitoring systems that provide a video-recording function as a main service, an intelligent video monitoring system is capable of extracting/tracking objects and detecting events such as car accidents, traffic congestion, pedestrian detection, and so on. Thus, the object tracking is an essential function for various intelligent video monitoring and surveillance systems. In this paper, we propose a background and local histogram-based object tracking approach for intelligent video monitoring systems. For robust object tracking in a live situation, the result of optical flow and local histogram verification are combined with the result of background subtraction. In the proposed approach, local histogram verification allows the system to track target objects more reliably when the local histogram of LK position is not similar to the previous histogram. Experimental results are provided to show the proposed tracking algorithm is robust in object occlusion and scale change situation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.3
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• pp.55-63
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• 2008

This paper presents a panorama background generation and object tracking technique using a Pan-Tilt-Zoom camera. The proposed method estimates local motion vectors rapidly using phase correlation matching at the prespecified multiple local regions, and it makes minimized estimation error by vector quantization. We obtain the required image patches, by estimating the overlapped region using local motion vectors, we can then project the images to cylinder and realign the images to make the panoramic image. The object tracking is performed by extracting object's motion and by separating foreground from input image using background subtraction. The proposed PTZ-based object tracking method can efficiently generated a stable panorama background, which covers up to 360 degree FOV The proposed algorithm is designed for real-time implementation and it can be applied to many commercial applications such as object shape detection and face recognition in various surveillance video systems.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.37-39
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• 2001

현재 이동 클라이언트의 발전과 무선 이동 데이터 통신의 발달에 의해 보다 정학한 공간 데이터 수정을 현장에서 수행 할 수 있게 되었다. 이러한 환경을 고려하여, 이 논문에서는 무선 이동 클라언트에서의 공간데이터 변경을 위해서 2-tier 트랜잭션 모델(2)을 이용한다. 이동 트랜잭션은 완료 즉시 서버에 재 접속할 필요가 없기 때문에 이동 트랜잭션의 완료 시점과 재 접속 후 베이스 트랜잭션으로 재 수행하는 시점 사이에 간격이 존재하게 된다. 그리고 고안 데이터 변경 트랜잭션은 교환가능한 트랜잭션이 아니며, 완전히 직렬가능(fully serializerability)해야 한다. 이러한 이유로 갱신 손실 문제(lost update problem)가 발생한다. 이 논문에서는 갱신 손실 문제를 해결하기 위하여 영역 잠금의 타입스탬프 값과 영역 잠금의 영역의 겹침을 이용하여 갱신 손실 가능한 공간객체 집합을 검출해내는 방법을 제시한다. 검출된 갱신 손실 가능한 공간 객체 집합의 완료 시점을 뒤로 연기(postpone)하는 프로토콜도 함께 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.127-129
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• 2003

최근 GPS기능을 탑재한 휴대폰ㆍPDA등의 모바일 장치를 사용하여 위치 기반 서비스 (LBS : Location Based Service)를 이용하는 사용자가 급증하고 있다. 이에 대용량의 공간 데이터베이스에 대해 효율적 검색을 가능하게 하기 위한 색인이 필요하다. 공간 데이터베이스를 위한 다차원 공간 색인 기법으로는 R-Tree가 널리 사용되고 있다. 기존의 R-Tree를 이용한 검색은 질의 영역과 관계없는 공간 데이터까지 검색하는 고비용의 연산이 요구되며, 사용자의 질의 위치 단위(Granularity)를 고려하지 않아 사용자의 빠른 검색 응답시간 및 질의 영역에 대한 정확한 공간 객체 검색에 대해 충족하지 못한다. 이에 본 논문에서는 임의의 셀 안에 존재하는 공간 데이터가 자신이 속한 노드의 전체 MBR(Union MBR)영역과 셀 영역에 따라 셀 레벨 값을 구성하는 CLR-Tree(Cell Leveling R-Tree)를 제안한다. CLR-Tree를 사용할 경우 사용자의 질의 영역 셀 레벨 값과 데이터베이스에 저장된 공간 데이터의 셀 레벨 값을 비교한 뒤 겹침 연산 대상이 되는 공간 객체 수를 줄임으로써 검색 시간을 향상시킬 수 있다.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.90-94
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• 2010

In this paper, we propose a vehicle tracking method that can be applied in the intelligent traffic information system. The proposed method mainly consists of two steps: overlap removal and background updating. In order to remove overlap, we detect the overlap based on the location of the vehicle from successive images. Background updating is to calculate a background using statistical analysis of successive images. We collected a set of test images from the traffic monitoring system and experimented. The experimental results show more than 96% of tracking accuracy.

• Spatial Information Research
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• v.19 no.2
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• pp.47-56
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• 2011

Typical tree-based spatial index structures are divided into a data-partitioning index structure such as R-Tree and a space-partitioning index structure such as KD-Tree. In recent years, researches on hybrid index structures combining advantages of these index structures have been performed extensively. However, because the split boundary extension of the node to which a new spatial object is inserted may extend split boundaries of other neighbor nodes in existing researches, overlaps between nodes are increased and the query processing cost is raised. In this paper, we propose a hybrid index structure, called SQR-Tree that can support efficient processing of spatial queries to solve these problems. SQR-Tree is a combination of SQ-Tree(Spatial Quad- Tree) which is an extended Quad-Tree to process non-size spatial objects and R-Tree which actually stores spatial objects associated with each leaf node of SQ-Tree. Because each SQR-Tree node has an MBR containing sub-nodes, the split boundary of a node will be extended independently and overlaps between nodes can be reduced. In addition, a spatial object is inserted into R-Tree in each split data space and SQ-Tree is used to identify each split data space. Since only R-Trees of SQR-Tree in the query area are accessed to process a spatial query, query processing cost can be reduced. Finally, we proved superiority of SQR-Tree through experiments.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.7
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• pp.488-498
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• 2007

This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs) in Ubiquitous Smart Space (USS). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and the grid-based approach for extracting the accurate object feature. In addition, we considered the appearance of people and the transition time between entry and exit zones for tracking objects across blind regions of multiple cameras with non-overlapping FOVs. The main contribution of this paper is to estimate transition times between various entry and exit zones, and to graphically represent the camera topology as an undirected weighted graph using the transition probabilities.

• Journal of KIISE:Databases
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• v.35 no.1
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• pp.54-66
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• 2008

A spatio-temporal join is an expensive operation that is commonly used in spatio-temporal database systems. In order to generate an efficient query plan for the queries involving spatio-temporal join operations, it is crucial to estimate accurate selectivity for the join operations. Given two dataset $S_1,\;S_2$ of discrete data and a timestamp $t_q$, a spatio-temporal join retrieves all pairs of objects that are intersected each other at $t_q$. The selectivity of the join operation equals the number of retrieved pairs divided by the cardinality of the Cartesian product $S_1{\times}S_2$. In this paper, we propose aspatio-temporal histogram to estimate selectivity of spatio-temporal join by extending existing geometric histogram. By using a wide spectrum of both uniform dataset and skewed dataset, it is shown that our proposed method, called Spatio-Temporal Histogram, can accurately estimate the selectivity of spatio-temporal join. Our contributions can be summarized as follows: First, the selectivity estimation of spatio-temporal join for discrete data has been first attempted. Second, we propose an efficient maintenance method that reconstructs histograms using compression of spatial statistical information during the lifespan of discrete data.

• Journal of KIISE:Databases
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• v.33 no.5
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• pp.495-512
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• 2006

Recently, researches on a future position prediction of moving objects have been progressed as the importance of the future position retrieval increases. New index structures are required to efficiently retrieve the consecutive positions of moving objects. Existing index structures significantly degrade the search performance of the moving objects because the search operation makes the unnecessary extension of the node in the index structure. To solve this problem, we propose a space partition based index structure considering the mobility of moving objects. To deal with the overflow of a node, our index structure first merges it and the sibling node. If it is impossible to merge them, our method splits the overflow node in which moving properties of objects are considered. Our index structure is always partitioned into overlap free subregions when a node is split. Our split strategy chooses the split position by considering the parameters such as velocities, the escape time of the objects, and the update time of a node. In the internal node, the split position Is determined from preventing the cascading split of the child node. We perform various experiments to show that our index structure outperforms the existing index structures in terms of retrieval performance. Our experimental results show that our proposed index structure achieves about $17%{\sim}264%$ performance gains on current position retrieval and about $107%{\sim}19l%$ on future position retrieval over the existing methods.