• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.397-406
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• 2014

In order to answer the questions successfully on behalf of the human in DeepQA environments such as Jeopardy! of the American quiz show, the computer is required to have the capability of fast temporal and spatial reasoning on a large-scale commonsense knowledge base. In this paper, we present a scalable spatial reasoning algorithm for deriving efficiently new directional and topological relations using the MapReduce framework, one of well-known parallel distributed computing environments. The proposed reasoning algorithm assumes as input a large-scale spatial knowledge base including CSD-9 directional relations and RCC-8 topological relations. To infer new directional and topological relations from the given spatial knowledge base, it performs the cross-consistency checks as well as the path-consistency checks on the knowledge base. To maximize the parallelism of reasoning computations according to the principle of the MapReduce framework, we design the algorithm to partition effectively the large knowledge base into smaller ones and distribute them over multiple computing nodes at the map phase. And then, at the reduce phase, the algorithm infers the new knowledge from distributed spatial knowledge bases. Through experiments performed on the sample knowledge base with the MapReduce-based implementation of our algorithm, we proved the high performance of our large-scale spatial reasoner.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.3
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• pp.101-110
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• 2003

Secure and reliable group communication is an increasingly active research area prompted by the growing popularity of many types of group-oriented and collaborative applications. The central challenge is secure and efficient group key management. While centralized methods are often appropriate for key distribution in large multicast-style groups, many collaborative group settings require distributed key agreement techniques. Most of prior group key agreement protocols have been focused on reducing the computational costs. One exception is STR protocol that optimizes communicational cost. On the other hand, it requires O(n) number of modular exponentiations. In this paper, we propose a new group key agreement protocol that modifies STR protocol by utilizing pairing based cryptography. The resulting protocol reduces computational cost of STR protocol while preserving the communication cost.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.664-667
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• 2014

기존 온톨로지들을 공유 및 재사용하기 위하여 온톨로지 정렬이 연구되고 있다. 기존 정렬 시스템은 온톨로지 데이터 양에 따라 매트릭스를 생성하고 과도한 계산을 통해 처리하여 대용량 데이터 집합에 대하여 공간적 및 계산적으로 부하를 발생하여 효율적이지 않다. 이를 해결하기 위하여 온톨로지 정렬을 휴리스틱 알고리즘을 적용하여 연구 진행하였다. 기존 휴리스틱 알고리즘은 계산이 간단하지만 조율해야 하는 파라미터가 많기에 특정 도메인에 최적 조합이 필요하며 만족한 성능을 얻지 못하였다. 이 논문에서는 Discrete Cuckoo Search(DCS) 기반 온톨로지 정렬 알고리즘을 제안한다. 제안한 알고리즘은 조율해야 하는 파라미터의 개수가 적고 Levy Flight 분포에 따라 탐색하여 계산이 간단하다. 제안된 알고리즘의 성능을 평가하기 위해 OAEI(Ontology Alignment Evaluation Initiative)에서 제공하는 벤치마크 데이터를 사용하여 정확률(Precision)과 재현율(Recall)을 구하고 기존 휴리스틱 정렬 알고리즘과 비교 평가하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.103-106
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• 2007

디지털 콘텐츠가 나타나면서 시대별 유통 방법이 제안되고 발전돼왔다. 초창기 저용량의 디지털 콘텐츠에서 현재 대용량의 DVD급 화질의 콘텐츠가 유통되고 있다. 아날로그 콘텐츠와는 달리 디지털 콘텐츠가 불법복제 되면 콘텐츠의 저작권자는 더욱 큰 물질적인 손해를 입게 된다. 그래서 DRM(Digital Rights Management)기술이 디지털 콘텐츠에 적용되어 콘텐츠를 보호하게 되었다. 하지만 표준화되지 못한 DRM 기술은 여러개의 전용 클라이언트 프로그램이 필요하게 되었고, 콘텐츠의 유통흐름을 원활하게 못하고 있는 실정이다. 제안된 시스템은 디지털 콘텐츠와 플레이어의 일체화된 구조로서 디지털콘텐츠를 1차 패키징 하고, 플레이어의 Secure Storage에 2차 패키징 하여 콘텐츠와 플레이어가 하나의 파일로 배포되어진다. 2차 패키징 된 결과물은 PackPlayer라 명칭한다. 콘텐츠와 플레이어가 하나의 파일로 배포되어지면 사용자가 쉽게 콘텐츠를 볼 수 있으며, 원활한 콘텐츠의 유통과 다양한 상품서비스를 가능하게 해줄 수 있을 것이다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.43-45
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• 2001

데이터로부터 숨겨진 패턴을 추출하는 데이터마이닝 기법 중에서 연관규칙은 대용량의 데이터베이스에서 단위 트랜잭션 당 동시에 발생할 확률이 높은 항목들의 유형을 발견하는 기법이다. 연관규칙 탐사에서 개념계층(taxonomy)을 사용하여 보다 포괄적인 의미를 갖는 규칙을 찾아내는 연구가 일반화된 연관규칙이며 이를 통해 일반화 이전에는 간과될 수 있는 중요한 규칙을 발견할 수 있다. 일반화된 연관규칙에 관한 기존의 접근방법은 후보항목집합의 각 항목에 대한 개념계층상의 모든 조상들을 트랜잭션에 추가한 후 확장된 트랜잭션에 대해 지지도를 계산하는 방법이며. 이렇게 되면 연관규칙의 단점중의 하나인 계산량 문제가 더욱 두드러지게 된다. 이에 본 연구에서는 모든 개념계층 레벨이 아닌, 사용자가 관심 있는 레벨로 제한된 환경에서 연관규칙 탐사를 수행하여 규칙생성의 복잡도를 줄이는 시스템을 구현하였다. 그러나 모든 항목을 한 레벨로 일반화하는데는 무리가 따르기 때문에 관심있는 항목의 경우 일반화 레벨을 따로 명시할 수 있도록 하여 사용자가 원하는 규칙을 발견하도록 하였다.

• Journal of Korea Game Society
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• v.13 no.5
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• pp.55-62
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• 2013

Since large terrain data can not be loaded on the GPU or CPU memory at once, out-of-core methods which read necessary part from the secondary storage such as a hard disk are commonly used. However, long delay may occur due to limited bandwidth while loading the data from the hard disk to memory. We propose efficient rendering method of large terrain data, which compresses the data with wavelet technique and save its coefficients in RGBA channel of an image us, then decompresses that in rendering stage. Entire process is performed in GPU using Direct Compute. By reducing the amount of data transfer, performing wavelet computations in parallel and doing decompression quickly on the GPU, our method can reduce rendering time effectively.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.45-56
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• 2017

In astronomy, the amount of data generated using wide-field optical telescope has increased exponentially. However, the fixed-size small-scale computing environment and the complexity of data analysis tools, results in difficulties to process the massive observation data collected. To resolve this problem, we propose a cloud-based automation system for the efficient processing of the enormous data gathered. The proposed system consists of a Workflow Execution Manager which manages various workflow templates and controls the execution of workflows instantiated from theses templates, and an Elastic Resource Manager that dynamically adds/deletes computing resources, according to the amount of data analysis requests. To show the effectiveness of our proposed system, we exhaustively explored a board spectrum of experiments, like elastic resources allocation, system load, etc. Finally, we describe the best practice case of DEEP-SOUTH scheduling system as an example application.

• Journal of Broadcast Engineering
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• v.2 no.1
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• pp.65-73
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• 1997

This paper propose a new triangular mesh approximation method using wavelet coefficients for large terrain data. Using spatio-freguency localization characteristics of wavelet coefficients, we determine the complexity of terrain data and approximate the data according to the complexity. This proposed algorithm is simple and requires low computational cost due to its top-down approach. Because of the similarity between the mesh approximation and data compression procedures based on wavelet transform, we combine the mesh approximation scheme with the Embedded Zerotree Wavelet (EZW) coding scheme for the effective management of large terrain data. Computer simulation results demonstrate that the proposed algorithm is very prospective for the 3-D visualization of terrain data.

• Information Systems Review
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• v.16 no.1
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• pp.37-50
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• 2014

Conventional case-based reasoning (CBR) does not perform efficiently for high volume dataset because of case-retrieval time. In order to overcome this problem, some previous researches suggest clustering a case-base into several small groups, and retrieve neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performances than the conventional CBR. This paper suggests a new hybrid case-based reasoning method which dynamically composing a searching pool for each target case. This method is applied to diagnose for the heart disease dataset. The results show that the suggested hybrid method produces statistically the same level of predictive performances with using significantly less computational cost than the CBR method and also outperforms the basic clustering-CBR (C-CBR) method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.9
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• pp.775-780
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• 2016

In this paper, a computational algorithm of an improved and versatile structural analysis applicable for large-size flexible nonlinear structures is developed. In more detail, nonlinear finite element based on the co-rotational (CR) framework is developed. Then, a finite element tearing and interconnecting method using local Lagrange multipliers (FETI-local) is combined with the nonlinear CR finite element. The resulting computational algorithm is presented and applied for nonlinear static analyses, i.e., cantilevered beam and multibody structure. Finally, the proposed analysis is evaluated with regard to its parallel computation performance, and it is compared with those obtained by serial computation using the sparse matrix linear solver, PARDISO.