• Journal of KIISE
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• v.42 no.10
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• pp.1195-1206
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• 2015

For the management of a knowledge system, systems that automatically infer and manage scalable knowledge are required. Most of these systems use ontologies in order to exchange knowledge between machines and infer new knowledge. Therefore, approaches are needed that infer new knowledge for scalable ontology. In this paper, we propose an approach to perform rule based reasoning for scalable SHIF ontologies in a spark framework which works similarly to MapReduce in distributed memories on a cluster. For performing efficient reasoning in distributed memories, we focus on three areas. First, we define a data structure for splitting scalable ontology triples into small sets according to each reasoning rule and loading these triple sets in distributed memories. Second, a rule execution order and iteration conditions based on dependencies and correlations among the SHIF rules are defined. Finally, we explain the operations that are adapted to execute the rules, and these operations are based on reasoning algorithms. In order to evaluate the suggested methods in this paper, we perform an experiment with WebPie, which is a representative ontology reasoner based on a cluster using the LUBM set, which is formal data used to evaluate ontology inference and search speed. Consequently, the proposed approach shows that the throughput is improved by 28,400% (157k/sec) from WebPie(553/sec) with LUBM.

• Journal of KIISE
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• v.41 no.9
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• pp.686-698
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• 2014

Recently, large-scale streaming sensor data have emerged due to explosive supply of smart phones, diffusion of IoT and Cloud computing technology, and generalization of IoT devices. Also, researches on combination of semantic web technology are being actively pushed forward by increasing of requirements for creating new value of data through data sharing and mash-up in large-scale environments. However, we are faced with big issues due to large-scale and streaming data in the inference field for creating a new knowledge. For this reason, we propose the RDFS rule based parallel reasoning scheme to service by processing large-scale streaming sensor data with the semantic web technology. In the proposed scheme, we run in parallel each job of Rete network algorithm, the existing rule inference algorithm and sharing data using the HBase, a hadoop database, as a public storage. To achieve this, we implement our system and evaluate performance through the AWS data of the weather center as large-scale streaming sensor data.

• Proceedings of the KIPE Conference
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• 2004.07a
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• pp.461-464
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• 2004

본 논문은 대용량 컨버터에 이용되는 Arrester에 대한 설계기법을 보여주고 있다. 본 논문에는 Arrester의 설계방법과 일반적인 AC계통에서 사용하는 Arrester와 다른 열방산 특성을 다루었다.

• Electric Engineers Magazine
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• v.187 no.3
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• pp.42-49
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• 1998

드문 경우이긴 하지만 설치 후 절연강도에 대한 전력용변압기의 운전 적합성을 판단하고 있다면 부분방전의 존재를 검출하기 위해 이 시험을 실시하는 경우가 있다. 이 시험은 보수나 수정작업이 실시된 변압기에 실시할 수도 있다. 일반적으로 유도전압시험은 대용량 초고압변압기에만 실시하는 것으로 알려져 있으나 모든 변압기에 실시할 수 있는 시험이다.

• The Magazine of the IEIE
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• v.32 no.10 s.257
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• pp.65-70
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• 2005

강 유전체 메모리(FRAM)와 상 변화 메모리(PRAM)는 기존의 메모리들이 갖고 있는 문제점들을 해결할 수 있는 이상적인 메모리로 주목 받고 있다. 현재 FRAM과 PRAM을 구현하는데 있어서 가장 큰 어려움은 셀의 크기와 대용량이다. 따라서 신 메모리의 셀 크기를 결정짓는 중요 요소들과 이를 해결할 수 있는 공정 기술들에 대하여 살펴보았다.

• Electric Engineers Magazine
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• v.185 no.1
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• pp.22-30
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• 1998

전력계통 가운데 중요한 역할을 담당하고 있는 대용량 전력용변압기는 효율이 높고 그 특성이 매우 안정되어 있는 설비이나 절연유의 여과와 같은 보수작업에도 불구하고 점진적인 열화과정이 누적진행되며 결국에는 교체를 필요로 하게 된다. 이러한 교첵 적절한 시기에 이루어지지 않는 경우 불시의 고장현상 또는 사고가 발생하게 되어 전력계통뿐 아니라 사회적 경제적으로도 큰 문제를 야기한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.221-222
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• 2014

빠른 웹의 성장으로 대용량 데이터를 효과적으로 처리할 수 있는 플랫폼 기술에 대한 관심이 높아지고 있다. 특히, HDFS는 이상적인 분산 파일 시스템으로 각광받고 있으며 대용량 파일의 처리를 목적으로 개발되었다. 하지만, 실제 파일들의 집합에서 소용량 파일이 차지하는 비중은 높은 편이다. 많은 수의 소용량 파일은 HDFS 성능 감소에 치명적인 원인이 된다. 많은 수의 소용량 파일들이 HDFS에 저장된다면 NameNode의 메모리 소비량이 증가하게 되며 많은 수의 소용량 파일은 많은 수의 DataNode와 NameNode를 요구하므로 상대적으로 처리시간이 많이 소모된다. 따라서 본 논문에서는 HDFS에서 소용량 파일의 저장과 액세스 효율성을 향상시키기 위하여 나이브 베이지안 분류기 알고리즘을 적용한 파일 그룹화 시스템을 설계하였다.

• Journal of Korea Spatial Information System Society
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• v.10 no.1
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• pp.1-14
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• 2008

Recently, because of the need to wide-area spatial data for spatlal analysis and military purpose, there are increasing demand for the efficient retrieval of mass spatial data in Geographic Information System(GIS) fields. Oracle Spatial and ESRI ArcSDE, that are GIS Software, are to manage mass spatial data stably and to support various services but they are inefficient to retrieve mass spatial data because of the complexity of their spatial data models and spatial operations. Therefore, in this paper, we developed a spatial middleware that can retrieve mass spatial data efficiently. The spatial middleware used Oracle which is a representative commercial DBMS as a repository for the stable management of spatial data and utilized OCCI(Oracle C++ Call Interface) for the efficient access of mass spatial data in Oracle. In addition, various spatial operating methods and the Array Fetch method were used in the spatial middleware to perform efficient spatial operations and retrieval of mass spatial data in Oracle, respectively. Finally, by comparing the spatial middleware with Oracle Spatial and ESRI ArcSDE through the performance evaluation, we proved its excellent retrieval and storage performance of mass spatial data.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.15-21
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• 2015

In support vector machine, the values of parameters included in kernels affect strongly generalization ability. It is often difficult to determine appropriate values of those parameters in advance. It has been observed through our studies that the burden for deciding the values of those parameters in support vector regression can be reduced by utilizing ensemble learning. However, the straightforward application of the method to large scale problems is too time consuming. In this paper, we propose a method in which the original data set is decomposed into a certain number of sub data set in order to reduce the burden for parameter tuning in support vector regression with large scale data sets and imbalanced data set, particularly.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.2
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• pp.195-201
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• 2008

This paper compares the performance of Mass Memory Unit(MMU) between Science and Technology Satellite 1(STSAT-1) and STSAT-2 from developed Proto Flight Model(PFM) for Miniaturization, lightweight and low power consumption. MMU receives the payload data at 200Kbps and transmits them to XTX at 10Mbps in the STSAT-2. The performance of PFM MMU in the Functional and space environments test satisfies the requirements of STSAT-2.