• 한국전자통신학회논문지
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• 제6권1호
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• pp.111-117
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• 2011

본 논문에서는 클라이언트 가상화 기술을 이용하여 로컬 컴퓨팅 환경의 성능 저하를 최소화 하고 가상화된 사용자 영역에서 사용자가 필요한 기능을 사용할 수 있도록 효율적으로 제공하며, 로컬 컴퓨팅 환경의 중요정보 보호와 성능의 안정성 및 지속성을 유지하였다. 또한 로컬 컴퓨팅 환경 뿐만 아니라 악성코드와 같은 공격으로부터 가상화된 영역을 보호하기 위한 방법을 제안함으로서 가상화된 영역에 있는 데이터들의 암호화를 통하여 가상화된 사용자 영역의 보안을 극대화시켰다. 가상화를 통해 로컬 컴퓨팅 자원을 그대로 사용하면서 효율적으로 로컬 컴퓨팅 시스템으로부터 하나의 사용자 컴퓨팅 리소스를 분리시키는 것과 같은 효과를 얻을 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.3972-3988
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• 2020

Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data - Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.

• 한국정보처리학회논문지
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• 제6권8호
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• pp.2222-2232
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• 1999

본 논문에서는 영상의 경계선 검출을 하는 시스톨릭 어레이를 제안하였다. 영상을 실시간 처리하는 것은 국부적인 연산자의 많은 연산으로 인하여 많은 어려움이 따른다. 경계선 검출을 위한 국부적 연산자는 한 화소의 이웃해 있는 다른 화소를 이용하여 경계선을 검출하는 데에 이용되나 기존의 컴퓨터에서는 빈번한 입출력의 요구로 인하여 실시간 처리에서 요구하는 계산능력을 충분하게 제공하지 못한다. 그래서 이 논문에서는 처리 방법이 규칙적이고, 보통의 대역폭을 가지고 있으면서도 경계선 검출이나 라플라시안 처리에 적합한 시스톨릭 어레이를 제안하였다.

• Structural Engineering and Mechanics
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• 제23권4호
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• pp.325-337
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• 2006

A hybrid structural design and optimization methodology that combines the strengths of genetic algorithms, local search techniques, and parallel computing is developed to evolve optimal truss systems in this research effort. The primary objective that is met in evolving near-optimal or optimal structural systems using this approach is the capability of satisfying user-defined design criteria while minimizing the computational time required. The application of genetic algorithms to the design and optimization of truss systems supports conceptual design by facilitating the exploration of new design alternatives. In addition, final shape optimization of the evolved designs is supported through the refinement of member sizes using local search techniques for further improvement. The use of the hybrid approach, therefore, enhances the overall process of structural design. Parallel computing is implemented to reduce the total computation time required to obtain near-optimal designs. The support of human-computer interaction during layout optimization and local optimization is also discussed since it assists in evolving optimal truss systems that better satisfy a user's design requirements and design preferences.

• ETRI Journal
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• 제42권5호
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• pp.658-668
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• 2020

In edge computing, most procedures, including data collection, data processing, and service provision, are handled at edge nodes and not in the central cloud. This decreases the processing burden on the central cloud, enabling fast responses to end-device service requests in addition to reducing bandwidth consumption. However, edge nodes have restricted computing, storage, and energy resources to support computation-intensive tasks such as processing deep neural network (DNN) inference. In this study, we analyze the effect of models with single and multiple local exits on DNN inference in an edge-computing environment. Our test results show that a single-exit model performs better with respect to the number of local exited samples, inference accuracy, and inference latency than a multi-exit model at all exit points. These results signify that higher accuracy can be achieved with less computation when a single-exit model is adopted. In edge computing infrastructure, it is therefore more efficient to adopt a DNN model with only one or a few exit points to provide a fast and reliable inference service.

• The Journal of the Acoustical Society of Korea
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• 제18권3E호
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• pp.37-43
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• 1999

In this paper, we present a new technique for the optimal local decomposition of convex structuring elements on a hexagonal grid, which are used as templates for morphological image processing. Each basis structuring element in a local decomposition is a local convex structuring element, which can be contained in hexagonal window centered at the origin. Generally, local decomposition of a structuring element results in great savings in the processing time for computing morphological operations. First, we define a convex structuring element on a hexagonal grid and formulate the necessary and sufficient conditions to decompose a convex structuring element into the set of basis convex structuring elements. Further, a cost function was defined to represent the amount of computation or execution time required for performing dilations on different computing environments and by different implementation methods. Then the decomposition condition and the cost function are applied to find the optimal local decomposition of convex structuring elements, which guarantees the minimal amount of computation for morphological operation. Simulation shows that optimal local decomposition results in great reduction in the amount of computation for morphological operations. Our technique is general and flexible since different cost functions could be used to achieve optimal local decomposition for different computing environments and implementation methods.

• 전기학회논문지
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• 제66권1호
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• pp.100-106
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• 2017

In this paper, we propose a parallel optimization method of Aho-Corasick (AC) algorithm and Parallel Failureless Aho-Corasick (PFAC) algorithm using Open Computing Language (OpenCL) on Field Programmable Gate Array (FPGA). The low throughput of string matching engine causes the performance degradation of network process. Recently, many researchers have studied the string matching engine using parallel computing. FPGA's vendors offer a parallel computing platform using OpenCL. In this paper, we apply the AC and PFAC algorithm on DE1-SoC board with Cyclone V FPGA, where the optimization that considers FPGA architecture is performed. Experiments are performed considering global id, local id, local memory, and loop unrolling optimizations using PFAC algorithm. The performance improvement using loop unrolling is 129 times greater than AC algorithm that not adopt loop unrolling. The performance improvements using loop unrolling are 1.1, 0.2, and 1.5 times greater than those using global id, local id, and local memory optimizations mentioned above.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.

• 한국콘텐츠학회논문지
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• 제10권7호
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• pp.81-87
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• 2010

본 논문에서는 현재 생물정보학 연구에서 가장 많이 사용하고 있는 BLAST의 문제점을 분석하고 이에 따른 해결책을 제시하기 위하여 그리드 컴퓨팅을 이용한 G-BLAST(Grid Computing을 이용한 Basic Local Alignment Search Tool)를 제안한다. 본 연구에서 제안하고 있는 G-BLAST을 이용한 시스템은 이기종 분산 환경에서 수행이 가능한 서열분석 통합 소프트웨어 패키지이며 기존 서열분석 서비스의 취약점인 검색 성능을 개선하여 BLAST 검색 기능을 강화 하였다. 또한, BLAST 결과를 사용자가 관리 및 분석이 용이하도록 데이터베이스 및 유전체 서열분석 서비스 시스템을 구현하였다. 본 논문에서는 G-BLAST시스템의 성능확인을 위하여 병렬컴퓨팅 성능테스트 기법을 도입하여 구현된 시스템을 기존 BLAST와 속도 및 효율부분에서 비교하여 성능개선을 확인하였으며 서열결과 분석에 필요한 자료를 사용자관점에서 제공해주고 있다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.224-226
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• 2005

Grid computing enables the fundamental computing shift from a localized resource computing model to a fully-distributed virtual organization with shared resources. In the grid computing environment, grid users usually get access rights by mapping their credential to local account. The mapped total account is temporally belongs to grid user. So, data on the secondary storage, which is produced by grid operation, can increase the load of system administration or can issue grid user's privacy. In this paper, we design a data management system for grid user to cover these problems. This system implements template account mechanism and manages local grid data.