• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.13-16
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• 2000

In fuzzy expert systems or database systems that have huge volumes of fuzzy data or large fuzzy rules, the inference time is much increased. Therefore, a high performance parallel fuzzy computing environment is needed. In this paper, we propose a parallel fuzzy inference mechanism in parallel computing environment. In this, fuzzy rules are distributed and executed simultaneously. The ONE_TO_ALL algorithm is used to broadcast the fuzzy input vector to the all nodes. The results of the MIN/MAX operations are transferred to the output processor by the ALL_TO_ONE algorithm. By parallel processing of fuzzy rules or data, the parallel fuzzy inference algorithm extracts effective parallel ism and achieves a good speed factor.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.928-933
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• 2003

For simulation of three-dimensional turbulent flow with LES and DNS takes much time and expense with current available computing resources. It is nearly impossible to simulate turbulent flow with high Reynolds number. So, the emerging alternative is the Grid computing for needed computation power and working environment. In this study, the CFD code was parallelized to adapt it for the parallel computing under the Grid environment. In the first place, the Grid environment was built to connect the PC-Cluster facilities belong to the different institutions using communication network system. And CFD applications were calculated to check the performance of the parallel code developed for the Grid environment. Although it is a fundamental study, it brings about a important meaning as first step in research of the Grid.

• 산업경영시스템학회지
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• 제32권2호
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• pp.85-93
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• 2009

GOMS model is a cognitive modeling method of human performance based on Goal, Operators, Methods, Selection rules. GOMS model was originally designed for desktop environment so that it is difficult for GOMS model to be implemented into the mobile environment. In addition, GOMS model would be inaccurate because the original GOMS model was based on serial processing, excluding one of most important human information processing characteristics, parallel processing. Therefore this study was designed to propose a modified GOMS model including mobile computing and parallel processing. In order to encompass mobile environment, an operator of 'look for' was divided into 'visual move to' and 'recognize' whereas 'point to' and 'click' were combined into 'tab.' The results showed that newly introduced operators were necessary to estimate more accurate mobile computing behaviors. In conclusion, modified-GOMS model could predict human performance more accurately than the original GOMS model in the mobile computing environment.

• 대한기계학회논문집A
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• 제31권12호
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• pp.1150-1156
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• 2007

Multidisciplinary design optimization methodologies play an essential role in modern engineering design which involves many inter-related disciplines. These methodologies usually require very long computing time and design tasks are hard to finish within a specified design cycle time. Parallel processing can be effectively utilized to reduce the computing time. The research on the parallel computing performance of MDO methodologies has been just begun and developing. This study investigates performances of MDF, IDF, SAND and CO among MDO methodologies in view of parallel computing. Finally, the best out of four methodologies is suggested for parallel processing purpose.

• International Journal of Contents
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• 제1권1호
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• pp.50-58
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• 2005

Generally, multidimensional data such as image and spatial data require large amount of storage space. There is a limit to store and manage those large amounts of data in single workstation. If we manage the data on parallel computing environment which is being actively researched these days, we can get highly improved performance. In this paper, we propose a parallel multidimensional index structure that exploits the parallelism of the parallel computing environment. The proposed index structure is nP(processor)-nxmD(disk) architecture which is the hybrid type of nP-nD and 1P-nD. Its node structure in-creases fan-out and reduces the height of an index. Also, a range search algorithm that maximizes I/O parallelism is devised, and it is applied to k-nearest neighbor queries. Through various experiments, it is shown that the proposed method outperforms other parallel index structures.

• 대한기계학회논문집B
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• 제28권5호
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• pp.604-611
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• 2004

Simulation of three-dimensional turbulent flow with LES and DNS lakes much time and expense with currently available computing resources and requires big computing resources especially for high Reynolds number. The emerging alternative to provide the required computing power and working environment is the Grid computing technology. We developed the CFD code which carries out the parallel computing under the Grid environment. We constructed the Grid environment by connecting different PC-cluster systems located at two different institutes of Pusan National University in Busan and KISTI in Daejeon. The specification of PC-cluster located at two different institutes is not uniform. We run our parallelized computer code under the Grid environment and compared its performance with that obtained using the homogeneous computing environment. When we run our code under the Grid environment, the communication time between different computer nodes takes much larger time than the real computation time. Thus the Grid computing requires the highly fast network speed.

• International Journal of Internet, Broadcasting and Communication
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• 제11권3호
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• pp.1-7
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• 2019

Previously, parallel computing was mainly used in areas requiring high computing performance, but nowadays, multicore CPUs and GPUs have become widespread, and parallel programming advantages can be obtained even in a PC environment. Various parallel programming frameworks using multicore CPUs such as OpenMP and PPL have been announced. Nvidia and AMD have developed parallel programming platforms and APIs for program developers to take advantage of multicore GPUs on their graphics cards. In this paper, we develop digital image transformation programs that runs on each of the major parallel programming frameworks, and measure the execution time. We analyze the characteristics of each framework through the execution time comparison. Also a constant K indicating the ratio of program execution time between different parallel computing environments is presented. Using this, it is possible to predict rough execution time without implementing a parallel program.

• International Journal of Contents
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• 제14권1호
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• pp.34-38
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• 2018

With the rapid growth of genomic data, new requirements have emerged that are difficult to handle with big data storage and analysis techniques. Regardless of the size of an organization performing genomic data analysis, it is becoming increasingly difficult for an institution to build a computing environment for storing and analyzing genomic data. Recently, cloud computing has emerged as a computing environment that meets these new requirements. In this paper, we analyze and compare existing distributed and parallel NGS (Next Generation Sequencing) analysis based on cloud computing environment for future research.

• 한국항공우주학회지
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• 제35권11호
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• pp.973-982
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• 2007

연구는 병렬 컴퓨팅 기반에서 자동화된 격자 생성 기법과 입자 군집 최적화(PSO) 알고리즘을 적용한 최적 설계 프레임워크를 개발하여 이를 인공위성 어댑터 모듈의 구조 최적 설계에 적용하였다. 자동화된 격자 생성 기법을 적용하여 구조 형상 변화를 가능하게 함으로써 폭넓은 범위에서 최적 형상 모델을 도출할 수 있었다. 또한 최적화 알고리즘인 PSO 알고리즘을 병렬 계산환경과 접목하고, 계산 성능을 최대화하기 위해 비동기식 PSO 알고리즘을 개발하였다. 그 결과 최적화에 걸리는 계산 시간을 줄일 수 있었다. 최적화 작업에서 제한 조건으로는 고유진동수와 어댑터에 발생하는 최대 응력 값을 고려하였다. 결과적으로 인공위성 어댑터 모듈의 최적 설계를 통해 인공위성 구조 질량 감소를 유도해 내었다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2018년도 제57차 동계학술대회논문집 26권1호
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• pp.89-92
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• 2018

본 논문에서는 Edge computing 환경에서 다수의 노드들로 구성된 네트워크의 디바이스를 효율적으로 관리하기 위한 방법을 제안한다. 기존의 클라이언트-서버 모델은 모든 데이터와 그에 대한 요청을 중심 서버에서 처리하기 때문에, 다수의 노드로부터 생성된 많은 양의 데이터를 처리하는 데 빠른 응답속도를 보장하지 못한다. Edge computing은 분담을 통해 네트워크의 부담을 줄일 수 있는 IoT 네트워크에 적합한 방법으로, 데이터를 전송하고 받는 과정에서 네트워크의 대역폭을 사용하는 대신 서로 연결된 노드들이 협력해서 데이터를 처리하고, 또한 네트워크 말단에서의 데이터 처리가 허용되어 데이터 센터의 부담을 줄일 수 있다. 여러병렬 기계학습 모델 중 본 연구에서는 Stale Synchronous Parallel(SSP) 모델을 이용하여 Edge 노드에서 분산기계 학습에 적용하였다.