• 한국정보처리학회논문지
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• 제7권1호
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• pp.306-312
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• 2000

본 논문에서는 소형 RS(Reed-Solomon) 디코우더의 효율적인 하드웨어 아키텍처를 제안하였다. 전체 아키텍쳐는 3단 파이프라인 구조를 택하였으며, 디코우딩 연산시, 에러위치다항식은 BMA(Berlekamp-Massey algortihm)에 의한 fast-iteration 방식으로 구하였으며, 계산의 복잡성이 요구도는 신드롬연산 부분은 ROM 테이블을 이용해서 병렬로 수행하고, 에러위치 다항식을 근을 구하는 부분은 Chein search 알고리즘을 응용한 방법을 ROM을 채택하여 계산하였다. 제안된 디코우더로 3심볼 랜덤에러정정을 수행하며, 시스템클록 25MHz를 사용하여 124Mbps의 디코우딩 데이터율을 가짐을 확인할 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제11권6호
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• pp.69-78
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• 2006

생물정보학 연구 있어서 아미노산이나 염기서열에 대한 유사성이나 상동성을 찾아내는 작업은 유전자의 기능에 대한 예측이나 단백질 구조를 예측하는 연구의 기반이 된다. 이러한 서열 데이터는 컴퓨터의 도입으로 매우 빠르게 증가하고 있다. 이러한 시점에서 서열에 대한 검색 속도는 매우 중요한 요소이기 때문에 대량의 서열정보를 다루기 위해서는 SMP(Sysmmetric Multi-Processors) 컴퓨터나 클러스터를 이용하고 있다. 본 논문에서는 서열 검색에 사용되는 BLAST(Basic Local Alignment Search Tool)의 속도향상을 위한 방법으로 클러스터 환경에서 병렬화 하는 nBLAST 알고리즘의 병렬화에 대해 제안한다. nBLAST는 기존의 BLAST 소스코드에 대한 수정 없이 병렬라이브러리인 MPI(Message Passing Interface)를 이용하여 질의를 분할하여 병렬화 하기 때문에 환경설정 등의 복잡한 과정을 거치지 않고 손쉽게 BLAST에 알고리즘에 대한 병렬화를 할 수 있다. 또한, 실험을 통하여 28대의 리눅스 클러스터에서 nBLAST를 수행하여 노드 수의 증가에 따른 성능 향상을 확인하였다.

• 한국전산유체공학회지
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• 제4권3호
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• pp.52-62
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• 1999

The development of a parallelized aerodynamic simulation process involving moving bodies is presented. The implementation of this process is demonstrated using a fully systemized Chimera methodology for steady and unsteady problems. This methodology consists of a Chimera hole-cutting, a new cut-paste algorithm for optimal mesh interface generation and a two-step search method for donor cell identification. It is fully automated and requires minimal user input. All procedures of the Chimera technique are parallelized on the Cray T3E using the MPI library. Two and three-dimensional examples are chosen to demonstrate the effectiveness and parallel performance of this procedure.

• 한국경영과학회지
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• 제30권1호
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• pp.27-41
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• 2005

This paper considers identical parallel machine scheduling problem. Each job has a processing time. due date. weight and family type. If a different type of job is followed by prior job. a family setup is incurred. A two phased heuristic is presented for minimizing the sum of weighted tardiness. In the first phase. using roiling horizon technique. group each job into same family and schedule each family. In the second phase. assign each job to machines using schedule obtained in the first phase. Extensive computational experiments and comparisons among other algorithms are carried out to show the efficiency of the proposed algorithm.

• 한국소음진동공학회논문집
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• 제19권5호
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• pp.482-490
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• 2009

In this paper, the author proposes a new method for acoustic radiation optimum design to minimize noise from a vibrating panel-like structure using a collaborative population-based search method called the particle swarm optimization algorithm(PSOA). The PSOA is a parallel evolutionary computation technique initially developed by Kennedy and Eberhart. The acoustic radiation optimization method based on the PSOA consists of two processes. In the first process, the acoustic radiation analysis by an integrated p-version FEM/BEM, which was developed by using MATLAB, is performed to evaluate the exterior acoustic radiation field of the panel. The second process is to search the optimum design variables: 1) Shape of Bezier curves and 2) Shape and position of ribs, to minimize noise from the panel using the PSOA. The optimization method based on the PSOA is compared to that based on the steady state genetic algorithm(SSGA) in order to verify the effectiveness and validity of the optimal solution by PSOA. Finally, it is shown that the optimal designs of the panel obtained by using the PSOA can achieve effective reductions in radiated sound power.

• 전자공학회논문지A
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• 제33A권12호
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• pp.110-119
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• 1996

A novel high speed VLSI architecture and its VLSI realization methodologies for a motion estimation processor based on full search block matching algorithm are presentd. The presented architecture is designed in order to be suitable for highly parallel and pipelined processing with identical PE's and adjustable in performance and hardware amount according to various application areas. Also, the throughput is maximized by enhancing PE utilization up to 100% and the chip pin count is reduced by reusing image data with embedded image memories. Also, the uniform and identical data processing structure of PE's eases VLSI implementation and the clock rate of external I/O data can be made slower compared to internal clock rate to resolve I/O bottleneck problem. The logic and spice simulation results of the proposed architecture are presented. The performances of the proposed architecture are evaluated and compared with other architectures. Finally, the chip layout is shown.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.104-106
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• 2007

In this paper, we propose an approach to design of optimized Cascade controller for Rotary Inverted Pendulum system using Hierarchical Fair Competition-based Genetic Algorithm(HFCGA). GAs may get trapped in a sub-optimal region of the search space thus becoming unable to find better quality solutions, especially for very large search space. The Parallel Genetic Algorithms(PGA) are developed with the aid of global search and retard premature convergence. HFCGA is a kind of multi-populations of PGA. In this paper, we design optimized Cascade controller by HFCGA for Rotary Inverted Pendulum system that is nonlinear and unstable. Cascade controller comprise two feedback loop, parameters of controller optimize using HFCGA. Then designed controller evaluate by apply to the real plant.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 A
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• pp.320-326
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• 2002

Distribution system loss minimization re-configuration is 0-1 planning problem, and the number of combinations requiring searches is extremely large when dealing with typical system scales. For this reason, the application of a genetic algorithm (GA) seems attactive to solve this problem. Although Genetic algorithms are a type of random number search method, they incorporate a multi-point search feature and are therefore superior to one-point search techniques. The efficiency of GAs for solving large combinational problem has received wide attention. Further, parallel searching can be performed and the optimal solution is more easily reached. In this paper, for improving GA convergence characteristics in the distribution system loss minimization re-configeration problem, a chromosome "Limited Life" concept is intro duced. Briefly, considering the population homogenization and genetic drift problems, natural selection is achieved by providing this new concept, in addition to natural selection by fitness. This is possible because individuals in a population have an age value. Simulations were carried out using a model system to check this method's validity.

• 제어로봇시스템학회논문지
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• 제12권10호
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• pp.1044-1049
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• 2006

Multiple sequence alignment is a method to compare two or more DNA or protein sequences. Most of multiple sequence alignment tools rely on pairwise alignment and Smith-Waterman algorithm to generate an alignment hierarchy. Therefore, in the existing multiple alignment method as the number of sequences increases, the runtime increases exponentially. In order to remedy this problem, we adopted a parallel processing suffix tree algorithm that is able to search for common subsequences at one time without pairwise alignment. Also, the cross-matching subsequences triggering inexact-matching among the searched common subsequences might be produced. So, the cross-matching masking process was suggested in this paper. To identify the function of the clusters generated by suffix tree clustering, BLAST and CDD (Conserved Domain Database)search were combined with a clustering tool. Our clustering and annotating tool consists of constructing suffix tree, overlapping common subsequences, clustering gene sequences and annotating gene clusters by BLAST and CDD search. The system was successfully evaluated with 36 gene sequences in the pentose phosphate pathway, clustering 10 clusters, finding out representative common subsequences, and finally identifying functional domains by searching CDD database.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2006년도 추계학술대회
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• pp.573-576
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• 2006

This paper deals with multi-objective optimization problem of finding a set of well-distributed solutions close to the true Pareto optimal solutions. In this paper, we present a two-leveled symbiotic evolutionary algorithm to efficiently solve the problem. Most of the existing multi-objective evolutionary algorithms (MOEAs) operate one population that consists of individuals representing the complete solution to the problem. The proposed algorithm maintains several populations, each of which represents a partial solution to the entire problem, and has a structure with two levels. The parallel search and the structure are intended to improve the capability of searching diverse and good solutions. The performance of the proposed algorithm is compared with those of the existing algorithms in terms of convergence and diversity. The experimental results confirm the effectiveness of the proposed algorithm.