• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3864-3877
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• 2022

We propose a new approach called PESA (Prioritized replay Evolutionary and Swarm Algorithms) combining prioritized replay of reinforcement learning with hybrid evolutionary algorithms. PESA hybridizes different evolutionary and swarm algorithms such as particle swarm optimization, evolution strategies, simulated annealing, and differential evolution, with a modular approach to account for other algorithms. PESA hybridizes three algorithms by storing their solutions in a shared replay memory, then applying prioritized replay to redistribute data between the integral algorithms in frequent form based on their fitness and priority values, which significantly enhances sample diversity and algorithm exploration. Additionally, greedy replay is used implicitly to improve PESA exploitation close to the end of evolution. PESA features in balancing exploration and exploitation during search and the parallel computing result in an agnostic excellent performance over a wide range of experiments and problems presented in this work. PESA also shows very good scalability with number of processors in solving an expensive problem of optimizing nuclear fuel in nuclear power plants. PESA's competitive performance and modularity over all experiments allow it to join the family of evolutionary algorithms as a new hybrid algorithm; unleashing the power of parallel computing for expensive optimization.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.11 s.254
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• pp.1447-1454
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• 2006

The evaluation of parallel performance of a high speed impact simulation is not an easy task because not only the development of parallel explicit code is difficult but also a large number of processors is not easily accessible. In this paper, the parallel performance of a new Lagrangian FEM impact code carried out on cluster supercomputer has been described in high speed range. In the case of metal sphere impacting to oblique plate, the overall speed-up continuously increases even up to 128 CPUs. Investigation of elapsed time of each part reveals that most of the inefficiency comes from the load imbalance of contact.

• Computers and Concrete
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• v.18 no.5
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• pp.1019-1039
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• 2016

In this paper, a parallel algorithm of nonlinear dynamic analysis of three-dimensional (3D) reinforced concrete (RC) frame structures based on the platform of graphics processing unit (GPU) is proposed. Time integration is performed using Newmark method for nonlinear implicit dynamic analysis and parallelization strategies are presented. Correspondingly, a parallel Preconditioned Conjugate Gradients (PCG) solver on GPU is introduced for repeating solution of the equilibrium equations for each time step. The RC frames were simulated using fiber beam model to capture nonlinear behaviors of concrete and reinforcing bars. The parallel finite element program is developed utilizing Compute Unified Device Architecture (CUDA). The accuracy of the GPU-based parallel program including single precision and double precision was verified in comparison with ABAQUS. The numerical results demonstrated that the proposed algorithm can take full advantage of the parallel architecture of the GPU, and achieve the goal of speeding up the computation compared with CPU.

• Journal of Digital Contents Society
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• v.10 no.2
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• pp.249-258
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• 2009

As wireless internet markets become larger than before, many mobile applications are also being developed actively. In this circumstances mobile devices such as cell phones, PDAs are playing an important role to satisfy the user's need of ubiquitous computing. Due to the hardware limitations, however, the mobile devices like PDA can not run large-scale softwares by itself. The main goal of this paper is to make large-scale applications runnable on PDA. To accomplish this, we used the PDA-JPVM cluster computing engine which has been already developed by us. We found out by running the applications and the performance evaluation that large-scale Java softwares can easily run on the hardware-limited PDA. And the performance evaluation results are also presented.

• Communications of the Korean Mathematical Society
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• v.10 no.3
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• pp.735-750
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• 1995

We present a domain decomposition algorithm for solving large sparse linear systems of equations arising from queuing networks. Such techniques are attractive since the problems in subdomains can be solved independently by parallel processors. Many of the methods proposed so far use some form of the preconditioned conjugate gradient method to deal with one large interface problem between subdomains. However, in this paper, we propose a "nested" domain decomposition method where the subsystems governing the interfaces are small enough so that they are easily solvable by direct methods on machines with many parallel processors. Convergence of the algorithms is also shown.lso shown.

• Proceedings of the KWS Conference
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• 2003.11a
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• pp.156-158
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• 2003

An implicit finite element implementation for Leblond's transformation plasticity constitutive equations, which are widely used in welded steel structure is proposed in the framework of parallel computing. The implementation is based upon the multiplicative decomposition of deformation gradient and hyper elastic formulation. We examine the efficiency of parallel computation for the finite element analysis of a welded structure using domain-wise multi-frontal solver.

• Proceedings of the KWS Conference
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• 2002.05a
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• pp.161-163
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• 2002

An implicit finite element implementation for Leblond's transformation plasticity constitutive equations, which are widely used in welded steel structure is proposed in the framework of parallel computing. The implementation is based upon the updated Lagrangian formulation. We examine the efficiency of parallel compuatation for the finite element analysis of a welded structure using multi-frontal solver.

• Journal of Korean Institute of Industrial Engineers
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• v.1 no.2
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• pp.51-56
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• 1975

A computer program for computing complex system reliability is described. The program is composed of three phases : Phase I program reduces all series, parallel and series-parallel components and subsequently obtains an irreducible non-series-parallel system. Phase II program enumerates all the possible paths from the source to the sink of the graph. Phase III program then computes system reliability based on the information obtained by the Phase II program. The program is based on a modified version of the algorithm published in [6]. An example of the use of the computer program is given.

• 한국전산유체공학회:학술대회논문집
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• 2006.05a
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• pp.185-187
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• 2006

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1490-1501
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• 2016

In this paper, we propose an image-based indoor localization system using parallel distributed computing. In order to reduce computation time for indoor localization, an scale invariant feature transform (SIFT) algorithm is performed in parallel by using Apache Spark. Toward this goal, we propose a novel image processing interface of Apache Spark. The experimental results show that the speed of the proposed system is about 3.6 times better than that of the conventional system.