• The KIPS Transactions:PartA
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• v.13A no.1 s.98
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• pp.53-56
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• 2006

Genetic algorithms(GA), which are based on the idea of natural selection and natural genetics, have proven successful in solving difficult problems that are not easily solved through conventional methods. The classical GA has the problem to spend much time when population is large. Parallel genetic algorithm(PGA) is an extension of the classical GA. The important aspect in PGA is migration and GA operation. This paper presents PGAs that use fuzzy logic. Experimental results show that the proposed methods exhibit good performance compared to the classical method.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.1
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• pp.37-50
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• 1998

This paper considers the nonidentical parallel machine scheduling problem in which n jobs having different due dates are to be scheduled on m nonidentical parallel machines. For the make-to-order manufacturing environment, the objective is to minimize the number of tardy jobs. A 0-1 nonlinear programming model is formulated and a heuristic algorithm that allocates and sequences jobs to machines is developed. The proposed algorithm makes use of the concept of assignment problem based on the suitability measure as the cost coefficient. Computational experiments show that the proposed algorithm is superior to the existing one in some performance measures such as number of tardy jobs. In addition, this algorithm is appropriate for solving real industrial problems efficiently.

• IE interfaces
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• v.25 no.3
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• pp.357-364
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• 2012

This paper considers a unrelated parallel machine scheduling problem with ready times, due times and sequence and machine-dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of machines to minimize the total tardy time. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, a genetic algorithm using an effective dispatching method is proposed. The performance of the proposed genetic algorithm is evaluated using several randomly generated examples.

• Journal of IKEEE
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• v.14 no.3
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• pp.205-209
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• 2010

We propose a new interference cancellation algorithm for W-CDMA repeater. The proposed algorithm uses the parallel multiple sub-filters instead of one long filter. We justify that convergence rate can be improved by using the proposed algorithm. The improvement of convergence rate is verified in a practical benchmark test condition.

• Proceedings of the KIPE Conference
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• 2002.07a
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• pp.734-738
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• 2002

In this paper, a novel wireless parallel operation algorithm of N+1 redundant UPS system with no control interconnections is presented. The load sharing of multiple UPS modules are controlled by Q-V droop and $P-\delta$ droop algorithm. This algorithm compensates for inverter parameter variation and line impedance imbalances with wireless auto-tuning method. And to increase the reliability of transient characteristic under parallel operation, a virtual injected Impedance is proposed to decrease a circulation current between inverter modules. Simulation results are provided to prove the novel wireless algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.4
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• pp.388-397
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• 1989

This paper describes an intelligent iterative parallel algorithm for solving large sparse linear systems of equations, and proposes a ststic dataflow computer architechture for the implementation of the algorithm. Implemented with the Jacobi interative method, the intelligent algorithm reduces the parallel execution time by reducing the individual inner product operation time.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.327-332
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• 2007

An engineering design problem involving Multi-Disciplinary Analysis(MDA) generally requires a large amounts of CPU time for the entire design process, and therefore Multiple Processing System (MPS) are essential to reduce the completion time. However, when applying conventional parallel processing techniques, all of the CAE S/W required for the MDA should be installed on all the servers making up NIPS because of characteristic of MDA and it would be a great expense in CAE S/W licenses. To solve this problem, we propose a Weight-based Multiqueue Load Balancing algorithm for a heterogeneous MPS where performance of servers and CAE S/W installed on each server are different of each other. To validate the performance, a computational experiments comparing the First Come First Serve algorithm and our proposed algorithm was accomplished.

• Journal of the Semiconductor & Display Technology
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• v.12 no.3
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• pp.13-18
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• 2013

Motion estimation is a key technique of modern video processing that significantly improves the coding efficiency significantly by exploiting the temporal redundancy between successive frames. Thread-level parallelism is a promising method to accelerate the motion estimation process for multithreading general-purpose processors. In this paper, we propose a parallel motion estimation algorithm which parallelizes the motion search process of the current H.264/AVC encoder. The proposed algorithm is implemented using the OpenMP application programming interface (API) and can be easily integrated into the current encoder. The experimental results show that the proposed parallel algorithm can reduce the processing time of the motion estimation up to 65.08% without any penalty in the rate-distortion (RD) performance.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.288-292
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• 1987

The purpose of this paper is to develope the parallel computation algorithm that enables it to minimize the completion tine of computation execution of the entire subtasks, under the constraints of the series-parallel precedence relation in each subtask. The developed algorithm was applied to the control of a robot manipulator functioned by multi-CPU's and to obtain the minimum time schedule so that real time control may be achieved. The completion time of computation execution was minimized by applying "Variable" Branch and Bound algorithm which was developed In this paper in determining the optimum ordered schedule for each CPU.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.471-474
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• 2011

In parallel job scheduling, each job can be executed simultaneously on multiple machines at a time. Thus in the input instance, a job $J_i$ requires the number $m_i$ of machines on which it shall be processed. The algorithm should determine not only the execution order of jobs but also the machines on which the jobs are executed. In this paper, when the jobs have deadlines, the problem is to maximize the total work of jobs which is completed by their deadlines. The problem is known to be strongly NP-hard [5] and we investigate the approximation algorithms for the problem. We consider a model in which the algorithm can have more machines than the adversary. With this advantage, the problem is how good solution the algorithm can produce against the optimal algorithm.