• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4587-4605
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• 2019

High Efficiency Video Coding (HEVC) suffers from high computational complexity due to its quad-tree structure in motion estimation (ME). This paper exposes an adaptive search range decision algorithm for accelerating HEVC integer-pel ME on GPU which estimates the optimal search range (SR) using a MAP (Maximum A Posteriori) estimator. There are three main contributions; First, we define the motion feature as the standard deviation of motion vector difference values in a CTU. Second, a MAP estimator is proposed, which theoretically estimates the motion feature of the current CTU using the motion feature of a temporally adjacent CTU and its SR without any data dependency. Thus, the SR for the current CTU is parallelly determined. Finally, the values of the prior distribution and the likelihood for each discretized motion feature are computed in advance and stored at a look-up table to further save the computational complexity. Experimental results show in conventional HEVC test sequences that the proposed algorithm can achieves high average time reductions without any subjective quality loss as well as with little BD-bitrate increase.

• Korean Management Science Review
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• v.17 no.1
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• pp.41-54
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• 2000

in the real world situations that some jobs need be processed only on certain limited machines frequently occur due to the capacity restrictions of machines such as tools fixtures or material handling equipment. In this paper we consider n-job non-preemptive and m parallel machines scheduling problem having two machines group. The objective function is to minimize the sum of earliness and tardiness with different release times and due dates. The problem is formulated as a mixed integer programming problem. The problem is proved to be Np-complete. Thus a heuristic is developed to solve this problem. To illustrate its suitability and efficiency a proposed heuristic is compared with a genetic algorithm and tabu search for a large number of randomly generated test problems in ship engine assembly shop. Through the experimental results it is showed that the proposed algorithm yields good solutions efficiently.

• Journal of the Korea Safety Management & Science
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• v.3 no.3
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• pp.191-200
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• 2001

Parallel Genetic Algorithms partition the whole population into several sub-populations and search the optimal solution by exchanging the information each others periodically. Distributed Genetic Algorithm, one of Parallel Genetic Algorithms, divides a large population into several sub-populations and executes the traditional Genetic Algorithm on each sub-population independently. And periodically promising individuals selected from sub-populations are migrated by following the migration interval and migration rate to different sub-populations. In this paper, for the Travelling Salesman Problems, we analyze and compare with Distributed Genetic Algorithms using different Genetic Algorithms and using same Genetic Algorithms on each separated sub-population The simulation result shows that using different Genetic Algorithms obtains better results than using same Genetic Algorithms in Distributed Genetic Algorithms. This results look like the property of rapidly searching the approximated optima and keeping the variety of solution make interaction in different Genetic Algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.59
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• pp.11-20
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• 2000

The Job Shop Scheduling Problem(JSSP) is one of the most general and difficult of all traditional scheduling problems. The goal of this research is to develop an efficient scheduling method based on single genetic algorithm(SGA) and parallel genetic algorithm (PGA) to address JSSP. In this scheduling method, new genetic operator, generating method of initial population are developed and island model PGA are proposed. The scheduling method based on PGA are tested on standard benchmark JSSP. The results were compared with SGA and another GA-based scheduling method. The PGA search the better solution or improves average of solution in benchmark JSSP. Compared to traditional GA, the proposed approach yields significant improvement at a solution.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.425-426
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• 2007

In power systems operation, state estimation takes an important role in security control. For the state estimation problem, conventional optimization algorithm, such as weighted least squares (WLS) method, has been widely used. But these algorithms have disadvantages of converging local optimal solution. In these days, a modern heuristic optimization methods such as Particle Swarm Optimization (PSO), are introducing to overcome the problems of classical optimization. In this paper, we suggested parallel particle swarm optimization (PPSO) to search an optimal solution of state estimation in power systems. To show the usefulness of the proposed method over the conventional PSO, proposed method is applied on the IEEE-57 bus system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.7A
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• pp.1080-1089
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• 1999

The union upper bounds to the bit error probability of maximum likelihood(ML) soft-decoding of parallel concatenated convolutional codes(PCCC) and serially concatenated convolutional codes(SCCC) can be evaluated through the weight enumerating function(WEF). This union upper bounds become the lower bounds of the BER achievable when iterative decoding is used. In this paper, to compute the WEF, an efficient error event search algorithm which is a combination of stack algorithm and bidirectional search algorithm is proposed. By computor simulation, it is shown that the union boounds obtained by using the proposed algorithm become the lower bounds to BER of concatenated convolutional codes with iterative decoding.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.317-322
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• 2002

The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.580-582
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• 2005

This paper presents a new method which applies a genetic algorithm(GA) for determining which sectionalizing switch to operate in order to solve the distribution system loss minimization re-configuration problem. The distribution system loss minimization re-configuration problem is in essence a 0-1 planning problem which means that for typical system scales the number of combinations requiring searches becomes extremely large. In order to deal with this problem, a new a roach which applies a GA was presented. Briefly, GA are a type of random number search method, however, they incorporate a multi-point search feature. Further, every point is not is not separately and respectively renewed, therefore, if parallel processing is applied, we can expect a fast solution algorithm to result.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.76-84
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• 1994

This paper presents a new high-performance VLSI architecture and VLSI implementation for full-search block matching algorithm. The proposed VLSI architecture has the feature of two directional parallel and pipeline processing, thereby reducing the PE idle time at which the direction of block matching operation within the search area is changed. Therfore, the proposed architecture is faster than the existing architectures under the same clock frequency. Based on HSPICE circuit simulation, it is verified that the implemented procesing element is operated successfully within 13 ns for 75 MHz operation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.825-836
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• 2016

In this paper, a speed-up technique for the non-local means (NLM) image denoising method based on local binary descriptor (LBD) is proposed. In the NLM, most of the computation time is spent on searching for non-local similar patches in the search window. The local binary descriptor which represents the structure of patch as binary strings is employed to speed up the search process in the NLM. The descriptor allows for a fast and accurate preselection of non-local similar patches by bitwise operations. Using this approach, a tradeoff between time-saving and noise removal can be obtained. Simulations exhibit that despite being principally constructed for speed, the proposed algorithm outperforms in terms of denoising quality as well. Furthermore, a parallel implementation on GPU brings NLM-LBD to real-time image denoising.