• Journal of Institute of Control, Robotics and Systems
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• v.5 no.4
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• pp.403-410
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• 1999

In this study, a parallel algorithm has been developed that can quickly solve the optiaml control problem of large-scale dynamic systems. The algorithm adopts the sequential quadratic programming methods and achieves domain decomposition-type parallelism in computing sensitivities for search direction computation. A silicon wafer thermal process problem has been solved using the algorithm, and a parallel efficiency of 45% has been achieved with 16 processors. Practical methods have also been investigated in this study as a way to further speed up the computation time.

• Ocean and Polar Research
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• v.23 no.2
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• pp.173-179
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• 2001

Parallel supercomputing is now a must for oceanographic numerical modelers. Most of today's parallel numerical schemes use synchronous algorithms, where some processors that have finished their tasks earlier than others must wait at synchronization points for correct computation. Hence, the load balancing is a crucial factor, however, it is, in general, difficult to achieve on heterogeneous workstation clusters. We devise an asynchronous algorithm that reduces the idle times of faster processors, and discuss application of the algorithm to some grid problems and implementation on a workstation cluster using Message Passing Interface (MPI).

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.225-230
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• 2019

There are several methods which efficiencies of database are uprise. One of the well-known methods is that segments of database satisfying a query was rapidly accessed and processed. So if it is possible to search completely parallel multiple database segment types which satisfy a query, the response time of the query will be reduced. The matter of obtaining CPS(Completely Parallel Searchable) distribution without redundancy can be viewed as graph theoretic problem, and the operation of ring sum on the graph is used for CPS. In this paper, the parallel algorithm is proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.6C
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• pp.442-449
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• 2005

In this paper, we introduce three kinds of simplified high-speed decoding algorithms for turbo product decoder. First, A parallel decoder structure, the row and column decoders operate in parallel, is proposed. Second, HAD(Hard Decision Aided) algorithm is used for early-stopping algorithm. Lastly, P-Parallel TPC decoder is a parallel decoding scheme, processing P rows and P columns in parallel instead of decoding one by one as that in the original scheme.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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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.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.54 no.4
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• pp.163-170
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• 2005

This paper deals with an improved fault location algorithm with compensation ground capacitance through distributed parameter for a long parallel T/L. For the purpose of fault locating algorithm non-influenced by source impedance and fault resistance, the loop method was used in the system modeling analysis. This algorithm uses a positive and negative sequence of the fault current for high accuracy of fault locating calculation. Power system model of 160km and 300km long parallel T/L was simulated using EMTP software. To evaluate of the proposed algorithm, we used the several different cases 64 sampled data per cycle. The test results show that the proposed algorithm was minimized the error factor and speed of fault location estimation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.7
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• pp.465-472
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• 2004

Analog CPPN-based optimal road boundary detection algorithm for autonomous vehicle is proposed. The CPPN is a massively connected analog parallel array processor. In the paper, the dynamic programming which is an efficient algorithm to find the optimal path is implemented with the CPPN algorithm. If the image of road-boundary information is utilized as an inter-cell distance, and goals and start lines are positioned at the top and the bottom of the image, respectively, the optimal path finding algorithm can be exploited for optimal road boundary detection. By virtue of the parallel and analog processing of the CPPN and the optimal solution of the dynamic programming, the proposed road boundary detection algorithm is expected to have very high speed and robust processing if it is implemented into circuits. The proposed road boundary algorithm is described and simulation results are reported.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.1
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• pp.1-6
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• 2009

A Thinning algorithm is a very Important factor in order to recognize the character, figure, and drawing. Until comparatively lately, the thinning algorithm was proposed by various methods. In this paper, we ascertain the point at issue of ZS(Zhang and Suen), LW(Lu and Wang) and WHF(Wang, Hui and Fleming) algorithms that are the parallel thinning algorithms. The parallel thinning algorithm means the first processing doesn't have to influence to the second processing. ZS algorithm has a problem which loses pixels in slanting lines and LW algorithm doesn't have one pixel width in slanting lines. So I propose an advanced parallel thinning algorithm that connects the pixels each other and preserve the end point.

• Journal of Advanced Information Technology and Convergence
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• v.9 no.2
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• pp.89-97
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• 2019

Recently, there is an increasing demand for applications utilizing maps and locations such as autonomous vehicles and location-based services. Since these applications are developed based on spatial data, interest in spatial data processing is increasing and various studies are being conducted. In this paper, I propose a parallel mining algorithm using the CUDA library to efficiently analyze large spatial data. Spatial data includes both geometric (spatial) and non-spatial (aspatial) attributes. The proposed parallel spatial data mining algorithm analyzes both the geometric and non-spatial relationships between two layers. The experiment was performed on graphics cards containing CUDA cores based on TIGER/Line data, which is the actual spatial data for the US census. Experimental results show that the proposed parallel algorithm using CUDA greatly improves spatial data mining performance.