• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.7
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• pp.9-14
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• 2003

In this work, a multi-block/multi-partitioning method is suggested for a multi-block parallelization. It has an advantage of uniform load balance via subdividing of each block on each processor. To make a comparison of parallel efficiency according to domain decomposition method, a multi-block/single-partitioning and a multi-block/ multi-partitioning methods are applied to the flow analysis solver. The multi-block/ multi-partitioning method has more satisfactory parallel efficiency because of optimized load balancing. Finally, it has applied to the CFDS code. As a result, the computing speed with sixteen processors is over twelve times faster than that of sequential solver.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.52-63
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• 2009

The problem of scheduling simply periodic task systems upon a uniform multiprocessor is considered. Partitioning of periodic task systems requires solving the bin-packing problem, which is known to be intractable (NP-hard in the strong sense). This paper presents a global scheduling algorithm which transforms a given simply periodic task system into another using a "task-splitting" technique. Each transformed simply periodic task system is guaranteed to be successfully scheduled upon any uniform multiprocessor using a partitioned scheduling algorithm. It is proven that the proposed algorithm achieves the theoretical maximum utilization bound upon any uniform multiprocessor platform.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.47-53
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• 2017

Recently, huge datasets have been generating rapidly in a variety of fields. Then, there is an urgent need for technologies that will allow efficient and effective processing of huge datasets. Therefore the problems of partitioning a huge dataset effectively and alleviating the processing overhead of the partitioned data efficiently have been a critical factor for scalability and performance in distributed database system. In our work we utilized multicore servers to provide scalable service to our distributed system. The partitioning of database over multicore servers have emerged from a need for new architectural design of distributed database system from scalability and performance concerns in today's data deluge. The system allows uniform access through a web service interface to concurrently distributed databases over multicore servers, using SQMD (Single Query Multiple Database) mechanism based on publish/subscribe paradigm. We will present performance results with the distributed database system built on multicore server, which is time intensive with traditional architectures. We will also discuss future works.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.3
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• pp.16-23
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• 1998

In this paper, we present a fast and efficient Iterative Improvement Partitioning(IIP) technique for VLSI circuits and hybrid bucket structures on its implementation. The IIP algorithms are very widely used in VLSI circuit partition due to their time efficiency. As the performance of these algorithms depends on choices of moving cell, various methods have been proposed. Specially, Cluster-Removal algorithm by S. Dutt significantly improved partition quality. We indicate the weakness of previous algorithms wjere they used a uniform method for choice of cells during for choice of cells during the improvement. To solve the problem, we propose a new IIP technique that selects the method for choice of cells according to the improvement status and present hybrid bucket structures for easy implementation. The time complexity of proposed algorithm is the same with FM method and the experimental results on ACM/SIGDA benchmark circuits show improvment up to 33-44%, 45%-50% and 10-12% in cutsize over FM, LA-3 and CLIP respectively. Also with less CUP tiem, it outperforms Paraboli and MELO represented constructive-partition methods by about 12% and 24%, respectively.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.9
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• pp.87-92
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• 1998

TM mode coupling coefficients for a generic five-layer DFB structure with trapezoidal and triangular gratings are calculated using the extended additional layer method. To determine the unperturbed field distributions of TM modes, a grating region is replaced by a new uniform layer whose inverse dielectric constant is the average value of the inverse dielectric constant of grating region in both longitudinal and transverse directions. Based on the self-consistent check, the validity of this method is established by comparing the results calculated by partitioning the grating region up to six uniform layers.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.565-569
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• 2006

This paper reviews some loop partitioning techniques such as loop splitting method by thresholds and Polychronopoulos' loop splitting method for exploiting parallelism from single loop which already developed. We propose improved loop splitting method for maximizing parallelism of single loops with non-constant dependence distances. By using the distance for the source of the first dependence, and by our defined theorems, we present generalized and optimal algorithms for single loops with non-uniform dependences. The algorithms generalize how to transform general single loops into parallel loops.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.516-524
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• 2004

In this paper, the initial value problem in clustering using K-means or Fuzzy-c-means is considered to reduce the number of iterations. Conventionally the initial values in clustering using K-means or Fuzzy-c-means are chosen randomly, which sometimes brings the results that the process of clustering converges to undesired center points. The choice of intial value has been one of the well-known subjects to be solved. The system of clustering using K-means or Fuzzy-c-means is sensitive to the choice of intial values. As an approach to the problem, the uniform partitioning method is employed to extract the optimal initial point for each clustering of data. Experimental results are presented to demonstrate the superiority of the proposed method, which reduces the number of iterations for the central points of clustering groups.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.118-127
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• 2017

We propose the dehazing algorithm which consists of two main parts, the derivation of the Atmospheric light and adaptive transmission map. In the getting the Atmospheric light value, we utilize the quad-tree partitioning where the depth of the partitioning is decided based on the difference between the averaged pixel values of the parent and children blocks. The proposed transmission map is adaptive for each pixel by using the parameter ${\beta}(x)$ to make the histogram of the pixel values in the map uniform. The simulation results showed that the proposed algorithm outperforms the conventional methods in the respect of the visual quality of the dehazed images and the computational complexity.

• The KIPS Transactions:PartD
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• v.15D no.4
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• pp.531-540
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• 2008

An Adaptive Random Testing(ART) is one of test case generation algorithms, which was designed to get better performance in terms of fault-detection capability than that of Random Testing(RT) algorithm by locating test cases in evenly spreaded area. Two ART algorithms, such as Distance-based ART(D-ART) and Restricted Random Testing(RRT), had been indicated that they have significant drawbacks in computations, i.e., consuming quadratic order of runtime. To reduce the amount of computations of D-ART and RRT, iterative partitioning of input domain strategy was proposed. They achieved, to some extent, the moderate computation cost with relatively high performance of fault detection. Those algorithms, however, have yet the patterns of non-uniform distribution in test cases, which obstructs the scalability. In this paper we analyze the distribution of test cases in an iterative partitioning strategy, and propose a new method of input domain enlargement which makes the test cases get much evenly distributed. The simulation results show that the proposed one has about 3 percent of improvement in terms of mean relative F-measure for 2-dimension input domain, and shows 10 percent improvement for 3-dimension space.

• Journal of the Korea Computer Graphics Society
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• v.15 no.3
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• pp.19-26
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• 2009

In this paper, a new adaptive vertex clustering using a KD-tree is presented for 3D mesh partitioning. A vertex clustering is used to divide a huge 3D mesh into several partitions for various mesh processing. An octree-based clustering and K-means clustering are currently leading techniques. However, the octree-based methods practice uniform space divisions and so each partitioned mesh has non-uniformly distributed number of vertices and the difference in its size. The K-means clustering produces uniformly partitioned meshes but takes much time due to many repetitions and optimizations. Therefore, we propose to use a KD-tree to efficiently partition meshes with uniform number of vertices. The bounding box region of the given mesh is adaptively subdivided according to the number of vertices included and dynamically determined axis. As a result, the partitioned meshes have a property of compactness with uniformly distributed vertices.