• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.165.2-165
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• 2001

A number of previous researches in evolutionary algorithm are based on the study of facets we observe in natural evolution. The individuals of species in natural evolution occupy their own niche that is a subdivision of the habitat. This means that two species with the similar requirements cannot live together in the same niche. This is known as the competitive exclusion principle, i.e., complete competitors cannot coexist. In this paper, a new evolutionary programming algorithm adopting this concept is presented. Similarly in the case of natural evolution , the algorithm Includes the concept of niche obtained by partitioning a search space and the competitive exclusion principle performed by migrating individuals. Cell partition and individual migration strategies are used to preserve search diversity as well as to speed up convergence of an ...

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5A
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• pp.702-708
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• 2000

In this paper, we designed one-dimensional VLSI array with optimal pipeline period for high speed processing fractal image compression. The algorithm is derived which is suitable for VLSI array from axed block partition algorithm. Also the algorithm satisfies high quality of image and high compression-ratio. The designed VLSI array has optimal pipeline relied because the required processing time of PEs is distributed as same as possible. As this result, we can improve the processing speed up to about 3 times. The number of input/output pins can be reduced by sharing the input/output and arithmetic unit of the domain blocks and the range blocks.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.111-114
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• 1997

Generally, fuzzy models have the capability of dividing input space into several subspaces. compared to liner ones. But hitherto suggested fuzzy modeling algorithms not take into consideration the correlations between components of sample input data and address them independently of each other, which results in ineffective partition of input space. Therefore, to solve this problem. this letter proposes a new fuzzy modeling algorithm which partitions the input space more efficiently than conventional methods by taking into consideration correlations between components of sample data. As a way to use correlation and divide the input space. the method of principal component is used. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.291-293
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• 1996

Logic simulation is playing a very important role for design verification as circuits are larger and more complicated. However unknown values in 3 value simulators may generate the X-propagation problem which makes inaccurate output values. In this paper, a new partitioning method and a new simulation algorithm are developed to deal with the X-propagation problem efficiently. The new algorithm can optimize simulation time and accuracy by controlling partition depth. Benchmark results prove the effectiveness of the new simulation algorithm.

• Convergence Security Journal
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• v.6 no.2
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• pp.53-60
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• 2006

In the applications such as CAD or image processing, a variety of geometric objects are manipulated. A polygon in which all the edges are parallel to x- or y-axis is decomposed into simple rectangles for efficient handling. But, depending on the partitioning algorithms, the same region can be decomposed into a completely different set of rectangles in the number, size and shape of rectangles. So, it is necessary an algorithm that compares two sets of rectangles extracted from two scenes such as CAD or image to see if they represent the same region. This paper proposes an efficient algorithm that compares two sets of rectangles. The proposed algorithm is not only simpler than the algorithm based on sweeping method, but also reduces the number $O(n^2)$ of overlapped rectangles from the algorithm based on a balanced binary tree to O(nlogn).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1188-1202
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• 2012

In this paper, we propose a new multi-scale connected coherence tree algorithm (MCCTA) by improving the connected coherence tree algorithm (CCTA). In contrast to many multi-scale image processing algorithms, MCCTA works on multiple scales space of an image and can adaptively change the parameters to capture the coarse and fine level details. Furthermore, we design a Multi-scale Connected Coherence Tree algorithm plus Spectral graph partitioning (MCCTSGP) by combining MCCTA and Spectral graph partitioning in to a new framework. Specifically, the graph nodes are the regions produced by CCTA and the image pixels, and the weights are the affinities between nodes. Then we run a spectral graph partitioning algorithm to partition on the graph which can consider the information both from pixels and regions to improve the quality of segments for providing image segmentation. The experimental results on Berkeley image database demonstrate the accuracy of our algorithm as compared to existing popular methods.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.175-178
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• 1998

In this paper, turbo codes with the DC-free trellis codes based on partition chain as constituent codes are presente. And efficient methods to design the DC-free turbo codes are introduced. An iterative decoding with the MAP algorithm is used for the decoding of the turbo codes designed by various methods. As results of simulations, the presented DC-free turbo codes show better error performances than the DC-free trellis codes.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.5-8
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• 2000

In this paper, we propose new algorithms for the partition of input space and the generation of fuzzy control rules. The one consists of Shannon and extended fuzzy entropy function, the other consists of adaptive fuzzy neural system with back propagation teaming rule. The focus of this scheme is to realize the optimal fuzzy rule base with the minimal number of the parameters of the rules, reducing the complexity of the system. The proposed algorithm is tested with the time series prediction problem using Mackey-Glass chaotic time series.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.33-36
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• 2000

관찰학습(OLA: Observational Learning Algorithm)은 앙상블 네트워크의 각 구성 모델들이 다른 모델들을 관찰함으로써 얻어진 가상 데이터와 초기에 bo otstrap된 실제 데이터를 학습에 함께 이용하는 방법이다. 본 논문에서는, 초기 학습 데이터 셋을 분할하고 분할된 각 데이터 셋에 대하여 앙상블의 구성 모델들을 전문화(specialize)시키는 방법을 적용하여 기존의 관찰학습 알고리즘을 개선시켰다. 제안된 알고리즘은 bagging 및 boosting과의 비교실험에 의하여, 보다 적은 수의 구성 모델로 동일 내지 보다 나은 성능을 나타냄이 실험적으로 검증되었다.

• Journal of Korean Institute of Industrial Engineers
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• v.10 no.1
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• pp.3-10
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• 1984

A materials acquisition planning (MAP) problem that involves the determination of how much to order of a number of different items from a number of different suppliers is considered. This particular problem is modelled as a nonlinear mixed integer programming problem. A solution procedure based upon the partition of variables is developed to handle the MAP problem. This solution procedure utilizes a modified Hooke-Jeeves Pattern Search procedure along with a linear programming simplex algorithm. An example problem is presented and the results of applying the suggested solution procedure to this problem are reported.