• Journal of Ocean Engineering and Technology
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• v.16 no.4
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• pp.25-31
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• 2002

This paper is to find optimum design of plane framed structures with discrete variables. Global search algorithms for this problem are Genetic Algorithms(GAs), Simulated Annealing(SA) and Shuffled Complex Evolution(SCE), and hybrid methods (GAs-SA, GAs-SCE). GAs and SA are heuristic search algorithms and effective tools which is finding global solution for discrete optimization. In particular, GAs is known as the search method to find global optimum or near global optimum. In this paper, reinforced concrete plane frames with rectangular section and steel plane frames with W-sections are used for the design of discrete optimization. These structures are designed for stress constraints. The robust and effectiveness of Genetic Algorithms are demonstrated through several examples.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1097-1100
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• 1999

Due to the significant computation of full search in motion estimation, extensive research in fast motion estimation algorithms has been carried out. However, most of the algorithms have the degradation in predicted images compared with the full search algorithm. To reduce an amount of significant computation while keeping the same prediction quality of the full search, we propose a fast block-matching algorithm based on gradient magnitude of reference block without any degradation of predicted image. By using Taylor series expansion, we show that the block matching errors between reference block and candidate block are proportional to the gradient magnitude of matching block. With the derived result, we propose fast full search algorithm with adaptively determined scan direction in the block matching. Experimentally, our proposed algorithm is very efficient in terms of computational speedup and has the smallest computation among all the conventional full search algorithms. Therefore, our algorithm is useful in VLSI implementation of video encoder requiring real-time application.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.696-704
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• 1998

Artificial neural networks (ANN) have successfully applied into various areas. But, How to effectively established network is the one of the critical problem. This study will focus on this problem and try to extensively study. Firstly, four different learning algorithms ANNs were constructed. The learning algorithms include backpropagation, simulated annealing, genetic algorithm, and tabu search. The experimental results of the above four different learning algorithms were tested by statistical analysis. The training RMS, training time, and testing RMS were used as the comparison criteria.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.206-211
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• 2011

In this paper, the evolutionary algorithm was specifically formulated for optimization with continuous parameter space. The proposal was motivated by the fact that the genetic algorithms have been most intensively reported for parameter identification problems with continuous search space. The difference of primary characteristics between genetic algorithms and the proposed algorithm, discrete or continuous individual representation has made different areas to which the algorithms should be applied. Results obtained by optimization of some well-known test functions indicate that the proposed algorithm is superior to genetic algorithms in all the performance, computation time and memory usage for continuous search space problems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5B
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• pp.285-295
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• 2008

Internet routers perform packet forwarding which determines a next hop for each incoming packet using the packet's destination IP address. IP address lookup becomes one of the major challenges because it should be performed in wire-speed for every incoming packet under the circumstance of the advancement in link technologies and the growth of the number of the Internet users. Many binary search algorithms have been proposed for fast IP address lookup. However, tree-based binary search algorithms are usually unbalanced, and they do not provide very good search performance. Even for binary search algorithms providing balanced search, they have drawbacks requiring prefix duplication. In this paper, a new binary search algorithm which provides the balanced binary search and the number of its entries is much less than the number of original prefixes. This is possible because of composing the binary search tree only with disjoint prefixes of the prefix set. Each node has a prefix vector that has the prefix nesting information. The number of memory accesses of the proposed algorithm becomes much less than that of prior binary search algorithms, and hence its performance for IP address lookup is considerably improved.

• The Journal of the Korea Contents Association
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• v.7 no.1
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• pp.58-64
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• 2007

In pattern recognition field, there are many optimization problems having exponential search spaces. To solve of sequential search algorithms seeking sub-optimal solutions have been used. The algorithms have limitations of stopping at local optimums. Recently lots of researches attempt to solve the problems using genetic algorithms. This paper explains the huge search spaces of typical problems such as feature selection, classifier ensemble selection, neural network pruning, and clustering, and it reviews the genetic algorithms for solving them. Additionally we present several subjects worthy of noting as future researches.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.153-167
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• 2005

In this paper, we consider the problem of exploring unknown environments with a mobile robot or an autonomous character agent. Traditionally, research efforts to address the space exploration problem havefocused on the graph-based space representations and the graph search algorithms. Recently EXPLORE, one of the most efficient search algorithms, has been discovered. It traverses at most min$min(mn, d^2+m)$ edges where d is the deficiency of a edges and n is the number of edges and n is the number of vertices. In this paper, we propose DFS-RTA* and DFS-PHA*, two real-time graph search algorithms for directing an autonomous agent to explore in an unknown space. These algorithms are all built upon the simple depth-first search (DFS) like EXPLORE. However, they adopt different real-time shortest path-finding methods for fast backtracking to the latest node, RTA* and PHA*, respectively. Through some experiments using Unreal Tournament, a 3D online game environment, and KGBot, an intelligent character agent, we analyze completeness and efficiency of two algorithms.

• Journal of Broadcast Engineering
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• v.10 no.3
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• pp.357-371
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• 2005

In this Paper, we propose a new motion search algorithm called CDTS (Cross-Diamond-Triangle Search algorithm) that uses optimal search pattern according to the position of a search area to improve the performance of CDS(Cross-Diamond Search algorithm) as well as CDHSs(Cross-Diamond-Hexagonal Searches algorithms). We analyze motion distributions in various test video sequences to apply optimal search pattern according to a position of search area. Based on the result of this analysis, we propose a new triangle-shaped search pattern whose structure is asymmetric while previous search patterns are generally symmetric in conventional algorithms. In CDTS, we apply cross- and diamond-shaped search patterns to central search areas, and triangle- and diamond-shaped patterns to the other areas. Applying CDTS to test video sequences, the proposed scheme can reduce search points more than CDS and CDHSs by 16.22$\%$ and 3.09$\%$, respectively, without any visual quality degradation.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.183-186
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• 2000

Motion estimation technique has been used to increase video compression rates in motion video applications. One of the important algorithms to implement the motion estimation technique is search algorithm. Among many search algorithms, the H.263 adopted the Nearest Neighbors algorithm for fast search. In this paper, motion estimation block for the Nearest Neighbors algorithm is designed on FPGA and coded using VHDL and simulated under the Xilinx foundation environments. In the experiment results, we verified that the algorithm was properly designed and performed on the Xilinx FPGA(XCV300Q240)

• Steel and Composite Structures
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• v.18 no.2
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• pp.289-303
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• 2015

This study presents a strategy so-called Subspace Search Mechanism (SSM) for reducing the computational time for convergence of population based metaheusristic algorithms. The selected metaheuristic for this study is the Cuckoo Search algorithm (CS) dealing with size optimization of trusses. The complexity of structural optimization problems can be partially due to the presence of high-dimensional design variables. SSM approach aims to reduce dimension of the problem. Design variables are categorized to predefined groups (subspaces). SSM focuses on the multiple use of the metaheuristic at hand for each subspace. Optimizer updates the design variables for each subspace independently. Updating rules require candidate designs evaluation. Each candidate design is the assemblage of responsible set of design variables that define the subspace of interest. SSM is incorporated to the Cuckoo Search algorithm for size optimizing of three small, moderate and large space trusses. Optimization results indicate that SSM enables the CS to work with less number of population (42%), as a result reducing the time of convergence, in exchange for some accuracy (1.5%). It is shown that the loss of accuracy can be lessened with increasing the order of complexity. This suggests its applicability to other algorithms and other complex finite element-based engineering design problems.