• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.12
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• pp.756-763
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• 2014

In this paper, we introduce a new sparse signal recovery algorithm referred to as the matching pursuit with greedy tree search (GTMP). The tree search in our proposed method is implemented to minimize the cost function to improve the recovery performance of sparse signals. In addition, a pruning strategy is employed to each node of the tree for efficient implementation. In our performance guarantee analysis, we provide the condition that ensures the exact identification of the nonzero locations. Through empirical simulations, we show that GTMP is effective for sparse signal reconstruction and outperforms conventional sparse recovery algorithms.

• Journal of the Korea Computer Industry Society
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• v.2 no.2
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• pp.203-210
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• 2001

To estimate relational models and test the theoretical hypotheses of binary tree search algorithms, we built binary search trees with random permutations of n (number of nodes) distinct numbers, which ranged from three to seven. Probabilities for building binary search trees corresponding to each possible height and balance factor were estimated. Regression models with variables of number of nodes, height, and average number of comparisons were estimated and the theorem of O(1g(n)) was accepted experimentally by a Lack of Test procedure. Analysis of Variance model was applied to compare the average number of comparisons with three groups by height and balance factor of the trees to test theoretical hypotheses of a binary search tree performance statistically.

• Journal of KIISE:Databases
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• v.36 no.5
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• pp.366-373
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• 2009

In this paper, we propose a similarity search algorithm for image databases. One of the central problems regarding content-based image retrieval (CBIR) is the semantic gap between the low-level features computed automatically from images and the human interpretation of image content. Many search algorithms used in CBIR have used the Minkowski metric (or $L_p$-norm) to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information. Our new search algorithm tackles this problem by employing new similarity measures and ranking strategies that reflect the nonlinearity of human perception and contextual information. Our search algorithm yields superior experimental results on a real handwritten digit image database and demonstrates its effectiveness.

• Journal of Distribution Science
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• v.15 no.7
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• pp.35-41
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• 2017

Purpose - A product bundling is a marketing approach where multiple products or components are packaged together into one bundle solution. This paper aims to introduce an optimal bundle search problem (hereinafter called "OBSP") which may be embedded with online recommendation system to provide an optimized service considering pairwise discount and delivery cost. Research design, data, and methodology - Online retailers have their own discount policy and it is time consuming for online shoppers to find an optimal bundle. Unlike an online system recommending one item for each search, the OBSP considers multiple items for each search. We propose a mathematical formulation with numerical example for the OBSP and analyzed the complexity of the problem. Results - We provide two results from the complexity analysis. In general case, the OBSP belongs to strongly NP-Hard which means the difficulty of the problem while the special case of OBSP can be solved within polynomial time by transforming the OBSP into the minimum weighted perfect matching problem. Conclusions - In this paper, we propose the OBSP to provide a customized service considering bundling price and delivery cost. The results of research will be embedded with an online recommendation system to help customers for easy and smart online shopping.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3B
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• pp.143-152
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• 2007

In order to provide new value-added services such as a policy-based routing and the quality of services in next generation network, the Internet routers need to classify packets into flows for different treatments, and it is called a packet classification. Since the packet classification should be performed in wire-speed for every packet incoming in several hundred giga-bits per second, the packet classification becomes a bottleneck in the Internet routers. Therefore, high speed packet classification algorithms are required. In this paper, we propose an efficient packet classification architecture based on a hierarchical binary search fee. The proposed architecture hierarchically connects the binary search tree which does not have empty nodes, and hence the proposed architecture reduces the memory requirement and improves the search performance.

• Magazine of the Korea Concrete Institute
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• v.9 no.1
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• pp.133-141
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• 1997

This work presents a method of optimum design for reinforced concrete building frames with rectangular cross sections. To overcome difficulties arising from the presence of two materials in one element(concrete and steel) , the principle of divided parameters is adopted. The design variable parameters are divided into two groups - external and internal. The optimization is also divided into external and internal procedure. Several scarxh algorithms are tested to verify their accuracy for the external optimization. This work proposes a new search method, a modified pattern search, and sample problems prove its accuracy and uscf'ulness. The design obtained by this method is an optimum and in full accord with ACI Building Code Ftequirements(ACI'318-89).

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.161-169
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• 1996

This paper presents a new search region prediction method using vector quantization for the motion estimation. We find motion vectors using the full search BMA from two successive frame images first. Then the motion vectors are used for training a codebook. The trained codebook is the predicted search region. We used the unsupervised neural network for VQ encoding and codebook design. A major advantage of formulating VQ as neural networks is that the large number of adaptive training algorithm that are used for neural networks can be applied to VQ. The proposed method reduces the computation and reduce the bits required to represent the motion vectors because of the smaller search points. The computer simulation results show the increased PSNR as compared with the other block matching algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7A
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• pp.828-835
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• 2004

An adaptive hexagon based search(AHBS) algorithm is proposed in this paper to perform block motion estimation in video coding. The AHBS evaluates the value of a given objective function starting from a diamond-shaped checking block and then continues its process using two hexagon-shaped checking blocks until the minimum value is found at the center of checking blocks. Also, the determination of which checking block is used depends on the position of minimum value occurred in previous searching step. The AHBS is compared with other fast searching algorithms including full search(FS). Experimental results show that the proposed algorithm provides competitive performance with slightly reduced computational complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3516-3541
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• 2018

Task scheduling is one of the most challenging aspects of cloud computing nowadays, and it plays an important role in improving overall performance in, and services from, the cloud, such as response time, cost, makespan, and throughput. A recent cloud task-scheduling algorithm based on the symbiotic organisms search (SOS) algorithm not only has fewer specific parameters, but also incurs time complexity. SOS is a newly developed metaheuristic optimization technique for solving numerical optimization problems. In this paper, the basic SOS algorithm is reduced, and chaotic local search (CLS) is integrated into the reduced SOS to improve the convergence rate. Simulated annealing (SA) is also added to help the SOS algorithm avoid being trapped in a local minimum. The performance of the proposed SA-CLS-SOS algorithm is evaluated by extensive simulation using the Matlab framework, and is compared with SOS, SA-SOS, and CLS-SOS algorithms. Simulation results show that the improved hybrid SOS performs better than SOS, SA-SOS, and CLS-SOS in terms of convergence speed and makespan.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.327-329
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• 2001

This paper presents the application of Parallel genetic algorithm and parallel tabu search to search an optimal solution of a unit commitment problem. The proposed method previously searches the solution globally using the parallel genetic algorithm, and then searches the solution locally using tabu search which has the good local search characteristic to reduce the computation time. This method combines the benefit of both method, and thus improves the performance. To show the usefulness of the proposed method, we simulated for 10 units system. Numerical results show the improvements of cost and computation time compared to previous obtained results.