• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5A
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• pp.611-617
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• 2000

Asynchronous mode W-CDMA system is kmown to be quite appropriate to the next generation mobile communication system, especially in a non-homogenious cellular architecture. In this case, however, each base station needs to use different spreading code for identification, so it is a demeanding task for a mobile terminal to find the best cell site and get an accurate code synchronization at the beginning of a communication. Since slow acquisition of a base station could mean the failure of initiation, a fast algorithm to accelerate the cell search process is essential. In this paper, a new cell search algorithm based on the binary code position modulation within the code block is proposed. Different cell sites are identified by different hopping code sequences, andeach position modulation is performed by the hopping code. The proposed algorithm is proved to make the cell search time in most places in a cell much shorter than the previous algorithms, and to make the receiver implementation simpler.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.2
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• pp.211-219
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• 1988

In this paper effective block matching algorithms are proposed to find the motion vector. There are two approaches to the estimation of the motion vector in MCC (motion compensated coding), i.e.pel(pixel element) recursive algorithm and block matching algorithm. The search algorithm in this paper is based on the block matching method. The advantage of this algorithm is the reduction of the computation time. In order to reduce the computation time, three mathods are proposed in this paper. These new algorithms are faster than other methods. Compared with the three step algorithm by Koga et al., the average ratio of the computational savings obtained from the proposed algorithm is about 3-4.

• Journal of Computing Science and Engineering
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• v.1 no.2
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• pp.162-176
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• 2007

Machine Learning (ML) has become very popular within Data Mining (KDD) and Artificial Intelligence (AI) research and their applications. In the ML and KDD contexts, two main approaches can be used for inducing a Bayesian Network (BN) from data, namely, Conditional Independence (CI) and the Heuristic Search (HS). When a BN is induced for classification purposes (Bayesian Classifier - BC), it is possible to impose some specific constraints aiming at increasing the computational efficiency. In this paper a new CI based approach to induce BCs from data is proposed and two algorithms are presented. Such approach is based on the Markov Blanket concept in order to impose some constraints and optimize the traditional PC learning algorithm. Experiments performed with the ALARM, as well as other six UCI and three artificial domains revealed that the proposed approach tends to execute fewer comparison tests than the traditional PC. The experiments also show that the proposed algorithms produce competitive classification rates when compared with both, PC and Naive Bayes.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.67-76
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• 1996

In the present paper we propose two new improved iterative restoration algorithms. One is to accelerate convergence of the steepest descent method using the improved search directions, while the other accelerates convergence by using preconditioners. It is also shown that the proposed preconditioned algorithm can accelerate iteration-adaptive iterative image restoration algorithm. The preconditioner in the proposed algorithm can be implemented by using the FIR filter structure, so it can be applied to practical application with manageable amount of computation. Experimental results of the proposed methods show good perfomance improvement in the sense of both convergence speed and quality of the restored image. Although the proposed methods cannot be directly included in spatially-adaptive restoration, they can be used as pre-processing for iteration-adaptive algorithms.

• Journal of Broadcast Engineering
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• v.18 no.6
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• pp.808-815
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• 2013

In this paper, we propose a novel fast and low-complexity Motion Estimation (ME) algorithm for Ultra High Definition (UHD) High Efficiency Video Coding (HEVC). Motion estimation occupies 77~81% of the amount of computation in HEVC. After all, the main key of video codec implementation is to find a fast and low-complexity motion estimation algorithm and architecture. We analyze the previous motion estimation algorithms and propose three optimal algorithm to reduce the computation proportion for HEVC. The proposed algorithm uses only 0.36% of the amount of operations compared to full search algorithm while maintaining compression performance with slight loss of 1.1%.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.317-322
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• 2002

The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.580-582
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• 2005

This paper presents a new method which applies a genetic algorithm(GA) for determining which sectionalizing switch to operate in order to solve the distribution system loss minimization re-configuration problem. The distribution system loss minimization re-configuration problem is in essence a 0-1 planning problem which means that for typical system scales the number of combinations requiring searches becomes extremely large. In order to deal with this problem, a new a roach which applies a GA was presented. Briefly, GA are a type of random number search method, however, they incorporate a multi-point search feature. Further, every point is not is not separately and respectively renewed, therefore, if parallel processing is applied, we can expect a fast solution algorithm to result.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.2
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• pp.50-60
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• 1997

Reconfiguration of a memory array using spare rows and columns has been known to be a useful technique to improve the yield. When the numbers of spare rows and scolumns are limited, respectively, the repair problem is known to be NP-complete. In this paper, we propose the reconfiguration algorithm for an array of memory cells using faulty cel clustering, which removes rows and columns algrithm is the simplest reconfiguration method with the time complexity of $O(n^2)$, where n is the number of faulty cells, however the repair rate is very low. Whereas the exhaustive search algorithm has a high repair rate, but the time complexity is $O(2^n)$. The proposed algorithm provides the same repair rate as the exhaustive search algorithm for almost all cases and runs as fast as the greedy method. It has the time complexity of $O(n^3)$ in the worst case. We show that the propsed algorithm provides more efficient solutions than other algorithms using simulations.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.383-384
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• 2007

In this paper, we propose the new and simple method for sub-pixel block search algorithm by only using integer-pixel for motion estimation and compensation. In many papers, the fast search block match algorithms based on TSS have been proposed. However, these methods could be achieved a little reduction of the computational complexity. All of searching points by 1/4-pixel have own predicted integer-pixel SAD array. Therefor, if we know initial nine SAD values by integer, which is on the searching area of the reference frame, then we can find optimal searching point by 1/4-pixel, directly.

• Journal of Magnetics
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• v.18 no.1
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• pp.74-79
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• 2013

This paper proposes a sampling-based optimization method for electromagnetic design problems, where design sensitivities are obtained from the elaborate surrogate models based on the universal Kriging method and a local window concept. After inserting additional sequential samples to satisfy the certain convergence criterion, the elaborate surrogate model for each true performance function is generated within a relatively small area, called a hyper-cubic local window, with the center of a nominal design. From Jacobian matrices of the local models, the accurate design sensitivity values at the design point of interest are extracted, and so they make it possible to use deterministic search algorithms for fast search of an optimum in design space. The proposed method is applied to a mathematical problem and a loudspeaker design with constraint functions and is compared with the sensitivity-based optimization adopting the finite difference method.