• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.12
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• pp.27-34
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• 1994

Block matching motion estimation is the most widely used method for motion compensated coding of image sequences. Based on a two dimensional systolic array, VLSI architecture and implementation of the full search block matching algorithm are described in this paper. The proposed architecture improves conventional array architecture by designing efficient processing elements that can control the data prodeuced by efficient search window division method. The advantages are that 1) it allows serial input to reduce pin counts for efficient composition of local memories but performs parallel processing. 2) It is flexible and can adjust to dimensional changes of search windows with simple control logic. 3) It has no idel time during the operation. 4) It can operate in real/time for low and main level in MPEG-2 standard. 5) It has modular and regular structure and thus is sutiable for VLSI implementation.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.1006-1010
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• 1995

This paper describes Sexual Reproduction Genetic Algorithm(SRGA) for function optimization. In SRGA, each individual utilize a diploid chromosome structure. Sex cells(gametes) are produced through artificial meiosis in which crossover and mutation occur. The proposed method has two selection operators, one, individual selection which selects the individual to fertilize, and the other, gamete selection which makes zygote for offspring production. We consider the effects of multi-selection and diploidy on search performance. SRGA improves local and global search(exploitation and exploration) and show optimum tracking performance in nonstationary environments. Gray coding is incorporated to transforming the search space and Genic uniform distribution method is proposed to alleviate the problem of premature convergence.

• Structural Engineering and Mechanics
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• v.50 no.3
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• pp.257-273
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• 2014

This paper introduces a novel optimization technique based on gravitational search algorithm (GSA) for numerical optimization and multi-objective optimization of foundation. In the proposed method, a chaotic time varying system is applied into the position updating equation to increase the global exploration ability and accurate local exploitation of the original algorithm. The new algorithm called global-local GSA (GLGSA) is applied for optimization of some well-known mathematical benchmark functions as well as two design examples of spread foundation. In the foundation optimization, two objective functions include total cost and $CO_2$ emissions of the foundation subjected to geotechnical and structural requirements are considered. From environmental point of view, minimization of embedded $CO_2$ emissions that quantifies the total amount of carbon dioxide emissions resulting from the use of materials seems necessary to include in the design criteria. The experimental results demonstrate that, the proposed GLGSA remarkably improves the accuracy, stability and efficiency of the original algorithm.

• Korean Journal of Construction Engineering and Management
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• v.21 no.1
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• pp.50-60
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• 2020

Estimates of project costs in the early stages of a construction project have a significant impact on the operator's decision-making in important matters, such as the site's decision or the construction period. However, it is difficult to carry out the initial stage with confidence because information such as design books and specifications is not available. In previous studies, case-based reasoning was used to predict initial construction costs, and genetic algorithms were used to calculate the weight of the inquiry phase among them. However, some say that it is difficult to perform better than the current year because existing genetic algorithms are calculated in random numbers. To overcome these limitations, correlation numbers using correlation analysis rather than random numbers are reflected in the genetic algorithm by method of local search, and weights are calculated using a hybrid genetic algorithm that combines local search and genetic algorithms. A case-based reasoning model was developed using the weights calculated and validated with the data. As a result, it was found that the hybrid GA-CBR applied with local search performed better than the existing GA-CBR.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.857-862
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• 2004

This paper analyzes properties of the recently developed nonlinear optimization method, Dynamic Encoding Algorithm for Searches (DEAS) [1]. DEAS locates local minima with binary strings (or binary matrices for multi-dimensional problems) by iterating the two operators; bisectional search (BSS) and unidirectional search (UDS). BSS increases binary strings by one digit (i.e., zero or one), while UDS performs increment or decrement to binary strings with no change of string length. Owing to these search routines, DEAS retains the optimization capability that combines the special features of several conventional optimization methods. In this paper, a special feature of BSS and UDS in DEAS is analyzed. In addition, a effective global search strategy is established by using information of DEAS. Effectiveness of the proposed global search strategy is validated through the well-known benchmark functions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8A
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• pp.669-678
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• 2012

In this paper, we propose an efficient fast integer-pel motion estimation for H.264/AVC using local statistics of local motion vectors. Using neighboring motion vectors, we define a new statistical property that is used to determine a mode of motion search range of current block. In addition, an adaptive motion search range compensated method that is based on cumulative statistics of previous coded blocks is addressed to solve the problem of the statistical motion search range decision method. Experimental results show that proposed algorithm has the capability to reduce the computational cost over the other methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.15-19
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• 2004

Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the number of visiting times and the distance between visited cities. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.339-344
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• 2019

Global sequence alignment in search of similarity or homology favors larger size of the sequence because it keeps looking for more similar section between two sequences in the hope that it adds up scores for matched part in the rest of the sequence. If a substantial size of mismatched section exists in the middle of the sequence, it greatly reduces the total alignment score. In this case a whole sequence would be better to be divided into multiple sections. Overall alignment score over the multiple sections of the sequence would increase as compared to global alignment. This method is called multiple local alignment. In this paper, we implement a multiple local alignment algorithm, an extension of Smith-Waterman algorithm and show the experimental results for the algorithm that is able to search for sub-optimal sequence.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.92-98
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• 2017

Data clustering determines a group of patterns using similarity measure in a dataset and is one of the most important and difficult technique in data mining. Clustering can be formally considered as a particular kind of NP-hard grouping problem. K-means algorithm which is popular and efficient, is sensitive for initialization and has the possibility to be stuck in local optimum because of hill climbing clustering method. This method is also not computationally feasible in practice, especially for large datasets and large number of clusters. Therefore, we need a robust and efficient clustering algorithm to find the global optimum (not local optimum) especially when much data is collected from many IoT (Internet of Things) devices in these days. The objective of this paper is to propose new Hybrid Simulated Annealing (HSA) which is combined simulated annealing with K-means for non-hierarchical clustering of big data. Simulated annealing (SA) is useful for diversified search in large search space and K-means is useful for converged search in predetermined search space. Our proposed method can balance the intensification and diversification to find the global optimal solution in big data clustering. The performance of HSA is validated using Iris, Wine, Glass, and Vowel UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KSAK (K-means+SA+K-means) and SAK (SA+K-means) are better than KSA(K-means+SA), SA, and K-means in our simulations. Our method has significantly improved accuracy and efficiency to find the global optimal data clustering solution for complex, real time, and costly data mining process.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.6 s.177
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• pp.1529-1539
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• 2000

A newly developed optimization method which uses the genetic algorithm combined with the sensitivity analysis is presented in this paper. The genetic algorithm is a probabilistic method, searching the optimum at several points simultaneously, requiring only the values of the object and constraint functions. It has therefore more chances to find global solution and can be applied various problems. Nevertheless, it has such shortcomings that even it approaches the optimum rapidly in the early stage, it slows down afterward and it can't consider the constraints explicitly. It is only because it can't search the local area near the current points. The traditional method, on the other hand, using sensitivity analysis is of great advantage in searching the near optimum. Thus the combination of the two techniques makes use of the individual advantages, that is, the superiority both in global searching by the genetic algorithm and in local searching by the sensitivity analysis. Application of the method to the several test functions verifies that the method suggested is very efficient and powerful to find the global solutions, and that the constraints can be considered properly.