• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.318-319
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• 1999

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5464-5488
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• 2019

The multi-population genetic algorithm in multi-target paths coverage has become a top choice for many test engineers. Also, information sharing strategy can improve the efficiency of multi-population genetic algorithm to generate multi-target test data; however, there is still space for some improvements in several aspects, which will affect the effectiveness of covering the target path set. Therefore, a multi-target paths coverage strategy is proposed by improving multi-population genetic algorithm based on individual information sharing among populations. It primarily contains three aspects. Firstly, the behavior of the sub-population covering corresponding target path is improved, so that it can continue to try to cover other sub-paths after covering the current target path, so as to take full advantage of population resources; Secondly, the populations initialized are prioritized according to the matching process, so that those sub-populations with better path coverage rate are executed firstly. Thirdly, for difficultly-covered paths, the individual chromosome features which can cover the difficultly-covered paths are extracted by utilizing the data generated, so as to screen those individuals who can cover the difficultly-covered paths. In the experiments, several benchmark programs were employed to verify the accuracy of the method from different aspects and also compare with similar methods. The experimental results show that it takes less time to cover target paths by our approach than the similar ones, and achieves more efficient test case generation process. Finally, a plug-in prototype is given to implement the approach proposed.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.10 s.101
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• pp.995-1002
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• 2005

An orthogonal frequency division multiplexing(OFDM) system has the problem of the peak-to-average power ratio(PAR). In general, in order to obtain optimal PAR reduction using the partial transmitted sequence(PTS), the total search for the number of sub-blocks and the rotation factors must be accomplished. As the number of sub-blocks and rotation factors increases, PAR reduction improves, such that complexity increases exponentially and the process delay occurs simultaneously. Therefore a technique that reduces PAR, which is almost close to optimal, and the amount of calculation is desired. In this paper a new method using genetic algorithm(GA), which is widely used to search for a point that is globally optimal in many problems, is proposed to search for a rotation factor that reduces simultaneously both the PAR and the amount of calculation, such that the complexity of calculation and the process time are reduced at the same time, Comparison is performed between the proposed method and the various techniques developed previously. The superiority of proposed method is presented by demonstrating the reduction of complexity while a similar PAR reduction is obtained.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.43-48
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• 2014

Usually, image deconvolution is applied as a preprocessing step in surveillance systems to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose a blind-image deconvolution filtering approach based on genetic programming (GP). A numerical expression is developed using GP process for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighborhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded image. The performance of developed function is estimated using various degraded image sequences. Our comparative analysis highlights the effectiveness of the proposed filter.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.206-215
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• 2005

In this paper, we propose a new design method of Genetic Algorithm Processor(GAP) and Evolvable Hardware(EHW). All sorts of creature evolve its structure or shape in order to adapt itself to environments. Evolutionary Computation based on the process of natural selection not only searches the quasi-optimal solution through the evolution process, but also changes the structure to get best results. On the other hand, Genetic Algorithm(GA) is good fur finding solutions of complex optimization problems. However, it has a major drawback, which is its slow execution speed when is implemented in software of a conventional computer. Parallel processing has been one approach to overcome the speed problem of GA. In a point of view of GA, long bit string length caused the system of GA to spend much time that clear up the problem. Evolvable Hardware refers to the automation of electronic circuit design through artificial evolution, and is currently increased with the interested topic in a research domain and an engineering methodology. The studies of EHW generally use the XC6200 of Xilinx. The structure of XC6200 can configure with gate unit. Each unit has connected up, down, right and left cell. But the products can't use because had sterilized. So this paper uses Vertex-E (XCV2000E). The cell of FPGA is made up of Configuration Logic Block (CLB) and can't reconfigure with gate unit. This paper uses Vertex-E is composed of the component as cell of XC6200 cell in VertexE

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.704-710
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• 2017

Our research team is developing a 6-DOF manipulator that is adequate for the narrow workspace of press forming processes. This paper addresses the task sequence optimization methods for the manipulator to minimize the task-finishing time. First, a kinematic model of the manipulator is presented, and the anticipated times for moving among the task locations are computed. Then, a mathematical model of the task sequence optimization problem is presented, followed by a comparison of three meta-heuristic methods to solve the optimization problem: an ant colony system, simulated annealing, and a genetic algorithm. The simulation shows that the genetic algorithm is robust to the parameter settings and has the best performance in both minimizing the task-finishing time and the computing time compared to the other methods. Finally, the algorithms were implemented and validated through a simulation using Mathworks' Matlab and Coppelia Robotics' V-REP (virtual robot experimentation platform).

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.6
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• pp.24-30
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• 2018

Dental implants are currently widely used as artificial teeth due to their good chewing performance and long life cycle. A dental implant consists of an abutment as the upper part and a fixture as the lower part. When chewing forces are repeatedly applied to a dental implant, gap at the interface surface between the abutment and the fixture is often occurred, and results in some deteriorations such as loosening of fastening screw, dental retraction and fixture fracture. To cope with such problems, a sealing-type abutment having a number of grooves along the conical-surface circumference was previously developed, and shows better sealing performance than the conventional one. This study carries out optimization of the groove shape by genetic algorithm(GA) as well as structural analysis in consideration of external chewing force and pretension between the abutment and the fixture. The overall optimization system consists of two subsystems; the one is the genetic algorithm with MATLAB, and the other is the structural analysis with ANSYS. Two subsystems transmit and receive the relevant data with each other throughout the optimization processes. The optimization result is then compared with that of the conventional one with respect to the contact pressure and the maximum stress. The result shows that the optimized model gives better sealing performance than the conventional sealing abutment.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.49-55
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• 2021

Recently, with the development of database, it is possible to store a lot of data generated in finance, security, and networks. These data are being analyzed through classifiers based on machine learning. The main problem at this time is data imbalance. When we train imbalanced data, it may happen that classification accuracy is degraded due to over-fitting with majority class data. To overcome the problem of data imbalance, oversampling strategy that increases the quantity of data of minority class data is widely used. It requires to tuning process about suitable method and parameters for data distribution. To improve the process, In this study, we propose a strategy to explore and optimize oversampling combinations and ratio based on various methods such as synthetic minority oversampling technique and generative adversarial networks through genetic algorithms. After sampling credit card fraud detection which is a representative case of data imbalance, with the proposed strategy and single oversampling strategies, we compare the performance of trained classifiers with each data. As a result, a strategy that is optimized by exploring for ratio of each method with genetic algorithms was superior to previous strategies.

• Animal Bioscience
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• v.34 no.7
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• pp.1105-1115
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• 2021

Objective: To conserve and utilize the genetic resources of a traditional Chinese indigenous pig breed, Liangshan pig, we assessed the genetic diversity, genetic structure, and genetic distance in this study. Methods: We used 50K single nucleotide polymorphism (SNP) chip for SNP detection of 139 individuals in the Liangshan Pig Conservation Farm. Results: The genetically closed conserved population consisted of five overlapping generations, and the total effective content of the population (Ne) was 15. The whole population was divided into five boar families and one non-boar family. Among them, the effective size of each generation subpopulation continuously decreased. However, the proportion of polymorphic markers (P_N) first decreased and then increased. The average genetic distance of these 139 Liangshan pigs was 0.2823±0.0259, and the average genetic distance of the 14 boars was 0.2723±0.0384. Thus, it can be deduced that the genetic distance changed from generation to generation. In the conserved population, 983 runs of homozygosity (ROH) were detected, and the majority of ROH (80%) were within 100 Mb. The inbreeding coefficient calculated based on ROH showed an average value of 0.026 for the whole population. In addition, the inbreeding coefficient of each generation subpopulation initially increased and then decreased. In the pedigree of the whole conserved population, the error rate of paternal information was more than 11.35% while the maternal information was more than 2.13%. Conclusion: This molecular study of the population genetic structure of Liangshan pig showed loss of genetic diversity during the closed cross-generation reproduction process. It is necessary to improve the mating plan or introduce new outside blood to ensure long-term preservation of Liangshan pig.

• Journal of Korean Association for Spatial Structures
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• v.4 no.2 s.12
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• pp.89-97
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• 2004

The objective of this study is the development of a scheme and discrete optimum design algorithm, which is based on the genetic algorithm. The algorithm can perform both scheme and size optimum designs of plane trusses. The developed Scheme genetic algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of structures and the constraints are limits on loads and serviceability. The basic search method for the optimum design is the genetic algorithm. The algorithm is known to be very efficient for the discrete optimization. However, its application to the complicated structures has been limited because of the extreme time need for a number of structural analyses. This study solves the problem by introducing the size & scheme genetic algorithm operators into the genetic algorithm. The genetic process virtually takes no time. However, the evolutionary process requires a tremendous amount of time for a number of structural analyses. Therefore, the application of the genetic algorithm to the complicated structures is extremely difficult, if not impossible. The scheme genetic algorithm operators was introduced to overcome the problem and to complement the evolutionary process. It is very efficient in the approximate analyses and scheme and size optimization of plane trusses structures and considerably reduces structural analysis time. Scheme and size discrete optimum combined into the genetic algorithm is what makes the practical discrete optimum design of plane fusses structures possible. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying the algorithm to various optimum design examples: plane pratt, howe and warren truss.