• Journal of Korean Society for Quality Management
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• v.32 no.4
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• pp.125-139
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• 2004

Generally, parallel redundancy is used to improve reliability in many systems. However, redundancy increases system cost, weight, volume, power, etc. Due to limited availability of these resources, the system designer has to maximize reliability subject to various constraints or minimize resources while satisfying the minimum requirement of system reliability. This paper presents GAs (Genetic Algorithms) to solve redundancy allocation in series-parallel systems. To apply the GAs to this problem, we propose a genetic representation, the method for initial population construction, evaluation and genetic operators. Especially, to improve the performance of GAs, we develop heuristic operators (heuristic crossover, heuristic mutation) using the reliability-resource information of the chromosome. Experiments are carried out to evaluate the performance of the proposed algorithm. The performance comparison between the proposed algorithm and a pervious method shows that our approach is more efficient.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.968-970
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• 2001

Recently, the power supply equipments have tendency to take multiple feedback loop paths. In this paper the state space averaging technique is applied for the analysis of flyback type current mode control circuit. We made real converter for the gurantee of stable output characteristic and proper design of feedback circuit. The validity of proposed method is verified from test results. The improvement of stability is confirmed by sinusoidal signal injection method with isolated transformer. It is known that phase margin is sufficient and gain crossover frequency $f_c$ is nearly 1/5 of switching frequency $f_s$, from the experimental result with frequency response analyzer.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.96-100
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• 2007

Condenser lens and objective lens are used to demagnify the image of the crossover to the final spot size. In lens, electrons are focused by magnetic fields. This fields is fringing field. It is important in electron focusing. Electron focusing occurs the radial component field and axial component field. Radial component produces rotational force and axial component produces radial force. Radial force causes the electron's trajectory to curve toward the optic axis and corss it. Focal length decreases as the current of lens increases. In this paper, we use the focal length for desiging the hardware of lens current control and present the results.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.254-254
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• 2000

This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. genetic programming can be useful control method for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent tree structured chromosome on hardware, and it is difficult to use crossover operator on hardware. Therefore, genetic programming is not so popular as genetic algorithms in evolvable hardware community in spite of its possible strength. We propose a chromosome representation methods and a hardware implementation method that can be helpful to this situation. Our method uses context switchable identical block structure to implement genetic tree on evolvable hardware. We composed an evolutionary strategy (or evolvable hardware by combining proposed method with other's striking research results. Proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10c
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• pp.214-216
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• 2001

인터넷에서 멀티미디어 서비스와 실시간 서비스의 요구가 증대하고 사용자가 요구하는 서비스의 품질이 높아짐에 따라 기존의 한정된 무선 자원은 부족하게 되었다. 이에 여러 가지 이동성 관리 방안들이 제시되었다. 본 논문에서는 셀룰러 IP를 기반으로 효율적인 이동성 관리를 위해 가상 대신을 이용한 이동성 관리 방안을 제안한다. 기존의 셀룰러 IP에서 구현한 Semisoft 핸드오프는 Crossover 베이스 스테이션을 통한 핸드오프 알고리즘으로 디자인 되어있다. 빠른 핸드오프를 위해 기존의 Semisoft 핸드오프 알고리즘을 확장하여 Semisoft 지연을 최소화하도륵 셀룰러 IP 프로토콜을 확장한다. 이 논문에서 Semisoft 핸드오프 알고리즘과 이를 위한 네트웍 구조를 설계한다. 이는 가상머신을 적용된 FA(Foreign Agent)인 VFA(Virtual Foreign Agent)를 통해 구현한다. 또한 VFA를 바탕으로 Semisoft 지연을 최소화하여 QoS의 보장을 해결한다.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.624-636
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• 2006

This paper proposes a hybrid approach involving Genetic Algorithm (GA) and Bacterial Foraging (BF) for tuning the PID controller of an AVR. Recently the social foraging behavior of E. coli bacteria has been used to solve optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the life time of the bacteria. Further, the proposed algorithm is used for tuning the PID controller of an AVR. Simulation results are very encouraging and this approach provides us a novel hybrid model based on foraging behavior with a possible new connection between evolutionary forces in social foraging and distributed non-gradient optimization algorithm design for global optimization over noisy surfaces.

• Journal of Korean Clinical Health Science
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• v.8 no.1
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• pp.1355-1361
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• 2020

Purpose: This study was conducted to investigate the effect of flipped-learning method on dental hygiene practice satisfaction. Methods: The study was a patient-group crossover design involving 53 third-year students at D's Department of Dental Hygiene. The study tools used self-questionnair and the analysis program used SPSS Ver 25.0. Results: Class satisfaction increased to 3.85 in the first semester and 4.23 in the second semester (p <0.05). Satisfaction with the flip learning method was 4.26, and most answered yes. In addition, it showed a positive effect on class satisfaction (p <0.01). As a result, it can be seen that the flip-learning learning method has a positive effect on the learners' learning motivation, academic achievement, and class satisfaction. Conclusions: it is considered that the flip learning method for hands-on classes should be expanded for the purpose of fostering job competency and high quality clinical practice experts.

• Advances in Computational Design
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• v.1 no.3
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• pp.275-296
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• 2016

This article presents an adaptive directional differential evolution (ADDE) algorithm and its application in solving discrete sizing truss optimization problems. The algorithm is featured by a new self-adaptation approach and a simple directional strategy. In the adaptation approach, the mutation operator is adjusted in accordance with the change of population diversity, which can well balance between global exploration and local exploitation as well as locate the promising solutions. The directional strategy is based on the order relation between two difference solutions chosen for mutation and can bias the search direction for increasing the possibility of finding improved solutions. In addition, a new scaling factor is introduced as a vector of uniform random variables to maintain the diversity without crossover operation. Numerical results show that the optimal solutions of ADDE are as good as or better than those from some modern metaheuristics in the literature, while ADDE often uses fewer structural analyses.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.17-24
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• 2013

Release planning in a software product line (SPL) is to select and assign the features of the multiple software products in the SPL in sequence of releases along a specified planning horizon satisfying the numerous constraints regarding technical precedence, conflicting priorities for features, and available resources. A greedy genetic algorithm is designed to solve the problems of release planning in SPL which is formulated as a precedence-constrained multiple 0-1 knapsack problem. To be guaranteed to obtain feasible solutions after the crossover and mutation operation, a greedy-like heuristic is developed as a repair operator and reflected into the genetic algorithm. The performance of the proposed solution methodology in this research is tested using a fractional factorial experimental design as well as compared with the performance of a genetic algorithm developed for the software release planning. The comparison shows that the solution approach proposed in this research yields better result than the genetic algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.5
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• pp.295-304
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• 2003

In this paper, we proposed a hardware-oriented Genetic Algorithm Processor(GAP) based on subpopulation architecture for high-performance convergence and reducing computation time. The proposed architecture was applied to enhancing population diversity for correspondence to premature convergence. In addition, the crossover operator selection and linear ranking subpop selection were newly employed for efficient exploration. As stochastic search space selection through linear ranking and suitable genetic operator selection with respect to the convergence state of each subpopulation was used, the elapsed time of searching optimal solution was shortened. In the experiments, the computation speed was increased by over $10\%$ compared to survival-based GA and Modified-tournament GA. Especially, increased by over $20\%$ in the multi-modal function. The proposed Subpop GA processor was implemented on FPGA device APEX EP20K600EBC652-3 of AGENT 2000 design kit.