• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.310-315
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• 2005

This paper proposes a fast optimization method by combining queen-bee evolution and derivative evaluation in genetic algorithms. These two operations make it possible for genetic algorithms to focus on highly fitted individuals and rapidly evolved individuals, respectively. Even though the two operations can also increase the probability that genetic algorithms fall into premature convergence phenomenon, that can be controlled by strong mutation rates. That is, the two operations and the strong mutation strengthen exploitation and exploration of the genetic algorithms, respectively. As a result, the genetic algorithm employing queen-bee evolution and derivative evaluation finds optimum solutions more quickly than those employing one of them. This was proved by experiments with one pattern matching problem and two function optimization problems.

• Journal of Applied Reliability
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• v.18 no.2
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• pp.173-183
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• 2018

Purpose: It is critical to design a set of experiments in Test and Evaluation Process for a weapon system. Because there is no sufficient resources in real-world, one must choose a subset of experiments which is considered to be more important. Methods: We introduce an optimization model for choosing the subset of experiments by considering a priority of experimental variable and level and restrictions of resources. We describe in detail how we construct objective function and constraints which must be a right realization of our logic and assumption. Conclusion: Since our optimization model turns out to be computationally difficult to solve, we introduce an algorithm for reducing the size of problem. Various computational results follows.

• International Journal of Control, Automation, and Systems
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• v.4 no.2
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• pp.155-164
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• 2006

This paper focuses on a new optimization technique of a fuzzy logic based proportional integral (FLPI) load frequency controller by the multiple tabu search (MTS) algorithm. Conventionally, the membership functions and control rules of fuzzy logic control are obtained by trial and error method or experiences of designers. To overcome this problem, the MTS algorithm is proposed to simultaneously tune proportional integral gains, the membership functions and control rules of a FLPI load frequency controller in order to minimize the frequency deviations of the interconnected power system against load disturbances. The MTS algorithm introduces additional techniques for improvement of the search process such as initialization, adaptive search, multiple searches, crossover and restart process. Simulation results explicitly show that the performance of the proposed FLPI controller is superior to conventional PI and FLPI controllers in terms of overshoot and settling time. Furthermore, the robustness of the proposed FLPI controller under variation of system parameters and load change are higher than that of conventional PI and FLPI controllers.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.11-19
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• 2024

The increased utilization of the FFT in signal processing, cryptography, and various other fields has highlighted the importance of optimization. In this paper, we propose the implementation of an accelerator that processes the radix-2 16 points FFT algorithm more rapidly and efficiently than FFT implementation of existing studies, using FPGA(Field Programmable Gate Array) hardware. Leveraging the hardware advantages of FPGA, such as parallel processing and pipelining, we design and implement the FFT logic in the PL (Programmable Logic) part using the Verilog language. We implement the FFT using only the Zynq processor in the PS (Processing System) part, and compare the computation times of the implementation in the PL and PS part. Additionally, we demonstrate the efficiency of our implementation in terms of computation time and resource usage, in comparison with related works.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.2
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• pp.88-99
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• 1992

This paper presents an efficient method of mapping Boolean equations to a set of library gates. The proposed system performs technology mapping by graph covering. To select optimal area cover, a new cost function and local area optimization are proposed. Experimental results show that the proposed algorithm produces effective mapping using given library.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.8
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• pp.627-635
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• 2012

This study aims at developing an artificial neural network(ANN)-based predictive and adaptive temperature control method to control the openings at internal and external skins, and heating systems used in a building with double skin envelope. Based on the predicted indoor temperature, the control logic determined opening conditions of air inlets and outlets, and the operation of the heating systems. The optimization process of the initial ANN model was conducted to determine the optimal structure and learning methods followed by the performance tests by the comparison with the actual data measured from the existing double skin envelope. The analysis proved the prediction accuracy and the adaptability of the ANN model in terms of Root Mean Square and Mean Square Errors. The analysis results implied that the proposed ANN-based temperature control logic had potentials to be applied for the temperature control in the double skin envelope buildings.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1471-1480
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• 2020

This research presents a case study on the remediation of a radioactive waste (uranium: U) utilizing a multi-objective fuzzy optimization in an electrocoagulation process for the iron-stainless steel and aluminum-stainless steel anode/cathode systems. The incorporation of the cumulative uncertainty of result, operational cost and energy consumption are essential key elements in determining the feasibility of the developed model equations in satisfying specific maximum contaminant level (MCL) required by stringent environmental regulations worldwide. Pareto-optimal solutions showed that the iron system (0 ㎍/L U: 492 USD/g-U) outperformed the aluminum system (96 ㎍/L U: 747 USD/g-U) in terms of the retained uranium concentration and energy consumption. Thus, the iron system was further carried out in a multi-objective analysis due to its feasibility in satisfying various uranium standard regulatory limits. Based on the 30 ㎍/L MCL, the decision-making process via fuzzy logic showed an overall satisfaction of 6.1% at a treatment time and current density of 101.6 min and 59.9 mA/㎠, respectively. The fuzzy optimal solution reveals the following: uranium concentration - 5 ㎍/L, cumulative uncertainty - 25 ㎍/L, energy consumption - 461.7 kWh/g-U and operational cost based on electricity cost in the United States - 60.0 USD/g-U, South Korea - 55.4 USD/g-U and Finland - 78.5 USD/g-U.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.325-328
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• 2008

본 논문에서는 비선형 모델의 설계를 위해 Type-2 퍼지 논리 집합을 이용하여 불확실성 문제를 다룬다. 퍼지 논리 시스템의 멤버쉽 함수와 규칙의 구조는 불확실성이 존재하는 언어적인 정보 또는 수치적 데이터를 바탕으로 설계된다. 기존의 Type-1 퍼지 논리 시스템은 외부의 노이즈와 같은 불확실성을 효율적으로 취급할 수 없다. 그러나 Type-2 퍼지 논리 시스템은 불확실한 정보까지 멤버쉽 함수로 표현함으로서 불확실성을 효과적으로 다룰 수 있다. 따라서 본 논문에서는 규칙의 전 ${\cdot}$ 후반부가 Type-2 퍼지 집합으로 구성된 Type-2 퍼지 논리 시스템을 설계하고 불확실성의 변화에 대한 비선형 모델의 성능을 비교한다. 여기서 규칙 전반부 멤버쉽 함수의 정점 선택은 C-means 클러스터링 알고리즘을 이용하고, 규칙 후반부 퍼지 집합의 정점 결정에는 입자 군집 최적화(PSO : Particle Swarm Optimization) 알고리즘을 사용한다. 마지막으로, 비선형 모델 평가에 대표적으로 이용되는 가스로 시계열 데이터를 제안된 모델에 적용하고, 입력 데이터에 인위적인 노이즈가 포함되었을 경우 Type-2 퍼지 논리 시스템이 기존의 Type-1 퍼지 논리 시스템보다 우수함을 보인다.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.43-51
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• 1996

Fuzzy rules in fuzzy logic control play a major role in deciding the control dynamics of a fuzzy logic controller. Thus, control performance is mainly determined by the quality of fuzzy rules. This paper introduces an optimization method for fuzzy rules using GAS with adaptive probabilies of crossover and mutation. Also we design two fitness measures to satisfy control objectives by partitioning the response of a plant into two parts. An initial population is generated by an automatic fuzzy rule generation method instead of random selection for fast a.pproaching to the final solution. We employed a nonlinear plant to simulate our method. It is shown through simulation that our method is reasonable and can be useful for optimizing fuzzy rules.

• Journal of Power Electronics
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• v.11 no.5
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• pp.734-742
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• 2011

In this paper a robust method is presented for the combined design of STATCOM and Power System Stabilizer (PSS) controllers in order to enhance the damping of the low frequency oscillations in power systems. The combined design problems among PSS and STATCOM internal ac and dc voltage controllers has been taken into consideration. The equations that describe the proposed system have been linearized and a Fuzzy Logic Controller (FLC) has been designed for the PSS. Then, the Particle Swarm Optimization technique (PSO) which has a strong ability to find the most optimistic results is employed to search for the optimal STATCOM controller parameters. The proposed controllers are evaluated on a single machine infinite bus power system with the STATCOM installed in the midpoint of the transmission line. The results analysis reveals that the combined design has an excellent capability in damping a power system's low frequency oscillations, and that it greatly enhances the dynamic stability of power systems. Moreover, a system performance analysis under different operating conditions and some performance indices studies show the effectiveness of the combined design.