• Journal of Communications and Networks
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• v.10 no.3
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• pp.233-238
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• 2008

In this paper, the application of genetic algorithms (GAs) for orthogonal frequency division multiplexing (OFDM) signal peak-to-average power ratio (PAPR) reduction is investigated. A GA is applied in order to enhance the performance of some known techniques for OFDM PAPR reduction and the potential benefits are analyzed. Using the proposed techniques, the system designer can take advantage of the GA versatility, robustness, and adaptability to specific system requirements, in order to achieve a convenient trade-off between effectiveness and computational burden.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1892-1896
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• 2005

In this paper, an effective method for selecting significant input variables in building ANFIS (Adaptive Neuro-Fuzzy Inference System) for nonlinear system modeling is proposed. Dominant inputs in a nonlinear system identification process are extracted by evaluating the performance index and they are applied to ANFIS. The availability of our proposed model is verified with the Box and Jenkins gas furnace data. The comparisons with other methods are also given in this paper to show our proposed method is superior to other models.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.429-432
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• 2004

본 논문은 기존의 퍼지 다항식 뉴럴 네트워크 (Fuzzy Polynomial Neural Networks ; FPNN) 모델을 이용하여 비선형성 데이터에 대한 추론을 제안한다. 복잡한 비선형 시스템의 모델동정을 위하여 생성된 GMDH 방법에 기초한 FPNN의 각 노드는 퍼지 규칙을 기반으로 구축되었으며, 층이 진행되는 동안 모델 스스로 노드의 선택과 제거를 통해 최적의 네트워크 구조를 생성할 수 있는 유연성을 가지고 있다. FPNN 각각의 활성노드를 퍼지다항식 뉴론(Fuzzy Polynomial Neuron ; FPN)이라고 표현한다. FPNN의 후반부 구조는 입출력 변수 사이 의 간략과 회귀다항식 (1차, 2차, 변형된 2차식) 함수에 의해 구현된다. 규칙의 전반부 멤버쉽 함수는 삼각형과 가우시안형의 멤버쉽 함수가 사용된다. 또한 유전자 알고리즘을 사용하여 각노드의 부분표현식을 구성하는 입력변수의 수, 입력변수와 차수의 선택 동조를 통하여 최적의 Genetic Algorithms(GAs)을 이용한 FPNN모델을 설계하는 것이 유용하고 효과적임을 보인다.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.478-486
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• 2000

This paper proposes a design method that can minimize the power dissipation of CMOS digital circuits without affecting their optimal operation speeds. The proposed method is based on genetic algorithms(GAs) combined to the retiming technique, a circuit transformation technique of repositioning flip-flops. The proposed design method consists of two phases: the phase of retiming for optimizing clock periods and the phase of GA retiming for minimizing power dissipation. Experimental results using Synopsys Design Analyzer show that the proposed design method can reduce the critical path delay of example circuits by about 30-50% and improve the dynamic power performance of the circuits by about 1.4~18.4%.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.112-117
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• 1997

This paper presents a GA(Genetic Algorithms)-Optical control strategy for the control of the swing motion and the transverse position of the overhead crane. The overhead crane system is defined uncertain due to unknown system parameters such as payload and trolly mass. To control the overhead crane. the GA-Optimal control scheme is suggested. which transfers a trolly to a desired place as fast as possible and minimizes the swing of the payload during the transfer. The genetic algorithms are applied to fine digital optimal feedback gains. A computer simulation demonstrate the performance of the proposed the GA-digital optimal controller for the overhead crane.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.347-355
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• 2002

Obstacle avoidance of underactuated robot manipulators using switching computed torque method (SCTM) is presented. One fundamental feature of this novel method is to use partly stable controllers (PSCs) in order to fulfill the ultimate control objective. Here, we use genetic algorithms (GAs) to acquire the optimum switching sequence of the control actions for a given time frame with the available set of elemental controllers, depending on which links/variables are controlled. The effectiveness of the concept is illustrated by taking a three-degrees-of-freedom (DOF) manipulator and showing enhanced performance of the proposed control methodology.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.117-120
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• 2001

This paper presents a new global state-matching intelligent digital redesign method for nonlinear systems by using genetic algorithms (GAs). The proposed method results in global matching of the states of the analogously controlled system with those of the digitally controlled system while the conventional intelligent digital redesign method does not. The proposed method provides a new approach for the digital redesign of a class of fuzzy-model-based controllers.

• Journal of Korean Association for Spatial Structures
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• v.3 no.1 s.7
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• pp.35-46
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• 2003

Conventional analysis and design of steel structures are performed using the assumption of a either fully rigid or pinned. However, every steel connection lies in between fully rigid and pinned connection. So, It is important to consider the connection for steel structure design. In this paper Computer-based second-order elastic analysis is used to calculate one story two bay and two story three bay for steel structures with semi-rigid connection. Genetic Algorithms(GAs) and Sequential Unconstrained Minized Technique(SUMT) dynamic programming is used to the method for optimum design of steel structures. The efficiency and validity of the developed continuous and discrete optimum design algorithm was verified by applying the algorithm to optimum design examples.

• Industrial Engineering and Management Systems
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• v.3 no.1
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• pp.22-37
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• 2004

An integrated heuristic approach based on genetic algorithms (GAs) is proposed for solving the container selection and loading problems. The GA for container selection solves a two-dimensional knapsack problem, determining a set of containers to minimize the transportation or shipment cost. The GA for container loading solves for the weighted coefficients in the evaluation functions that are applied in selecting loading positions and boxes to be loaded, so that the volume utilization is maximized. Several loading constraints such as box orientation, stack priority, stack stability, and container stability are also incorporated into the algorithm. In general, our computational results based on randomly generated data and problems from the literature suggest that the proposed heuristic provides a good solution in a reasonable amount of computational time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.391-398
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• 2009

In this study, we introduce the design methodology of an optimized fuzzy cascade controller with the aid of hierarchical fair competition-based genetic algorithm(HFCGA) for ball & beam system. The ball & beam system consists of servo motor, beam and ball, and remains mutually connected in line in itself. The ball & beam system determines the position of ball through the control of a servo motor. The displacement change the position of ball leads to the change of the angle of the beam which determines the position angle of a servo motor. Consequently the displacement change of the position of the moving ball and its ensuing change of the angle of the beam results in the change of the position angle of a servo motor. We introduce the fuzzy cascade controller scheme which consists of the outer(1st) controller and the inner(2nd) controller as two cascaded fuzzy controllers, and auto-tune the control parameters(scaling factors) of each fuzzy controller using HFCGA. The inner controller controls the position of lever arm which corresponds to the position angle of a servo motor and the outer controller decides the set-point value of the inner controller. HFCGA is a kind of parallel genetic algorithms(PGAs), and helps alleviate the premature convergence being generated in conventional genetic algorithms (GAs). For a detailed comparative analysis from the viewpoint of the performance results and the design methodology, the proposed method for the ball & beam system which is realized by the fuzzy cascade controller based on HFCGA, is presented in comparison with the conventional PD cascade controller based on serial genetic algorithms.