• 한국학교수학회논문집
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• 제20권2호
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• pp.91-117
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• 2017

본 논문에서는 수학적 지식을 스스로 구성하여 개념적으로 이해할 수 있도록 개발된 발생적 모델링을 활용하여 로그 단원에 대한 교수 학습 자료를 개발하고 발생적 모델링 활동을 통해 학생들이 로그 개념을 이해해 나가는 과정을 분석하고자 한다. 이를 위해 로그 단원을 3가지 소주제로 나누고 각각의 소주제별로 발생적 모델링의 교수학적 4단계인 적용, 추출, 압축, 구성 틀에 맞추어 발생적 근원 맥락을 담고 학생 스스로 개념을 구성해 나갈 수 있는 교수 학습 자료를 개발하였다. 개발된 자료를 이용하여 중하 수준 학생 2명과 중상 수준 학생 2명을 대상으로 수업을 진행하였다. 이를 통해 발생적 모델링의 교수학적 4단계를 따르는 로그 단원에 대한 개념 구성 과정을 살펴보고 van Hiele이 제시한 일반적인 수학학습수준을 바탕으로 학생들의 로그 단원에 대한 이해정도를 분석하여 몇 가지 교수학적 시사점을 제안하였다.

• 한국세라믹학회지
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• 제40권12호
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• pp.1170-1176
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• 2003

진화최적방법을 이용하여 alkali earth borosilicate 계열(Eu, Mg, Ca, Sr, Ba)$_{x}$ $B_{y}$S $i_{z}$ $O_{d}$에 E $u^{3+}$ 를 도핑 하여 고효율 적색 형광체를 합성하였다. 본 연구는 삼원색 백색 LED로의 적용을 목적으로 한다. 진화최적방법은 유전알고리즘과 조합화학을 연계하여, LED형광체 개발을 위해 개발하였다. 유전알고리즘을 조합화학에 접목함으로써 시간과 자원의 낭비 없이 매우 효율적인 형광체 탐색을 꾀할 수 있었다. 실질적인 실험에 앞서 다양한 목적함수를 이용하여 시뮬레이션을 실시하여 본 연구의 타당성을 증명하고 실제 합성한 결과 삼원색 백색 LED용 적색형광체(E $u_{0.14}$M $g_{0.18}$C $a_{0.07}$B $a_{0.12}$ $B_{0.17}$S $i_{0.32}$ $O_{{\delta}}$)를 얻었다.얻었다.다.얻었다.얻었다.다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.707-712
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• 1998

This paper presents several softcomputing techniques such as neural networks, fuzzy logic and genetic algorithms : Neural networks as brain metaphor provide fundamental structure, fuzzy logic gives a possibility to utilize top-down knowledge from designer, and genetic algorithms as evolution metaphor determine several system parameters with the process of bottom up development. With these techniques, we develop a pattern recognizer which consists of multiple neural networks aggregated by fuzzy integral in which genetic algorithms determine the fuzzy density values. The experimental results with the problem of recognizing totally unconstrained handwritten numeral show that the performance of the proposed method is superior to that of conventional methods.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.607-612
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• 1998

In this paper, a controller using fuzzy-genetic algorithms is proposed for pat-tracking of WMR. A fuzzy controller is implemented so as to adjust appropriate crossover rate and mutation rate. A genetic algorithms is also implemented to have adaptive adjustment of control gain during optimizing process. To check effectiveness of this algorithms, computer simulation is applied.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.863-868
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• 1992

This paper presents a method on applying Genetic Algorithm(GA), which is a well-known high performance optimizing algorithm, to construct the self-organizing fuzzy logic controller. Fuzzy logic controller considered in this paper utilizes Sugeno's hybrid inference method, which has an advantage of simple defuzzification process in the inference engine. Genetic algorithm is used to find the optimal parameters in the FLC. The proposed approach will be demonstrated using 2 d.o.f robot manipulator to verify its effectiveness.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.1353-1355
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• 2003

Genetic algorithms (GA) is a well known technique that is capable of handling multiobjective functions and discrete constraints in the process of numerical optimization. Together with the Pareto ranking scheme, more than one possible solution can be obtained despite the imposed constraints and multi-criteria design functions. In view of this unique capability, the design of the log-periodic dipole antenna array (LPDA) using this special feature is proposed in this paper. This method also provides gain, front-back level and S parameter design tradeoff for the LPDA design in broadband application at no extra computational cost.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1305-1308
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• 2003

A new technique is presented to construct a predictive model of plasma etch process. This was accomplished by combining a backpropagation neural network (BPNN) and a genetic algorithm (GA). The predictive model constructed in this way is referred to as a GA-BPNN. The GA played a role of controlling training factors simultaneously. The training factors to be optimized are the hidden neuron, training tolerance, initial weight magnitude, and two gradients of bipolar sigmoid and linear functions. Each etch response was optimized separately. The proposed scheme was evaluated with a set of experimental plasma etch data. The etch process was characterized by a $2^3$ full factorial experiment. The etch responses modeled are aluminum (A1) etch rate, silica profile angle, A1 selectivity, and dc bias. Additional test data were prepared to evaluate model appropriateness. The GA-BPNN was compared to a conventional BPNN. Compared to the BPNN, the GA-BPNN demonstrated an improvement of more than 20% for all etch responses. The improvement was significant in the case of A1 etch rate.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.358-363
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• 2003

Optimal tuning plays an important role in operations or tuning of the complex process such as the main steam temperature of the thermal power plant. However, it is very difficult to maintain the steam temperature of power plant using conventional optimization methods, since these processes have the time delay and the change of the dynamic characteristics in the reheater. Up to the present time, the Pm controller has been used. However, it is not easy to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. This paper suggests immune algorithm based tuning technique for PID Controller on steam temperature process with long dead time and its results are compared with genetic algorithm based tuning technique.

• 동력기계공학회지
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• 제3권4호
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• pp.63-68
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• 1999

The determination of the optimal machining parameters in metal cutting, such as cutting speed, feed rate, and depth of cut, is an important aspect in an economic manufacturing process. The main objective in general is either to minimize the production cost or to maximize the production rate. Also there are constraints on all the machining operations which put restrictions on the choice of the machining parameters. In this paper as an objective function the production cost is considered with two constraints, surface finish and cutting power. Genetic Algorithm is applied to determine the optimum machining parameters, and the effectiveness of the applied algorithm is demonstrated by means of an example, turning operation.

• Journal of Electrical Engineering and Technology
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• 제5권4호
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• pp.653-665
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• 2010

In this study, we introduce a neurofuzzy approach to the design of fuzzy controllers. The development process exploits key technologies of Computational Intelligence (CI), namely, genetic algorithms (GA) and neurofuzzy networks. The crux of the design methodology deals with the selection and determination of optimal values of the scaling factors of fuzzy controllers, which are essential to the entire optimization process. First, the tuning of the scaling factors of the fuzzy controller is carried out. Next, we form a nonlinear mapping for the scaling factors, which are realized by GA-based neurofuzzy networks by using a fuzzy set or fuzzy relation. The proposed approach is applied to control nonlinear systems like the inverted pendulum. Results of comprehensive numerical studies are presented through a detailed comparative analysis.