• 제어로봇시스템학회논문지
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• 제13권2호
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• pp.173-178
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• 2007

This paper is to establish an appropriate model for predicting the temperature decreases in the batch transferred from the refining process to the caster in steel-making companies. Mathematical modeling of the temperature decreases between the processes is difficult, since the reaction mechanism by which the temperature changes in a molten steel batch is dynamic, uncertain and complex. Three soft computing techniques are examined using the same data, namely the multiple regression, fuzzy regression, and neural net (NN) models. To compare the accuracy of these three models, a limited number of input variables are selected from those variables significantly affecting the temperature decrease. The results show that the difference in accuracy between the three models is not statistically significant. Nonetheless, the NN model is recommended because of its adaptive ability and robustness. The method presented in this paper allows the temperature decrease to be predicted without requiring any precise metallurgical knowledge.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권4호
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• pp.271-276
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• 2006

To implement a generalization of value functions in Adaptive Search Element (ASE)-reinforcement learning, CMAC (Cerebellar Model Articulation Controller) is integrated into ASE controller. ASE-reinforcement learning scheme is briefly studied to discuss how CMAC is integrated into ASE controller. Neighbourhood Sequential Training for CMAC is utilized to establish the look-up table and to produce discrete control outputs. In computer simulation, an ASE controller and a couple of ASE-CMAC neural network are trained to balance the inverted pendulum on a cart. The number of trials until the controllers are established and the learning performance of the controllers are evaluated to find that generalization ability of the CMAC improves the speed of the ASE-reinforcement learning enough to realize the cartpole control system.

• 조명전기설비학회논문지
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• 제25권7호
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• pp.15-25
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• 2011

This proposes an online loss minimization algorithm for series PI(SPI) based interior permanent magnet synchronous motor(IPMSM) drive to yield high efficiency and high dynamic performance over wide speed range. The loss minimization algorithm is developed based on the motor model. In order to minimize the controllable electrical losses of the motor and thereby maximize the operating efficiency, the d-axis armature current is controlled optimally according to the operating speed and load conditions. For vector control purpose, a SPI is used as a speed controller which enables the utilization of the reluctance torque to achieve high dynamic performance as well as to operate the motor over a wide speed range. Also, this paper proposes current control of model reference adaptive fuzzy controller(MFC), and estimation of speed using artificial neural network(ANN) controller. The proposed efficiency optimization control, SPI, MFC, ANN in this paper is applied to IPMSM drive system, the validity of this paper is proved by analyzing response characteristics in variety operating conditions.

• 한국정보통신학회논문지
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• 제2권2호
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• pp.223-230
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• 1998

본 논문은 뉴랄퍼지를 이용한 디지탈식 제어기를 고주파 유도 가열기의 전력 조절을 위해 IGBT를 사용한 위상 전이(Phase-Shift) 펄스폭 변조(PWM)와 펄스 주파수 변조(PFM)가 조절되는 공진 고주파 인버터를 응용한 유도가열기를 설명한다. 이는 실제로 산업 현장에서 20KHz~500KHz 유도 가열 및 유도 용해 전원 장치용으로 쓰인다. 위상 전이(Phase-Shift) PWM 정전력 조절 기술을 바탕으로 한 적응 주파수 추종 기법은 스위칭 손실을 최소화하고 전력조절을 용이하게 하기 위해 소개되어졌다. IGBT를 사용하여 실험적으로 만들어진 실험장치는 성공적으로 논증과 토의가 되어졌다.

• Smart Structures and Systems
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• 제19권4호
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• pp.341-350
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• 2017

Based on the super elastic properties of the shape memory alloy (SMA) and the inverse piezoelectric effect of piezoelectric (PZT) ceramics, a kind of hybrid semi-active control device was designed and made, its mechanical properties test was done under different frequency and different voltage. The local search ability of genetic algorithm is poor, which would fall into the defect of prematurity easily. A kind of adaptive immune memory cloning algorithm(AIMCA) was proposed based on the simulation of clone selection and immune memory process. It can adjust the mutation probability and clone scale adaptively through the way of introducing memory cell and antibody incentive degrees. And performance indicator based on the modal controllable degree was taken as antigen-antibody affinity function, the optimization analysis of damper layout in a space truss structure was done. The structural seismic response was analyzed by applying the neural network prediction model and T-S fuzzy logic. Results show that SMA and PZT friction composite damper has a good energy dissipation capacity and stable performance, the bigger voltage, the better energy dissipation ability. Compared with genetic algorithm, the adaptive immune memory clone algorithm overcomes the problem of prematurity effectively. Besides, it has stronger global searching ability, better population diversity and faster convergence speed, makes the damper has a better arrangement position in structural dampers optimization leading to the better damping effect.

• Advances in nano research
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• 제14권2호
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• pp.155-164
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• 2023

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.

• 조명전기설비학회논문지
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• 제24권5호
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• pp.44-54
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• 2010

SynRM 효율최적화 제어는 다른 교류전동기에 비해 SynRM의 효율이 낮기 때문에 에너지 절약과 환경보존의 관점에서 매우 중요하다. 본 논문에서는 다중 AFLC를 이용하여 철손을 고려한 SynRM의 새로운 효율 최적화 제어를 제안하였다. 최대효율에서 SynRM을 구동하기 위해 토크전류와 여자전류사이의 최적전류비를 분석하여 구한다. 본 논문에서는 동손과 철손을 최소로 하는 SynRM의 효율 최적화 제어를 제안하였다. 특정한 모터토크를 제공하는 d축과 q축 전류의 다양한 조합이 존재한다. 효율 최적화의 목적은 정상상태에서 최소 손실을 제공하는 d축과 q축 전류의 조합을 찾는 것이며, 제안된 제어기의 제어 성능은 다양한 동작조건의 분석을 통해 평가되었다. 분석된 결과는 제안된 알고리즘의 타당성을 입증한다.

• 조명전기설비학회논문지
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• 제25권4호
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• pp.45-56
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• 2011

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• 한국수자원학회논문집
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• 제31권6호
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• pp.821-832
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• 1998

본 논문에서는 상수도시설을 효율적으로 운영하는 데 필요한 1일 급수량 수요를 예측하는 방식에 대하여 인공지능(Artificial Inteligence)이라 불리는 퍼지 뉴론(fuzzy neuron)을 이용하여 연구하였다. 퍼지뉴론이란 퍼지정보(fuzzy information)를 입력으로 받아들이고 처리하는 퍼지 신경망을 일컫는 말이다. 본 연구에서는 소속함수와 퍼지규칙을 신경망으로 학습하는 기능인 적응식 학습방법을 통하여 1일 급수량을 예측하였으며 연구대상 지역으로는 광주광역시를 선정하였다. 또한 1일 급수량 예측에 있어서 필요한 변수 선택을 위해 입력자료를 상관분석, 자기상관, 부분자기상관, 교차상관 분석 등을 하였으며 동정된 입력변수는 급수량, 평균기온, 급수인구이다. 먼저 급수량, 평균기온, 급수인구로 모델을 구성하였고, 한편으론 기상청의 기후예보자료를 신뢰할 수 없는 경우에는 급수량을 예측할 수 있도록 급수량 자료만으로 모델을 구성하여 그 유효성을 검증하였다. 제안된 모형식은 사고 등의 인위적인 조작(단수 등)이 가해지는 시기를 포함하고도 실측치와 모형의 예측치와의 오차율이 최대 18.46%, 평균2.36% 이내로 나타나, 모형의 결과는 상수도 시설의 운용 및 급·배수관망의 실시간 제어에 많은 도움을 주리라 생각된다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
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• pp.408-411
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• 2009

In this paper, conventional PI, fuzzy neural network(FNN) and adaptive teaming mechanism(ALM)-FNN for rotor field oriented controlled(RFOC) induction motor are studied comparatively. The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. Comparative study of PI, FNN and ALM-FNN are carried out from various aspects which is dynamic performance, steady-state accuracy, parameter robustness and complementation etc. To have a clear view of the three techniques, a RFOC system based on a three level neutral point clamped inverter-fed induction motor drive is established in this paper. Each of the three control technique: PI, FNN and ALM-FNN, are used in the outer loops for rotor speed. The merit and drawbacks of each method are summarized in the conclusion part, which may a guideline for industry application.