• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.4
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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.

• Korean Journal of Optics and Photonics
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• v.12 no.3
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• pp.235-239
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• 2001

In this paper, we propose an optical configuration of a reflective AFLC display mode using a half-wave AFLC cell in which inplane tilt angle is $22.5^{\circ}C$. To check the validity of our design, we fabricate a reflective half-wave AFLC cell of which the in-plane tilt angle is $24.9^{\circ}C$, and measure viewing angle and visible spectra characteristics. In the results, the half-wave AFLC cell in the reflective configuration exhibits wide viewing-angle as well as high brightness and contrast. trast.

• Journal of information and communication convergence engineering
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• v.3 no.1
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• pp.43-48
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• 2005

In this paper, the adaptive fuzzy logic controller(AFLC) is proposed, which uses real-coding genetic algorithm showing a good performance on convergence velocity and diversity of population among evolutionary computations. The effectiveness of the proposed AFLC was demonstrated by computer simulation for speed control system of AC servo motor. As a result of simulation for the AC servo motor, it is shown the proposed AFLC has the better performance on overshoot, settling time and rising time than the PI controller which is used when tuning AFLC.

• Proceedings of the Optical Society of Korea Conference
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• 2001.08a
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• pp.124-125
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• 2001

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.727-728
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• 2006

This paper is proposed robust control based on the vector controlled induction motor drive with adaptive fuzzy learning control(AFLC). The fuzzy logic principle is first utilized for the control rotor speed. AFLC scheme is then proposed in which the adaptation mechanism is executed using fuzzy logic. Also, this paper is proposed estimation of speed of induction motor using ANN Controller. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. This paper is proposed the analysis results to verify the effectiveness of the new method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.5
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• pp.44-54
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• 2010

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using multi adaptive fuzzy learning controller(AFLC). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Journal of Information Display
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• v.1 no.1
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• pp.48-51
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• 2000

In this work, we proposed a reflective antiferroelectric liquid crystal display (AFLCD) using a half-wave cell whose inplane tilt angle is $22.5^{\circ}$. To check the validity of our design, we fabricated a reflective half-wave AFLC cell of which inplane tilt angle is $24.9^{\circ}$, and measured VIS reflection spectra, contrast ratio and response time. In the results, the half-wave AFLC cell in the reflective configuration exhibits high brightness, high contrast ratio of 20:1, and fast response time of $700{\mu}s$.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07b
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• pp.1157-1160
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• 2005

In order to improve the image quality of a large size LCD-TV, the Area-Focused Luminance Control (AFLC) technology with data processing algorithm has been developed. The AFLC backlight consists of 16 U-shaped lamps, and controllable areas are divided into 8 blocks. Based on the AFLC technology, the backlight luminance of each block is automatically and separately controllable. Consequently, the contrast ratio is greatly enhanced whereas the corresponding power consumption is decreased as compared with those of conventional backlights.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.577-582
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• 1999

Using a sole input variable simplifies the design process for the fuzzy logic controller(FLC). This is called single-input fuzzy logic controller(SFLC). However it is still deficient in the capability of adapting to the varying operating conditions. We here design a single-input adaptive fuzzy logic controller(AFLC) using a switching function of the sliding mode control. The AFLC can directly incorporate linguistic fuzzy control rules into the controller. Hence some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules can be adjusted by an adaptive law. In the proposed AFLC center values of fuzzy sets are directly adjusted by a fuzzy logic system. We prove that 1) its closed-loop system is globally stable in the sense that all signals involved are bounded and 2)its tracking error converges to zero asymptotically. We perform computer simulation using a nonlinear plant.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2158-2160
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• 2002

In this paper, we designed the adaptive fuzzy controller(AFLC) using neural network and tabu search. We tuned the weights of neural network changing adaptively input/output gain of fuzzy logic controller and the gain of fuzzy logic controller using tabu search. To evaluate the proposed method's effectiveness, we apply the proposed AFLC to the speed control of an actual AC servomotor system. The experimental results show that AFLC has the better control performance than PI controller in terms of settling time, rising time and overshoot.