• 대한전기학회논문지:시스템및제어부문D
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• 제53권8호
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• pp.545-550
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• 2004

This paper is proposed a fuzzy neural network controller based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed estimation and control of speed of induction motor using ANN Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. 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. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• 전력전자학회논문지
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• 제4권2호
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• pp.166-174
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• 1999

최적효율제어를 통한 유도전동기의 효율향상은 에너지 절감측면에서 매우 중요하며 인공신경망을 사용하면 시스템의 특성이 충분히 해석되지 않은 상태에서도 우수한 제어특성을 얻을 수 있다. 본 논문은 유도전동기 구동시스템에서 최적 슬립주파수를 추종하는 실시간 인공신경망 회로를 구성하여 운전효율을 최적화하는 제어방법을 제안한다. 제안된 최적 효율제어기는 인공신경망 제어기에 의해 시스템의 비선형성을 포함하여 전동기의 내부손실이 최소가 되는 운전점을 추종한다. 시뮬레이션과 실험을 통하여 기존의 일정v/f 방식에 비하여 고속 경부하시 경제성 있는 에너지 절감효과를 충분히 확보할 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 추계학술대회 논문집 학회본부
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• pp.112-114
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• 1991

This paper describes an application of neural network to diagnose defect of induction motor and investigates possibility to construct defect diagnosis system to be operated without special knowledge. For defect diagnosis, frequency spectrum of vibration is utilized. Learning method of applied neural network is back propagation.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권7호
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• pp.437-442
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• 2005

In order to realize the speed control of an induction motor, the information of the rotor speed is needed. So the speed sensor as an encoder or a pulse generator is used to obtain it. But the use of speed sensor occur the some problems in the control system of an induction motor. To solve the problems, the appropriate speed estimation algorithm is used instead of the speed sensor. Also there is the limitation to improve the speed control performance of an induction motor using the existing speed estimation algorithm. Therefore, in this paper, intelligent speed estimator using Fuzzy-Neural systems as adaptive laws in Model Reference Adaptive System is proposed so as to improve the existing estimation algorithm and ,using the rotor speed estimated by the Proposed estimator, the speed control of an induction motor without speed sensor is performed. The computer simulation and the experiment is executed to prove the performance of the speed control system usinu the proposed speed estimator.

• Reproductive and Developmental Biology
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• 제36권1호
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• pp.33-37
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• 2012

Differential capacity of the parthenogenetic embryonic stem cells (PESCs) is still under controversy and the mechanisms of its neural induction are yet poorly understood. Here we demonstrated neural lineage induction of PESCs by addition of insulin-like growth factor-2 (Igf2), which is an important factor for embryo organ development and a paternally expressed imprinting gene. Murine PESCs were aggregated to embryoid bodies (EBs) by suspension culture under the leukemia inhibitory factor-free condition for 4 days. To test the effect of exogenous Igf2, 30 ng/ml of Igf2 was supplemented to EBs induction medium. Then neural induction was carried out with serum-free medium containing insulin, transferrin, selenium, and fibronectin complex (ITSFn) for 12 days. Normal murine embryonic stem cells derived from fertilized embryos (ESCs) were used as the control group. Neural potential of differentiated PESCs and ESCs were analyzed by immunofluorescent labeling and real-time PCR assay (Nestin, neural progenitor marker; Tuj1, neuronal cell marker; GFAP, glial cell marker). The differentiated cells from both ESC and PESC showed heterogeneous population of Nestin, Tuj1, and GFAP positive cells. In terms of the level of gene expression, PESC showed 4 times higher level of GFAP expression than ESCs. After exposure to Igf2, the expression level of GFAP decreased both in derivatives of PESCs and ESCs. Interestingly, the expression level of $Tuj1$ increased only in ESCs, not in PESCs. The results show that IGF2 is a positive effector for suppressing over-expressed glial differentiation during neural induction of PESCs and for promoting neuronal differentiation of ESCs, while exogenous Igf2 could not accelerate the neuronal differentiation of PESCs. Although exogenous Igf2 promotes neuronal differentiation of normal ESCs, expression of endogenous $Igf2$ may be critical for initiating neuronal differentiation of pluripotent stem cells. The findings may contribute to understanding of the relationship between imprinting mechanism and neural differentiation and its application to neural tissue repair in the future.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 A
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• pp.251-254
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• 1995

The Fuzzy-Neural Controller is constructed to resolve some dificulties taking place in decision of membership functions, input and output gains and an inferenced method for desinging fuzzy logic controller. In addition Neural network emulator is used to emulate induction motor forward dynamics and to get error signal at fuzzy-neural controller output layer. Error signal is backpropagated through neural network emulator. A back propagation algorithm is used to train fuzzy-neural controller and emulator. The experimental results show that this control system can provide good dynamical responses.

• Molecules and Cells
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• 제37권10호
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• pp.705-712
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• 2014

The early steps of neural development in the vertebrate embryo are regulated by sets of transcription factors that control the induction of proliferative, pluripotent neural precursors, the expansion of neural plate stem cells, and their transition to differentiating neural progenitors. These early events are critical for producing a pool of multipotent cells capable of giving rise to the multitude of neurons and glia that form the central nervous system. In this review we summarize findings from gain- and loss-of-function studies in embryos that detail the gene regulatory network responsible for these early events. We discuss whether this information is likely to be similar in mammalian embryonic and induced pluripotent stem cells that are cultured according to protocols designed to produce neurons. The similarities and differences between the embryo and stem cells may provide important guidance to stem cell protocols designed to create immature neural cells for therapeutic uses.

• 지능정보연구
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• 제7권2호
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• pp.51-63
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• 2001

데이터로부터 학습하여 룰을 추출하는 귀납적 학습기법은 데이터 마이닝의 주요 도구 중 하나이다. 귀납적 학습 기법은 불필요한 변수나 잡음이 섞인 변수를 포함하여 학습하는 경우 생성된 룰의 예측 성능이 떨어지고 불필요하게 룰이 복잡하게 구성될 수 있다. 따라서 귀납적 학습 기법의 예측력을 높이고 룰의 구성도 간단하게 할 수 있는 주요 변수 부분집합을 선정하는 방안이 필요하다. 귀납적 학습에서 예측력을 높이기 위해 많이 사용되는 부분집합 선정을 위한 포장 기법은 최적의 부분집합을 찾기 위해 전체 부분집합을 탐색한다. 이때 전체 변수의 수가 많아지면 부분집합의 탐색 공간이 너무 커져서 탐색하기 어려운 문제가 된다. 본 연구에서는 포장 기법에 신경망 민감도 분석을 결합한 귀납적 학습 기법의 변수 부분집합 선정 방안을 제시한다. 먼저, 신경망의 민감도 분석 기법을 이용하여 전체 변수를 중요도 순으로 순서화 한다. 다음에 순서화된 정보를 이용하여 귀납적 학습 기법의 예측력을 높일 수 있는 부분집합을 찾아 나간다. 제안된 방법을 세 데이터 셋에 적용한 결과 일정한 반복 회수 이내에 예측력이 향상된 부분집합을 얻을 수 있음을 볼 수 있다.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권4호
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• pp.154-157
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• 2006

This paper is proposed hybrid artificial intelligent(HAI) controller for high performance of induction motor drive. The design..of this algorithm based on fuzzy-neural network(FNN) controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.152-154
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• 2005

This paper is proposed a fuzzy control and neural network based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability Also, this paper is proposed estimation and control of speed of Induction motor using fuzzy and neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. This paper is proposed the experimental results to verify the effectiveness of the new method.