• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.8
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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.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.2
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• pp.166-174
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• 1999

On-line efficiency optimization control of an induction motor drive using neural network is important from the v viewpoints of energy saving and controlling a nonlinear system whose charact81istics are not fully known. This paper p presents a neural networklongleftarrowbased on-line efficiency optimization control for an induction motor drive, which adopts an optimal slip an밍J.lar frequency control. In the proposed scheme, a neuro-controller provides minimal loss operating point i in the whole range of the measured input power. Both simulation and experimental results show that a considerable e energy saving is achieved compared with the conventional constant vlf ratio operation.

• Proceedings of the KIEE Conference
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• 1991.11a
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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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.7
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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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• v.36 no.1
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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.

• Proceedings of the KIEE Conference
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• 1995.07a
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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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• v.37 no.10
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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.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.51-63
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• 2001

In supervised machine learning, an induction algorithm, which is able to extract rules from data with learning capability, provides a useful tool for data mining. Practical induction algorithms are known to degrade in prediction accuracy and generate complex rules unnecessarily when trained on data containing superfluous features. Thus it needs feature subset selection for better performance of them. In feature subset selection on the induction algorithm, wrapper method is repeatedly run it on the dataset using various feature subsets. But it is impractical to search the whole space exhaustively unless the features are small. This study proposes a heuristic method that uses sensitivity analysis of neural networks to the wrapper method for generating rules with higher possible accuracy. First it gives priority to all features using sensitivity analysis of neural networks. And it uses the wrapper method that searches the ordered feature space. In experiments to three datasets, we show that the suggested method is capable of selecting a feature subset that improves the performance of the induction algorithm within certain iteration.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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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.

• Proceedings of the KIEE Conference
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• 2005.04a
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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.