• Journal of the Korean Society for Library and Information Science
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• v.15
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• pp.267-280
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• 1988

After the Koryo government moved to Kanghwado Island to escape the invading Mongols, Ch'oe I who was the government irstman ordered in 1239 the recarving of Nanmingchuan Huoshangsung Chengtao-ko (남명천화상송증도가) widely used by an adherent of Son Buddhism to gain a greater understanding of Son principles. Ch'oe I's postscript indicate that the original edition printed with metal type was no longer available and woodblocks were carved to produce facsimiles of that edition. It is assumed that the copies of the original edition were limited in number due to the primitiveness of printing skills and had either not been taken to Kanghwado Island in the flurry of evacuation or were almost all used up. Demand for the book seemed to have surged sharply due to need for spiritual solace during the period of invasion. It is believed that the original edition of Chengtao-ko was published with metal type in Kaegyong(개경), the capitial, before the government took refuge on Kanghwado Island. Two copies of the recarved woodblocks were recently found in the collectiion of Kim Chong-gyu(김종규). and Park Tong-sop (박동섭). It is presumed that one of the copies was recarved again the other one some time, But the carving of the woodblocks were very sophisticated and faithfully reproduced the characteristics of the original metal type edition; first, there were many slanted characters; second, the lines of the main text were not straight but wavy; third, no bottom stroke of any character overlaps the upper stroke of the following character; and fourth, the characters are relatively uniform in size and shape. The printing techniques reflected in the woodblock reproduction were much better than that at the end of the Koryo dynasty and the beginning of the Yi dynasty. It can be assumed that printing with official type had reached a high level of skill before the transfer to Kanghwado Island.

• The Korean Journal of Physiology and Pharmacology
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• v.25 no.4
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• pp.355-363
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• 2021

Dynamic changes in adipose tissue blood flow (ATBF) with nutritional status play a role in the regulation of metabolic and endocrine functions. Activation of the sympathetic nervous system via β-adrenergic receptors (β-AR) contributes to the control of postprandial enhancement of ATBF. Herein, we sought to identify the role of each β-AR subtype in the regulation of ATBF in mice. We monitored the changes in visceral epididymal ATBF (VAT BF), induced by local infusion of dobutamine, salbutamol, and CL316,243 (a selective β1-, β2-, and β3-AR agonist, respectively) into VAT of lean CD-1 mice and global adipose triglyceride lipase (ATGL) knockout (KO) mice, using laser Doppler flowmetry. Administration of CL316,243, known to promote lipolysis in adipocytes, significantly increased VAT BF of CD-1 mice to a greater extent compared to that of the vehicle, whereas administration of dobutamine or salbutamol did not produce significant differences in VAT BF. The increase in VAT BF induced by β3-AR stimulation disappeared in ATGL KO mice as opposed to their wild-type (WT) littermates, implying a role of ATGL-mediated lipolysis in the regulation of VAT BF. Different vascular reactivities occurred despite no significant differences in vessel density and adiposity between the groups. Additionally, the expression levels of the angiogenesis-related genes were significantly higher in VAT of ATGL KO mice than in that of WT, implicating an association of ATBF responsiveness with angiogenic activity in VAT. Our findings suggest a potential role of β3-AR signaling in the regulation of VAT BF via ATGL-mediated lipolysis in mice.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.155-157
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive learning mechanism-fuzzy neural network(ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.152-154
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• 2006

This paper investigates the adaptive control of a fuzzy logic based speed and flux controller for a vector controlled induction motor drive. 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 fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed adaptive fuzzy controller is confirmed by performance results for induction motor drive system

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.146-148
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications. This paper proposes efficiency optimization control of IPMSM drive using AFLC-FNN(Adaptive Fuzzy Learning Control Fuzzy Neural Network)controller. In order to maximize the efficiency in such applications, this paper proposes the optimal control method of the armature current. The optimal current can be decided according to the operating speed and the load conditions. This paper proposes speed control of IPMSM using AFLC-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled AFLC-FNN controller, the operating characteristics controlled by efficiency optimization control are examined in detail.

• 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.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.737-738
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• 2006

This paper is proposed hybrid artificial intelligent controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. The control performance of the hybrid artificial intelligent 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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• 2006.07b
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• pp.729-730
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive fuzzy neural network(A-FNN) controller and artificial neural network(ANN). For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled A-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the A-FNN and ANN controller.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.761-762
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• 2006

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.743-744
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• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the hybrid artificial intelligent(HAI) controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm