• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.372-374
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• 2006

This paper reported the experimental results of the 2 stage converter using a Buck converter and a soft switching dc-to-dc converter that employs two separate transformers. A 2 stage converter module is designed with the specifications of an 3.3V output voltage, 20A output current, 66W output power and 36-75V input voltage. A prototype converter module is successfully implemented within 85.2% efficiency and 3% voltage regulation for the entire input voltage range, thereby demonstrating its application potentials to future telecommunication electronics.

• Proceedings of the KIEE Conference
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• 2002.04a
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• pp.112-115
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• 2002

Previous AC/DC PFC Boost Converter perceives feed forward signal of input and feedback signal of output for average current-mode control. Previous Boost Converter, the quantity of input current will be decreased by the decrease of output current in light load, and also power factor comes to be decreased. Also the efficiency of converter will be decreased by the decrease of power factor. The proposed converter presents the good PFC(Power Factor Correction), low line current hormonic distortions and tight output voltage regulations using non-dissipative snubber. The proposed converter also has a high efficiency by non-dissipative snubber circuit. To show the superiority of this converter is verified through the experiment with a 640W, 100kHz prototype converter.

• Journal of Sensor Science and Technology
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• v.21 no.3
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• pp.235-239
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• 2012

This paper presents frequency response compensation technique, and a self-oscillation circuit for capacitive microresonator with the compensation technique using programmable capacitor array, to compensate for the frequency response distorted by parasitic capacitances, and to obtain stable oscillation condition. The parasitic capacitances between the actuation input port and capacitive output port distort the frequency response of the microresonator. The distorted non-ideal frequency response can be compensated using two programmable capacitor arrays, which are connected between anti-phased actuation input port and capacitive output port. The simulation model includes the whole microresonator system, which consists of mechanical structure, transimpedance amplifier with automatic gain control, actuation driver and compensation circuit. The compensation operation and oscillation output of the system is verified with the simulation results.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.647-649
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• 1998

This paper describes a robust stabilization of single input nonlinear systems with parametric uncertainty. We first investigate differential flatness of the nominal nonlinear systems. If a single input system is differentially flat, it possesses a flat output. And we define coordinate transformation functions via successively differentiating the flat output, and we also consider the robust fictitious controls at every differentiation of the flat output. In the new coordinates the nonlinear system is transformed into the Brunovsky normal form with matched uncertainty. With a robust control based on the Lyapunov method, the robust stabilization is achieved.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.189-192
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• 2001

A new phase-shifted parallel-input/series-output dual converter for high-power step-up applications has been proposed. It features a high efficiency due to the low switch turn-off voltage, low device stresses, low ripple contents, and a fast control-to-output dynamics compared to its PWM counterpart. To confirm the validity of the proposed converter, experimental results from an 800W, 350Vdc prototype are presented.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.134-139
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• 1994

In this paper the comparison between the neural networks and traditional approaches as system identification method are considered. Two model structures of neural networks are the state space model and the input output model neural networks. The traditional methods are the AutoRegressive eXogeneous Input model and the Nonlinear AutoRegressive eXogeneous Input model. The examples considered do not represent any physical system, no a priori knowledge concerning their structure has been used in the identification process. Testing inputs for comparison are the sinusoidal, ramp and the noise ramp.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.42.6-42
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• 2002

Kohonen's self organizing feature map (SOFM) converts arbitrary dimensional patterns into one or two dimensional arrays of nodes. Among the many competitive learning algorithms, SOFM proposed by Kohonen is considered to be powerful in the sense that it not only clusters the input pattern adaptively but also organize the output node topologically. SOFM is usually used for a preprocessor or cluster. It can perform dimensional reduction of input patterns and obtain a topology-preserving map that preserves neighborhood relations of the input patterns. The traditional SOFM algorithm[1] is a competitive learning neural network that maps inputs to discrete points that are called nodes on a lattice...

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.168-173
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• 1987

The optimal Input design problem for linear system Which have the common parameters in the system and noise transfer functions. Exploiting the assumed Model structure and deriving the information matrix structure in detail, D-optimal open-loop stochastic input can be realized as an ARMA process under the Input or output variance constraints. In spite of the reduced order, It Is necessary to develop an efficient algorithms for the optimation with respect to the .rho..

• Proceedings of the KIEE Conference
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• 2001.04a
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• pp.301-303
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• 2001

This paper proposes the boost input type active clamp DC-DC converter featuring the high efficiency and improved EMI characteristics. The main characteristic of the converter is to operate with the non-pulsating input and output currents. Besides, it has the zero-voltage switching (ZVS) and low voltage stress characteristics. For the proposed converter, the detailed operation principles and the simulation results are presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.433-439
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• 1998

In this paper, we present an approach to the structure identification based on the input space partition methods and to the parameter identification by hybrid learning method in neuro-fuzzy system. The structure identification can automatically estimate the number of membership function and fuzzy rule using grid partition, tree partition, scatter partition from numerical input-output data. And then the parameter identification is carried out by the hybrid learning scheme using back-propagation and least squares estimate. Finally, we sill show its usefulness for neuro-fuzzy modeling to truck backer-upper control.