• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.34-42
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• 1993

Due to the low loss, broadband and accurate short time delay properties of optical fiber, it has attracted as a delay medium for high speed and broad-band signal processing. In this paper, we consider the coherent optical fiber filter of lattice structure, which uses coherent light sources and consists of directional couplers and optical fiber delay elements.The differences between the optical fiber filter and the ordinary digital filter are 1) the coupling coefficients of directional couplers are restricted between 0 and 1. 2) the optical signal is divided into ${j\sqrt{a}}and\;{j\sqrt{1-a}}$ at the directional coupler. Considering these restrictions, the design formulae and condition of realibility for optical fiber filter of lattice structure which makes the optimal use of optical signal energy are derived.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.247-250
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• 2003

Mobile wire technology is a publicly available general purpose for Radio Frequency(RF) communication for short-range and point-to-multi point voice and data transfer. In this paper, we proposed a new technology of the synchronized frame structure which is based on the CDMA technique, and it's implemented into the VLSI design by FPGA. The developmental technique consists of an assigned coded type mobile communication equipments and available local mobile or wireless communication scheme quasi BT(Blue Tooth)'s functions as multiplexer or do-multiplexer for each other applications. We implement this architecture with special proposed frame structure in the local area network. Also, we expect the above proposed structure extend into the DS3 network architecture and applicable to the TE(Terminal Equipment) in the local communication area and the other one etc.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.12
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• pp.69-77
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• 1999

This paper describes the design of fuzzy digital PID controller using simplified indirect inference method. First, the fuzzy digital PID controller is derived from the conventional continuous time linear digital PID controller. Then the fuzzification, control-rule base, and defuzzification using SIM in the design of the fuzzy digital controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional digital PID controller, which has the same linear structure, but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability, particularly when the process to be controlled is nonlinear. When the SIM is applied, the fuzzy inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control performance of the one proposed by D. Misir et al.

• Proceedings of the KAIS Fall Conference
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• 2010.11a
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• pp.344-348
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• 2010

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• Proceedings of the KAIS Fall Conference
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• 2010.11a
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• pp.332-336
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• 2010

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1998.04a
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• pp.664-669
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• 1998

This research is concerned with the active vibration controller design for smart structures by a modified LQG controller. The smart structure is defined as the structure equipped with smart actuators and sensors. Various analog and digital control, techniques aimed for the piezoceramic sensors and actuators have been proposed for the active vibration control of smart structures. In this paper, the modified LQG controller is developed for the active vibration suppression of smart structures to implement the predefined decay rate on modal displacements. The proposed modified LQG controller proved its effectiveness by experiments.

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.150-153
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• 2007

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.68-68
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• 2000

This paper presents a fuzzy modified PID controller that uses linear fuzzy inference method. In this structure, the proportional and derivative gains vary with the output of the system under control. 2-input PD type fuzzy controller is designed to obtain the varying gains. The proposed fuzzy PID structure maintains the same performance as the general-purpose linear PID controller, and enhances the tracking performance over a wide range of input. Numerical simulations and experimental results show the effectiveness of the fuzzy PID controller in comparison with the conventional PID controller.

• Proceedings of the KAIS Fall Conference
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• 2008.11a
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• pp.191-196
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• 2008

The intent of this paper is to describe a neural network structure called dynamic neural processor(DNP), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning using the DNP.

• Proceedings of the KAIS Fall Conference
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• 2006.11a
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• pp.122-128
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• 2006

The intent of this paper is to describe a neural network structure called multi dynamic neural network(MDNN), and examine how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the MDNN, are described. Computer simulations are demonstrate the effectiveness of the proposed learning using the MDNN.