• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.329-332
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• 1999

Currently, the most important technology is compression methods in the multimedia society. In audio compression, the method using human auditory nervous property is used. This method using psychoacoustical model is applied to perceptual audio coding, because human's audibility is limited. MPEG-II AAC(Advanced Audio Coding) is the most advanced coding scheme that is of benefit to high quality audio coding. The compression ratio is 1.4 times compared with MPEG-I layer-III. In this paper, the vector processor for MDCT/IMDCT(Modified Discrete Cosine Transform /Inverse Modified Discrete Cosine Transform) of MPEG-II AAC is designed.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.724-727
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• 1999

The previous LQ-servo PI design methods have some serious design problems happened from the frequency matching of the maximum and minimum singular values of loop transfer function at both low and high frequency regions on the Bode plot. To solve these problems, this paper proposes a new design technique based on the inverse-optimal control and convex optimization.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.24-29
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• 1990

In this paper ,ie propose a hybrid position/force controller of a robot manipulator using double-layer neural network. Each layer is constructed from inverse dynamics and Jacobian transpose matrix, respectively. The weighting value of each neuron is trained by using a feedback force as an error signal. If the neural networks are sufficiently trained it does not require the feedback-loop with error signals. The effectiveness of the proposed hybrid position/force controller is demonstrated by computer simulation using a PUMA 560 manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.23-28
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• 1987

A Computer Aided Design (CAD) program of robot application engineering has been developed for the efficient examination of HR8000 robot. For the Simulation of robot motion. direct and inverse kinematics of robot manipulator was analyzed and robot motion was visualized. The program could contribute to upgrade accuracy and to minimize the time for the robot application engineering.

• 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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• 2009.05a
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• pp.454-457
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• 2009

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 KIEE Conference
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• 1988.07a
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• pp.165-167
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• 1988

In this paper, the design of decoder for R-S code using discrete finite-field Fourier transform is presented. An important ingredient of this design is a modified Euclid algorithm for computing the error-locator polynomial. The computation of inverse elements is completely avoided in this modification of Euclid algorithm. This decoder is regular and simple, and naturally suitable for VLSI implementation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.897-903
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• 1991

In this paper we propose a hybrid position/force controller of a robot manipulator using feedback error learning rule and neural networks. The neural network is constructed from inverse dynamics. The weighting value of each neuron is trained by using a feedback force as an error signal. If the neural networks are sufficiently trained well, it does not require the feedback-loop with error signals. The effectiveness of the proposed hybrid position/force controller is demonstrated by computer simulation using PUMA 560 manipulator.

• 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.

• Journal of Korea Multimedia Society
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• v.11 no.8
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• pp.1093-1100
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• 2008

Feistel and SPN are the two main structures in designing a block cipher algorithm. Unlike Feistel, an SPN has an asymmetric structure in encryption and decryption. In this paper we propose an SPN algorithm which has a symmetric structure in encryption and decryption. The whole operations in our SPN algorithm are composed of the even numbers of N rounds where the first half of them, 1 to N/2, applies function and the last half of them, (N+1)/2 to N, employs inverse function. Symmetry layer is executed to create a symmetry block in between function layer and inverse function layer. AES encryption and decryption algorithm, whose safety is already proved, are exploited for function and inverse function, respectively. In order to be secure enough against the byte or word unit-based attacks, 32bit rotation and simple logical operations are performed in symmetry layer. Due to the simplicity of the proposed encryption and decryption algorithm in hardware configuration, the proposed algorithm is believed to construct a safe and efficient cipher in Smart Card and RFID environments where electronic chips are built in.