• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.1
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• pp.17-32
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• 1992

The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manupulator.

• Journal of Korea Multimedia Society
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• v.8 no.2
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• pp.201-210
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• 2005

This paper proposes a power efficient 2-dimensional DCT/IDCT architecture driven by input data to be processed. The architecture achieves low power by taking advantage of the typically large fraction of zero and small-valued input processing data in video and image data compression. In particular, it skips multiplication by zero and dynamically activates/deactivates required bit-slices of fine-grain bit partitioned adders within multipliers and accumulators using simple input ANDing and bit-slice MASKing. The processed results from 1-D DCT/IDCT do not have unnecessary sign extension bits (SEBs), which are used for further power reduction in matrix transposer. The results extracted by bit-level transition activity simulations indicate significant power reduction compared to conventional designs.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.133-137
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• 1996

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have bevome increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking arre indispensable capabilities for their versatile application. the need to meet demanding control requirement in increasingly complex dynamical control systems under sygnificant uncertainties leads toward design of implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme the ntworks intrduced are neural nets with dynamic neurouns whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure fast in computation and suitable for implementation of real-time control, Performance of the neural controller is illustrated by simulation and experimental results for a SCAEA robot.

• Proceedings of the KIPE Conference
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• 2013.07a
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• pp.88-89
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• 2013

본 논문은 영구자석 동기전동기의 동적 특성 개선과 토크 리플 저감 기법을 제안한다. 제안하는 방법은 영구자석 동기전동기의 인가전압과 토크의 관계를 수학적인 분석을 통해 유효 벡터 인가 시간을 결정하는 기법이다. 결정된 유효벡터 인가 시간과 동기전동기의 토크 오차와의 관계를 이용하여 최종적인 지령전압벡터를 계산한다. 계산된 지령전압벡터는 영구자석 동기전동기의 빠른 응답성과 우수한 정상상태 특성을 보인다. 시뮬레이션을 이용하여 제안하는 영구자석 동기기 토크 제어 알고리즘의 타당성을 확인한다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.36-41
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• 2016

In this study, numerical analyses were used to investigate the performance of a coupling control method for the dynamic responses of adjacent buildings under multiple hazards. Numerical simulations were done using the earthquake loads of regions with strong seismicity in Los Angeles, California, and the wind loads in regions with strong winds in Charleston, North Carolina. The artificial earthquake and wind loads were made using SIMQKE and Kaimal Spectrum based on ASCE 7-10. Ten-story and twenty-story adjacent buildings were selected as example structures, and nonlinear hysteretic dampers were used to connect them. The Bouc-Wen model was used to model the nonlinear hysteretic dampers. The results show that the proposed control method could effectively reduce the dynamic responses, and the optimal control designs were different for each hazard.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.764-767
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• 1999

In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.315-320
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• 1992

This paper presents the simulation architecture and strategy for dynamic simulation of chemical process and describes key features of developed dynamic simulation system, MOSA(Multi-Objective Simulation Architecture). A plant structure may be partioned into several strong coupling units, called cluster. If this cluster is solved simultaneously, it is possible to simulate whole plant without introducing convergence problem of tear streams. In this study, a flexible modular approach based on clusters was proposed as a promising architecture for dynamic chemical process simulator.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1995.04a
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• pp.28-33
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• 1995

본 논문에서는 조합변형이 각각의 모드에 따라 매우 다양하게 달라질 수 있으며 각 모드의 역학적 거동에 따라 제어기의 최적 위치가 달라지는 것을 보였으며 이러한 최적의 위치를 효과적으로 파악할 수 있는 각 모드의 기하학적인 중심에 대하여 기술하였다.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12C
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• pp.199-207
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• 2001

We propose an adaptive shaping controller equipped with the technologies of shaping and buffering VoIP packets arriving at the receiving end by the CAM-type controller. In order to conceal interarrival packet delay variation, the conventional jitter buffers force them to be too large, thereby causing the audio quality to suffer excessive delay. However, by using our proposed method, the delay caused by shaping operation dynamically increases or decreases on the level of jitter that exists with in the IP network. This makes the delay accommodates adaptively the network jitter condition. The less jitter network has the fewer delay the shaping controller requires for jitter elimination. And the CAM-type method generally makes the shaping operation faster and leads to processing packets in as little time as can. We analyse the packet loss and delay performance dependency on the average talk ratio and the number of jitter buffer entries in shaping controller. Surprising, we show that the average delay using our shaping controller is about 70msec. This performance is much better than with the delay equalization method which forces the receiving end to delay about 60msec.