• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.65-69
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• 1992

In this paper, an implementation of neuro-controller with an application of artificial neural network for an adjustment and tuning process for the completed electronics devices is presented. Multi-layer neural network model is employed with the learning method of error back-propagation. For the intelligent control of adjustment and tuning process, the neural network emulator (NNE) and the neural network controller(NNC) are developed. Computer simulation reveals that the intelligent controllers designed can function very effectively as tools for computer aided adjustment system. The applications of the controllers to the real systems are also demonstrated.

• Journal of electromagnetic engineering and science
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• 제7권4호
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• pp.183-189
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• 2007

When the M-ary signal experiences the Rayleigh fading, the diversity schemes can reduce the effect of fading since the probability that all the signals components will fade simultaneously is reduced considerably. The symbol error probabilities for various M-ary signals, such as MDPSK(M-ary DPSK) and MPSK(M-ary PSK), are mathematically derived for the Selection Combining 2(SC-2) and Selection Combining 3(SC-3) demodulation system which requires a less complex receiver than maximum ratio combining(MRC). The propagation model used in this paper is the frequency-nonselective slow Rayleigh fading channel corrupted by the additive white gaussian noise(AWGN). The numerical results presented in this paper are expected to provide information for the design of radio system using M-ary modulation method for above mentioned channel environment.

• Journal of electromagnetic engineering and science
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• 제7권4호
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• pp.201-206
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• 2007

When the M-ary signal experiences the Rayleigh fading, the diversity schemes can reduce the effects of fading since the probability that all the signals components will fade simultaneously are reduced considerably. The symbol error probabilities for various M-ary signals, such as MDPSK, MPSK and MQAM, are mathematically derived for the SC-2(Selection Combining 2) demodulation system, whereby the two signals with the two largest amplitudes are coherently combined among the L branches. On the other hand, maximum ratio combining(MRC) requires the individual signals from each path to be time-aligned, cophased, optimally weighted by their own fading amplitude, and then summed. The propagation model used in this paper is the frequency-nonselective slow Rayleigh fading channel corrupted by the Additive White Gaussian Noise(AWGN). The numerical results presented in this paper are expected to provide information for the design of radio system using M-ary modulation method for above mentioned channel environment.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.193-200
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• 2004

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권2호
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• pp.48-54
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• 2002

In this study, a neuro-fuzzy controller which has the characteristic of fuzzy control and artificial neural network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to fuzzy rules are created by an expert. To adapt the more precise model is implemented by error back-propagation learning algorithm to adjust the link-weight of fuzzy membership function in the neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of dual mode method. In order to verify the effectiveness of the proposed algorithm designed above, an operating characteristic of a DC servo motor with variable load is investigated.

• 소음진동
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• 제10권6호
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• pp.941-948
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• 2000

Active noise control is an approach to reduce the noise by utilizing a secondary noise source that destructively interferes with the unwanted noise. In general, active noise control systems rely on multiple sensors to measure the unwanted noise field and the effect of the cancellation. This paper develops an approach that utilizes a single sensor. The noise field is modeled as a stochastic process, and an adaptive algorithm is used to adaptively estimate the parameters of the process. Based on these parameter estimates, a canceling signal is generated. Oppenheim assumed that transfer function characteristics from the canceling source to the error sensor is only a propagation delay. This paper proposes a modified Oppenheim algorithm by considering transfer characteristics of speaker-path-sensor This transfer characteristics is adaptively cancelled by the proposed adaptive modeling technique. Feasibility of the proposed method is proved by computer simulations with artificially generated random noises and sine wave noise. The details of the proposed architecture. and theoretical simulation of the noise cancellation system for three dimension enclosure are presented in the Paper.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 추계학술대회 논문집
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• pp.256-261
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• 2002

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.195-198
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• 2006

One of the major sources of error in precise GPS positioning since the turn-off the Selective Availability(SA) is the ionospheric propagation delay. For the last decades, a lot of the ionospheric researches based on a GPS network have been implemented throughout the world. Especially researches of the ionospheric modeling for Wide Area Argumentation System(WAAS) have been undertaken and published. In mid-latitude regions, typical spatial and temporal variations in ionospheric models delay tend to minimal. The developed ionospheric model calls for a 1.25 degree grid at latitudes and a 2.5 degree grid at longitudes. The precise grid TEC estimated by the inversion technique is also compared with global ionosphere maps(GIMs) which have been provided by several analysis centers(ACs). The results of initial investigations into the suitability of the proposed ionospheric modeling scheme in north-east Asia are presented.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.199-202
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• 2006

One of the major sources of error in precise GPS positioning since the turn-off the Selective Availability(SA) is the ionospheric propagation delay. For the last decades, a lot of the ionospheric researches based on a GPS network have been implemented throughout the world. Especially researches of the ionospheric modeling for Wide Area Argumentation System(WAAS) have been undertaken and published. In mid-latitude regions, typical spatial and temporal variations in ionospheric models delay tend to minimal. The developed ionospheric model calls for a 1.25 degree grid at latitudes and a 2.5 degree grid at longitudes. The precise grid TEC estimated by the inversion technique is also compared with global ionosphere maps(GIMs) which have been provided by several analysis centers(ACs). The results of initial investigations into the suitability of the proposed ionospheric modeling scheme in north-east Asia are presented.