• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3C
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• pp.279-285
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• 2003

In this paper we propose a blind decision feedback equalizer (DFE) that is characterized by the fact that it does not require channel estimation. Because the output of the optimized multistep prediction error filter (PEF) can be represented as a product of the channel partial impulse response and the transmitted sequence, a backward multistep PEF can be used as the blind DFE feedforward filter (FFF). The corresponding feedback filter (FBF) is obtained from the symbol -rate partial channel impulse response. The proposed algorithm has several advantages over existing blind channel estimation techniques, including stable performance without the necessity of exact channel order estimation.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.631-634
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• 2004

In this paper, the Bayesian recurrent neural network (BRNN) is proposed to predict time series data. Among the various traditional prediction methodologies, a neural network method is considered to be more effective in case of non-linear and non-stationary time series data. A neural network predictor requests proper learning strategy to adjust the network weights, and one need to prepare for non-linear and non-stationary evolution of network weights. The Bayesian neural network in this paper estimates not the single set of weights but the probability distributions of weights. In other words, we sets the weight vector as a state vector of state space method, and estimates its probability distributions in accordance with the Bayesian inference. This approach makes it possible to obtain more exact estimation of the weights. Moreover, in the aspect of network architecture, it is known that the recurrent feedback structure is superior to the feedforward structure for the problem of time series prediction. Therefore, the recurrent network with Bayesian inference, what we call BRNN, is expected to show higher performance than the normal neural network. To verify the performance of the proposed method, the time series data are numerically generated and a neural network predictor is applied on it. As a result, BRNN is proved to show better prediction result than common feedforward Bayesian neural network.

• Journal of Welding and Joining
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• v.12 no.1
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• pp.59-72
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• 1994

This paper presents an on-line quality monitoring and control method to obtain a uniform weld quality in gas metal arc welding (GMAW) processes. The geometrical parameters of the weld pool such as the top bead width and the penetration depth plus half back width are utilized to assess the integrity of the weld quality. Since a good quality weld is characterized by a relatively high depth-to-width ratio in its dimensions, the second geometrical parameter is regulated to a desired one. The monitoring variables are the surface temperatures measured at various points on the top surface of the weldment which are strongly related to the formation of the weld pool The relationship between the measured temperatures and the weld pool size is implemented on the multilayer perceptrons which are powerful for realization of complex mapping characteristics through training by samples. For on-line quality monitoring and control, it is prerequisite to estimate the weld pool sizes in the region of transient states. For this purpose, the time history of the surface temperatures is used as the input to the neural estimator. The control purpose is to obtain a uniform weld quality. In this research, the weld pool size is directly regulated to a desired one. The proposed controller is composed of a neural pool size estimator, a neural feedforward controller and a conventional feedback controller. The pool size estimator predicts the weld pool size under growing. The feedforward controller compensates for the nonlinear characteristics of the welding process. A series of simulation studies shows that the proposed control method improves the overall system response in the presence of changes in torch travel speed during GMA welding and guarantees the uniform weld quality.

• Korean Journal of Optics and Photonics
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• v.14 no.6
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• pp.583-587
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• 2003

In this paper, the optimal performance of optical second-order polarization mode dispersion (PMD) compensation has been investigated theoretically in terms of minimization of the root-mean-square (RMS) pulse broadening. The optimal compensation vector in feedforward-type second-order PMD compensation has been derived, and the RMS pulse broadening factor after the optimal second-order PMD compensation has been analytically calculated. The calculated result has been compared with the previously reported simulation result where numerically optimized feedback scheme was adopted. They are in good agreement, which verifies the validity of the derivation. The investigation in this work will form the basis for the implementation of the feed-forward-type second-order PMD compensation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.10
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• pp.1143-1153
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• 2010

In this paper, we propose an alternative method to increase the distortion cancellation bandwidth of an analog RF feedback power amplifier by using a negative group delay circuit(NGDC). A limited distortion cancellation bandwidth due to the group delay(GD) mismatch discouraged the use of feedback technique in spite of its powerful linearization performance. With the fabricated NGDC with positive phase slope over frequency, the feedback amplifier of the proposed topology experimentally achieved adjacent channel leakage ratio(ACLR) improvement of 15 dB over 50 MHz bandwidth at wideband code division multiple access(WCDMA) downlink band when tested with 2-carrier WCDMA signal. At an average output power of 28 dBm, ACLR of 25.1 dB is improved to obtain -53.2 dBc at 5 MHz offset.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.3
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• pp.17-24
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• 2004

We present a P-type iterative learning control(ILC) scheme for uncertain robotic systems that perform the same tasks repetitively. The proposed ILC scheme comprises a linear feedback controller consisting of position error, and a feedforward and feedback teaming controller updated by current velocity error. As the learning iteration proceeds, the joint position and velocity mrs converge uniformly to zero. By adopting the learning gain dependent on the iteration number, we present joint position and velocity error bounds which converge at the arbitrarily tuned rate, and the joint position and velocity errors converge to zero in the iteration domain within the adopted error bounds. In contrast to other existing P-type ILC schemes, the proposed ILC scheme enables analysis and tuning of the convergence rate in the iteration domain by designing properly the learning gain.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.643-648
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• 2003

The problem considered in this paper is that the tip position of a flexible cantilever beam is controlled to follow a command signal, using a pair of piezoelectric actuators at the clamped end. The beam is lightly damped and so the natural transient response is rather long, and also since the sensor and actuator are not collocated, the plant response is non-minimum phase. Two control strategies were investigated. The first involved conventional PID control in which the feedback gains were adjusted to give the fastest closed-loop response to a step input. The second control strategy was based on an internal model control (IMC) architecture. The control filter in the IMC controller was a digital FIR device designed to minimize the expectation of the mean square tracking error. The IMC controller designed fur the beam was found to have very much reduced settling times to a step input compared with those of the PID controller.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.11
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• pp.1186-1193
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• 1991

This paper proposed a trajectory controlmethod for a robot manipulator by using neural networks. The total torque for a manipulator is a sum of the linear feedback controller torque and the neural network feedfoward controller torque. The proposed neural network is a multilayer neural network with time delay elements, and learns the inverse dynamics of manipulator by means of PD(propotional denvative)controller error torque. The error backpropagation (BP) learning neural network controller does not directly require manipulator dynamics information. Instead, it learns the information by training and stores the information and connection weights. The control effects of the proposed system are verified by computer simulation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.3
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• pp.502-510
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• 1997

A disturbance rejection controller using eccentricity filtering and LQ control techniques is proposed to alleviate the effecto of major roll eccentricity in multivariable cold-rolling processes. Fundamental problems in multivariable cold-rolling processes such as process time delay inherent in exit thickness measurement and non-stationary characteristics of roll eccentricity signals can be overcome by the proposed control method. The filtered instantaneous estimate of roll eccentricity may be exploited to improve instantaneous estimate of the exit thickness variation based on roll force and roll gap measurements, and a feedforward compensator is augmented as a reference for a gaugemeter thickness estimator. LQ feedback controller is combined with eccentricity filter for the attenuation of the exit thickness variation due to the entry thickness variation. The simulation results show that the roll eccentricity disturbance is significantly eliminated and other disturbances also are attenuated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.4
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• pp.567-576
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• 2003

In this study, a robust controller to control pressure in a pneumatic pressure vessel with a long transmission line is proposed. Frequency response of transmission line using compressible fluid is changed by the flowing state of the fluid. So, it a fixed gain controller designed based on a model supposed the flowing state to a specific state, the performance of the control system could be degraded because of the modelling error. The controller designed in this study is composed of two parts. One is a feedback controller to improve a feedback characteristics and to compensate the influence of the variation of transfer characteristics of a transmission line owing to the change of flowing state and the other is a feedforward controller to regulate command fallowing performance. The experimental results with the designed controller show that the robustness of the control system is achieved regardless of the change of the model or the transmission line. Therefore, the designed controller can be utilized for the Performance improvement of a Pressure control system with a long transmission line using compressible fluid.