• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4A
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• pp.370-377
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• 2004

In this paper, we present an adaptive modulation technique for orthogonal frequency division multiplexing (OFDM) for broadband wireless communications. Also, using improved channel prediction, we enhance the performance of adaptive OFDM in high mobility environments. Adaptive modulation technique has been shown to achieve reliable high-rate data transmission over frequency-selective fading channel when OFDM is employed. This scheme requires the accurate channel information between two stations for a better performance. In an outdoor high mobility environment, most of adaptive OFDM systems have to be given the channel information transmitted from the receiver. Even if it is possible, there is some delay. Moreover, the channel impulse response between two stations is very rapidly varied. If the channel information is obsolete at the time of transmission, then poor system performance will result. In order to solve this problem, we propose adaptive OFDM with improved channel predictor. The proposed bit allocation algorithm has a lower complexity and the proposed scheme mitigates the effect of channel delay. Robust approach is less sensitive to outdated channel information. Performance results show that the proposed scheme can achieve considerable performance enhancement.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1041-1044
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• 1988

This paper considers the design problem of adaptive filters based an the state-space models for linear discrete-time stationary stochastic signal processes. The adaptive state estimator consists of both the predictor and the sequential prediction error estimator. The discrete Chandrasakhar filter developed by author is employed as the predictor and the nonlinear least-squares estimator is used as the sequential prediction error estimator. Two models are presented for calculating the parameter sensitivity functions in the adaptive filter. One is the exact model called the linear innovations model and the other is the simplified model obtained by neglecting the sensitivities of the Chandrasekhar X and Y functions with respect to the unknown parameters in the exact model.

• Journal of the military operations research society of Korea
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• v.13 no.1
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• pp.1-18
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• 1987

This paper presents several adaptive linear predictive coding techniques based upon extension of recursive ladder filters. A 2-D recursive ladder filter is extended to a 3-D case which can adaptively track the variation of both spatial and temporal changes of moving images. Using the 2-D/3-D ladder filter and a previous farme predictor, two types of adaptive predictor-control schemes are proposed in which the prediction error at each pel can be obtained at or close to a minimum level. We also investigate several modifications of the basic encoding methods. Performance of the 2-D/3-D ladder filters, their adaptive control schemes, and variations in coding methods are evaluated by computer simulations on a real sequence and compared to the results of motion compensation and frame differential coders. As a validity test of the ladder filters developed, the error signals for the different predictors are compared and evaluated.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.1
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• pp.77-84
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• 2017

Generally, a time-varying vibration signal is generated in a rotating machine system, and when there is a failure in the rotating machine, the signal contains noise. In this paper, we propose a system consisting of an adaptive predictor and a binary tree filter bank for analyzing time - varying vibration signals with noise. And the vibration signal analyzed results in this system is used for fault diagnosis of the rotating machine. The adaptive predictor of the proposed system predicts the periodic signal components, and the filter bank system decomposes the difference signal between the input signal and the predicted periodic signal into subband. Since each subband signal includes a noise signal component due to a failure, it is possible to diagnose the failure of the using rotary machine. The validity of the proposed vibration signal analysis method is shown in the simulations, where the periodic components cancelled vibrating signals are decomposed to 32 subband, and the signal characteristics related faults are analyzed.

• 전자공학회논문지 IE
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• v.47 no.2
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• pp.27-31
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• 2010

In this paper, a predictor switching algorithm for lossless compression is proposed. It uses adaptively one of two predictors using errors obtained by MED(median edge detector) and GAP(gradient adaptive prediction). The reduced error is measured by existing entropy method. Experimental results show that the proposed algorithm can compress higher than existing predictive methods.

• Journal of Power Electronics
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• v.4 no.2
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• pp.102-111
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• 2004

This paper is concerned with a deadbeat current control implementation of shunt-type three-phase active power filter (APF). Although the one-dimensional deadbeat control method can attain time-optimal response of APF compensating current, one sampling period is actually required fur its settling time. This delay is a serious drawback for this control technique. To cancel such a delay and one more delay caused by DSP execution time, the desired APF compensating current has to be predicted two sampling periods ahead. Therefore an adaptive predictor is adopted for the purpose of both predicting the control error of two sampling periods ahead and bringing the robustness to the deadbeat current control system. By adding the adaptive predictor output as an adjustment term to the reference value of half a source voltage period before, settling time is made short in a transient state. On the other hand, in a steady state, THD (total harmonic distortion) of the utility grid side AC source current can be reduced as much as possible, compared to the case that ideal identification of controlled system could be made.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.12
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• pp.1806-1814
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• 1993

Efficient speech coders using tree coding combined with harmonic scaling are designed at the rate of 4.8 kilobitts/sec (kbps). A time domain harmonic scaling algorithm (TDHS) is used to compress input speech by a factor of two. This process allows the tree coder have 1.5 bits/sample for 4.8 kbps in the case of a 6.4 kHz sampling rate. In the backward adaptive tree coder, there are three components of the code generator, including a hybrid adaptive quantizer, a short-term predictor and a pitch predictor. The robustness of the tree coder is achieved by carefully choosing the input of the short term predictor adaptation. Also, inclusion of a smoother in the pitch predictor improves the error performance of tree coder in the noisy channel. Subjectively, tree coding combined with TDHS provides good quality speech at 4.8 kbps.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.4
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• pp.736-739
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• 2009

In this paper, an efficient lossless compression algorithm using motion adaptation is proposed. It is divided into two parts: a motion adaptation based nonlinear predictor part and a residual data coding part. The proposed nonlinear predictor can reduce prediction error by learning from its past prediction errors using motion adaption. The predictor decides the proper selection of the intra and inter prediction values according to the past prediction error. The reduced error is coded by existing context adaptive coding method. Experimental results show that the proposed algorithm has the higher compression ratio than context modeling methods, such as FELICS, CALIC, and JPEG-LS.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.609-615
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• 2007

We propose an optimized adaptive PID control scheme. This paper is focused on the development of model reduction as well as a new adoptive control structure (viz. a recursive least square estimation (RLSE) method-based structure) that is constructed with smith-predictor structure and a real time estimator. The estimator adjust parameters of a reduced model in real time. It leads to robust and superb control performance for the noise or variation of parameters of process. Experimental study reveals that the proposed control structure exhibits more superb output performance in comparison to some previous methods.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.43-55
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• 2005

In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.