• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.623-626
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• 1991

Crucial to the success of the vector control scheme without speed sensor is up to computing instantaneous position of the rotor flux. In tracing this flux depending on the machine parameter, variations of those factor lead to the non-linear charlcteristic between I/O value and decrease overall efficiency of the vector control scheme. This paper, using recursive least square method estimating instantaneous value of the machine speed and parameter from the shift of current and voltage, proposes an algorithm for compensating the I/O error of the scheme.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.286-287
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• 2011

Kalman filter(KF) algorithm is a very useful application being used in many engineering fields. Through the KF, the next time step's estimation can be almost simultaneously calculated by the recursive least square optimization method with the present measurement data. It provides us with the superior detection performance of power quality events. This paper deals with the concrete programming example of KF to detect various kinds of PQ disturbances, such as voltage sag, swell, harmonics, voltage fluctuation and Frequency variation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.5
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• pp.45-52
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• 2002

This paper presents fault detection scheme of Brushless DC(BLDC) motor drive system by estimating BLDC motor resistance using motor input and output data which is transmitted from data acquisition board to host computer over serial communication channel. Since communication time delay has a serious effect on performance, we use periodic and fixed communication protocol. Hence, the delay time is priory known. Simplified BLDC motor model and recursive least square algorithm is used for estimating motor resistance. By experiment result, we confirm the proposed scheme.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06c
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• pp.357-360
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• 1998

DS/CDMA system rejected narrow-band interference and additional White Gaussian noise which are occured at multipath, intentional jammer and multiuser to share same bandwidth in mobile communication systems. Because of having not sufficiently obtained processing gain which is related to system performance, they were not effectively suppressed. In this paper, an matched filter channel model using backpropagation neural network based on complex multilayer perceptron is presented for suppressing interference of narrow-band of direct sequence spread spectrum receiver in DS/CDMA mobile communication systems. Recursive least square backpropagation algorithm with backpropagation error is used for fast convergence and better performance in matched filter receiver scheme. According to signal noise ratio and transmission power ratio, computer simulation results show that bit error ratio of matched filter using backpropagation neural network improved than that of RAKE receiver of direct sequence spread spectrum considering of con-channel and narrow-band interference.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.2
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• pp.26-30
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• 2000

In this paper, we obtain a mathematical model of a gas turbine control system from experimental data. The gas turbine in Gunsan power plant is selected as controlled system. The recursive least square algorithm is used to model the plant. For parameter estimation, plant is assumed as second order system and forgetting factor is 0.98 and the period of input and output signal period is 1sec. As a result, input and output characteristics of real system and modeling are identified.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.2
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• pp.373-380
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• 1994

In this paper, we proposed a method of extracting optimum features of speech signal to adjust signal level. For extracting features of speech signal we used FRLS(Fast Recursive Least Square) algorithm, we adjusted each frames of equal to constant level, and extracted optimum features of speech signal by using equalized autocorrelation function proposed in this paper.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.5
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• pp.193-198
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• 1986

In this paper, 1-168 hours ahead load prediction algorithm is developed for power system economic weekly operation. Total load is composed of three components, which are base load, week load and weather-sensitive load. Base load and week load are predicted by moving average and exponential smoothing method, respectively. The days of moving average and smoothing constant are optimally determined. Weather-sensitive load is modeled by linear form. The paramiters of weather load model are estimated by exponentially weighted recursive least square method. The load prediction of special day is very tedious, difficult and remains many problems which should be improved. Test results are given for the day of different types using the actual load data of KEPCO.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1787-1788
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• 2006

In this paper, a noisy passive telemetry sensor system using Unscented Kalman Filter (UKF) is proposed. To overcome these trouble problems such as a power limitation and a estimation complexity that the general passive telemetry sensor system including IC chip has, the principle of inductive coupling was applied to the modelling of a passive telemetry sensor system (PTSS) and its noisy capacitive parameter was estimated by the UKF algorithm. Specialty, to show the effective tracking performance of the UKF, we compared with the tracking performance of Recursive Least Square Estimation (RLSE) using linearization

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.295-298
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• 1996

In this paper, a self-tuning optimal control algorithm is proposed to retain the optimal performance of an active suspension system, when the vehicle has some time varying parameters and parameter uncertainties. We consider a 2 DOF time-varying quarter car model which has the parameter variation of sprung mass, suspension spring constant and suspension damping constant. Instead of solving algebraic riccati equation on line, we propose a neural network approach as an alternative. The optimal feedback gains obtained from the off line computation, according to parameter variations, are used as the neural network training data. When the active suspension system is on, the parameters are identified by the recursive least square method and the trained neural network controller designer finds the proper optimal feedback gains. The simulation results are represented and discussed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.495-498
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• 2004

In this paper, passive telemetry capacitive humidity sensor system using a RLSE(Recursive Least Square Estimation) technique Is proposed. To overcome the problem like power limits and complications that general passive telemetry sensor system including IC chip has, the principle of inductive coupling is applied to model the sensor system. Specially, by applying the forgetting factor, we show that the accuracy of its estimation can be improved even in the case of time varying parameter and also the convergence time can be reduced.