• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.4
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• pp.427-432
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• 2010

To reduce the outage probability and to increase the transmission capacity, the importance of repeaters in cellular systems is increasing. But a RF(Radio Frequency) repeater has a problem that the output of the transmit antenna is partially feedback to the receive antenna, which is feedback interference. In this paper, we proposed adaptive Sign-Sign LMS(Least Mean Square) algorithm using correlations for the performance enhancement of RF repeater. The weight vector is updated by using sign of input signal and error signal to the least squared error of the conventional algorithms. When compared with the conventional method, the proposed canceller achieves the maximum 10 dB performance gain in terms of the MSE(Mean Square Error).

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.686-689
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• 2002

This paper proposes a weighted least-squares(WLS) method for designing variable one-dimensional (1-D) FIR digital filters with simultaneously variable magnitude and variable non-integer phase-delay responses. First, the coefficients of a variable FIR filter are represented as the two-dimensional (2-D) polynomials of a pair of spectral parameters: one is for tuning the magnitude response, and the other is for varying its non-integer phase-delay response. Then the optimal coefficients of the 2-D polynomials are found by minimizing the total weighted squared error of the variable frequency response. Finally, we show that the resulting variable FIR filter can be implemented in a parallel form, which is suitable for high-speed signal processing.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.430-433
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• 2003

This paper presents a system identification (SI) scheme in time domain using measured acceleration data. The error function is defined as the time integral of the least square errors between the measured acceleration and the calculated acceleration by a mathematical model. Damping parameters as well as stiffness properties of a structure are considered as system parameters. The structural damping is modeled by the Rayleigh damping in SI. The regularization technique is applied to alleviate the ill-posed characteristics of inverse problems. The validity of the proposed method is demonstrated by an experimental study on a shear building model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.2C
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• pp.166-173
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• 2007

In Orthogonal Frequency Division Multiplexing (OFDM) systems, Narrow-Band Jamming (NBJ) over pilot tones used for channel estimation degrades the system performance. In this paper, we propose a new jammed pilot detection and elimination algorithm to overcome this problem. Moreover, the average Mean-Squared Error (MSE) on one OFDM symbol both under jammed and removed pilot subcarrier is analyzed. And then, the Symbol Error Rate (SER) performance of the channel estimation scheme using the proposed algorithm is evaluated by simulation. We can confirm that the channel estimator with the proposed algorithm improves the channel estimation performance at a high jamming power.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.723-731
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• 2009

In this paper, we propose a novel sound field reconstruction algorithm using a three dimensional loudspeaker array for providing realistic sound field to multiple listeners. The proposed algorithm is based on minimization of the squared error between the original sound field and the reconstructed sound field by the loudspeaker array over a predefined three dimensional region of the space using a loudspeaker array surrounding the listening area. For evaluating the proposed algorithm, we constructed the three dimensional array composed of 40 loudspeakers and discuss the relevant experiment results.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2A
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• pp.97-108
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• 2012

Dynamic responses were measured using long-term monitoring system for Uldolmok tidal current pilot power plant which is one of jacket-type offshore structures. Among the dynamic quantities, the tilt angle was chosen because the low frequency response components can be precisely measured by dynamic tiltmeter, and the natural frequencies and modal damping ratio were successfully identified using proposed LS-FDD (least squared frequency domain decomposition) method. And the effects of tidal height and tidal current velocity on the variation of natural frequencies and modal damping ratios were investigated in time and frequency domain. Also the non-parametric models were tested to model the relationship between tidal conditions and modal properties such as natural frequencies and damping ratios.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.91-102
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• 2019

In this paper, we propose a new estimation method based on a weighted linear regression framework to obtain some estimators for unknown parameters in a two-parameter Rayleigh distribution under a progressive Type-II censoring scheme. We also provide unbiased estimators of the location parameter and scale parameter which have a nuisance parameter, and an estimator based on a pivotal quantity which does not depend on the other parameter. The proposed weighted least square estimator (WLSE) of the location parameter is not dependent on the scale parameter. In addition, the WLSE of the scale parameter is not dependent on the location parameter. The results are compared with the maximum likelihood method and pivot-based estimation method. The assessments and comparisons are done using Monte Carlo simulations and real data analysis. The simulation results show that the estimators ${\hat{\mu}}_u({\hat{\theta}}_p)$ and ${\hat{\theta}}_p({\hat{\mu}}_u)$ are superior to the other estimators in terms of the mean squared error (MSE) and bias.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.9
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• pp.1051-1059
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• 1992

In this paper, two general methods using time-series analysis in the functional separation of the myoelectric signal of human arm movements are developed. Autocorrelation, covariance method and sequential least squares algorithm were used to determine the model parameters and the order of signal model to describe six arm movement patterns` the forearm flexion and extension, the wrist pronation and supination, rotation-in and rotation-out. The confidence interval to classify the functions of arm movement was defined by the mean and standard deviation of total squared error. With the error signals of autoregressive(AR) model, the result showed that the highest success rate was obtained in the case of 4th order, and success rate was decreased with increase of order. Autocorrelation was the method of choice for better success rate. This technique might be applied to biomedical and rehabilitation engineering.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.803-805
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• 2000

In this paper, we propose fuzzy-neural Networks(FNN) which is useful for identification algorithms. The proposed FNN model consists of two steps: the first step, which determines premise and consequent parameters approximately using FCM_RI method, the second step, which adjusts the premise and consequent parameters more precisely by gradient descent algorithm. The FCM_RI algorithm consists FCM clustering algorithm and Recursive least squared(RLS) method, this divides the input space more efficiently than convention methods by taking into consideration correlations between components of sample data. To evaluate the performance of the proposed FNN model, we use the time series data for gas furnace.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.526-530
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• 1999

This paper presents a newly developed speed sensorless drive using Neural Network algorithm. Neural Network algorithm can be divided into three categories. In the first one, a Back Propagation-based NN algorithm is well-known to gradient descent method. In the second scheme, a Extended Kalman Filter-based NN algorithm has just the time varying learning rate. In the last scheme, a Recursive Least Square-based NN algorithm is faster and more stable than the classical back-propagation algorithm for training multilayer perceptrons. The number of iterations required to converge and the mean-squared error between the desired and actual outputs is compared with respect to each method. The theoretical analysis and experimental results are discussed.