• ETRI Journal
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• v.37 no.1
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• pp.1-10
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• 2015

A blind hopping phase estimator is proposed for the demodulation of received signals in frequency-hopping spread spectrum systems. The received signals are assumed to be bandwidth limited with a shaping filter, modulated as frequency modulation (FM) or binary frequency shift keying (BFSK), and hopped by predetermined random frequency sequences. In the demodulation procedure in this paper, the hopping frequency tracking is accomplished by choosing a frequency component with maximum amplitude after taking a discrete Fourier transform, and the hopping phase estimator performs the conjugated product of two consecutive signals and moving-average filtering. The probability density function and Cramer-Rao low bound (CRLB) of the proposed estimator are evaluated. The proposed scheme not only is very simple to implement but also performs close to the CRLB in demodulating hopped FM/BFSK signals.

• Journal of the Korean Statistical Society
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• v.35 no.4
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• pp.441-452
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• 2006

Here an efficient regression type estimator for a stratified population mean is proposed under the two-phase sampling scheme. While constructing the proposed estimator, it is assumed that the first auxiliary variable x is directly and highly correlated with the study variable y, and the second auxiliary variable z is directly and highly correlated with the first auxiliary variable x. However the variable z is not directly correlated with the variable y, but they are just correlated with each other only due to their direct and high correlation with the variable x. The proposed regression type estimator is found to be always more efficient than the existing estimators defined under the same situation.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.5
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• pp.332-337
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• 2004

A number of techniques have been developed for estimation of speed or position in motor drives. The accuracy of these techniques is affected by the variation of motor parameters such as the stator resistance, stator inductance or torque constant. This paper is proposed a neural network based estimator for torque and stator resistance in IPMSM Drives. The neural weights are initially chosen randomly and a model reference algorithm adjusts those weights to give the optimum estimations. The neural network estimator is able to track the varying. parameters quite accurately at different speeds with consistent performance. The neural network parameter estimator has been applied to slot and flux linkage torque ripple minimization of the IPMSM. The validity of the proposed parameter estimator is confirmed by the operating characteristics controlled by neural networks control.

• Journal of Korean Society for Quality Management
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• v.26 no.3
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• pp.71-81
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• 1998

In reliability analysis, the time difference between the expected next failure time and the current failure time or the Mean Time Between Failure(MTBF) is of significant interest. Until recently, in reliability growth studies, the reciprocal of the intensity function at current failure time has been used as being equal to MTBE($t_n$)at the n-th failure time $t_n$. That is MTBF($t_n$)=l/$\lambda (t_n)$. However, such a relationship is only true for Homogeneous Poisson Process(HPP). Tsokos(1995) obtained the upper bound and lower bound for the MTBF($t_n$) and proposed an estimator for the MTBF($t_n$) as the mean of the two bounds. In this paper, we provide the estimator for the MTBF($t_n$) which does not depend on the value of the shape parameter. The result of the Monte Carlo simulation shows that the proposed estimator has better efficiency than Tsokos's estimator.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.669-683
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• 2006

Model-based methods are generally used for small area estimation. Recently Shin and Lee (2003) suggested a method which used spatial correlations between areas for data set including some auxiliary variables. However in case of absence of auxiliary variables, Direct estimator is used. Even though direct estimator is unbiased, the large variance of the estimator restricts the use for small area estimation. In this paper, we suggest new estimators which take into account spatial correlation when auxiliary variables are not available. We compared Direct estimator and the newly suggested estimators using MSE, MAE and MB.

• Journal of Broadcast Engineering
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• v.6 no.1
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• pp.72-81
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• 2001

An adaptive channel estimation technique for OFDM-based DTV receivers is proposed using a new mobility estimator. Sample mean techniques for channel estimation have displayed good performance in slow fading channels, because averaging reduces noise In channel estimation operation. This paper suggests an algorithm which selects the optimal number of symbols within which the sample mean of consecutive pilot data can be obtained. The designed mobility estimator determines the optimal number by comparing mobility variance and estimated noise valiance. The algorithm using the mobility estimator obtains an optimal channel function under time-invariant or time-variant multipath fading channels, thereby making the best BER performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.843-850
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• 2017

The EMG amplitude estimator, which has been investigated as an indicator of muscle force, is utilized as the control input to artificial prosthetic limbs. This paper describes an application of the optimal EMG amplitude estimator to the surface EMG signals recorded during constant isometric %MVC (maximum voluntary contraction) for 30 seconds and reports on assessing performance of the amplitude estimator from the application. Surface EMG signals, a total of 198 signals, were recorded from biceps brachii muscle over the range of 20-80%MVC isometric contraction. To examine the estimator performance, a SNR(signal-to-noise ratio) was computed from each amplitude estimate. The results of the study indicate that ARV(average rectified value) and RMS(root mean square) amplitude estimation with forth order whitening filter and 250[ms] moving average window length are optimal and showed the mean SNR improvement of about 50%, 40% and 20% for each 20%MVC, 50%MVC and 80%MVC surface EMG signals, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5A
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• pp.484-489
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• 2007

The main disadvantage of orthogonal frequency division multiplexing (OFDM) systems is its sensitivity to carrier frequency offset and timing offset. This paper proposes a simple way of improving the performance of the integer frequency offset (IFO) estimator in OFDM-based digital video broadcasting (DVB) system. By modifying the conventional maximum likelihood (ML) estimator to have multi-stage estimation strategy, IFO estimator is derived. Simulations indicate that the proposed IFO estimator works robustly with reduced computational burden when compared to ML estimator.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.221-235
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• 1999

Let be $X_1$,...,$X_p$, $p\geq2$ independent random variables where each $X_i$ has a gamma distribution with $\textit{k}_i$ and $\theta_i$ The problem is to simultaneously estimate $\textit{p}$ gamma parameters $\theta_i$ and $\theta_i{^-1}$ under entropy loss where the parameters are believed priori. Hierarch ical Bayes(HB) and empirical Bayes(EB) estimators are investigated. And a preference of HB estimator over EB estimator is shown using Gibbs sampler(Gelfand and Smith 1990). Finally computer simulation is studied to compute the risk percentage improvements of the HB estimator and the estimator of Dey Ghosh and Srinivasan(1987) compared to UMVUE estimator of $\theta^{-1}$.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.515-529
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• 1998

Burman (1987) and Hall and Titterington (1987) studied kernel smoothing for sparse multinomial data in detail. Both of their estimators for cell probabilities are sparse asymptotic consistent under some restrictive conditions on the true cell probabilities. Dong and Simonoff (1994) adopted boundary kernels to relieve the restrictive conditions. We propose a local linear kernel estimator which is popular in nonparametric regression to estimate cell probabilities. No boundary adjustment is necessary for this estimator since it adapts automatically to estimation at the boundaries. It is shown that our estimator attains the optimal rate of convergence in mean sum of squared error under sparseness. Some simulation results and a real data application are presented to see the performance of the estimator.