• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.457-459
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• 2005

In this paper the BER consideration of a quadrature receiver that has an autocalibration method is considered. The analysis is based on the derivation of the statistical characteristics of the imbalances in gain and phase between in-phase and quadrature components that may cause severe performance degradation of the receiver. The density. mean and variance functions of the estimates of gain and phase imbalances are discussed. Then it is shown that the estimates are asymptotically minimum variance unbiased with respect to the integration time in sampling. A brief consideration on the BER calculation follows.

• Water for future
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• v.27 no.1
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• pp.133-140
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• 1994

The augmenting hydrologic data using a correlation procedue has been used to improve the estimates of the mean and variance at the site of interest with short record when one or more nearby sites with longer records are available. The variance of the unbiased maximum likelihood estimator of ${{\sigma}_v}^2$ derived by Moran based on the multivariate normal distribution is modified into the form of Matalas and jacobs for the bivariate normal distribution to get the critical minimum values of the multiple correlation coefficient which give the improvement for estimation the variance at the site of interest. Those values are tabulated for various lengths of records and the number of sites.

• Industrial Engineering and Management Systems
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• v.8 no.4
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• pp.257-263
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• 2009

Focusing on the idea that the equation of exponential smoothing method (ESM) is equivalent to (1, 1) order ARMA model equation, new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. Theoretical solution was derived in a simple way. Mere application of ESM does not make good forecasting accuracy for the time series which has non-linear trend and/or trend by month. A new method to cope with this issue is required. In this paper, combining the trend removal method with this method, we aim to improve forecasting accuracy. An approach to this method is executed in the following method. Trend removal by a linear function is applied to the original shipping data of consumer goods. The combination of linear and non-linear function is also introduced in trend removal. For the comparison, monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful especially for the time series that has stable characteristics and has rather strong seasonal trend and also the case that has non-linear trend. The effectiveness of this method should be examined in various cases.

• Journal of the Korean Institute of Gas
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• v.15 no.3
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• pp.11-18
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• 2011

Ultrasonic guided waves have been widely utilized for long range inspection of structures. Recently, many researchers have paid attention to the tomographic imaging using guided wave for the diagnosis of plate-like structures because group velocity of guided waves is changed by central frequency of transducer and thickness of plate. Currently, Delay and Sum imaging technique and MVDR(Minimum Variance Distortionless Response) imaging technique are performed. So the performance of these two imaging techniques are investigated in this paper.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.10 no.5
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• pp.83-93
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• 1996

In this paper, a minimum variance control system is proposed and practically implemented, which is adequate to the induction motor speed control system with frequent load variation. Minimum variance control method is used as a control law and recursive least square method with selective forgetting factor is proposed and analyzed with general forgetting algorithm as an estimation method. Designed control system is based on PC-DSP structure for the purposed of easiness of applying adaptive algorithm. Through computer simulation and experimental results, it is verified that proposed control system is robust to the load variation and practical implementation is possible.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.703-708
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• 2022

K-means clustering is a representative clustering technique. However, there is a limitation in not being able to integrate the performance evaluation scale and the method of determining the minimum number of clusters. In this paper, a method for numerically determining the minimum number of clusters is introduced. The explained variance is presented as an integrated measure. We propose that the k-means clustering method should be performed in the subspace of the PCA in order to simultaneously satisfy the minimum number of clusters and the threshold of the explained variance. It aims to present an explanation in principle why principal component analysis and k-means clustering are sequentially performed in pattern recognition and machine learning.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.103-110
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• 2022

This paper deals with portfolio optimization problem that you could lower the total risk of an investment portfolio by adding risky assets to the mix than the minimum risk of single asset. Popular Markowitz's mean-variance(MV) model construct the portfolio with the point in the efficient frontier using principle of domination where the variance is minimized for a given mean return. While this paper suggest the portfolio with minimum risk asset with non-positive(negative and uncorrelated) correlation assets to it. As a result of experiments, the proposed method shows lower risk(standard deviation) than MV.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.8-13
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• 2023

We propose a method for removing interference and noise to estimate target information. In wireless channels, information signals are subject to interference and noise, making it is difficult to accurately estimate the desired signal. To estimate the desired information signal, it is essential to remove the noise and interference from the received signal, extracting only the desired signal. If the received signal noise and interference are not removed, the estimated information signal will have a large error in distance and direction, and the exact location of the target cannot be estimated. This study aims to accurately estimate the desired target in space. The objective is to achieve more presice target estimation than existing methods and enhance target resolution.An estimation method is proposed to improve the accuracy of target estimation. The proposed target estimation method obtains optimal weights using linear constraints and the minimum variance method. Through simulation, the performance of the proposed method and the existing method is analyzed. The proposed method successfully estimated all four targets, while the existing method only estimated two targets. The results show that the proposed method has better resolutiopn and superior estimation capability than the existing method.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.1
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• pp.124-132
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• 2004

A hydraulic system is difficult to keep the performance due to non-linearity, load pressure which changes according to working condition and system parameter variation, the requirement of control algorithm has been risen in order to satisfy them. An adaptive control is a control method which is suggested to achieve a control object though plant characteristics change. In spite of the case that plant characteristics and the degree of variation are difficult to grasp, adaptive control can keep the characteristics of closed-loop system regularly. In this study GMVAC(generalized minimum variance adaptive control) combined with output error feedback is proposed in order to solve problems of non-minimum phase, vibration and overshoot in initial response of the plant. The control performance according to the variation of characteristics of the plant is evaluated by changing the supply pressure only.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.6
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• pp.510-520
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• 1988

In this paper, the output error variance due to the differential vcariation of the state-space coefficient [ABCD], which is the coefficient quentization error, is normalized on the variance for cases that infinite wordlength state-space digital filter is realized by the finite one. That is, defining S as the statistical sensitivity and extending controllability gramian, observability gramian, and 2nd order mode analysis method to the state space digital filter, we synthesize the realization structure with the minimum statistical sensitivity and prove the effecency of the minimum statistical sensitivity structure synthesis by the simulation.