• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.53-58
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• 2012

In this paper, we evaluate and analyze representative estimation methods for the sparse channel. In order to evaluate error performance of matching pursuit(MP) and minimum mean square error(MMSE) algorithm, lower bound of MMSE is determined by Cramer-Rao bound and compared with upper bound of MP. Based on analysis of those bounds, mean square error of MP which is effective in the estimation of sparse channel can be larger than that of MMSE according to the number of estimated tap and signal-to-noise ratio. Simulation results show that the performances of both algorithm are reversed on the sparse channel with Rayleigh fading according to signal-to-noise ratio.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.5
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• pp.61-65
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• 2002

RM(Switched Reluctance Motor)＇s Torque is generated by phase-current and inductance profile. A new analytical concept is proposed to determine the turn-off angle for maximization of the torque output. This paper describes a new method to maximization the average torque of a current control Switched Reluctance Motor. It is based on the simplified turn-off angle equation using least square method. Simulations carried out on a three-phase 6/4 pole SRM justify the algorithm is described. The suggested maximization average torque is verified by simulation in this paper.

• The Korean Journal of Financial Studies
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• v.14 no.1
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• pp.41-56
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• 2008

주식 수익률이 정상적 과정이 아니라 비정상적 과정에 의해서 생성되고 있다는 사실이 여러 실증 분석에서 제시되고 있다. 시계열의 평균이 시간의 흐름에 따라 변하면 이 시계열은 비정상적 과정에 의하여 생성된다. 시간의 흐름에 따라 평균이 변하는 비정상 시계열은 단위근과 공적분에 의하여 시계열의 운동을 모형화하고 있다. 한편 시계열의 비정상성은 분산이 시간의 흐름에 따라 변할 때에도 발생한다. 시간의 흐름에 따라 무조건부 분산은 변하지 않고 있지만 이용 가능한 정보 집합을 조건으로 하는 조건부 분산이 변하는 경우도 있다. 이 같은 성질을 가진 주가 시계열은 자기회귀 조건부 이분산(ARCH) 계통의 과정으로 모형화하고 있다. 그러나 무조건부 분산이 시간의 흐름에 따라 변하면 ARCH 계통은 중대한 모형정립과오(misspecification)에 직면하게 된다. 따라서 본 논문은 무조건부 분산이 시간의 흐름에 따라 변할 때 자기 회귀 과정의 모수를 추정하는 방법을 검토하고, 이 방법을 한국 종합주가 지수에 적용하여 자기회귀 과정의 모수를 추정하였다. 이 방법에 의하여 추정된 2계 자기회귀 과정의 모수값 중 상수항과 제1계 항의 계수는 통상 최소자승법에 의한 값과 유사하다. 그러나 제2계 항 모수의 값은 양자가 상당히 다르다. 최소자승에 의한 제2계 값이 과대 추정되고 있다.

• The Journal of the Acoustical Society of Korea
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• v.35 no.4
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• pp.272-279
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• 2016

In this paper, the realization of a hearing aid adaptively cancelling feedback noise was considered. Conventional least mean square method in time domain was transformed into frequency domain in order to minimize computational burden. The adaptive filter algorithm was evaluated by Matlab (Matrix laboratory), and it was confirmed by CSR 8675 Bluetooth DSP IC (Digital Signal Processor Integrated Circuit) chip firmware realization. Some remote control features by a smart phone was added to the smart hearing aid for user interface easiness.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.51-53
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• 2001

전력 수요 예측은 전력 수급 안정과 양질의 전력을 공급하기 위한 필수 기법이며 경쟁적인 전력시장에서 전력요금과 밀접한 관련이 있다. 그러므로, 경쟁적인 전력시장 구조하의 시장 참여자에게 있어서 전력 수요 예측은 매우 관심 있는 사항이다. 최근의 전력 수요 예측 기법으로 예측한 오차율을 살펴보면 평일과는 다르게 특수일의 전력 수요예측은 평균 5%를 상회하는 수준으로 예측의 정확도가 평일 예측에 비해 크게 낮은데 이유는 특수일이 평일에 비하여 부하의 크기가 다소 낮게 나타나고 특수일 마다 계절적인 차이가 있으며 각각의 특수일 마다 고유한 부하의 특성이 있으므로 과거 데이터를 이용할 때 동일 특수일을 이용하게 되며 따라서 평일과는 다르게 일년 단위로 과거 데이터 값들이 취득되므로 오차율이 커진다. 따라서 데이터들을 퍼지화하여 선형계획법을 수행하여 평균 $2{\sim}3%$ 정도의 우수한 결과를 도출한 바 있다. 본 논문에서는 퍼지 선형회귀분석법을 이용한 예측 기법에 최소자승법을 도입하여 특수일 전력 수요예측의 정확도를 개선하였다.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.687-693
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• 2015

Using 378,086 lactation records on dairy cattle, environmental factors influencing acetone and ${\beta}$-hydroxybutyrate acid contents in raw milk which are used as ketosis diagnosis indicator traits were analyzed in this experiment. Significance testing was conducted on farm, lactation stage, parity, milking time and month of age by traits. The results of this experiment indicated that there was a highly significant (p < 0.01) difference in all factors and lactation stage was the most significant factor. Linear regression coefficients of month of age on daily milk yields and acetone and ${\beta}$-hydroxybutyrate acid contents were all positive, while their quadratic linear regression coefficients were negative. Least square means for milk yield at second lactation stage (36~65 days) was 19.06kg which was higher than that of late lactation stage by 6.51kg. Least square means for acetone and ${\beta}$-hydroxybutyrate acid contents at the first lactation stage (5~35 days) were highest (0.1929mM/L and 0.0742mM/L, respectively), and there was a trend that they decreased as the milking progressed, but increased slightly at the late stage of milking. However, least square means for acetone and ${\beta}$-hydroxybutyrate acid contents at the first parity were 0.1414mM/L and 0.0522mM/L, respectively, which were higher than the average milk yield after the second parity. Least square means for acetone and ${\beta}$-hydroxybutyrate acid contents of PM milk yield (0.1372mM/L and 0.0534mM/L, respectively) were higher than those of AM milk yield collectively.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.675-684
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• 1998

This paper presents the empirical formulas for determining the design-width for medium rivers in the Han river basin. The design flood, the watershed ares, and the channel slope of 216 medium rivers in the Han river basin are collected. the design width formulas are then determined by 1) the least squares (LS) method, 2)the least median squares (LMS) method, and 3) the reweighted least squares method based on the LMS (RLS). The six types of formulas are considered to determine the acceptable type for medium streams in the Han river basin. The root mean squared errors (RMSE), the absolute mean (AME) errors, and the mean errors (ME) are computed to test the formulas derived by three regression methods. It si found that the equation related stream width to the watershed area and the channel slope is acceptable for determining the design width for medium streams in the Han river basin. It is expected that the equations proposed by this study be used an index for determining the design-width for medium streams in the Han river basin.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.5
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• pp.805-810
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• 2019

Recently, a majority of 4G mobile telecommunication manufacturers prefer repeaters with good adaptability. In this paper, we propose a new LMF(: Least Means Fourth) algorithm for LTE(: Long Term Evolution) RF(: Radio Frequency) Repeater. The proposed algorithm is a modification of the LMF, which appropriately adjusts the step size and improves performance according to the Sign function. The steady state MSE(: Mean Square Error) performance of the proposed LMF algorithm with step size of 0.009 is low level at about -25dB, and the proposed LMF algorithm requires 500 less iterations than the conventional algorithms at MSE of -25dB.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.66-78
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• 2015

With the development of bio computing technology, DNA watermarking to do as a medium of DNA information has been researched in the latest time. However, DNA information is very important in biologic function unlikely multimedia data. Therefore, the reversible DNA watermarking is required for the host DNA information to be perfectively recovered. This paper presents a reversible DNA watermarking using least square based prediction error expansion for noncodng DNA sequence. Our method has three features. The first thing is to encode the character string (A,T,C,G) of nucleotide bases in noncoding region to integer code values by grouping n nucleotide bases. The second thing is to expand the prediction error based on least square (LS) as much as the expandable bits. The last thing is to prevent the false start codon using the comparison searching of adjacent watermarked code values. Experimental results verified that our method has more high embedding capacity than conventional methods and mean prediction method and also makes the prevention of false start codon and the preservation of amino acids.

• Korean Chemical Engineering Research
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• v.53 no.2
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• pp.236-242
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• 2015

We present two data-driven modeling methods, partial least square (PLS) and artificial neural network (ANN), to predict the major operating and performance variables of a polymer electrolyte membrane (PEM) fuel cell stack. PLS and ANN models were constructed using the experimental data obtained from the testing of a 30 kW-class PEM fuel cell stack, and then were compared with each other in terms of their prediction and computational performances. To reduce the complexity of the models, we combined a variables importance on PLS projection (VIP) as a variable selection method into the modeling procedure in which the predictor variables are selected from a set of input operation variables. The modeling results showed that the ANN models outperformed the PLS models in predicting the average cell voltage and cathode outlet temperature of the fuel cell stack. However, the PLS models also offered satisfactory prediction performances although they can only capture linear correlations between the predictor and output variables. Depending on the degree of modeling accuracy and speed, both ANN and PLS models can be employed for performance predictions, offline and online optimizations, controls, and fault diagnoses in the field of PEM fuel cell designs and operations.