• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.1079-1086
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• 2005

Most discussions of single imputation methods and the EM algorithm concern point estimation of population quantities with missing values. A second concern is how to get standard errors of the point estimates obtained from the filled-in data by single imputation methods and EM algorithm. Now we focus on how to estimate standard errors with incorporating the additional uncertainty due to nonresponse. There are some approaches to account for the additional uncertainty. The general two possible approaches are considered. One is the jackknife method of resampling methods. The other is multiple imputation(MI). These two approaches are reviewed and compared through simulation studies.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.27-30
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• 2007

This paper deals with an equivalent circuit model for RF passive components. Rational functions are obtained from the frequency responses of EM simulation by using Foster canonical partial fraction expressions. The Vector Fitting(VF) and the Adaptive Frequency Sampling(AFS) scheme are also implemented to obtain the rational functions. A passivity enforcement algorithm is applied to ensure the stability of the equivalent circuit model. In order to verify the schemes, S parameters of the equivalent circuit model is compared to those of EM simulation in case of the microstrip line structure with 3 slots in ground.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.12
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• pp.125-130
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• 2004

The characteristics of on-chip spiral rectangular inductors in CMOS process are investigated through the simulation and experiment. The ADS-momentum is used for EM simulation, and the spiral inductors are fabricated with Hynix 0.35㎛ CMOS process. This research mainly concerned the effects of the geometric change in terms of the number of turns and the width of micro strip line. The measured and simulated results show that the Hynix 0.35㎛ process could support a top metal spiral inductor of 1nH to 6nH with Q-factor less than 5.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.9
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• pp.915-921
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• 2013

This work presents a 77 GHz low noise amplifier(LNA) for automotive radar systems using 65 nm RF CMOS process. The LNA is composed of three stage common source amplifiers and includes transmission line matching networks. To reduce the time for three dimensional EM simulation, we optimize the transmission line impedance matching network using a pre-built EM library. The proposed compact simulation technique is confirmed by measurement results. The peak gain of the LNA is 10 dB at 77 GHz and input/output return losses are below -10 dB around the design frequency.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.15 no.6
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• pp.492-498
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• 2002

In this paper, we present the simulation results of multi-layer VCO(voltage controlled oscillator), which is composed of resonator, oscillator, and buffer circuit, using EM simulator and nonlinear RF circuit simulator. EM simulator is used for obtaining the EM(Electromagnetic) characteristics of conductor pattern as well as designing the multi-layer VCO. Obtained EM characteristics were used as real components in nonlinear RF circuit simulation. Finally the overall VCO was simulated by the nonlinear RF circuit simulator. The material for the circuit pattern was Ag and the dielectric was Dupont 951AT, which will be applied for LTCC process. The structure of multi-layer VCO is constructed with 4 conducting layer. Simulated results showed that the output level was about 4.5 [dBm], the phase noise was -104 [dBc/Hz] at 30 [kHz] offset frequency, the harmonics -8 dBc, and the control voltage sensitivity of 30 [MHz/V] with a DC current consumption of 9.5 [mA]. The size of VCO is $6{\times}9{\times}2 mm$(0.11 [cc]).

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.07b
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• pp.653-656
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• 2002

In this paper, we present the simulation results of multi-layer VCO(voltage controlled oscillator), which is composed of resonator, oscillator, and buffer circuit, using EM simulator and nonlinear RF circuit simulator. EM simulator is used for obtaining the EM(Electromagnetic) characteristics of conductor pattern as well as designing the multi-layer VCO. Obtained EM characteristics were used as real components in nonlinear RF circuit simulation. Finally the overall VCO was simulated by the nonlinear RF circuit simulator. The material for the circuit pattern was Ag and the dielectric was DuPont 951AT, which will be applied for LTCC process. The structure of multi-layer VCO is constructed with 4 conducting layer. Simulated results showed that the output level was about 4.5 [dBm], the phase noise was -104 [dBc/Hz] at 30 [kHz] offset frequency, the harmonics -8 dBc, and the control voltage sensitivity of 30 [MHz/V] with a DC current consumption of 9.5 [mA]. The size of VCO is $6{\times}9{\times}2$ mm(0.11[cc]).

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.157-168
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• 2012

The EM algorithm is the most important tool to obtain the maximum likelihood estimator in finite mixture models due to its stability and simplicity. However, its convergence rate is often slow because the conventional EM algorithm is based on a large missing data space. Several techniques have been proposed in the literature to reduce the missing data space. In this paper, we review existing methods and propose a new EM algorithm for Gaussian mixtures, which reduces the missing data space while preserving the stability of the conventional EM algorithm. The performance of the proposed method is evaluated with other existing methods via simulation studies.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2615-2617
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• 2003

The Kalman filter is a recursive Linear Estimator for the linear dynamic systems(LDS) affected by two different noises called process noise and measurement noise both of which are uncorrelated white. The Expectation Maximization(EM) algorithm is employed in this paper as a preprocessor to reinforce the effectiveness of Kalman estimator. Particularly, we focus on the relation between Kalman filter and EM algorithm in the LDS. In this paper, we propose a new algorithm to improve the performance on the parameter estimation via EM algorithm, which improves the overall process of Kalman filtering. Since Kalman filter algorithm not only needs the system parameters but also is very sensitive the initial state conditions, the initial conditions decided through EM turns out to be very effective. In experiments, the computer simulation results ate provided to demonstrate the superiority of the proposed algorithm.

• Journal of Internet Computing and Services
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• v.8 no.2
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• pp.105-115
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• 2007

The up-to-moment dataset is built by combining the past dataset and the recent dataset. The proposal is to compute association rules in real time. This study proposed the model, $EM_{past'}$ and algorithm that is sensitive to time. It can be utilized in real time by applying partitioned combination law after dividing the past dataset into(k-1). Also, we suggested $EM^{ES}_{past}$ applying the exponential smoothing method to $EM^p_{past'}$ When the association rules of $EM_{past'}\;EM^w_{past'\;and\;EM^{ES}_{past}$ were compared, The simulation results showed that $EM^{ES}_{past}$ is most accurate for testing dataset than $EM_{past}$ and $EM^w_{past}$ in huge dataset.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1347-1359
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• 2016

Finite mixtures of regression models provide an effective tool to explore a hidden functional relationship between a response variable and covariates. However, it is common in practice that data are not fully observed due to several reasons. In this paper, we derived an expectation-maximization (EM) algorithm to obtain the maximum likelihood estimator when some covariates are missing at random in the finite mixture of regression models. We conduct some simulation studies and we also provide some real data examples to show the validity of the derived EM algorithm.