• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.7 s.337
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• pp.71-78
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• 2005

A K-Best algerian is known as optimal for implementing the maximum-likelihood detector (MLD), since it has a fixed maximum complexity compared with the sphere decoding or the maximum-likelihood decoding algorithm. However the computational complexity of the K-Best algrithm is still prohibitively high for practical applications when K is large enough. If small value of K is used, the maximum complexity decreases but error flooring at high SNR is caused by error propagation. In this paper, a K-reduction scheme, which reduces K according to each search level, is proposed to solve error propagation problems. Simulations showed that the proposed scheme provides the improved performance in the bit error rate and also reduces the average complexity compared with the conventional scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.7A
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• pp.710-716
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• 2007

In this paper, a new scheme of downlink co-channel interference (CCI) cancellation in OFDM cellular networks is introduced for users at the cell-edge. Coordinated symbol transmission between base stations (BS) is operated where the same symbol is transmitted from different BS on different sub-carriers. At the mobile station (MS) receiver, we introduce a soft decision maximum likelihood CCI canceler and a modified maximum ratio combining (M-MRC) to obtain an estimate of the transmitted symbols. Weights used in the combining method are derived from the channels coefficients between the cooperated BSs and the MS. Simulations show that the proposed scheme works well under frequency-selective channels and frequency non-selective channels. A gain of 9 dB and 6 dB in SIR is obtained under multipath fading and flat-fading channels, respectively.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.2
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• pp.21-28
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• 2011

We consider an RFID network configured as a star in which tags stationarily move into and out of the vicinity of the reader. To cognize the neighboring tags in the RFID network, we propose a scheme based on dynamic framed and slotted ALOHA which determines the number of slots belonging to a frame in a dynamic fashion. The tag cognizance scheme distinctively employs a rule for estimating the expected number of neighboring tags, identified as R-retrospective maximum likelihood rule, where the observations attained in the R previous frames are used in maximizing the likelihood of expected number of tags. Simulation result shows that a slight increase in depth of retrospect is able to significantly improve the cognizance performance.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.385-395
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• 2020

We present a new maximum likelihood approach to estimate demographic history using genomic data sampled from two populations. A demographic model such as an isolation-with-migration (IM) model explains the genetic divergence of two populations split away from their common ancestral population. The standard probability model for an IM model contains a latent variable called genealogy that represents gene-specific evolutionary paths and links the genetic data to the IM model. Under an IM model, a genealogy consists of two kinds of evolutionary paths of genetic data: vertical inheritance paths (coalescent events) through generations and horizontal paths (migration events) between populations. The computational complexity of the IM model inference is one of the major limitations to analyze genomic data. We propose a fast maximum likelihood approach to estimate IM models from genomic data. The first step analyzes genomic data and maximizes the likelihood of a coalescent tree that contains vertical paths of genealogy. The second step analyzes the estimated coalescent trees and finds the parameter values of an IM model, which maximizes the distribution of the coalescent trees after taking account of possible migration events. We evaluate the performance of the new method by analyses of simulated data and genomic data from two subspecies of common chimpanzees in Africa.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.957-981
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• 2017

A generalized linear mixed model is an extension of a generalized linear model that allows random effect as well as provides flexibility in developing a suitable model when observations are correlated or when there are other underlying phenomena that contribute to resulting variability. We describe maximum likelihood estimation methods for logistic regression models that include random effects - the Laplace approximation, Gauss-Hermite quadrature, adaptive Gauss-Hermite quadrature, and pseudo-likelihood. Applications are provided with social science problems by analyzing the effect of mental health and life satisfaction on volunteer activities from Korean welfare panel data; in addition, we observe that the inclusion of random effects in the model leads to improved analyses with more reasonable inferences.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.384-393
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• 2019

An experimental validation of a nuclear forensics methodology for the source reactor-type discrimination of separated weapons-useable plutonium is presented. The methodology uses measured values of intra-element isotope ratios of plutonium and fission product contaminants. MCNP radiation transport codes were used for various reactor core modeling and fuel burnup simulations. A reactor-dependent library of intra-element isotope ratio values as a function of burnup and time since irradiation was created from the simulation results. The experimental validation of the methodology was achieved by performing two low-burnup experimental irradiations, resulting in distinct fuel samples containing sub-milligram quantities of weapons-useable plutonium. The irradiated samples were subjected to gamma and mass spectrometry to measure several intra-element isotope ratios. For each reactor in the library, a maximum likelihood calculation was utilized to compare the measured and simulated intra-element isotope ratio values, producing a likelihood value which is proportional to the probability of observing the measured ratio values, given a particular reactor in the library. The measured intra-element isotope ratio values of both irradiated samples and its comparison with the simulation predictions using maximum likelihood analyses are presented. The analyses validate the nuclear forensics methodology developed.

• Communications for Statistical Applications and Methods
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• v.21 no.3
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• pp.253-260
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• 2014

The intraclass correlation model has a long history of applications in several fields of research. Case deletion diagnostic methods for the intraclass correlation model are proposed. Based on the likelihood equations, we derive a formula for a case deletion diagnostic method which enables us to investigate the influence of observations on the maximum likelihood estimates of the model parameters. Using the Taylor series expansion we develop an approximation to the likelihood distance. Numerical examples are provided for illustration.

• Journal of the Korean Statistical Society
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• v.27 no.2
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• pp.205-220
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• 1998

Dependence concepts for ordered two-way contingency tables have been of considerable interest. We consider a dependence concept which is less restrictive than likelihood ratio dependence and more restrictive than regression dependence. Maximum likelihood estimation of cell probability under this dependence restriction is studied. The likelihood ratio statistics for and against this dependence are proposed and their large sample distributions are derived. A real data is analyzed to illustrate the estimation and testing procedures.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1309-1317
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• 2013

There are various parameter estimation methods for the generalized exponential distribution under progressive type I interval censoring. Chen and Lio (2010) studied the parameter estimation method by the maximum likelihood estimation method, mid-point approximation method, expectation maximization algorithm and methods of moments. Among those, mid-point approximation method has the smallest mean square error in the generalized exponential distribution under progressive type I interval censoring. However, this method is difficult to derive closed form of solution for the parameter estimation using by maximum likelihood estimation method. In this paper, we propose two type of approximate maximum likelihood estimate to solve that problem. The simulation results show the obtained estimators have good performance in the sense of the mean square error. And proposed method derive closed form of solution for the parameter estimation from the generalized exponential distribution under progressive type I interval censoring.

• Journal of Biomedical Engineering Research
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• v.30 no.1
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• pp.56-65
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• 2009

Compton imaging is often recognized as a potentially more valuable 3-D technique in nuclear medicine than conventional emission tomography. Due to inherent computational limitations, however, it has been of a difficult problem to reconstruct images with good accuracy. In this work we show that the row-action maximum likelihood algorithm (RAMLA), which have proven useful for conventional tomographic reconstruction, can also be applied to the problem of 3-D reconstruction of cone-beam projections from Compton scattered data. The major advantage of RAMLA is that it converges to a true maximum likelihood solution at an order of magnitude faster than the standard expectation maximiation (EM) algorithm. For our simulations, we first model a Compton camera system consisting of the three pairs of scatterer and absorber detectors placed at x-, y- and z-axes, and generate conical projection data using a software phantom. We then compare the quantitative performance of RAMLA and EM reconstructions in terms of the percentage error. The net conclusion based on our experimental results is that the RAMLA applied to Compton camera reconstruction significantly outperforms the EM algorithm in convergence rate; while computational costs of one iteration of RAMLA and EM are about the same, one iteration of RAMLA performs as well as 128 iterations of EM.