• Journal of Veterinary Clinics
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• v.24 no.2
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• pp.77-81
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• 2007

Diagnostic performance of polymerase chain reaction (PCR) for detecting Dirofilaria immitis in dogs was evaluated when no gold standard test was employed. An enzyme-linked immunosorbent assay test kit (SnapTM, IDEXX, USA) with unknown parameters was also employed. The sensitivity and specificity of the PCR from two-population model were estimated by using both maximum likelihood using expectation-maximization (EM) algorithm and Bayesian method, assuming conditional independence between the two tests. A total of 266 samples, 133 samples in each trial, were randomly retrieved from the heartworm database records during the year 2002-2004 in a university animal hospital. These data originated from the test results of military dogs which were brought for routine medical check-up or testing for heartworm infection. When combined 2 trials, sensitivity and specificity of the PCR was 96.4-96.7% and 97.6-98.8% in EM and 94.4-94.8% and 97.1-98% in Bayesian. There were no statistical differences between estimates. This finding indicates that the PCR assay could be useful screening tool for detecting heartworm antigen in dogs. This study was provided further evidences that Bayesian approach is an alternative approach to draw better inference about the performance of a new diagnostic test in case when either gold test is not available.

• Review of KIISC
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• v.16 no.3
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• pp.63-74
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• 2006

본 논문에서는 한국 산업규격 KS 표준 블록암호 알고리즘인 ARIA의 핵심논리들을 64비트 프로세서 환경에서 효율적으로 구현하는 방법을 제안하고, 제안된 방법으로 구현된 ARIA를 대표적인 64비트 마이크로프로세서인 Intel Pentium4 x64, Intel Itanium, Intel XEON(EM64T)을 이용하여 그 효율성을 평가하였으며, 32비트 프로세서에 적합하게 구현된 ARIA와 효율성을 비교하고 문제점을 분석하였다.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.441-450
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• 2003

Collaborative filtering is one of the most widely used methodologies for recommendation system. Collaborative filtering is based on a data matrix of each customer's preferences and frequently, there exits missing data problem. We introduced two imputation approach (multiple imputation via Markov Chain Monte Carlo method and multiple imputation via bootstrap method) to improve the prediction performance of collaborative filtering and evaluated the performance using EachMovie data.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.265-267
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• 2000

본 논문에서는 텍스트 문서의 주제어를 추출하고 문서를 주제별로 분류하기 위해 확률적 그래프 모델을 사용하는 방법을 제안하였다. 텍스트 문서 데이터를 문서와 단어의 쌍으로(dyadic)표현하여 확률적 생성 모델을 학습하였다. 확률적 그래프 모델의 학습에는 정의된 likelihood를 최대화하기 위한 EM(Expected Maximization)알고리즘을 사용하였다. TREC-8 AdHoc 텍스트 에이터에 대하여 학습된 확률 그래프 모델의 성능을 실험적으로 평가하였다. 이로부터 찾아 낸 문서에 대한 주제어가 사람이 제시한 주제어와 유사한 지와, 사람이 각 주제에 대해 분류한 문서가 이 확률모델로부터의 분류와 유사한 지를 실험적으로 검토하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.391-392
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• 2022

본 논문에서는 SparkHDE-EM이라는 생태학적 모델 알고리즘에 기반한 하이브리드 DE를 제안한다. 그리고 Spark 기반 아일랜드 모델을 도입하여 다양한 DE 변종의 병렬화를 구현한다. 또한 Monod 모델을 활용하여 자원 간의 균형을 유지하는 방법을 제안한다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.10
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• pp.1021-1030
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• 2009

We propose optimization method of two-inductor loaded small loop antennas using simple genetic algorithm. To optimize the loop antennas for the RFID and the mobile phone band, we changed positions and values of the two inductors in the loop antenna. Visual basic was used to make genetic algorithm and to calculate fitness values by controlling the commercial EM software. The bandwidth of the optimized RFID loop antenna is 10 MHz at the center frequency of 922 MHz and that of the mobile phone antenna are 84 MHz and 266 MHz at the center frequency of 948 MHz(GSM band) and 1.81 GHz(DCS band), respectively.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.183-196
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• 2012

The inference for incomplete data such as missing data, truncated distribution and censored data is a phenomenon that occurs frequently in statistics. To solve this problem, Expectation Maximization(EM), Monte Carlo Expectation Maximization(MCEM) and Stochastic Expectation Maximization(SEM) algorithm have been used for a long time; however, they generally assume known distributions. In this paper, we propose the Metropolis-Hastings Expectation Maximization(MHEM) algorithm for unknown distributions. The performance of our proposed algorithm has been investigated on simulated and real dataset, KOSPI 200.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.21-30
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• 2014

Ordinary survival analysis cannot be applied when a significant fraction of patients may be cured. A cure rate model is the combination of cure fraction and survival model and can be applied to several types of cancer. In this article, the cure rate model is considered in the interval censored data with a cluster effect. A shared frailty model is introduced to characterize the cluster effect and an EM algorithm is used to estimate parameters. A simulation study is done to evaluate the performance of estimates. The proposed approach is applied to the smoking cessation study in which the event of interest is a smoking relapse. Several covariates (including intensive care) are evaluated to be effective for both the occurrence of relapse and the smoke quitting duration.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.885-894
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• 2015

It is necessary to forecast the electricity demand for reliable and effective operation of the power system. In this study, we try to categorize a functional data, the mean curve in accordance with the time of daily power demand pattern. The data were collected between January 1, 2009 and December 31, 2011. And it were converted to time series data consisting of seasonal components and error component through log transformation and removing trend. Functional clustering by Ma et al. (2006) are applied and parameters are estimated using EM algorithm and generalized cross validation. The number of clusters is determined by classifying holidays or weekdays. Monday, weekday (Tuesday to Friday), Saturday, Sunday or holiday and season are described the mean curve of daily power demand pattern.

• The Journal of the Acoustical Society of Korea
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• v.21 no.1
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• pp.11-18
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• 2002

In this paper, a gain-adapted speech recognition method in noise is developed in the time domain. Noise is assumed to be colored. To cope with the notable nonstationary nature of speech signals such as fricative, glides, liquids, and transition region between phones, the nonstationary autoregressive (NAR) hidden Markov model (HMM) is used. The nonstationary AR process is represented by using polynomial functions with a linear combination of M known basis functions. When only noisy signals are available, the estimation problem of noise inevitably arises. By using multiple Kalman filters, the estimation of noise model and gain contour of speech is performed. Noise estimation of the proposed method can eliminate noise from noisy speech to get an enhanced speech signal. Compared to the conventional ARHMM with noise estimation, our proposed NAR-HMM with noise estimation improves the recognition performance about 2-3%.