• Genomics & Informatics
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• 제6권1호
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• pp.14-17
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• 2008

GENDISCAN study (Gene Discovery for Complex traits in Asian population of Northeast area) was designed to incorporate methodologies which enhance the power to identify genetic variations underlying complex disorders. Use of population isolates as the target population is a unique feather of this study. However, population isolates may have hidden inbreeding structures which can affect the validity of the study. To understand how this issue may affect results of GENDISCAN, we estimated inbreeding coefficients in two study populations in Mongolia. We analyzed the status of Hardy-Weinberg Equilibrium (HWE), polymorphism information contents (PIC), heterozygosity, allelic diversity, and inbreeding coefficients, using 317 and 1,044 STR (short tandem repeat) markers in Orkhontuul and Dashbalbar populations. HWE assumptions were generally met in most markers (88.6% and 94.2% respectively), and single marker PIC ranged between 0.2 and 0.9. Inbreeding coefficients were estimated to be 0.0023 and 0.0021, which are small enough to assure that conventional genetic analysis would work without any specific modification. We concluded that the population isolates used in GENDISCAN study would not present significant inflation of type I errors from inbreeding effects in its gene discovery analysis.

• 전자공학회논문지S
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• 제35S권4호
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• pp.67-75
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• 1998

In general, Evoluationary Algorithm(EAs) are refered to as methods of population-based optimization. And EAs are considered as very efficient methods of optimal sytem design because they can provice much opportunity for obtaining the global optimal solution. This paper presents a co-evolution scheme of artifical neural networks, which has two different, still cooperatively working, populations, called as a host popuation and a parasite population, respectively. Using the conventional generatic algorithm the host population is evolved in the given environment, and the parastie population composed of schemata is evolved to find useful schema for the host population. the structure of artificial neural network is a diagonal recurrent neural netork which has self-feedback loops only in its hidden nodes. To find optimal neural networks we should take into account the structure of the neural network as well as the adaptive parameters, weight of neurons. So we use the genetic algorithm that searches the structure of the neural network by the co-evolution mechanism, and for the weights learning we adopted the evolutionary stategies. As a results of co-evolution we will find the optimal structure of the neural network in a short time with a small population. The validity and effectiveness of the proposed method are inspected by applying it to the stabilization and position control of the invered-pendulum system. And we will show that the result of co-evolution is better than that of the conventioal genetic algorithm.

• Genomics & Informatics
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• 제18권4호
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• pp.45.1-45.5
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• 2020

With the ongoing rise of coronavirus disease 2019 (COVID-19) pandemic across the globe, interests in COVID-19 antibody testing, also known as a serology test has grown, as a way to measure how far the infection has spread in the population and to identify individuals who may be immune. Recently, many countries reported their population based antibody titer study results. South Korea recently reported their third antibody formation rate, where it divided the study between the general population and the young male youths in their early twenties. As previously stated, these simple point estimates may be misinterpreted without proper estimation of standard error and confidence intervals. In this article, we provide an updated 95% confidence intervals for COVID-19 antibody formation rate for the Korean population using asymptotic, exact and Bayesian statistical estimation methods. As before, we found that the Wald method gives the narrowest interval among all asymptotic methods whereas mid p-value gives the narrowest among all exact methods and Jeffrey's method gives the narrowest from Bayesian method. The most conservative 95% confidence interval estimation shows that as of 00:00 November 23, 2020, at least 69,524 people were infected but not confirmed. It also shows that more positive cases were found among the young male in their twenties (0.22%), three times that of the general public (0.051%). This thereby calls for the quarantine authorities' need to strengthen quarantine managements for the early twenties in order to find the hidden infected people in the population.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅲ
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• pp.1307-1310
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• 2003

본 논문에서는 사용자 개개인에 최적화된 아바타를 생성하기 위해 대화형 진화연산(Interactive Genetic Algorithm, IGA)을 적용하는 방법을 제안하고 있다. 대화형 진화연산은 사용자의 선택을 적합도 평가에 사용하는 방법이기 때문에, 사용자의 개인적인 취향을 아바타 생성 과정에 반영할 수 있다. 본 연구에서는 기존의 대화형 진화연산이 가지고 있는 단점을 극복하기 위해 hidden population 기법과, simplified genotype 기법을 제안한다. 이러한 방법들은 단시간 내에 최적화된 결과물을 생성하도록 유도함으로써 IGA 시스템의 최대 문제점인 사용자의 피로도를 최소화한다 마지막으로, 제안하고 있는 알고리즘의 우수성을 증명하기 위해 사용자의 만족도나 신뢰도를 측정할 수 있는 독자적인 평가 방법을 소개하고 있다

• 한국가정과교육학회지
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• 제27권4호
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• pp.67-91
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• 2015

본 연구에서는 30-40대 싱글여성의 의식주생활, 소비 및 여가 생활과 같은 일상생활에서의 의미를 분석하였다. 그 결과 자신, 관계, 생존, 미래 준비, 자유와 그 이면과 같이 5개의 대주제를 찾을 수 있었다. '자신'이라는 대주제에서는 오직 '나'를 위해, '나'를 완성시키고 싶은, '나'만의 스타일이라는 3개의 중주제를 찾았다. '관계'라는 대주제에서는 가족과 더불어서, 다른 사람들과 더불어서 라는 2개의 중주제를 찾았고, 가족과 더불어서 라는 중주제에서는 '가족'을 위해, 아직은 '가족'의 그늘에서 라는 2개의 소주제를 찾았다. 다른 사람들과 더불어서 라는 중주제에서는 함께 하는 즐거움, 싱글들끼리의 편안함, 세상과의 소통 이라는 3개의 소주제를 찾았다. '생존'이라는 대주제에서는 건강, 안전, 혼자 살아내는 연습 이라는 3개의 중주제를 찾았고, '미래 준비'라는 대주제에서는 착한 소비, 노후대비 저축, 노후대비 여가 라는 3개의 중주제를, '자유와 그 이면'이라는 대주제에서는 홀로라서 자유, 자유의 그 이면들 이라는 2개의 중주제를 찾았고, 홀로라서 자유라는 중주제에서는 가족으로부터 벗어난 자유로움, 홀로이기에 나에 대한 보상 이라는 2개의 소주제를, 자유의 그 이면들이라는 중주제에서는 혼자라서 '불안' '싫음' '두려움', 외로움과 쓸쓸함 극복하기 라는 2개의 소주제를 찾았다. 이와 같이 주제를 찾는 과정을 통하여 싱글 여성들의 일상생활에서의 의미를 알아낼 수 있었고, 이러한 의미를 종합해 봄으로써 인구교육에서의 시사점을 도출해 내었다.

• 천문학회보
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• 제37권2호
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• pp.232.2-232.2
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• 2012

Submillimeter Galaxies (SMGs) are high-redshift galaxies undergone extremely intense starbursts. Their UV radiation is heavily extinguished by dust and is re-radiated in the far-IR and submillimeter. They are thought to be progenitors of present-day giant elliptical galaxies and can be tracers of the highest density environment at high redshift. However, because of the low angular resolution of existing single-dish submillimeter telescopes, the progress in understanding the SMG population has been remarkably slow. In this talk, I will outline the outstanding issues in this field, and introduce our Submillimeter Array interferometric studies of SMGs. I will also discuss possible new research that will be enabled by next-generation instruments such as ALMA and LMT.

• Journal of Information Processing Systems
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• 제15권4호
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• pp.986-1016
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• 2019

Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1998년도 학술발표대회 논문집 제17권 1호
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• pp.71-74
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• 1998

HMM (hidden Markov model)을 이용한 음성인식은 현재 가장 널리 쓰여지고 있는 방법으로, 이 중 CDHMM (continuous observation density HMM)은 상태에서 관측심볼확률을 연속확률밀도를 사용하여 표현한다. 본 논문에서는 가우스 혼합밀도함수를 사용하는 CDHMM의 상태천이확률과, 관측심볼확률을 표현하기 위한 인자인 평균벡터, 공분산 행렬, 가지하중값을 유전자 알고리듬을 사용하여 최적화하는 방법을 제안하였다. 유전자 알고리듬은 매개변수 최적화문제에 대하여 자연의 진화원리를 모방한 알고리듬으로, 염색체 형태로 표현된 개체군 (population) 중에서 환경에 대한 적합도 (fitness)가 높은 개체가 높은 확률로 살아남아 재생 (reproduction)하게 되며, 교배 (crossover)와 돌연변이 (mutation) 연산 후에 다음 세대 개체군을 형성하게 되고, 이러한 과정을 반복하면서 최적의 개체를 구하게 된다. 본 논문에서는 상태천이확률, 평균벡터, 공분산행렬, 가지하중값을 부동소수점수 (floating point number)의 유전자형으로 표현하여 유전자 알고리듬을 수행하였다. 유전자 알고리듬은 복잡한 탐색공간에서 최적의 해를 찾는데 효과적으로 적용되었다.

• 천문학회보
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• 제35권2호
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• pp.45.1-45.1
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• 2010

Using SOHO particle and EUV detection and radio spectrograms from both ground-based and spaceborne instruments, we have studied the first phase of major solar energetic particle (SEP) events associated with wide and fast coronal mass ejections (CMEs) centered at different solar longitudes. Observations support the idea that acceleration of SEPs starts in the helium-rich plasma of the eruption's core well behind the CME leading edge, in association with coronal shocks and magnetic reconnection caused by the CME liftoff; and those "coronal" components dominate during the first ~1.5 hour of the SEP event, not yet being hidden by the CME-bow shock in solar wind. At magnetic connection to the eruption's periphery, onset of SEP emission is delayed for a time of the lateral expansion that is visualized by global coronal (EIT) wave. The first, "coronal" phase of SEP acceleration is followed by a second phase associated with CME-driven shock wave in solar wind, which accelerates high-energy ions from a helium-poor particle population until the interplanetary shock slows down to below 1000 km/s. Based on these and other SOHO observations, we discuss what findings can be expected from STEREO in the SOHO era perspective.

• 한국포장학회지
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• 제29권3호
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• pp.163-174
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• 2023

Though box compression strength (BCS) is commonly used as a performance criterion for shipping containers, estimating BCS remains a challenge. In this study, artificial neural networks (ANN) are implemented as a new tool, with a focus on building up ANN architectures for BCS estimation. An Artificial Neural Network (ANN) model can be constructed by adjusting four modeling factors: hidden neuron numbers, epochs, number of modeling cycles, and number of data points. The four factors interact with each other to influence model accuracy and can be optimized by minimizing model's Mean Squared Error (MSE). Using both data from the literature and "synthetic" data based on the McKee equation, we find that model estimation accuracy remains limited due to the uncertainty in both the input parameters and the ANN process itself. The population size to build an ANN model has been identified based on different data sets. This study provides a methodology guide for future research exploring the applicability of ANN to address problems and answer questions in the corrugated industry.