• 응용통계연구
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• 제28권2호
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• pp.335-342
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• 2015

경시적 자료는 각 환자마다 시간에 따라 반복 측정되는 코호트 연구 등에서 많이 쓰인다. 본 연구는 반응변수 간 상관성을 고려할 수 있는 결합 다단계 일반화 선형모형을 이용하여, 다변량 경시적 자료 분석을 수행하였다. 한국 유전체 역학 연구에서 실시한 코호트 자료를 적합하고 결과를 해석한다. 조건부 아카이케 정보 기준을 이용하여 모형 선택을 하고, 변량효과들의 추정치들을 설명한다.

• 제어로봇시스템학회논문지
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• 제5권6호
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• pp.714-723
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• 1999

This paper presents a short-term scheduling algorithm for the operation of steelmaking processes. The scope of the problem covers refining of the hot iron transferred form a blast furnace, ladle treatment, continuous casting, hot-rolling, and coiling for the final products that should satisfy the given demand. The processing time at each unit depends on how much the batch amount is treated, and te dedicated intermediate storage with finite capacity between the units is considered. Resource constraints and initial amount of each state are incorporated into the presented scheduling model for the algorithm of on-line scheduling. We propose amixed integer linear programming (MILP) model with two objectives for the scheduling. The first is to maximize the total profit while atisfying the due date constraint for each product. And the second is to minimize the total processing time, makespan, while satisfying the demand for each product. Especially, we observe the effect of penalizing the intermediate storage and the inventory level of the final product on the scheduling results.

• 한국컴퓨터정보학회논문지
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• 제24권11호
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• pp.41-49
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• 2019

본 연구는 비지도 기계학습 기술과 코퍼스의 각 단어를 이용하여 한국어 단어를 형태소 분석하는 언어 모델을 구축하는데 목적을 둔다. 그리고 이 언어 모델의 단어 형태소 분석의 결과와 언어 심리 실험결과에서 얻은 한국어 언어사용자의 단어 이해/판단 시간이 상관관계을 갖는지를 규명하고자 한다. 논문에서는 한국어 세종코퍼스를 언어 모델로 학습하여 형태소 분리 규칙을 통해 한국어 단어를 자동 분리하는데 발생하는 단어 정보량(즉, surprisal(놀라움) 정도)을 측정하여 실제 단어를 읽는데 걸리는 반응 시간과 상관이 있는지 분석하였다. 이를 위해 코퍼스에서 단어에 대한 형태 구조 정보를 파악하기 위해 Morfessor 알고리즘을 적용하여 단어의 하위 단위 분리와 관련한 문법/패턴을 추출하고 형태소를 분석하는 언어 모델이 예측하는 정보량과 반응 시간 사이의 상관관계를 알아보기 위하여 선형 혼합 회귀(linear mixed regression) 모형을 설계하였다. 제안된 비지도 기계학습의 언어 모델은 파생단어를 d-형태소로 분석해서 파생단어의 음절의 형태로 처리를 하였다. 파생단어를 처리하는 데 필요한 사람의 인지 노력의 양 즉, 판독 시간 효과가 실제로 형태소 분류하는 기계학습 모델에 의한 단어 처리/이해로부터 초래될 수 있는 놀라움과 상관함을 보여 주었다. 본 연구는 놀라움의 가설 즉, 놀라움 효과는 단어 읽기 또는 처리 인지 노력과 관련이 있다는 가설을 뒷받침함을 확인하였다.

• Journal of Korean Dental Science
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• 제15권1호
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• pp.1-8
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• 2022

Purpose: Intraoral scanners, desktop scanners, and cone-beam computed tomography (CBCT) are being used in a complementary way for diagnosis and treatment planning. Limited patient-based results are available about dimensional reproducibility among different three-dimensional imaging systems. This study aimed to evaluate dimensional reproducibility among patient-derived digital models created from an intraoral scanner, desktop scanner, and two CBCT systems. Materials and Methods: Twenty-nine arches from sixteen patients who were candidates for implant treatments were enrolled. Different types of CBCT systems (KCT and VCT) were used before and after the surgery. Polyvinylsiloxane impressions were taken on the enrolled arches after the healing period. Gypsum casts were fabricated and scanned with an intraoral scanner (CIOS) and desktop scanner (MDS). Four test groups of digital models, each from CIOS, MDS, KCT, and VCT, respectively, were compared to the reference gypsum cast group. For comparison of linear measurements, intercanine and intermolar widths and left and right canine to molar lengths were measured on individual gypsum cast and digital models. All measurements were triplicated, and the averages were used for statistics. Bland-Altman plots were drawn to assess the degree of agreement between each test group with the reference gypsum cast group. A linear mixed model was used to analyze the fixed effect of the test groups compared to the reference group (α=0.05). Result: The Bland-Altman plots showed that the bias of each test group was -0.07 mm for CIOS, -0.07 mm for MDS, -0.21 mm for VCT, and -0.25 mm for KCT. The linear mixed model did not show significant differences between the test and reference groups (P>0.05). Conclusion: The linear distances measured on the digital models created from CIOS, MDS, and two CBCT systems showed slightly larger than the references but clinically acceptable reproducibility for diagnosis and treatment planning.

• Asian-Australasian Journal of Animal Sciences
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• 제21권5호
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• pp.633-639
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• 2008

The genetic relationship of gestation length (GL) with birth and weaning weight (BW, WW) was investigated using data collected from the Hanwoo Experiment Station, National Institute of Animal Science, RDA, Republic of Korea. Analytical mixed models including birth year‐season, sex of calf, linear and quadratic covariates of age of dam (days) and linear covariate of age at weaning (days) as fixed effects were used. Corresponding restricted maximum likelihood (REML) and Bayesian estimates of variance components and heritability were obtained with two models; Model 1 included only direct genetic effect and Model 2 included direct genetic, maternal genetic and permanent environmental effect. All the genetic parameter estimates from REML were corresponding to the Bayesian estimates. Direct heritability estimates for GL, BW, and WW were 0.48, 0.33 and 0.25 by Model 1. From Model 2, direct and maternal heritability estimates were 0.38 and 0.03 for GL, 0.14 and 0.05 for BW, and 0.08 and 0.05 for WW. Genetic correlation estimates between direct and maternal effects were 0.05 for GL, 0.59 for BW, and 0.52 for WW. Estimates of direct genetic correlation between GL and BW (WW) were 0.44 (0.21). Positive genetic correlation of GL with BW and WW imply that selection for greater BW or WW would lead to prolonged gestation length.

• Asian-Australasian Journal of Animal Sciences
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• 제21권6호
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• pp.784-788
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• 2008

Studies to detect genes responsible for economic traits in farm animals have been performed using parametric linear models. A non-parametric, model-free approach using the 'expanded multifactor-dimensionality reduction (MDR) method' considering high dimensionalities of interaction effects between multiple single nucleotide polymorphisms (SNPs), was applied to identify interaction effects of SNPs responsible for carcass traits in a Hanwoo beef cattle population. Data were obtained from the Hanwoo Improvement Center, National Agricultural Cooperation Federation, Korea, and comprised 299 steers from 16 paternal half-sib proven sires that were delivered in Namwon or Daegwanryong livestock testing stations between spring of 2002 and fall of 2003. For each steer at approximately 722 days of age, the Longssimus dorsi muscle area (LMA) was measured after slaughter. Three functional SNPs (19_1, 18_4, 28_2) near the microsatellite marker ILSTS035 on BTA6, around which the QTL for meat quality were previously detected, were assessed. Application of the expanded MDR method revealed the best model with an interaction effect between the SNPs 19_1 and 28_2, while only one main effect of SNP19_1 was statistically significant for LMA (p<0.01) under a general linear mixed model. Our results suggest that the expanded MDR method better identifies interaction effects between multiple genes that are related to polygenic traits, and that the method is an alternative to the current model choices to find associations of multiple functional SNPs and/or their interaction effects with economic traits in livestock populations.

• 한국수정란이식학회지
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• 제31권3호
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• pp.243-247
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• 2016

Growth traits, such as body weight, directly influence productivity and economic efficiency in the swine industry. In this study, we estimate heritability for body weight traits usinginformation from pedigree and genome-wide single nucleotide polymorphism (SNP) chip data. Four body weight phenotypes were measured in 1,105 $F_2$ progeny from an intercross between Landrace and Jeju native black pigs. All experimental animals were subjected to genotypic analysis using PorcineSNP60K BeadChip platform, and 39,992 autosomal SNP markers filtered by quality control criteria were used to construct genomic relationship matrix for heritability estimation. Restricted maximum likelihood estimates of heritability were obtained using both genomic- and pedigree- relationship matrix in a linear mixed model. The heritability estimates using SNP information were smaller (0.36-0.55) than those which were estimated using pedigree information (0.62-0.97). To investigate effect of common environment, such as maternal effect, on heritability estimation, we included maternal effect as an additional random effect term in the linear mixed model analysis. We detected substantial proportions of phenotypic variance components were explained by maternal effect. And the heritability estimates using both pedigree and SNP information were decreased. Therefore, heritability estimates must be interpreted cautiously when there are obvious common environmental variance components.

• 한국수자원학회논문집
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• 제35권5호
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• pp.501-510
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• 2002

가뭄상황 또는 가뭄이 임박한 상황에서의 저수지 운영은 수요관리개념(단계별 급수)에서의 운영률을 필요로 한다. 본 연구는 저수지 갈수대응 차원에서 수문상황에 따른 단계별 방류량 감소를 고려할 수 있는 heding 효과를 고려한 단일 저수지 운영률 개발을 목표로 하였다. hedging 효과를 고려한 최적운영률 결정에는 혼합정수계획기법이 적용되었으며, 정식화단계에는 Shih 등(1994)의 hedging효과를 고려한 운영률을 개선하여 정식화 요소에 Hashimoto 등(1982)의 위험도 평가기준을 포함시켰다. 또한 hedging항의 비선형 해석을 수행하기 위하여 축차 선형계획기법을 도입ㆍ정식화에 적용하였다. 본 hedging운영률의 적용결과 대청다목적댐에 대하여 hedging 매개변수론 산정하였으며, 이를 통하여 각 월별 갈수대응 제한공급 시점 저수량(trigger volume)을 산정할 수 있었다.

• Genomics & Informatics
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• 제20권3호
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• pp.34.1-34.11
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• 2022

Multilevel analysis is an appropriate and powerful tool for analyzing hierarchical structure data widely applied from public health to genomic data. In practice, however, we may lose the information on multiple nesting levels in the multilevel analysis since data may fail to capture all levels of hierarchy, or the top or intermediate levels of hierarchy are ignored in the analysis. In this study, we consider a multilevel linear mixed effect model (LMM) with single imputation that can involve all data hierarchy levels in the presence of missing top or intermediate-level clusters. We evaluate and compare the performance of a multilevel LMM with single imputation with other models ignoring the data hierarchy or missing intermediate-level clusters. To this end, we applied a multilevel LMM with single imputation and other models to hierarchically structured cohort data with some intermediate levels missing and to simulated data with various cluster sizes and missing rates of intermediate-level clusters. A thorough simulation study demonstrated that an LMM with single imputation estimates fixed coefficients and variance components of a multilevel model more accurately than other models ignoring data hierarchy or missing clusters in terms of mean squared error and coverage probability. In particular, when models ignoring data hierarchy or missing clusters were applied, the variance components of random effects were overestimated. We observed similar results from the analysis of hierarchically structured cohort data.

• 한국도로학회논문집
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• 제14권3호
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• pp.25-32
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• 2012

현재 도로포장 설계법에 따르면, 동상방지층의 두께는 지역별 온도조건에 따라 결정되는 동결깊이에 의해 결정되며 동상방지층의 지지력은 설계에서 고려되지 않고 있다. 동상방지층을 도로포장체에서 구조층으로 고려할 경우에는 기존 도로포장층의 두께를 감소시킬 수 있으며 보다 경제적인 도로 포장단면을 구성할 수 있다. 본 연구에서는 동상방지층의 지지력을 평가하기 위한 통계적 모형을 개발하였다. 동상방지층의 구조적 역할을 규명하고 동상방지층 구조적 평가 모형 개발을 위하여 2m 이하 저성토부, 절토부 및 절성경계부 등을 구분하여 포장 하부층에서 Falling Weight Deflectormeter(FWD) 시험을 계절별로 수행하였다. 본 시험은 동방방지층의 유무에 따른 지지력 차이를 규명하기 위하여 동방방지층이 있는 구간과 없는 구간으로 구분하여 수행하였다. 본 시험결과, 동상방지층이 설치된 구간에서의 FWD 처짐량이 동상방지층 미설치 구간에 비해 0.4~82.6% 작게 측정되어 동상방지층이 포장체에서 지지력을 검증하였다. 다양한 FWD 처짐지수와 동상방지층 두께와의 상관관계를 조사한 결과, 보조기층 파손지수의 차이값(${\Delta}BDI$)과 동상방지층 두께와의 상관도가 가장 높았다. 본 논문에서는 ${\Delta}BDI$값을 선형혼합효과 모형에 적용하여 동상방지층을 구조적으로 평가할 수 있는 모형을 개발하였다.