• Journal of Veterinary Clinics
• /
• v.20 no.2
• /
• pp.145-149
• /
• 2003

Color Doppler imaging(CDI) was carried out to determine CDI-derived resistive index(RI) values of normal canine ophthalmic vasculature and its reproducibility. CDI was performed on 58 dogs. and normal ranges of RI value were calculated for the medial long posterior ciliary artery(mLPCA), ciliary artery(CA), and ophthalmic artery(OA). Ophthalmic vascular RI values of normal dogs were 0.67$\pm$0.07, 0.70$\pm$0.06, and 0.80$\pm$0.04 in mLPCA, CA, and OA, respectively. Means of RT value of all vessel had no statistically significant difference by sex, fellow orbits, and skull type. The results suggest that color Doppler imaging is a noninvasive test which has the advantage of providing objective measurements of blood flow velocity parameter in the canine eye and orbit.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.4
• /
• pp.661-672
• /
• 2009

This study was conducted to investigate whether mat exercise and field exercise have effectiveness on the balance and gait in older adults. Thirty subjects were participated in this study. And they were all randomly divided into mat exercise and field exercise group. To evaluate the effects of mat and field exercise, subjects were evaluated by using One Leg Stand test which was static balance test, and Berg Balance test which was dynamic balance test. Finally Gait pattern was assessed by using 3-dimensional gait analysis system. The assessment parameters were evaluated before, after 3 weeks, and after 6 weeks treatments. And we received a consent form from all subjects. The results of repeated measures analysis of variance showed that One Leg Stand, Berg Balance, Stride length, Cadence, Velocity were significantly increased after than before exercise in both groups. So we conclude that therapeutic exercise that is mat and field exercise has effectiveness on the balance and gait in older adults.

• The Korean Journal of Applied Statistics
• /
• v.29 no.1
• /
• pp.113-122
• /
• 2016

Longitudinal data often occur in prospective follow-up studies. Joint model for longitudinal data and failure time has been applied on several works. In this paper, we extend it to the case where longitudinal data involve informative observation time process as well as competing risks survival times. We use a likelihood approach and derive an EM algorithm to obtain maximum likelihood estimate of parameters. A suggested joint model allows us to make inferences for three components: longitudinal outcome, observation time process and competing risk failure time. In addition, we can test the association among these components. In this paper, liver cirrhosis patients' data is analyzed. The relationship between prothrombin times measured at irregular visiting times and drop outs is investigated with a joint model.

• The Korean Journal of Applied Statistics
• /
• v.16 no.2
• /
• pp.407-426
• /
• 2003

We consider the missing covariates problem in generalized estimating equations(GEE) model. If the covariate is partially missing, GEE can not be calculated. In this paper, we study the performance of 7 imputation methods to handle missing covariates in GEE models, and the properties of GEE estimators are investigated after missing covariates are imputed for ordinal data of repeated measurements. The 7 imputation methods include i) Naive Deletion ii) Sample Average Imputation iii) Row Average Imputation iv) Cross-wave Regression Imputation v) Carry-over Imputation vi) Bayesian Bootstrap vii) Approximate Bayesian Bootstrap. A Monte-Carlo simulation is used to compare the performance of these methods. For the missing mechanism generating the missing data, we assume ignorable nonresponse. Furthermore, we generate missing covariates with or without considering wave nonresp onse patterns.

• The Korean Journal of Applied Statistics
• /
• v.28 no.2
• /
• pp.289-294
• /
• 2015

Linear mixed models are commonly used in the clinical pharmaceutical studies to analyze repeated measures such as the crossover study data of bioequivalence studies. In these models, random effects describe the correlation between repeated outcomes and variance-covariance matrix explain within-subject variabilities. Bioequivalence analysis verifies whether a 90% confidence interval for geometric mean ratio of Cmax and AUC between reference drug and test drug is included in the bioequivalence margin [0.8, 1.25] performed using linear mixed models with period, sequence and treatment effects as fixed and sequence nested subject effects as random. A Levofloxacin study is referred to for an example of real data analysis.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.6
• /
• pp.1439-1447
• /
• 2014

Most of literatures on Korean child aggression are based on using the cross-sectional data sets. Although there is a related study with a longitudinal data set, it is assumed that the data sets measured repeatedly in the longitudinal data are mutually independent. A longitudinal data analysis for Korean child aggression is then necessary. This study is to analyze the effect of child development outcomes including academic achievement, self-esteem, depression anxiety, delinquency, victimization by peers, abuse by parents and internet using time on child aggression with Korea Welfare Panel Study data observed three times between 2006 and 2012. Since Korea Welfare Panel Study data have missing values, the missing at random is assumed. The linear mixed effect model and the restricted maximum likelihood estimation are considered.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 1999.11a
• /
• pp.139-144
• /
• 1999

본 논문에서는 45명의 남자 대학생들에게 연산을 수행하게 한 후, 연산스트레스를 측정하기 위한 생리 파라메터의 추출에 대하여 연구하였다. 파라메터를 추출하기 위해서 1) 정규분포화를 위한 변환 2) 상관관계를 통해 상호관련성이 높은 파라메터를 조사 3) 휴식기간과 연산작업간의 파라메터의 값 비교를 통한 파라메터 표준화 4) 각 파라메터에 대해서 반복측정자료의 분산분석법을 통하여 검정함으로써 통계적으로 유의적인 차이가 있는 파라메터를 선정하였다. 위와 같은 절차를 통하여 연산스트레스의 지수화에 필요한 생리 파라메터로 Heart Rate, HRV의 LF/HF, HRV의 MF/(LF+HF), Return Map의 분산, Mean Temperature, GSR-Mean과 호흡수가 최종적으로 선정되었다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.219-219
• /
• 2020

하천의 수질을 나타내는 환경지표 중 국가 TMS(Tele Monitoring system)의 수질측정망을 통해 관리되고 있는 지표로는 DO, BOD, COD, SS, TN, TP 등 여러 인자들이 있다. 이러한 수질인자는 하천의 자정작용에 있어 많은 영향을 나타내고 있다. 이를 활용한 경제적이고 합리적인 수질관리를 위해 하천의 자정작용을 활용하는 것이 중요하다. 생물학적 작용을 가장 효과적으로 활용하기 위해서는 수질오염 데이터에 기초한 수질예측을 채택하여 적절한 대책이 필요하다. 이를 위해서는 수질인자의 데이터를 측정하고 축적해 수질오염을 예측하는 것이 필수적인데, 실제적으로 수질인자의 일일 측정은 비용 관점에서 쉽게 접근할 수 없다. 본 연구에서는 시계열 학습으로 알려진 RNN-LSTM(Recurrent Neural Network-Long Term Memory) 알고리즘을 활용하여 기존에 측정된 수질인자의 데이터를 통해 시간당 및 일일 수질인자를 예측하려고 했다. 연구에 앞서, 기존에 시간단위로 측정된 수질인자 데이터의 이상 유무를 확인 후, 에러값은 제거하고 12시간 이하 데이터가 누락되었을 때는 선형 보간하여 데이터를 사용하고, 1일 데이터도 10일 이하 데이터가 누락되었을 때 선형 보간하여 데이터를 활용하여 수질인자를 예측하였다. 수질인자를 예측하기 위해 구글이 개발한 딥러닝 오픈소스 라이브러리인 텐서플로우를 활용하였고, 연구지역으로는 대한민국 부산에 위치한 온천천의 유역을 선정하였다. 수질인자 데이터 수집은 부산광역시에서 운영하는 보건환경정보 공개시스템의 자료를 활용하였다. 모델의 연구를 위해 하천의 수질인자, 기상자료 데이터를 입력자료로 활용하였다. 분석에서는 입력자료와, 반복횟수, 시계열의 길이 등을 조절해 수질 요인을 예측했고, 모델의 정확도도 분석하였다.

• Journal of Korean Society of Transportation
• /
• v.28 no.3
• /
• pp.119-130
• /
• 2010

Reconstruction analysis of traffic accident is done by analyzing diverse data such as the road, accident traces and damage on the automobile. Most data can be a variable in the process of analysis, and measurement error of the data occurs from the investigator, tool and the given environment. Therefore, accident analysis always has some risks of measurement uncertainty. This research quantify the uncertainty in traffic accident analysis by conducting repetitive measurement experiments for variables with high probability of uncertainly such as length (i.e. geometric structure of the road, tire marks) and coefficient of friction. This paper also suggests an analysis result for the uncertainly of photographic observation of automobile crush measurement. These statistical distributions can help determine appropriate ranges for the input data in order to estimate the accident reconstruction uncertainty.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.1
• /
• pp.1-10
• /
• 2017

Longitudinal data of Korean child academic achievement have been used to find the significant exploratory variables under the assumption of independent repeated measured data. Using the exploratory variables in previous research works, we analyze the linear mixed model incorporating the fixed and random effects for child academic achievement to detect the significant exploratory variables. Korea welfare panel study data observed three times between 2006 and 2012 by additional survey for children. The child academic achievement is evaluated by the sum of academic achievements of Korean, English and Mathematics. We also investigate the multicollinearity and the missing mechanism and select some popular correlation matrices to analyze the linear mixed model.