• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.287-296
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• 2011

Thirty six female students participated in the experiment of the fat mass weight loss. they kept diary for foods they ate every day, took a picture of the foods, transmitted the picture to the experimenter by the camera phone, and consulted him about fat mass loss once a week for 8 weeks period. Fat mass weight and its related factors of the students had been measured repeatedly every week during 8 weeks, The repeated measurement data were used for applying various random coefficient models. And hence optimal random coefficient model was selected. From the optimal model, the baseline, body mass index, diastolic blood pressure, total cholesterol and time of the fixed factors were very significant. The fixed quadratic time effect existed. The variance components corresponding to the subject effect, linear time effect of the random coefficients were all positive. Thus random coefficients up to the linear terms were considered as the optimal model. The treatment effect reduced the weight loss to an average of 2.1kg at the end of the period.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.303-310
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• 2013

Forty two female students whose fat mass ratio was over 30% were participated in the experiment of fat mass loss of two treatments for 8 weeks. They kept diary for foods they ate every day, took a picture of the foods, transmitted the picture to the experimenter by the camera phone. Among those, 28 students took the picture by regular camera phone (Treatment A), and the other students used smart phone (Treatment B). Fat mass weight and its related variables had been measured repeatedly four times at an interval of two weeks during 8 weeks. It was shown from mixed model analysis of repeated measurements data that AR(1) covariance matrix was selected as the optimal covariance matrix pattern. The correlation between two successive times is highly correlated as 0.838. Based upon the AR(1) covariance matrix structure, the students using smart phones were somewhat more effective in losing fat mass weight than the students using regular camera phones. The time effect was highly significant, but the treatment-time interaction effect was insignificant. The baseline effect and total cholesterol were found to be significant, but the calories with taking foods were somewhat significant, but the waist to hip ratio was found to be insignificant.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.04a
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• pp.92-96
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• 2004

본 연구에서는 지하수위 변동 분석을 위한 프로그램을 개발하고, 건설교통부에서 설치, 운영하고 있는 169개 국가 지하수 관측소의 264개 지하수관측정에서 측정된 지하수위자료에 적용하였다. 분석결과 암반대수층과 충적층대수층의 평균수위 및 변동양상이 대체적으로 비슷하게 나타났으며, 이는 관측소 설치 지역의 대부분에서 충적층(10m 내외)과 암반층(70m 내외)이 수리적으로 연결되어 있다는 것을 시사한다. 6시간 간격의 지하수위 관측 자료를 이용하여 지하수위가 상승하는 횟수, 상승량의 합계 산정 등 변동양상을 분석하였다. 분석 결과 해양 및 지구 조석의 영향을 받는 관측정의 경우 지하수위 상승 개수가 450개/yr 이상이 대부분이며, 수위 변동량은 0.1 ～ 1m 정도이고, 수위변동 자료를 시계열로 나타내 보면 하루에 약 2번의 상승과 하강을 반복하는 수위변동 형태를 볼 수 있었다. 양수의 영향이 우세한 관측정에서는 수위 상승 개수가 약 360개/yr, 수위 변동량은 1m 이상의 값이 우세하게 나타났다. 지하수위 상승량은 암반/충적 관측정 모든 관측정에서 전반적으로 강수량과의 상관계수가 높았으며, 같은 관측정의 .자료라도 6시간 간격의 관측 자료보다, 12시간 및 24시간 관측 간격으로 분석한 결과에서 상관관계가 더 높게 나타났다. 12시간 및 24시간 관측 간격으로 분석할 경우 조석 및 양수에 의해 발생된 주기적인 지하수위 변동 성분이 제거되면서 강수에 대한 지하수위 반응의 상관도가 높아진 것으로 해석된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.39-39
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• 2019

인구의 도시 집중화로 인하여 다량의 생활용수의 사용에 따라 하천의 자정능력을 초과하여 오염을 유발시키고 있다. 이에 도시하천들의 오염은 점점 심해져 경제적으로 많은 문제를 유발하고 있다. 이러한 하천오염 문제를 과학적으로 대응하기 위해서는 오염물질의 농도 측정 및 데이터 축척을 통한 오염예측이 필수적이라 할 수 있으며, 부산광역시 보건환경정보 공개시스템에서는 하천수질 자동측정망을 설치하여 시간 단위로 오염물질을 측정하고 있다. 그러나 온천천의 하천수질 데이터는 계속 쌓여가고 있는데 이 데이터를 활용해서 하천수질 인자 예측이 거의 이뤄지지 않고 있다. 본 연구에서는 순환신경망 알고리즘을 활용하여 일 단위의 하천수질 인자 예측을 시도하였다. 순환신경망은 인공신경망의 발전된 형태인 시계열 학습에 강한 RNN, LSTM 알고리즘을 활용한 일단위 하천수질 인자 예측을 하고자 하였다. 연구에 앞서 시간 단위로 쌓여있는 데이터를 평균 내어 일 단위로 변경하였고 이 데이터를 가지고 일 단위 하천수질 인자 예측을 진행하였다. 연구에는 Google에서 개발한 딥러닝 오픈소스 라이브러리인 텐서플로우를 활용하여 DO, 탁도 등 항목을 예측하였다. 하천오염의 학습과 예측을 위해 대상지로는 부산지역 온천천의 부곡교, 세병교, 이섭교 관측소를 선택하였다. 연구를 위해 DO, 탁도 등 자료 수집은 부산광역시 보건환경정보 공개시스템의 자료를 활용하였다. 모형의 학습을 위해 입력자료로는 하천수질 인자 자료를 이용하였고, 자료의 학습에는 2014년~2017년 4년간의 자료를 학습자료로 사용하였고, 2018년 1년간의 자료는 모형의 검증을 위해 사용하였다. RNN, LSTM 알고리즘을 활용하여 분석 시 은닉층의 개수, 반복시행횟수, sequence length 등의 값을 조절하여 하천수질 인자 예측을 하였다. 모형의 검증을 위해 $R^2$(r square)와 RMSE(root mean square error)을 이용하여 통계분석을 실시하였다.

• Korean Journal of Remote Sensing
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• v.30 no.6
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• pp.817-827
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• 2014

In the satellite remote sensing, the operational environment of the satellite sensor causes image degradation during the image acquisition. The degradation results in noise and blurring which badly affect identification and extraction of useful information in image data. This study proposes a maximum a posteriori (MAP) estimation using Point-Jacobian iteration to restore a degraded image. The proposed method assumes a Gaussian additive noise and Markov random field of spatial continuity. The proposed method employs a neighbor window of spoke type which is composed of 8 line windows at the 8 directions, and a boundary adjacency measure of Mahalanobis square distance between center and neighbor pixels. For the evaluation of the proposed method, a pixel-wise classification was used for simulation data using various patterns similar to the structure exhibited in high resolution imagery and an unsupervised segmentation for the remotely-sensed image data of 1 mspatial resolution observed over the north area of Anyang in Korean peninsula. The experimental results imply that it can improve analytical accuracy in the application of remote sensing high resolution imagery.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.581-594
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• 2013

A group sequential design can end a clinical trial early if a confirmed efficacy or a futility of study medication is found during clinical trials. Adaptation can adjust the design of clinical trials based on accumulated data. The key to this methodology is considered to control the overall type 1 error rate while maintaining the integrity of clinical trials. The estimation would be more complex and the sample size calculation will be more difficult if the clinical trials have repeated measurement data. Lee et al. (2002) suggested a repeated observation case by using the independent increments properties of the interim test statistics and investigated the properties of the proposed confidence interval based on the stage-wise ordering. This study extend Lee et al. (2002) to adaptive group sequential design. We suggest test statistics for the adaptation as redesigning the second stage of clinical trials and induce the stage-wise confidence interval of parameter of interests. The simulation will help to confirm the suggested method.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.281-296
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• 2020

Repeated outcomes from the same subjects are referred to as longitudinal data. Analysis of the data requires different methods unlike cross-sectional data analysis. It is important to model the covariance matrix because the correlation between the repeated outcomes must be considered when estimating the effects of covariates on the mean response. However, the modeling of the covariance matrix is tricky because there are many parameters to be estimated, and the estimated covariance matrix should be positive definite. In this paper, we consider analysis of multivariate longitudinal data via two modeling methodologies for the covariance matrix for multivariate longitudinal data. Both methods describe serial correlations of multivariate longitudinal outcomes using a modified Cholesky decomposition. However, the two methods consider different decompositions to explain the correlation between simultaneous responses. The first method uses enhanced linear covariance models so that the covariance matrix satisfies a positive definiteness condition; in addition, and principal component analysis and maximization-minimization algorithm (MM algorithm) were used to estimate model parameters. The second method considers variance-correlation decomposition and hypersphere decomposition to model covariance matrix. Simulations are used to compare the performance of the two methodologies.

• Journal of Science Education
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• v.33 no.2
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• pp.271-289
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• 2009

The purpose of the research was to analyze the characteristics and the process elements which appeared to the process of designing experiment for biologists and gifted students in science. Four biologists and thirty-two gifted students were participated in this study. The findings indicated that (a) the researcher figured out the process elements could constructed in fifteen elements such as confirming questions, arrange materials, consideration for experimental subjects, searching variables, eliminating variables, selecting variables, planning operation of variables, planning control environmental variables, planning control biological variables, planning the methods of observation and assessment, planning the methods of collecting data, planning the interpretation of data, planning the repetition of experiments, planning the repetition of assessment, safety regulations as well. The biologists concentrated in the particular process elements related with variables, since the science-gifted students concerned the performance whole process elements themselves without deeper consideration, (b) the characteristics of biologists and science-gifted students in the process of designing experiment were as follows; 1) biologists and students showed the process elements which include the domain specific process skills as well, 2) biologists accurately conducted the designing experiments processes with repetition of specific process, since students designed experiments conventionally, and 3) biologists possessed the domain specific skills and know-how about their experiments, but students did not. The results show that the programs of designing experiment activity should be constructed with the process elements which were concentrated by biologists, should provide feedbacks to design experiment more accurately, and should be developed with concern of the process skills and know-hows of biologists.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.445-459
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• 2014

As a certain job is repeatedly done by a worker, the outcome comparative to the effort to complete the job gets more remarkable. The outcome may be the time required and fraction defective. This phenomenon is referred to a learning-curve effect. We focus on the parametric modeling of the learning-curve effects on count data using a logistic cumulative distribution function and some probability mass functions such as a Poisson and negative binomial. We conduct various simulation scenarios to clarify the characteristics of the proposed model. We also consider a real application to compare the two discrete-type distribution functions.

• 대한예방의학회:학술대회논문집
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• 1999.10a
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• pp.177-178
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• 1999