• Title/Summary/Keyword: model factor

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A case study on the random coefficient model for diet experimental data (변량계수모형의 식이요법 실험자료에 관한 사례연구)

  • Jo, Jin-Nam;Baik, Jai-Wook
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.5
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    • pp.787-796
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    • 2009
  • A random coefficient model is applied when times of the repeated measurements are not fixed in experiments with respect to the subjects. The procedures of the inference of a random coefficient model are same as those of a mixed model. Diet experimental data was used for applying the random coefficient model. Various random coefficient models are investigated for the experimental data, and are compared each other. Finally, optimal random coefficient model would be selected. It resulted from the analysis that for the fixed effect factor, the baseline, treatment, height, and time effect were very significant. The treatment effect of the diet foods and exercises were more effective in losing weight than the effect of the diet foods only. The fixed cubic time effect was very significant. The variance components corresponding to the subject effect, linear time effect, quadratic time effect, and cubic time effect of the random coefficients are all positive. When quartic time effect was added as random coefficients the model did not converge. Thus random coefficients up to the cubic terms was considered as the optimal model.

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Model selection method for categorical data with non-response (무응답을 가지고 있는 범주형 자료에 대한 모형 선택 방법)

  • Yoon, Yong-Hwa;Choi, Bo-Seung
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.4
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    • pp.627-641
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    • 2012
  • We consider a model estimation and model selection methods for the multi-way contingency table data with non-response or missing values. We also consider hierarchical Bayesian model in order to handle a boundary solution problem that can happen in the maximum likelihood estimation under non-ignorable non-response model and we deal with a model selection method to find the best model for the data. We utilized Bayes factors to handle model selection problem under Bayesian approach. We applied proposed method to the pre-election survey for the 2004 Korean National Assembly race. As a result, we got the non-ignorable non-response model was favored and the variable of voting intention was most suitable.

Multiobjective Decision Model with Consideration of Flexibility in Sequential Capital Budgeting

  • Min, Kye-Ryo;Park, Kyung-Soo
    • Journal of the military operations research society of Korea
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    • v.7 no.1
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    • pp.53-80
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    • 1981
  • This paper explores a rational investment decision model in sequential capital allocation process under capital rationing. A method is proposed for measuring the new investment decision factor which is the flexibility that describes the future availability of invested funds. This flexibility is important in sequential decision process. Also presented is a multiobjective (MO) decision model into which flexibility is incorporated with the profit and risk factors. The effectiveness of this criterion is compared with the expected present value and the mean-semivariance criteria through a simulation model.

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A Marginal Probability Model for Repeated Polytomous Response Data

  • Choi, Jae-Sung
    • Journal of the Korean Data and Information Science Society
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    • v.19 no.2
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    • pp.577-585
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    • 2008
  • This paper suggests a marginal probability model for analyzing repeated polytomous response data when some factors are nested in others in treatment structures on a larger experimental unit. As a repeated measures factor, time is considered on a smaller experimental unit. So, two different experiment sizes are considered. Each size of experimental unit has its own design structure and treatment structure, and the marginal probability model can be constructed from the structures for each size of experimental unit. Weighted least squares(WLS) methods are used for estimating fixed effects in the suggested model.

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Time to change from a simple linear model to a complex systems model

  • Hong, Yun-Chul
    • Environmental Analysis Health and Toxicology
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    • v.31
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    • pp.8.1-8.2
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    • 2016
  • A simple linear model to test the hypothesis based on one-on-one relationship has been used to find the causative factors of diseases. However, we now know that not just one, but many factors from different systems such as chemical exposure, genes, epigenetic changes, and proteins are involved in the pathogenesis of chronic diseases such as diabetes mellitus. So, with availability of modern technologies to understand the intricate nature of relations among complex systems, we need to move forward to the future by taking complex systems model.

Autopilot Design with Two Degree of Freedom $H_{\infty}$ Control Method (2자유도 $H_{\infty}$제어기를 이용한 비행체 자동조종장치 설계)

  • 최광진;황준하;권오규
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.1304-1307
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    • 1996
  • In this paper, we present a robust Two Degree of Freedom (TDF) $H_{\infty}$ controllers for a missile system. The feedback controller is designed to meet robust stability and disturbance rejection specifications while the prefilter is used to improve the robust model matching properties of the closed loop system. As the perturbed model, we use the normalized coprim factor perturbations. These controllers are designed using $H_{\infty}$ optimization procedures, and applied to a missile model via simulation.

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Computational analysis of cancer angiogenesis using two dimensional model (2차원 모델을 이용한 암의 혈관생성에 대한 수치적 연구)

  • Shim Eun Bo;Ko Hyung Jong;Deisboeck Thomas
    • Proceedings of the KSME Conference
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    • 2002.08a
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    • pp.709-710
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    • 2002
  • Cancer angiogenesis is simulated using a two dimensional model. Governing equation of angiogenesis is a TAE (Tumor angiogenesis factor) conservation equation in time and space. A stochastic process model is utilized to simulate vessel formation, proliferation, and migration to a cancer pellet. Numerical results are presented especially in case of growing cancer.

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Nonlinear model predictive control of chemical reactors

  • Lee, Jongku;Park, Sunwon
    • 제어로봇시스템학회:학술대회논문집
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    • 1992.10b
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    • pp.419-424
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    • 1992
  • A robust nonlinear predictive control strategy using a disturbance estimator is presented. The disturbance estimator is comprised of two parts: one is the disturbance model parameter adaptation and the other is future disturbance prediction. RLSM(recurrsive least square method) with a forgetting factor is used to de the uncertain distance model parameters and for the future disturbance prediction, future process outputs and inputs projected by the process model are used. The simulation results for chemical reactors indicate that a substantial improvement in nonlinear predictive control performance is possible using the disturbance estimator.

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Numerical Simulation Model of Alternative Refrigerants Flow Through Capillary Tubes (대체냉매의 모세관내 유동 시뮬레이션)

  • 장세동;노승택
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.8 no.1
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    • pp.55-64
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    • 1996
  • A numerical model of refrigerant flow through a capillary tube is developed, which considers the effects of underpressure for vaporization, kinetic energy, and roughness of capillary tube. The numerical model is based on homogeneous flow assumptions for the two-phase flow region. A characteristic chart of HFC refrigerants flow through capillary tubes and correction factor chart of geometry and relative roughness of capillary tube to select a proper capillary for refrigerating machines using alternative refrigerants is presented by this numerical model.

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Oxygen Transfer in Microbial System (미생물 배양 시스템에서의 산소 전달)

  • 최동원
    • The Korean Journal of Food And Nutrition
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    • v.7 no.4
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    • pp.399-405
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    • 1994
  • Some method was introduced to explain oxygen transfer from broth to cell during aerobic microbe cultivation. It is explained by 5 steps that how desolved oxygen can reach to cell. Among these steps film resistance was the most important factor to describe oxygen transfer. Lumped model and distributed model was introduced to explain oxygen diffusion rate and oxygen consumption rate which occurs in the microbe pellet.

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