• Journal of Korean Society of Forest Science
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• v.88 no.1
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• pp.47-54
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• 1999

To estimate optimal tree diameter and height growth function in Pinus thunbergii stands with site index of 12 class, quoted from two linear models of linear transformation(1) and linear transformation (2) and four non-linear models of exponential, Gompertz, Chapman-Richards, and Weibull etc.. Analyzed correlation among the estimated tree diameter and height by these function models, and observed diameter and height growth were compared. In the results of tree diameter and height growth estimation by stand age, non-linear models showed better appropriation than linear model and Chapman-Richards model was most fitted for tree height growth but few, if any, differences among their nonlinear models. Therefore, it is consider to be much more study about non-linear model to estimate tree diameter and height growth in the actual stands hereafter.

• Journal of Information Science Theory and Practice
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• v.3 no.3
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• pp.45-63
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• 2015

The word ‘growth’ represents an increase in actual size, implying a change of state. In science and technology, growth may imply an increase in number of institutions, scientists, or publications, etc. The present study demonstrates the growth of neurology literature for the period 1961-2010. A total of 291,702 records were extracted from the Science Direct Database for fifty years. The Relative Growth Rate (RGR) and Doubling Time (Dt.) of neurology literature have been calculated, supplementing with different growth patterns to check whether neurology literature fits exponential, linear, or logistic models. The results of the study indicate that the growth of literature in neurology does not follow the linear, or logistic growth model. However, it follows closely the exponential growth model. The study concludes that there has been a consistent trend towards increased growth of literature in the field of neurology.

• Journal of Microbiology and Biotechnology
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• v.12 no.6
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• pp.962-971
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• 2002

The current study proposes a simple monodimensional model to estimate the linear growth rate of photosynthetic microorganisms in flat-plate photobioreactors (FPPBRs) during batch cultivation. As a model microorganism, Chlorella kessleri was cultivated photoautotrophically in FPPBRs using light-emitting diodes (LEDs) as the light sources to provide unidirectional irradiation in the photobioreactors. Various conditions were simulated by adjusting both the intensity of the light and the height of the culture. The validity of the proposed model was examined by comparing the linear growth rates measured with the predicted ones obtained from the proposed model. Accordingly, the value of $\frac{K\cdot\mu m}{\alpha\cdot L}log(I_0\cdot{I_s}^{\varepsilon 1)\cdot {I_c}^{-\varepsilon})$ was proposed as an approximate index for strategies to obtain the maximal lightn yield under light-limiting conditions for high-density algal cultures and as a control parameter to improve the photosynthetic productivity and efficiency.

• Korean Journal of Child Studies
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• v.27 no.3
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• pp.169-187
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• 2006

A hierarchical linear model(HLM) provides advantages over existing traditional statistical methods (e.g., ordinary least squares regression, repeated measures analysis of variance, etc.) for analyzing multilevel/longitudinal data or diary methods. HLM can gauge a more precise estimation of lower-level effects within higher-level units, as well as describe each individual's growth trajectory across time with improved estimation. This article 1) provides scholars who study children and families with an overview of HLM (i.e., statistical assumptions, advantages/disadvantages, etc.), 2) provides an empirical study to illustrate the application of HLM, and 3) discusses the application of HLM to the study of children and families. In addition, this article provided useful information on available articles and websites to enhance the reader's understanding of HLM.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.32 no.3
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• pp.181-189
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• 2008

GaAs film growth process from trimethylgallium(TMGa) and tertiary-butylarsine(TBAs) using a horizontal MOCVD reactor was numerically studied to explain the experimental result that the decreasing surface reaction rate as the increasing partial pressure of group III species. Using the non-linear model based on the Langmuir isotherm which considers the adsorption and desorption of molecules, film deposition over the entire reactor scale was predicted by computational fluid dynamics (CFD) with the aid of the parameters obtained from the selective area growth (SAG) technique. CFD Results using the non-linear surface reaction model with the parameters determined from the SAG experiments predicted too high film growth rate compared to the measured values at the downstream region where the temperature was decreased abruptly. The pairs of ($k_s^n$, K) from the numerical simulations was $(2.52{\times}10K^{-6}mol/m^2/s,\;1.6{\times}10^5m^3/mol)$, whereas the experimentally determined was $(3.58{\times}10^{-5}mol/m^2/s,\;6.9{\times}10^5m^3/mol)$.

• Korean Management Science Review
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• v.32 no.4
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• pp.19-28
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• 2015

This study presents a mathematical programming model to develop production planning in the manufacturing processes for photovoltaic silicon ingots and wafers. The model is formulated as a linear programming model that maximizes total growth margin, which is composed of production cost, inventory cost, shortage cost, and sales profit while considering the constraints associated with the production environments of photovoltaic materials. In order to demonstrate the utility of the model for production planning, we run operations for a planning horizon of a year for a case study. When the primary results of this mathematical programming are compared with the historical records, the model could have resulted in the considerable increase of the total growth margin by effectively reducing inventory cost if a decision maker had employed the model as a decision support system with perfect information for sales demand.

• Journal of Biosystems Engineering
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• v.24 no.1
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• pp.25-30
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• 1999

The effectiveness of many greenhouse environment control methodologies depends on the growth information of crops. Acquisition of the growth information of crops requires a non-invasive and continuous monitoring method. Crop growth monitoring system using digital imaging technique was developed to conduct non-destructive and intact plant growth analyses. The monitoring system automatically measures crop growth information sends an appropriate control signal to the nutrient solution supplying system. To develop the monitoring system, a linear model that explains the relationship between the fresh weight and the top projected leaf area of a lettuce plant was developed from an experiment. The monitoring system was evaluated buy successive lettuce growing experiments. Results of the experiments showed that the developed system could estimate the fresh weight of lettuce from a lettuce image by using the linear model and generate an EC control signal according to the lettuce growth stage.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.217-225
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• 2019

The Latent Growth Modeling(LGM) is known as the typical analysis method of longitudinal data and it could be classified into unconditional model and conditional model. It is common to assume that the growth trajectory of unconditional model of LGM is linear. In the case of quasi-linear, the methodology for improving the model fitness using Sequential Pattern of Association Rule Mining is suggested. To do this, we divide longitudinal data into quintiles and extract periodic changes of the longitudinal data in each quintiles and make sequential pattern based on this periodic changes. To evaluate the effectiveness, the LGM module in SPSS AMOS was used and the dataset of the Youth Panel from 2001 to 2006 of Korea Employment Information Service. Our methodology was able to increase the fitness of the model compared to the simple linear growth trajectory.

• Preventive Nutrition and Food Science
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• v.13 no.2
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• pp.104-111
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• 2008

As a way to account for the variability of the primary model parameters in the secondary modeling of microbial growth, three different regression approaches were compared in determining the confidence interval of the temperature-dependent primary model parameters and the estimated microbial growth during storage: bootstrapped regression with all the individual primary model parameter values; bootstrapped regression with average values at each temperature; and simple regression with regression lines of 2.5% and 97.5% percentile values. Temperature dependences of converted parameters (log $q_o$, ${\mu}_{max}^{1/2}$, log $N_{max}$) of hypothetical initial physiological state, maximum specific growth rate, and maximum cell density in Baranyi's model were subjected to the regression by quadratic, linear, and linear function, respectively. With an advantage of extracting the primary model parameters instantaneously at any temperature by using mathematical functions, regression lines of 2.5% and 97.5% percentile values were capable of accounting for variation in experimental data of microbial growth under constant and fluctuating temperature conditions.

• Journal of Environmental Science International
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• v.32 no.3
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• pp.161-171
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• 2023

The present study evaluated growth factors affecting oak community plantations through literature review and a field survey. Specifically, 41 related literature sources were analyzed and field surveys were conducted to collect growth data. Previous studies were analyzed to identify variables with high frequency of use. The frequency of use was in the order of tree size > environment > planting density > forest age. Analysis of factors impacting height and diameter growth revealed that the growth rate of species other than Quercus variabilis was negative in the field survey. This may be because of differences between the actual trees planted and specifications in the construction drawings, which may be attributed to the site conditions and decisions made by the project subject during construction. Furthermore, simple linear regression analysis was conducted with time, height at planting, density, and species code as the independent variables and growth rate as the dependent variable. A strong positive linear correlation was noted between height and diameter. This work builds a foundation for developing a forest restoration model and simulation program based on a regression model derived from the four variables tested.