• Journal of Environmental Policy
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• v.2 no.1
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• pp.77-86
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• 2003

Particle-size distributions (PSDs) are widely used for the estimation of soil hydraulic properties. The objective of this study was to select the best model among the nine PSD models with different underlying assumptions, by using a variety of Korean soils. The Fredlund model with four parameters, the logistic growth curve, and Weibull distribution model showed the highest performance compared to the other models with the majority of soils studied. It was interesting to find that the logistic growth function with no fitting parameters showed a great fitting performance.

• The Journal of Information Systems
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• v.16 no.3
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• pp.109-132
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• 2007

In the paper, we builds an advanced new credit evaluation system for Micro-small sized individual firms through appropriate evaluation factors derived by logistic regression analysis for credit evaluation model using in Korean Federation of Credit Guarantee Foundations, and the weights of factors computed by analytic hierarchy process(AHP). Industry characteristics are more applied to previous credit model with the additional the financial fact-information and non-financial judgement-information. Our results show that the financial factors have become more important than three years ago. Moreover, in the non-financial factors, the fact-information factors consider more important then the judgement-information factors. A new credit evaluation system is developed based on this credit evaluation model.

• Journal of Microbiology and Biotechnology
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• v.18 no.5
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• pp.889-896
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• 2008

A suitable simple model tested by experiments is required to address complex biological reactions like esterase synthesis by Saccharomyces cerevisiae. Such an approach might be the answer to a proper bioprocessing strategy. In this regard, a logistic model for esterase production from Saccharomyces cerevisiae has been developed, which predicts well the cell mass, the carbon source (glucose) consumption, and the esterase activity. The accuracy of the model has been statistically examined by using the Student's t-test. The parameter sensitivity analysis showed that all five parameters (${\mu}_m$, $K_a$, $X_m$, $Y_{x/s}$, and $Y_{p/x}$) have significant influence on the predicted values of esterase activity.

• The Pure and Applied Mathematics
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• v.17 no.1
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• pp.93-106
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• 2010

We employ the methods of Lattice Dynamical System to establish a global model extending the Walrasian evolutionary cobweb model in an independent single local market to the global market evolution over an infinite chain of many local markets with interaction of each other through a diffusion of prices between them. For brevity of the model, we assume linear decreasing demands and logistic supplies with naive predictors, and investigate the traveling wave behaviors of global price dynamics and show that their dynamics are conjugate to those of H$\acute{e}$non maps and hence can exhibit complicated behaviors such as period-doubling bifurcations, chaos, and homoclic orbits etc.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1052-1070
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• 2017

For the autoencoder (AE) implemented as a construction component, this paper uses the method of greedy layer-by-layer pre-training without supervision to construct the stacked autoencoder (SAE) to extract the abstract features of the original input data, which is regarded as the input of the logistic regression (LR) model, after which the click-through rate (CTR) of the user to the advertisement under the contextual environment can be obtained. These experiments show that, compared with the usual logistic regression model and support vector regression model used in the field of predicting the advertising CTR in the industry, the SAE-LR model has a relatively large promotion in the AUC value. Based on the improvement of accuracy of advertising CTR prediction, the enterprises can accurately understand and have cognition for the needs of their customers, which promotes the multi-path development with high efficiency and low cost under the condition of internet finance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.33
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• pp.93-102
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• 1995

In this study we are concerned with the diagnostics method of cross-classified categorical data using logistic regression model of binary response models for cell proportions. under this model, we could examine the goodness-of-fit of the models using Pearson's $x^2$test statistic and likelihood ratio statistic. Under this model, these statistics are assumed that sample survey schemes are with replacement sampling model. But these statistics are often inappropriate for analysing contingency tables consists of complex sampling schemes obtained sample survey data. In this study we are examined diagnostics procedures detecting any outlying cell proportions and influential observations on design space in logistic regression modeltake account of the survey design effects.

• Smart Structures and Systems
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• v.24 no.3
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• pp.361-377
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• 2019

The stiffness of shape memory alloy (SMA) spring while in actuation is represented by an empirical model that is derived from the logistic differential equation. This model correlates the stiffness to the alloy temperature and the functionality of SMA spring as active variable stiffness actuator (VSA) is analyzed based on factors that are the input conditions (activation current, duty cycle and excitation frequency) and operating conditions (pre-stress and mechanical connection). The model parameters are estimated by adopting the nonlinear least square method, henceforth, the model is validated experimentally. The average correlation factor of 0.95 between the model response and experimental results validates the proposed model. In furtherance, the justification is augmented from the comparison with existing stiffness models (logistic curve model and polynomial model). The important distinction from several observations regarding the comparison of the model prediction with the experimental states that it is more superior, flexible and adaptable than the existing. The nature of stiffness variation in the SMA spring is assessed also from the Dynamic Mechanical Thermal Analysis (DMTA), which as well proves the proposal. This model advances the ability to use SMA integrated mechanism for enhanced variable stiffness actuation. The investigation proves that the stiffness of SMA spring may be altered under controlled conditions.

• Fire Science and Engineering
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• v.33 no.6
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• pp.72-79
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• 2019

Through this study, we developed a model for predicting chemical accidents lead to casualties. The model was derived from the logistic regression analysis model and applied to the variables affecting the accident. The accident data used in the model was analyzed by studying the statistics of past chemical accidents, and applying independent variables that were statistically significant through data analysis, such as the type of accident, cause, place of occurrence, status of casualties, and type of chemical accident that caused the casualties. A significance of p < 0.05 was applied. The model developed in this study is meaningful for the prevention of casualties caused by chemical accidents and the establishment of safety systems in the workplace. The analysis using the model found that the most influential factor in the occurrence of casualty in accidents was chemical explosions. Therefore, there is an urgent need to prepare countermeasures to prevent chemical accidents, specifically explosions, from occurring in the workplace.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.1829-1831
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• 2012

Background: The generalized gamma distribution statistics constitute an extensive family that contains nearly all of the most commonly used distributions including the exponential, Weibull and log normal. A saturated version of the model allows covariates having effects through all the parameters of survival time distribution. Accelerated failure-time models assume that only one parameter of the distribution depends on the covariates. Methods: We fitted both the conventional GG model and the saturated form for each of its members including the Weibull and lognormal distribution; and compared them using likelihood ratios. To compare the selected parameter distribution with log logistic distribution which is a famous distribution in survival analysis that is not included in generalized gamma family, we used the Akaike information criterion (AIC; r=l(b)-2p). All models were fitted using data for 369 women age 50 years or more, diagnosed with stage IV breast cancer in BC during 1990-1999 and followed to 2010. Results: In both conventional and saturated parametric models, the lognormal was the best candidate among the GG family members; also, the lognormal fitted better than log-logistic distribution. By the conventional GG model, the variables "surgery", "radiotherapy", "hormone therapy", "erposneg" and interaction between "hormone therapy" and "erposneg" are significant. In the AFT model, we estimated the relative time for these variables. By the saturated GG model, similar significant variables are selected. Estimating the relative times in different percentiles of extended model illustrate the pattern in which the relative survival time change during the time. Conclusions: The advantage of using the generalized gamma distribution is that it facilitates estimating a model with improved fit over the standard Weibull or lognormal distributions. Alternatively, the generalized F family of distributions might be considered, of which the generalized gamma distribution is a member and also includes the commonly used log-logistic distribution.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6799-6806
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• 2015

Electric energy consumption is always followed by the introduction of diversity scale-up and state-of-the-art equipments in logistic centers. In order to analyze the status and the characteristic of the electric energy consumption quantitatively, and also to evaluate the efficiency of the electric energy, this research aims to develop an estimation model of standard electric energy consumption for logistic centers. The proposed model applies the thermodynamics theory so as to effectively reflect the peculiarity that the temperature in the logistic center influences the electric energy consumption. And the model consists of the energy consumed by the refrigerator, which can be subdivided into the heat conducted through the wall, the heat convected by the open doors and the heat lost into the goods, and the electric consumption of the machinery equipments. The model also includes a variety of explanatory variables to support an operator of logistics centers in evaluating the efficiency of energy consumption and establishing improvement strategies for energy efficiency. Application of the model developed in this study is discussed with observed data on energy consumption of a logistics center.