• Journal of The Korean Society of Grassland and Forage Science
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• v.11 no.4
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• pp.209-214
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• 1991

The germination of seeds is developmentally complex process requiring water uptake, which is regulated by both genotypic and environmental factors. The present study was undertaken to determine the difference in germination characteristics, and to compare the ability of the logistic and Weibull functions to describe the cumulative germination curve when two levels of osmotic potential(0, -5 bar) were put to seeds of alfalfa, tall fescue, orchardgrass, and Kentucky bluegrass. The effects of grass type, osmotic potential, and their interaction on the total germination and coefficient of germination velocity were significant(P<0.01). The Weibull equation for predicting percent cumulative germination curve of alfalfa had significantly lower residuals than the logistic equation regardless of osmotic potential(P<0.01), indicating that the Weibull equation was more efficient than the logistic equation to fit the data of the percent cumulative germination of alfalfa. The rate parameter from the logistic equation was decreased under water stress, whereas the scale and shape parameters were increased. There were significant differences in days to 20% germination estimated from the logistic and Weibull equations.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.30-35
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• 2010

This paper presents a quality enhancement of images by using a histogram equalization based on the symmetric logistic function. The histogram equalization is a simple and effective spatial processing method that it enhances the quality by adjusting the brightness of image. The logistic function that is a sigmoidal nonlinear transformation function, is applied to non-linearly enhance the brightness of the image according to its intensity level frequency. We propose a flexible and symmetrical logistic function by only using the intensity with maximum frequency in an histogram and the total number of pixels. The proposed function decreases the computation load of an exponential function in the traditional logistic function. The proposed method has been applied for equalizing 5 images with a different resolution and histogram distribution. The experimental results show that the proposed method has the superior enhancement performances compared with the source images and the traditional global histogram equalization, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.787-795
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• 2009

This paper presents a histogram equalization based on the logistic function for enhancing the quality of images. The histogram equalization is a simple and effective spatial processing method that it enhances the quality by adjusting the brightness of image. The logistic function that is a nonlinear transformation function is applied to adaptively enhance the brightness of the image according to its intensity level frequency. We propose a flexible and asymmetrical logistic function by only using the intensity level with maximum frequency and the maximum intensity level in an histogram, and the total number of pixels. The proposed function excludes both the computation load of an exponential function and the heuristic setting of an optimal parameter values in the traditional logistic function. The proposed method has been applied for equalizing many images with a different resolution and histogram distribution. The experimental results show that the proposed method has the superior enhancement performances and the faster equalizing speed compared with the traditional histogram equalization and the adaptively modified histogram equalization, respectively. And the proposed histogram equalization can be used in various multimedia systems in real-time.

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.71-80
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• 2001

Logistic regression model is one of the most popular linear models for a binary response variable and used for the estimation of probability function. In many practical situations, the probability function can be expressed by a bell shaped curve and such a function can be estimated by a second order logistic regression model. However, when the probability curve is asymmetric, the estimation results using a second order logistic regression model may not be precise because a second order logistic regression model is a symmetric function. In addition, even if a second order logistic regression model is used, the interpretation for the effect of second order term may not be easy. In this paper, in order to alleviate such problems, an estimation method for asymmetric probabiity curve based on a first order logistic regression model and iterative bi-section method is proposed and its performance is compared with that of a second order logistic regression model by a simulation study.

• Fire Science and Engineering
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• v.33 no.6
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• pp.20-27
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• 2019

In this study, a new function with higher accuracy for fire heat release rate prediction was developed. The 'αt²' curve, which is the major exponential function currently used for fire engineering calculations, must be improved to minimize the prediction gap that causes fire system engineering inefficiency and lower cost-effectiveness. The newly developed prediction function was designed to cover the initial fire stage that features rapid growth based on logistic function theory, which has a more logical background and graphical similarity compared to conventional exponential function methods for 'αt²'. The new function developed in this study showed apparently higher prediction accuracy over wider range of fire growth durations. With the progress of fire growth pattern studies, the results presented herein will contribute towards more effective fire protection engineering.

• Journal of Forest and Environmental Science
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• v.8 no.1
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• pp.35-49
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• 1992

Three theoretical growth equations, i.e., the Mitscherlich, the Gompertz, and the Logistic equation, were applied to the radical stem growth of 50 jack pines (Pinus banksiana Lamb.). For the determination of the parameters in these equations, NELDER-MEAD's method was used, which is one of the direct-search methods of optimization. It has been known to be very convenient in dealing with the issues related to optimization, specifically where the number of parameters are less than 6. It was found that although all the equations did not appropriately work as expected, the Mitscherlich equation revealed the least discrapancy from the obsered value among three. Using these equations and the first certain period data, i. e., 35, 55, 75 years, the predection of radius of age 95 was investigated. Comparing to the observed value, the most valid equation was the Mitscherlich, and the next were the Gompertz and the Logistic, in order.

• Convergence Security Journal
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• v.11 no.4
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• pp.85-91
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• 2011

Due to the large-scale application software syslmls, software reliability, software development has animportantrole. In this paper, software truncated software reliability growth model was proposed based on log-logistic distribution. According to fixed time, the intensity function, the mean value function, the reliability was estimated and the parameter estimation used to maximum likelihood. In the empirical analysis, Poisson execution time model of the existiog model in this area and the log-logistic model were compared Because log-logistic model is more efficient in tems of reliability, in this area, the log-logistic model as an alternative 1D the existiog model also were able to confim that you can use.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.269-272
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• 2005

For the efficient image encryption, we proposed the encryption algorithm using the chaotic function and elementary matrix operation defined on the bit plane decomposition. Though the chaotic encryption algorithm is faster than block encryption, it uses a real number computation. In this sense, we use the row and column operations on the bit-plane decomposed images combined with logistic function for the recursive rounding number, too.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.309-319
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• 2016

For binary classification models, we consider a risk score that is a function of linear scores and estimate the coefficients of the linear scores. There are two estimation methods: one is to obtain MLEs using logistic models and the other is to estimate by maximizing AUC. AUC approach estimates are better than MLEs when using logistic models under a general situation which does not support logistic assumptions. This paper considers imbalanced data that contains a smaller number of observations in the default class than those in the non-default for credit assessment models; consequently, the AUC approach is applied to imbalanced data. Various logit link functions are used as a link function to generate imbalanced data. It is found that predicted coefficients obtained by the AUC approach are equivalent to (or better) than those from logistic models for low default probability - imbalanced data.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1305-1315
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• 2015

Consider the risk score which is a function of a linear score for the classification models. The AUC optimization method can be applied to estimate the coefficients of linear score. These estimates obtained by this AUC approach method are shown to be better than the maximum likelihood estimators using logistic models under the general situation which does not fit the logistic assumptions. In this work, the VUS and HUM approach methods are suggested by extending AUC approach method for more realistic discrimination and prediction worlds. Some simulation results are obtained with both various distributions of thresholds and three kinds of link functions such as logit, complementary log-log and modified logit functions. It is found that coefficient prediction results by using the VUS and HUM approach methods for multiple categorical classification are equivalent to or better than those by using logistic models with some link functions.