• Journal of Korean Society of Water and Wastewater
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• v.20 no.2
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• pp.295-302
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• 2006

Over the past decades, a flocculation and/or sedimentation process have been adopted to remove pollutants from CSOs. It has been learned that major factors affecting settlement of pollutants are the particle size distribution, their settling velocities and their specific gravity. It is, therefore, a good idea to analyze the particle size distribution and settleability of CSOs pollutants in order to develop details in designing a process. Discussed in this study are pollutant characteristics of CSOs such as particle size distribution and settleability of pollutants. The power law function is applied and is found to be an effective and reliable index for expressing the particle size distribution of pollutants in CSOs. Based on the regression analysis it is observed that the derived constants of curves representing settling velocity profile are proportional to the initial concentration of particles and to the ${\beta}$-values of power law distributions.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.191-213
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• 2023

Unit distributions are frequently used in probability theory and statistics to depict meaningful variables having values between zero and one. Using convenient transformation, the unit inverse exponentiated weibull (UIEW) distribution, which is equally useful for modelling data on the unit interval, is proposed in this study. Quantile function, moments, incomplete moments, uncertainty measures, stochastic ordering, and stress-strength reliability are among the statistical properties provided for this distribution. To estimate the parameters associated to the recommended distribution, well-known estimation techniques including maximum likelihood, maximum product of spacings, least squares, weighted least squares, Cramer von Mises, Anderson-Darling, and Bayesian are utilised. Using simulated data, we compare how well the various estimators perform. According to the simulated outputs, the maximum product of spacing estimates has lower values of accuracy measures than alternative estimates in majority of situations. For two real datasets, the proposed model outperforms the beta, Kumaraswamy, unit Gompartz, unit Lomax and complementary unit weibull distributions based on various comparative indicators.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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• pp.207-217
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• 2000

A fire fighting characteristic by a single evaporating droplet in the case of a fire of military enclosure space was studied experimentally. Transient cooling of solid surface by water droplet evaporation has been investigated through controlled experiments using a heated brass cylinder. Quantitative predictions of droplet evaporation time and in-depth transient temperature distribution in solid have been made. The particular interest was in the removal of thermal energy from the heated cylinder by evaporative cooling. A $10{\mu}1$ single droplet is deposited on a horizontal brass surface with initial temperatures in the range of $90^{\circ}C{\sim}130^{\circ}C.$ The results can be summarized as follows; Evaporating droplet was divided into three different configuration. Evaporation time was predicted as a function of initial surface temperature ($t_c=492.62-6.89T_{s0}+0.0248T_{s0}^2).$ The contact temperature was predicted as a function of initial surface temperature( $T_{i}$=0.94 $T_{s0}$+1.4), The parameter ${\beta}_o$ was predicted as a function of initial surface temperature( ${\beta}_0$ : 0.O0312 $T_{s0}+0.932$)>)>)

• Journal of Biosystems Engineering
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• v.11 no.1
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• pp.76-85
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• 1986

This study was intended to examine the failure characteristics and breakdowns of the head-fed type combines generally used on farms. The failure distribution was assumed to follow Weibull distribution function and the Weibull parameters of the major parts, units and combine as whole were estimated by using the data collected in a survey. A computer program for the estimation of the Weibull parameter was developed. Monte Carlo method was used in predicting the time between failures. The results of study may be summarized as follows: 1. The number of failures per combine was 4.83 times per year and 0.3 times per hectare of combines of different ages. 2. According to the Kolmogorov-Smirnov test method, it was proved that the Weibull distribution function is well fitted to the characteristics of the failure and breakdowns of combines. 3. Weibull parameters of failure distribution of the combine as a whole were estimated to give the shape parameter ${\beta}$=1.3089 and the scale parameter ${\alpha}$=105.2409. The combining area with 80% reliability was 1.1 ha, and the probability of operating the combine without any failure for a year, was $2.76{\times}10^{-4}$. 4. The mean time between failures (MTBF) of the combines was predicted to be 3.2 ha of operation, which corresponds to 32 hours of operation.

• International Journal of Contents
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• v.3 no.1
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• pp.1-8
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• 2007

The distributions of the minimum correlated F-variable arises in many applied statistical problems including simultaneous analysis of variance (SANOVA), equality of variance, selection and ranking populations, and reliability analysis. In this paper, negative binomial sampling technique is employed to derive the distributions of the minimum of chi-square variables and hence the distributions of the minimum correlated F-variables. The work presented in this paper is divided in two parts. The first part is devoted to develop some combinatorial identities arised from the negative binomial sampling. These identities are constructed and justified to serve important purpose, when we deal with these distributions or their characteristics. Other important results including cumulants and moments of these distributions are also given in somewhat simple forms. Second, the distributions of minimum, chisquare variable and hence the distribution of the minimum correlated F-variables are then derived within the negative binomial sampling framework. Although, multinomial theory applied to order statistics and standard transformation techniques can be used to derive these distributions, the negative binomial sampling approach provides more information regarding the nature of the relationship between the sampling vehicle and the probability distributions of these functions of chi-square variables. We also provide an algorithm to compute the percentage points of the distributions. The computation methods we adopted are exact and no interpolations are involved.

• The Korean Journal of Physiology and Pharmacology
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• v.16 no.1
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• pp.71-77
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• 2012

Mitochondrial dynamics and distribution is critical for their role in bioenergetics and cell survival. We investigated the consequence of altered fission/fusion on mitochondrial function and motility in INS-1E rat clonal ${\beta}$-cells. Adenoviruses were used to induce doxycycline-dependent expression of wild type (WT-Mfn1) or a dominant negative mitofusin 1 mutant (DN-Mfn1). Mitochondrial morphology and motility were analyzed by monitoring mitochondrially-targeted red fluorescent protein. Adenovirus-driven overexpression of WT-Mfn1 elicited severe aggregation of mitochondria, preventing them from reaching peripheral near plasma membrane areas of the cell. Overexpression of DN-Mfn1 resulted in fragmented mitochondria with widespread cytosolic distribution. WT-Mfn1 overexpression impaired mitochondrial function as glucose- and oligomycin-induced mitochondrial hyperpolarization were markedly reduced. Viability of the INS-1E cells, however, was not affected. Mitochondrial motility was significantly reduced in WT-Mfn1 overexpressing cells. Conversely, fragmented mitochondria in DN-Mfn1 overexpressing cells showed more vigorous movement than mitochondria in control cells. Movement of these mitochondria was also less microtubule-dependent. These results suggest that Mfn1-induced hyperfusion leads to mitochondrial dysfunction and hypomotility, which may explain impaired metabolism-secretion coupling in insulin-releasing cells overexpressing Mfn1.

• IE interfaces
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• v.16 no.spc
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• pp.39-44
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• 2003

As one of many design variables, the role of dimension tolerances is to restrict the amount of size variation in a manufactured feature while ensuring functionality. In this study, a nonlinear integer model has been modeled to allocate the optimal tolerance to each individual feature at a minimum manufacturing cost. While a normal distribution determines statistically worst tolerances with its symmetrical property in many previous tolerance allocation studies, a asymmetrical distribution is more realistic because its mean is not always coincident with a process center. A nonlinear integer model is modeled to allocate the optimal tolerance to a feature based on a beta distribution at a minimum total cost. The total cost as a function of tolerances is defined by machining cost and quality loss. After the convexity of manufacturing cost is checked by the Hessian matrix, the model is solved by the Complex Method. Finally, a numerical example is presented demonstrating successful model implementation for a nonlinear design case.

• Journal of Korean Society for Quality Management
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• v.44 no.1
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• pp.167-180
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• 2016

Purpose: The problem for the traditional control chart is that it is unable to monitor the very small fraction of nonconforming and the underlying distribution is the normal distribution. $Z_p$ control chart is useful where it controls the vert small fraction on nonconforming. In this study, we will design the $Z_p$ control chart in order to use under non-normal process. Methods: $Z_p$ is calculated not by failure rate based on attribute data but using variable data. Control limit for non-normal $Z_p$ control chart is designed based on ${\alpha}$-risk calculated by cumulative distribution function of Burr distribution. ${\beta}$-risk, which is for performance evaluation, obtains in the Burr distribution's cumulative distribution function and control limit. Results: The control limit for non-normal $Z_p$ control chart is designed based on Burr distribution. The sensitivity can be checked through ARL table and OC curve. Conclusion: Non-normal $Z_p$ control chart is able to control not only the very small fraction of nonconforming, but it is also useful when $Z_p$ distribution is non-normal distribution.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.78-82
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• 2022

Phenology determines the timing of crop development, and the timing of phenological events is strongly influenced by the temperature during the growing season. In process-based model, leaf area is simulated dynamically by coupling of morphology and phenology module. Therefore, the prediction of leaf appearance rate and final leaf number affects the performance of whole crop model. The dataset for the model equation was collected from SPA R chambers with five different temperature treatments. Beta distribution function (proposed by Yan and Hunt (1999)) was used for describing the leaf appearance rate as a function of temperature. The optimum temperature and the critical value were estimated to be 26.0℃ and 35.3℃, respectively. For evaluation of the model, the accumulated number of onion leaves observed in a temperature gradient chamber was compared with model estimates. The model estimate is the result of accumulating the daily increase in the number of onion leaves obtained by inputting the daily mean temperature during the growing season into the temperature model. In this study, the coefficient of determination (R²) and RMSE value of the model were 0.95 and 0.89, respectively.

• Journal of Korean Ophthalmic Optics Society
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• v.1 no.2
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• pp.7-11
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• 1996

The size of pupil with pupillary light reflex is determined by the amount of a luminance. and it is dependent with the distribution function of the retinal illuminance which is the amount of transmittance for the external light due to the size of pupil, and the detector of cone and rod due to the amount of the luminance. The change of the pupil size with the luminance can be expressed with the mathematical model $$y(x)={\alpha}+{\beta}\frac{1}{1+{e}{x}{p}(x-x_0)/{\theta}}$$ where ${\alpha}$ is the size of the pupil diameter in a maximum value of the luminance, ${\beta}$ is the deviation of the pupil's diameter between maximum and minimum, ${\theta}$ is the parameter showed the degree of a sensitivity. Comparing with the experimental value of P.Moon et al, We known that the equation of the model is very compatible.