• Journal of Preventive Medicine and Public Health
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• v.20 no.2 s.22
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• pp.261-269
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• 1987

In order to investigate working conditions of underground coal mines, this work was undertaken to evaluate the respirable dust and the concentration of quartz in Taeback and Kangneung areas. The concentration of quartz was determined by Fourier Transform Infrared Spectrophotometry. The results were as follows; 1) The concentration of respirable dust of drilling and coal face in Taeback and Kangneung areas were as followed; Arithmetic $Mean{\pm}S.D.(mg/m^3)$ Taeback Drilling: $2.00{\pm}1.56$ Taeback Coal Face: $3.74{\pm}3.14$ Kangneung Drilling: $4.55{\pm}4.51$ Kangneung Coal Face: $5.77{\pm}4.53$ Geometric $Mean{\pm}S.D.(mg/m^3)$ Taeback Drilling: $1.34{\pm}2.81$ Taeback Coal Face : $2.55{\pm}2.61$ Kangneung Drilling : $2.44{\pm}3.63$ Kangneung Coal Face: $4.24{\pm}2.37$ 2) Distribution of respirable dust was well fitted to the log-normal distribution and geometric mean value was $log^{-1}\;0.37{\pm}log^{-1}\;0.47(2.34{\pm}2.95)mg/m^3$. 3) The difference of respirable dust concentrations in Taeback and Kangneung areas was not significant statistically (p>0.05). 4) The concentration of quartz of drilling and coal face in Taeback and Kangneung areas were as followed; Arithmetic $Mean{\pm}S.D.(%)$ Taeback Drilling: $6.18{\pm}5.52$ Taeback Coal Face: $1.89{\pm}1.54$ Kangneung Drilling: $3.54{\pm}2.12$ Kangneung Coal Face: $2.05{\pm}3.37$ Geometric $Mean{\pm}S.D.(%)$ Taeback Drilling: $4.24{\pm}2.59$ Coal Face: $1.39{\pm}2.22$ Kangneung Drilling : $2.55{\pm}3.08$ Kangneung Coal Face : $1.24{\pm}2.33$ 5) Distribution of quartz concentrations was well fitted to the log-normal distribution and geometric mean value was $log^{-1}\;0.33{\pm}log^{-1}\;0.45(2.14{\pm}2.82)%$. 6) The difference of quartz concentrations in Taeback and Kangneung areas was not significant (p>0.05), but significant at drilling sites and coal faces (p<0.05).

• Korean Journal of Food Science and Technology
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• v.43 no.2
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• pp.243-248
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• 2011

The prevalence and distribution of hazardous microorganisms were investigated from the major perilla cultivation area at Milyang, Gyeongnam province, Korea. Aerobic plate count (APC) and coliform count of perilla leaves were 4.82 log CFU/g and 3.85 log CFU/g, respectively. E. coli, S. aureus and B. cereus were detected in 3.0% (4/114), 7.9% (9/114) and 46.5% (53/114) of examined perilla leaves. However, E. coli O157:H7, Salmonella spp, and L. monocytogenes were not detected. The distribution of hazardous microorganisms in perilla leaf cultivation environment were compared and the concentration of APC and coliform counts were more than 3.0 log CFU/(mL, g, $100cm^2$, hand) from most of the samples. S. aureus were detected from irrigation water, packing table, packing vinyl, hand, and clothes. Also, B. cereus was frequently detected from the examined samples. Especially, packing table and collection container were contaminated with maximum 5.5 log $CFU/100cm^2$ of B. cereus. Good Agricultural Practice (GAP) system should be introduced to farms to enhance the safety of perilla leaves.

• Journal of Ocean Engineering and Technology
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• v.34 no.1
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• pp.26-36
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• 2020

An extreme value analysis of metocean data which include wave, wind, and current data is a prerequisite for the operation and survival of offshore structures. The purpose of this study was to provide information about the return wave, wind, and current values for the Barents Sea using extreme value analysis. Hindcast datasets of the Global Reanalysis of Ocean Waves 2012 (GROW2012) for a waves, winds and currents were obtained from the Oceanweather Inc. The Gumbel distribution, 2 and 3 parameters Weibull distributions and log-normal distribution were used for the extreme value analysis. The least square method was used to estimate the parameters for the extreme value distribution. The return values, including the significant wave height, spectral peak wave period, wind speed and current speed at surface, were calculated and it will be utilized to design offshore structures to be operated in the Barents Sea.

• Journal of Wetlands Research
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• v.2 no.1
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• pp.49-58
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• 2000

The frequency analysis of annual maximum rainfall data and the derivation of probable rainfall intensity formula at Masan station are performed in this study. Based on the eight different rainfall duration data from 10 minutes to 24 hours, eight types of probability distribution (Gamma, Lognormal, Log-Pearson type III, GEV, Gumbel, Log-Gumbel, Weibull, and Wakeby distributions), three types of parameter estimation scheme (moment, maximum likelihood and probability weighted methods) and three types of goodness-of-fit test (${\chi}^2$, Kolmogorov-Smirnov and Cramer von Mises tests) were considered to find an appropriate probability distribution at Masan station. The Lognormal-2 distribution was selected and the probable rainfall intensity formula was derived by regression analysis. The derived formula can be used for estimating rainfall quantiles of the Masan vicinity areas with convenience and reliability in practice.

• Journal of the Korean Data and Information Science Society
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• v.26 no.3
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• pp.593-610
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• 2015

Adaptive Rejection Sampling (ARS) method is a well-known random number generator to acquire a random sample from a probability distribution, and has the advantage of improving the proposal distribution during the sampling procedures, which update it closer to the target distribution. However, the use of ARS is limited since it can be used only for the target distribution in the form of the log-concave function, and thus various methods have been proposed to overcome such a limitation of ARS. In this paper, we attempt to compare five random number generators based on ARS in terms of adequacy and efficiency. Based on empirical analysis using simulations, we discuss their results and make a comparison of five ARS-based methods.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.3
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• pp.189-195
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• 2007

Using fractal dimensionality theory proposed by Riew and Sposito (1991), we attempted to analyze quantitatively the characteristics of porous distribution for built-in soils in the mini-lysimeter and artificial seed-bed media. The 2" stainless core soil samples were taken from lysimeter soils. Artificial seed-bed media were compacted in the acrylic core filled with raw materials consisted of cocopeat, zeolite and perlite. N (Constant number of partitioned group size smaller media volumes) and r (Self-similarity ratio) parameters consisting of fractal dimension D=log(N)/log(1/r) were obtained by Excel Programme using the Riew and Sposito's fractal model. The pore distribution of tested media was screened in pore size and its occurring frequency. The results reveal that the distribution range of pores is wider in the lysimeter soils than in the seed-bed media, while average size of pores in the media is smaller in lysimeter core soils than in seed-bed media.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.775-791
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• 2017

We frequently encounter binary data in real life. Logistic, Probit, Cauchit, Complementary log-log models are often used for binary data analysis. In order to analyze binary data, Liu (2004) proposed a Robit model, in which the inverse of cdf of the Student's t distribution is used as a link function. Kim et al. (2008) also proposed a generalized t-link model to make the binary regression model more flexible. The more flexible skewed distributions allow more flexible link functions in generalized linear models. In the sense, we propose a binary data regression model using skewed generalized t distributions introduced in Theodossiou (1998). We implement R code of the proposed models using the glm function included in R base and R sgt package. We also analyze Pima Indian data using the proposed model in R.

• Transactions of the Korean Society of Automotive Engineers
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• v.1 no.1
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• pp.131-139
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• 1993

Low cycle fatigue characteristics of cast aluminum alloy A356 with a yield strength and ultimate strength of 229 and 283 MPa respectively was evaluated using smooth axial specimen under strain controlled condition. Reversals to failure ranged from 16 to 107. The cast aluminum alloy exhibited cyclically strain-gardening behavior. The results of low cycle fatigue tests indicated that the conventional low cycle fatigue tests indicated that the conventional low cycle fatigue life model was not a satisfactory representation of the data. This occurred because the elastic strain-life curve was not-log-log linear and this phenomena caused a nonconservative and unsafe fatigue life prediction at both extremes of long and short lives. A linear log-log total strain-life model and a bilinear log-log elastic strain-life model were proposed in order to improve the representation of data compared to the conventional low cycle fatigue life model. Both proposed fatigue life models were statistically analyzed using F tests and successfully satisfied. However, the low cycle fatigue life model generated by the bilinear log-log elastic strain-life equation yielded a discontinuous curve with nonconservatism in the region of discontinuity. Among the models examined, the linear log-log total strain-life model provided the best representation of the low cycle fatigue data. Low cycle fatigue life prediction method based on the local strain approach could conveniently incorporated both proposed fatigue life models.

• Communications of the Korean Mathematical Society
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• v.30 no.1
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• pp.7-21
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• 2015

We give the characterization of H$\ddot{o}$lder differentiability points and non-differentiability points of the Riesz-N$\acute{a}$gy-Tak$\acute{a}$cs (RNT) singular function ${\Psi}_{a,p}$ satisfying ${\Psi}_{a,p}(a)=p$. It generalizes recent multifractal and metric number theoretical results associated with the RNT function. Besides, we classify the singular functions using the singularity order deduced from the H$\ddot{o}$lder derivative giving the information that a strictly increasing smooth function having a positive derivative Lebesgue almost everywhere has the singularity order 1 and the RNT function ${\Psi}_{a,p}$ has the singularity order $g(a,p)=\frac{a{\log}p+(1-a){\log}(1-p)}{a{\log}a+(1-a){\log}(1-a)}{\geq}1$.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.607-617
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• 2003

This paper offers a practical comparison of efficiency between local likelihood approach and conventional kernel approach in density estimation. The local likelihood estimation procedure maximizes a kernel smoothed log-likelihood function with respect to a polynomial approximation of the log likelihood function. We use two types of data driven bandwidths for each method and compare the mean integrated squares for several densities. Numerical results reveal that local log-linear approach with simple plug-in bandwidth shows better performance comparing to the standard kernel approach in heavy tailed distribution. For normal mixture density cases, standard kernel estimator with the bandwidth in Sheather and Jones(1991) dominates the others in moderately large sample size.