• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.10 no.11
• /
• pp.3274-3279
• /
• 2009

The elutriation characteristics of particle size distribution were investigated in a gas-solid fluidized bed. Experiments were carried out with the mulit-sized particles of Gaussian distributions. The elutriation rate constant obtained from the experiment was correlated with the standard deviation of particle size and the dimensionless group of the velocity ratio. The standard deviation of pressure fluctuation, mean pressure, major frequency and power spectrum density function were calculated by pressure fluctuation properties. Size distribution of elutriated particles and pressure fluctuations were measured for the particle size distribution of particle system depended largrly on the size distribution. Characteristics of fluidization and elutriation were greatly influenced by the particle size distribution and these characteristics could be interpreted with pressure fluctuation properties.

• Journal of The Korean Astronomical Society
• /
• v.42 no.4
• /
• pp.71-79
• /
• 2009

In this paper, statistical distribution functions are developed for distance determination of stellar groups. This method depends on the assumption that absolute magnitudes and apparent magnitudes follow a Gaussian distribution function. Due to the limits of the integrands of the frequency function of apparent and absolute magnitudes, we introduce Case A, B, and C Gaussian distributions. The developed approaches have been implemented to determine distances to some clusters and stellar associations. The comparison with the distances derived by different authors reveals good agreement.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.6
• /
• pp.1260-1265
• /
• 2012

This paper have presented the change for subthreshold characteristics for double gate(DG) MOSFET based on scaling theory and the shape of Gaussian function. To obtain the analytical solution of Poisson's equation, Gaussian function been used as carrier distribution and consequently potential distributions have been analyzed closely for experimental results, and the subthreshold characteristics have been analyzed for the shape parameters of Gaussian function such as projected range and standard projected deviation. Since this potential model has been verified in the previous papers, we have used this model to analyze the subthreshold chatacteristics. The scaling theory is to sustain constant outputs for the change of device parameters. As a result to apply the scaling theory for DGMOSFET, we know the subthreshold characteristics have been greatly changed, and the change of threshold voltage is bigger relatively.

• Journal of the Korean Society of Combustion
• /
• v.6 no.2
• /
• pp.23-28
• /
• 2001

In the present work, the effect of PDF selection and intermittency on the result of the numerical simulation are examined by the simulation of a turbulent methane-air jet diffusion flame. As to the PDFs, beta-function and clipped Gaussian are considered. Results for the pure mixing jet are compared with experimental results. Then, the turbulent flame is calculated for the same conditions and the results obtained for the several models are compared. It is found that the clipped Gaussian distribution coupled with consideration of intermittency recovers the experimental data very well. As to the reacting flow results, the main overall properties of the turbulent jet diffusion flame such as maximum flame temperature are less affected by the choice of the PDF. Flame height and NO emissions, on the contrary, appear to be significantly influenced.

• Journal of the Korean Statistical Society
• /
• v.25 no.4
• /
• pp.515-527
• /
• 1996

It has been known for mean field models that the limiting distribution reflecting the asymptotic behavior of the system is non-Gaussian at the critical state. Recently, however, Papangelow showed for the critical Curie-Weiss mean field model that there exist Gaussian structures in the asymptotic behavior of the total magnetization. We construct Gaussian structures existing in the internal fluctuation of the system for the critical case of a generalized Curie-Weiss mean field model.

• Journal of KIEE
• /
• v.10 no.1
• /
• pp.7-15
• /
• 2000

Source separation is a statistical method, the goal of which is to separate the linear instantaneous mixtures of statistically independent sources without resorting to any prior knowledge. This paper addresses a source separation algorithm which is able to separate the mixtures of sub- and super-Gaussian sources. The nonlinear function in the proposed algorithm is derived from the generalized Gaussian distribution that is a set of distributions parameterized by a real positive number (Gaussian exponent). Based on the relationship between the kurtosis and the Gaussian exponent, we present a simple and efficient way of selecting proper nonlinear functions for source separation. Useful behavior of the proposed method is demonstrated by computer simulations.

• ETRI Journal
• /
• v.7 no.4
• /
• pp.3-10
• /
• 1985

After the light from an incoherent LED was transmitted through the multimode fibers which were linked over 10 km, the nearfield power distribution reached the steady-state independent of launching conditions. It has been also found that the steady-state output power distribution showed the pattern of Gaussian function. In this steady-state Gaussian function pattern, the measured losses of fibers were very repeatable values. In case of using LD source, the speckle phenomena in near -field power distribution appeared until the distance of 10 km. And the output power distribution did not reach the steady-state shown in LED even over 20km on account of the coherence and the nonuniform lasing modes of LD. But the measured losses of fibers were nearly stable in this long distance.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.45 no.3
• /
• pp.1-11
• /
• 2008

This paper proposes a real-time method using a 3-axis accelerometer to enhance blurred images taken from a camera loaded in mobile devices. Blurring phenomenon is a smoothing effect occurring in photo images. Algorithms to cope with blurring phenomenon is essential since small-size mobile devices tremble severely by even a tiny hand-shaking of a user. In this paper, accurate sensing characteristics of the 3-axis accelerometer is acquired by applying the sensor in pendulum motion and the blurring phenomenon is modeled as a uniform distribution and Gaussian distribution. Also, non-Gaussian distributed model is observed in the experiment of real blurring phenomenon and a particular deblurring function is designed by reversing the model. It has been demonstrated that the application of trembling information to the deblurring function adequately removes the blurring phenomenon.

• Structural Engineering and Mechanics
• /
• v.86 no.3
• /
• pp.349-359
• /
• 2023

This paper presents a technique for determining the optimal number of elements in stochastic finite element analysis based on reliability analysis. Using the change-of-variable perturbation stochastic finite element approach, the probability density function of the dynamic responses of stochastic structures is explicitly determined. This method combines the perturbation stochastic finite element method with the change-of-variable technique into a united model. To further examine the relationships between the random fields, discretization of the random field parameters, such as the variance function and the scale of fluctuation, is also performed. Accordingly, the reliability index is calculated based on the explicit probability density function of responses with Gaussian or non-Gaussian random fields in any number of elements corresponding to the random field discretization. The numerical examples illustrate the effectiveness of the proposed method for a one-dimensional cantilever reinforced concrete column and a two-dimensional steel plate shear wall. The benefit of this method is that the probability density function of responses can be obtained explicitly without the use simulation techniques. Any type of random variable with any statistical distribution can be incorporated into the calculations, regardless of the restrictions imposed by the type of statistical distribution of random variables. Consequently, this method can be utilized as a suitable guideline for the efficient implementation of stochastic finite element analysis of structures, regardless of the statistical distribution of random variables.

• Journal of Digital Convergence
• /
• v.11 no.11
• /
• pp.409-414
• /
• 2013

Naive Bayesian classifiers are a powerful and well-known type of classifiers that can be easily induced from a dataset of sample cases. However, the strong conditional independence assumptions can sometimes lead to weak classification performance. Normally, naive Bayesian classifiers use Gaussian distributions to handle continuous attributes and to represent the likelihood of the features conditioned on the classes. The probability density of attributes, however, is not always well fitted by a Gaussian distribution. Another eminent type of classifier is the neuro-fuzzy classifier, which can learn fuzzy rules and fuzzy sets using supervised learning. Since there are specific structural similarities between a neuro-fuzzy classifier and a naive Bayesian classifier, the purpose of this study is to apply learning distribution graphs constructed by a neuro-fuzzy network to naive Bayesian classifiers. We compare the Gaussian distribution graphs with the fuzzy distribution graphs for the naive Bayesian classifier. We applied these two types of distribution graphs to classify leukemia and colon DNA microarray data sets. The results demonstrate that a naive Bayesian classifier with fuzzy distribution graphs is more reliable than that with Gaussian distribution graphs.