• Wind and Structures
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• v.23 no.4
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• pp.275-300
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• 2016

Synchronous multi-pressure measurements were carried out with relatively long time duration for a double-layer reticulated shell roof model in the atmospheric boundary layer wind tunnel. Since the long roof is open at two ends for the storage of coal piles, three different testing cases were considered as the empty roof without coal piles (Case A), half coal piles inside (Case B) and full coal piles inside (Case C). Based on the wind tunnel test results, non-Gaussian time-dependent statistics of net wind pressure on the shell roof were quantified in terms of skewness and kurtosis. It was found that the direct statistical estimation of high-order moments and peak factors is quite sensitive to the duration of wind pressure time-history data. The maximum value of COVs (Coefficients of variations) of high-order moments is up to 1.05 for several measured pressure processes. The Mixture distribution models are proposed for better modeling the distribution of a parent pressure process. With the aid of mixture parent distribution models, the existing translated-peak-process (TPP) method has been revised and improved in the estimation of non-Gaussian peak factors. Finally, non-Gaussian peak factors of wind pressure, particularly for those observed hardening pressure process, were calculated by employing various state-of-the-art methods and compared to the direct statistical analysis of the measured long-duration wind pressure data. The estimated non-Gaussian peak factors for a hardening pressure process at the leading edge of the roof were varying from 3.6229, 3.3693 to 3.3416 corresponding to three different cases of A, B and C.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.5
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• pp.569-581
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• 2003

A method of synthetic time series generation was developed and applied to the simulation of homogeneous turbulence in a periodic 3 - D box and the hourly wind data simulation. The method can simulate almost exact sample auto and cross correlations of multiple time series and control non-Gaussian distribution. Using the turbulence simulation, influence of correlations, non-Gaussian distribution, and one-direction anisotropy on homogeneous structure were studied by investigating the spatial distribution of turbulence kinetic energy and enstrophy. An hourly wind data of Typhoon Robin was used to illustrate a capability of the method to simulate sample cross correlations of multiple time series. The simulated typhoon data shows a similar shape of fluctuations and almost exactly the same sample auto and cross correlations of the Robin.

• Journal of The Korean Astronomical Society
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• v.48 no.6
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• pp.325-331
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• 2015

We study an association between the duration of solar activity and characteristics of the latitude distribution of sunspots by means of center-of-latitude (COL) of sunspots observed during the period from 1878 to 2008 spanning solar cycles 12 to 23. We first calculate COL by taking the area-weighted mean latitude of sunspots for each calendar month to determine the latitudinal distribution of COL of sunspots appearing in the long and short cycles separately. The data set for the long solar cycles consists of the solar cycles 12, 13, 14, 20, and 23. The short solar cycles include the solar cycles 15, 16, 17, 18, 19, 21, and 22. We then fit a double Gaussian function to compare properties of the latitudinal distribution resulting from the two data sets. Our main findings are as follows: (1) The main component of the double Gaussian function does not show any significant change in the central position and in the full-width-at-half-maximum (FWHM), except in the amplitude. They are all centered at ~ 11° with FWHM of ~ 5°. (2) The secondary component of the double Gaussian function at higher latitudes seems to differ in that even though their width remains fixed at ~ 4°, their central position peaks at ~ 22.1° for the short cycles and at ~ 20.7° for the long cycles with quite small errors. (3) No significant correlation could be established between the duration of an individual cycle and the parameters of the double Gaussian. Finally, we conclude by briefly discussing the implications of these findings on the issue of the cycle 4 concerning a lost cycle.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.3
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• pp.253-258
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• 2015

The purpose of this study is to find suitable probability distribution function of complex distribution data like multimodal. Normal distribution is broadly used to assume probability distribution function. However, complex distribution data like multimodal are very hard to be estimated by using normal distribution function only, and there might be errors when other distribution functions including normal distribution function are used. In this study, we experimented to find fit probability distribution function in multimodal area, by using AIS(Automatic Identification System) observation data gathered in Mokpo port for a year of 2013. By using chi-squared statistic, gaussian mixture model(GMM) is the fittest model rather than other distribution functions, such as extreme value, generalized extreme value, logistic, and normal distribution. GMM was found to the fit model regard to multimodal data of maritime traffic flow distribution. Probability density function for collision probability and traffic flow distribution will be calculated much precisely in the future.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.3
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• pp.171-176
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• 2016

The distribution shapes of air and water temperatures are basic and essential information, which determine the frequency patterns of their occurrence. It is also very useful to understand the changes in long-term air and water temperatures with respect to climate change. The typical distribution shapes of air and water temperatures cannot be well fitted using widely used/accepted normal distributions because their shapes show multimodal distributions. In this study, Gaussian mixture distributions and kernel distributions are suggested as the more suitable models to fit their distribution shapes. Based on the results, the tail shape exhibits different patterns. The tail is long in higher temperature regions of water temperature distribution and in lower temperature regions of air temperature distribution. These types of shape comparisons can be useful to identify the patterns of long-term air and water temperature changes and the relationship between air and water temperatures. It is nearly impossible to identify change patterns using only mean-temperatures and normal distributions.

• Journal of Wetlands Research
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• v.11 no.1
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• pp.49-64
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• 2009

Rainfall-runoff models are used for efficient management, distribution, planning, and design of water resources in accordance with the process of hydrologic cycle. The models simplify the transition of rainfall to runoff as rainfall through different processes including evaporation, transpiration, interception, and infiltration. As the models simplify complex physical processes, gaps between the models and actual rainfall events exist. For more accurate simulation, appropriate models that suit analysis goals are selected and reliable long-term hydrological data are collected. However, uncertainty is inherent in models. It is therefore necessary to evaluate reliability of simulation results from models. A number of studies have evaluated uncertainty ingrained in rainfall-runoff models. In this paper, Meta-Gaussian method proposed by Montanari and Brath(2004) was used to assess uncertainty of simulation outputs from rainfall-runoff models. The model, which estimates upper and lower bounds of the confidence interval from probabilistic distribution of a model's error, can quantify global uncertainty of hydrological models. In this paper, Meta-Gaussian method was applied to analyze uncertainty of simulated runoff outputs from $Vflo^{TM}$, a physically-based distribution model and HEC-HMS model, a conceptual lumped model.

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

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.1-49
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• 2001

The skin color model is a very important concept in face detection, face recognition and face tracking. Usually, this model is obtained by estimating a probability density function of skin color distribution. In many cases, it is assumed that the underlying density function follows a Gaussian distribution. In this paper, a new method for non-parametric estimation of the probability density function, by using feed-forward neural network, is used to estimate the underlying skin color model. By using this method, the resulting skin color model is better than the Gaussian estimation and substantially approaches the real distribution. Applications to face detection and face ...

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

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.655-666
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• 2008

Small sample likelihood based inference for the shape parameter of the inverse Gaussian distribution is the purpose of this paper. When shape parameter is of interest, the signed log-likelihood ratio statistic and the modified signed log-likelihood ratio statistic are derived. Hsieh (1990) gave a statistical inference for the shape parameter based on an exact method. Throughout simulation, we will compare the statistical properties of the proposed statistics to the statistic given by Hsieh (1990) in term of confidence interval and power of test. We also discuss a real data example.