• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.78.2-78.2
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• 2014

Many techniques for data analysis also based on gaussian noise assumption which is often valid in various situations. However, the sensitivity of gravitational wave searches are limited by their non-gaussian and non-stationary noise. We introduce various on-going efforts to overcome this limitation in Korean Gravitational Wave Group. First, artificial neural networks are applied to discriminate non-gaussian noise artefacts and gravitational-wave signals using auxiliary channels of a gravitational wave detector. Second, viability of applying Hilbert-Huang transform is investigated to deal with non-stationary data of gravitational wave detectors. We also report progress in acceleration of low-latency gravitational search using GPGPU.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.353-360
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• 2015

Various statistical models have been proposed over the last decade for spatially correlated Gaussian outcomes. The spatial linear mixed model (SLMM), which incorporates a spatial effect as a random component to the linear model, is the one of the most widely used approaches in various application contexts. Employing link functions, SLMM can be naturally extended to spatial generalized linear mixed model for non-Gaussian outcomes (SGLMM). We review popular SGLMMs on non-Gaussian spatial outcomes and demonstrate their applications with available public data.

• Environmental Engineering Research
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• v.11 no.2
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• pp.63-76
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• 2006

Multivariate analysis and batch monitoring on a pilot-scale sequencing batch reactor (SBR) are described for integrated wastewater treatment management system, where a batchwise multiway independent component analysis method (MICA) are used to extract meaningful hidden information from non-Gaussian wastewater treatment data. Three-way batch data of SBR are unfolded batch-wisely, and then a non-Gaussian multivariate monitoring method is used to capture the non-Gaussian characteristics of normal batches in biological wastewater treatment plant. It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. The batchwise multivariate monitoring results of a pilot-scale SBR for integrated wastewater treatment management system showed more powerful monitoring performance on a WWTP application than the conventional method since it can extract non-Gaussian source signals which are independent and cross-correlation of variables.

• 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.

• Wind and Structures
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• v.36 no.1
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• pp.15-29
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• 2023

To better estimate the non-Gaussian extreme wind pressures for high-rise buildings, a data-driven revised Hermitetype peak factor estimation model is proposed in this papar. Subsequently, a comparative study on three types of methods, such as Hermite-type models, short-time estimate Gumbel method (STE), and new translated-peak-process method (TPP) is carried out. The investigations show that the proposed Hermite-type peak factor has better accuracy and applicability than the other Hermite-type models, and its absolute accuracy is slightly inferior to the STE and new TPP methods for non-Gaussian wind pressures by comparing with the observed values. Moreover, these methods generally overestimate the Gaussian wind pressures especially the STE.

• 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 Soil and Groundwater Environment
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• v.21 no.6
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• pp.67-79
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• 2016

Gaussian process regression (GPR) is proposed as a tool of long-term groundwater quality predictions. The major advantage of GPR is that both prediction and the prediction related uncertainty are provided simultaneously. To demonstrate the applicability of the proposed tool, GPR and a conventional non-parametric trend analysis tool are comparatively applied to synthetic examples. From the application, it has been found that GPR shows better performance compared to the conventional method, especially when the groundwater quality data shows typical non-linear trend. The GPR model is further employed to the long-term groundwater quality predictions based on the data from two domestically operated groundwater monitoring stations. From the applications, it has been shown that the model can make reasonable predictions for the majority of the linear trend cases with a few exceptions of severely non-Gaussian data. Furthermore, for the data shows non-linear trend, GPR with mean of second order equation is successfully applied.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.31-41
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• 2014

Biplot is a useful graphical method to simultaneously explore the rows and columns of a two-way data matrix. In particular, principal component factor biplot is a graphical method to describe the interrelationship among many variables in terms of a few underlying but unobservable random variables called factors. If we consider the unobservable variables (which are mutually independent and also non-Gaussian), we can apply the independent component analysis decomposing a mixture of non-Gaussian in its independent components. In this case, if we apply the principal component factor analysis, we cannot clearly describe the interrelationship among many variables. Therefore, in this study, we apply the independent component analysis of Jutten and Herault (1991) decomposing a mixture of non-Gaussian in its independent components. We suggest an independent component biplot to interpret the independent component analysis graphically.

• Journal of Korean Society of Disaster and Security
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• v.6 no.2
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• pp.1-8
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• 2013

This study is concerned with the estimation of fluctuate wind velocity statistic properties in the major cities reflecting the recent meteorological with largest data samples (yearly 2003-2012). The basic wind speeds were standardized homogeneously to the surface roughness category C, and to 10m above the ground surface. The estimation of the extreme of non-Gaussian load effects for design applications has often been treated tacitly by invoking a conventional wind design (gust load peak factor) on the basis of Gaussian processes. This assumption breaks down when the loading processes exhibits non-Gaussianity, in which a conventional wind design yields relatively non conservative estimates because of failure to include long tail regions inherent to non-Gaussian processes. This study seeks to ascertain the probability distribution function from recently wind data with effected typhoon & maximum instantaneous wind speed.

• Ocean Systems Engineering
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• v.4 no.4
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• pp.293-308
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• 2014

Unlike the traditional displacement type vessels, the high speed planing crafts are supported by the lift forces which are highly non-linear. This non-linear phenomenon causes their motions in an irregular seaway to be non-Gaussian. In general, it may not be possible to express the probability distribution of such processes by an analytical formula. Also the process might not be stationary or ergodic in which case the statistical behavior of the motion to be constantly changing with time. Therefore the extreme values of such a process can no longer be calculated using the analytical formulae applicable to Gaussian processes. Since closed form analytical solutions do not exist, recourse is taken to fitting a distribution to the data and estimating the statistical properties of the process from this fitted probability distribution. The peaks over threshold analysis and fitting of the Generalized Pareto Distribution are explored in this paper as an alternative to Weibull, Generalized Gamma and Rayleigh distributions in predicting the short term extreme value of a random process.