• Structural Engineering and Mechanics
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• v.77 no.3
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• pp.355-368
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• 2021

The time-varying mean (TVM) component of non-stationary wind speeds is commonly extracted utilizing empirical mode decomposition (EMD) in practice, whereas the accuracy of the extracted TVM is difficult to be quantified. To deal with this problem, this paper proposes an approach to identify and extract the optimal TVM from several TVM results obtained by the EMD. It is suggested that the optimal TVM of a 10-min time history of wind speeds should meet both the following conditions: (1) the probability density function (PDF) of fluctuating wind component agrees well with the modified Gaussian function (MGF). At this stage, a coefficient p is newly defined as an evaluation index to quantify the correlation between PDF and MGF. The smaller the p is, the better the derived TVM is; (2) the number of local maxima of obtained optimal TVM within a 10-min time interval is less than 6. The proposed approach is validated by a numerical example, and it is also adopted to extract the optimal TVM from the field measurement records of wind speeds collected during a sandstorm event.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.350-358
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• 2021

In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms.

• Journal of Environmental Science International
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• v.15 no.4
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• pp.319-324
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• 2006

Release rate is one of the important items for the environmental impact assessment caused by radioactive materials in case of an accidental release from the nuclear facilities. In this study, the uncertainty of the estimated release rate is evaluated using Monte Carlo method. Gaussian plume model and linear programming are used for estimating the release rate of a source material. Tracer experiment is performed at the Yeoung-Kwang nuclear site to understand the dispersion characteristics. The optimized release rate was 1.56 times rather than the released source as a result of the linear programming to minimize the sum of square errors between the observed concentrations of the experiment and the calculated ones using Gaussian plume model. In the mean time, 95% confidence interval of the estimated release rate was from 1.41 to 2.53 times compared with the released rate as a result of the Monte Carlo simulation considering input variations of the Gaussian plume model. We confirm that this kind of the uncertainty evaluation for the source rate can support decision making appropriately in case of the radiological emergencies.

• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.3
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• pp.257-263
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• 2013

Acoustic nonlinearity of surface waves is an effective method to evaluate the micro damage on the surface of materials. In this method, the $A_1$ (magnitude of the fundamental wave) and $A_2$ (magnitude of the second-order harmonic wave) are measured for evaluation of acoustic nonlinearity. However, if there is another source of second-order harmonic wave other than the material itself, the linear relationship between $A_1{^2}$ and $A_2$ will not be guaranteed. Therefore, the second-order harmonic generation by another source should be fully suppressed. In this paper, we investigated the initial second-order harmonic generation in narrowband surface waves by multi-line laser beams. The spatial profile of laser beam was considered in the cases of Gaussian and square-like. The temporal profile was assumed to be Gaussian. In case of Gaussian spatial profile, the generation of the initial second-order harmonic wave was inevitable. However, when the spatial profile was square-like, the generation of the initial second-order harmonic wave was able to be fully suppressed at specific duty ratio. These results mean that the multi-line laser beams of square-like profile with a proper duty ratio are useful to evaluate the acoustic nonlinearity of the generated surface waves.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.203-213
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• 2017

We study estimation and inference of joint conditional distributions of bivariate longitudinal outcomes using regression models and copulas. We consider a class of time-varying transformation models and combine the two marginal models using Gaussian copulas to estimate the joint models. Our models and estimation method can be applied in many situations where the conditional mean-based models are inadequate. Gaussian copulas combined with time-varying transformation models may allow convenient and easy-to-interpret modeling for the joint conditional distributions for bivariate longitudinal data. We apply our method to an epidemiological study of repeatedly measured bivariate cholesterol data.

• 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.

• Phonetics and Speech Sciences
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• v.7 no.3
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• pp.131-138
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• 2015

In this paper, we propose L1-norm regularization for state vector adaptation of subspace Gaussian mixture model (SGMM). When you design a speaker adaptation system with GMM-HMM acoustic model, MAP is the most typical technique to be considered. However, in MAP adaptation procedure, large number of parameters should be updated simultaneously. We can adopt sparse adaptation such as L1-norm regularization or sparse MAP to cope with that, but the performance of sparse adaptation is not good as MAP adaptation. However, SGMM does not suffer a lot from sparse adaptation as GMM-HMM because each Gaussian mean vector in SGMM is defined as a weighted sum of basis vectors, which is much robust to the fluctuation of parameters. Since there are only a few adaptation techniques appropriate for SGMM, our proposed method could be powerful especially when the number of adaptation data is limited. Experimental results show that error reduction rate of the proposed method is better than the result of MAP adaptation of SGMM, even with small adaptation data.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.27-34
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• 2019

This paper presents a chamber pressure model development of K2 rifle by applying Gaussian mixture model. In order to materialize a real recoil force of a virtual reality shooting rifle in military combat training, the chamber pressure which is one of major components of the recoil force needs to be investigated and modeled. Over 200,000 data of the chamber pressure were collected by implementing live fire experiments with both K100 and M193 of 5.56 mm bullets. Gaussian mixture method was also applied to create a mathematical model that satisfies nonlinear, asymmetry, and deviations of the chamber pressure which is caused by irregular characteristics of propellant combustion. In addition, Polynomial and Fourier Regression were used for comparison of results, and the sum of squared errors, the coefficient of determination and root-mean-square errors were analyzed for performance measurement.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.37-47
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• 2013

A Q-Q plot is an effective and convenient graphical method to assess a distributional assumption of data. The primary step in the construction of a Q-Q plot is to obtain a closed-form expression to represent the relation between observed quantiles and theoretical quantiles to be plotted in order that the points fall near the line y = a + bx. In this paper, we introduce a Q-Q plot to assess goodness-of-fit for inverse Gaussian distribution. The procedure is based on the distributional result that a transformed random variable $Y={\mid}\sqrt{\lambda}(X-{\mu})/{\mu}\sqrt{X}{\mid}$ follows a half-normal distribution with mean 0 and variance 1 when a random variable X has an inverse Gaussian distribution with location parameter ${\mu}$ and scale parameter ${\lambda}$. Simulations are performed to provide a guideline to interpret the pattern of points on the proposed inverse Gaussian Q-Q plot. An illustrative example is provided to show the usefulness of the inverse Gaussian Q-Q plot.

• Journal of the Korean Mathematical Society
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• v.35 no.3
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• pp.741-753
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• 1998

Let G(*) be a Borel function applied to a stationary long memory sequence {X$_{i}$} of standard Gaussian random variables. Focusing on the process {G(X$_{i}$)}, the present paper establishes the almost sure representation for the empirical quantile process, that is, Bahadur's representation, and for the empirical process with respect to sample mean. Statistical applications of the representations are also addressed.sed.