• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.243-250
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• 2000

In the partial lubrication regime, the roughness effects are most important due to the presence of interacting asperities. An average Reynolds equation using flow factors is very useful to determine effects of surface roughness on partial lubrication. In this paper, the pressure flow factors for Gaussian and non-Gaussian surfaces are evaluated in terms of kurtosis. The effect of kurtosis on pressure flow factor is investigated using random rough surface generated numerically. The pressure flow factor increases with increasing kurtosis in partial lubrication regime(h/$\sigma$<3). As h/$\sigma$increases, the pressure flow factor approach to 1 asymptotically regardless of kurtosis.

• Tribology and Lubricants
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• v.16 no.6
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• pp.448-454
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• 2000

The roughness effects are very important due to the presence of interacting asperities in partial lubrication regime. An average Reynolds equation using flow factors is very useful to determine the effects of surface roughness on mixed lubrication. In this paper, the pressure flow factors for surfaces having Gaussian and non-Gaussian distribution of roughness height are evaluated in terms of various kurtosis. The effect of kurtosis on pressure flow factors is investigated using random rough surface generated numerically. The pressure flow factor increases with increasing kurtosis in mixed lubrication regime (h/$\sigma$<3). As h/$\sigma$ increases, the pressure flow factors approach to 1 asymptotically regardless of kurtosis.

• Industrial Engineering and Management Systems
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• v.4 no.2
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• pp.192-197
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• 2005

Among many amplitude parameters, Kurtosis (4-th normalized moment of probability density function) is recognized to be the sensitive good parameter for machine diagnosis. Kurtosis has a value of 3.0 under normal condition and the value generally goes up as the deterioration proceeds. In this paper, simplified calculation method of kurtosis is introduced for the analysis of impact vibration with one sided affiliated impact vibration which occurs towards the progress of time. That phenomenon is often watched in the failure of such as bearings’ outer race. One sided affiliated impact vibration is approximated by one sided triangle towards the progress of time and simplified calculation method is introduced. Varying the shape of one sided triangle, various models are examined and it is proved that new index is a sensitive good index for machine failure diagnosis. Utilizing this method, the behavior of kurtosis is forecasted and analyzed while watching machine condition and correct diagnosis is executed.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.361-375
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• 2015

A measure of skewness and kurtosis is proposed to test multivariate normality. It is based on an empirical standardization using the scaled residuals of the observations. First, we consider the statistics that take the skewness or the kurtosis for each coordinate of the scaled residuals. The null distributions of the statistics converge very slowly to the asymptotic distributions; therefore, we apply a transformation of the skewness or the kurtosis to univariate normality for each coordinate. Size and power are investigated through simulation; consequently, the null distributions of the statistics from the transformed ones are quite well approximated to asymptotic distributions. A simulation study also shows that the combined statistics of skewness and kurtosis have moderate sensitivity of all alternatives under study, and they might be candidates for an omnibus test.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1142-1151
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• 2021

Objective: To evaluate microstructural damage in high myopia (HM) patients using 3T diffusion kurtosis imaging (DKI). Materials and Methods: This prospective study included 30 HM patients and 33 age- and sex-matched healthy controls (HCs) with DKI. Kurtosis parameters including kurtosis fractional anisotropy (FA), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK) as well as diffusion metrics including FA, mean diffusivity, axial diffusivity (AD), and radial diffusivity derived from DKI were obtained. Group differences in these metrics were compared using tract-based spatial statistics. Partial correlation analysis was used to evaluate correlations between microstructural changes and disease duration. Results: Compared to HCs, HM patients showed significantly reduced AK, RK, MK, and FA and significantly increased AD, predominately in the bilateral corticospinal tract, right inferior longitudinal fasciculus, superior longitudinal fasciculus, inferior fronto-occipital fasciculus, and left thalamus (all p < 0.05, threshold-free cluster enhancement corrected). In addition, DKI-derived kurtosis parameters (AK, RK, and MK) had negative correlations (r = -0.448 to -0.376, all p < 0.05) and diffusion parameter (AD) had positive correlations (r = 0.372 to 0.409, all p < 0.05) with disease duration. Conclusion: HM patients showed microstructural alterations in the brain regions responsible for motor conduction and vision-related functions. DKI is useful for detecting white matter abnormalities in HM patients, which might be helpful for exploring and monitoring the pathogenesis of the disease.

• KSTLE International Journal
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• v.3 no.1
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• pp.7-11
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• 2002

The roughness effects are very important due to the presence of interacting asperities in mixed lubrication regime. An average Reynolds equation using flow factors is useful to determine the effects of surface roughness on mixed lubrication. In this study, the effect of kurtosis on flow factors is investigated using random rough surfaces generated numerically, The results show that flow factors are very sensitive to h/$\sigma$ according to the value of kurtosis in the partial lubrication regime.

• Wind and Structures
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• v.36 no.6
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• pp.393-404
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• 2023

Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NIST-UWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.06a
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• pp.34-41
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• 2000

Surface roughness plays a significant role in friction, wear, and lubrication in machine components. Most engineering surfaces have the nongaussian distrubution. So, in this study, contact simulation are conducted for not only gaussian surfaces but also nongaussian surfaces. Nongaussian rough surface censidering the kurtosis is generated numerically, And the effects of kurtosis on real contact area fraction, average gap, and mean asperity contact pressure are studied. It will be shown that the real contact area fraction and the mean asperity contact pressure are sensitive to the characteristics of surface geometry according to kurtosis.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.501-510
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• 2020

Mardia (Biometrika, 57, 519-530, 1970) defined measures of multivariate skewness and kurtosis. Based on these measures, omnibus test statistics of multivariate normality are proposed using normalizing transformations. The transformations we consider are normal approximation and a Wilson-Hilferty transformation. The normalizing transformation proposed by Enomoto et al. (Communications in Statistics-Simulation and Computation, 49, 684-698, 2019) for the Mardia's kurtosis is also considered. A comparison of power is conducted by a simulation study. As a result, sum of squares of the normal approximation to the Mardia's skewness and the Enomoto's normalizing transformation to the Mardia's kurtosis seems to have relatively good power over the alternatives that are considered.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2000.11a
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• pp.280-288
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• 2000

In this study, flow factors are evaluated in terms of kurtosis using random rough surface generated numerically. As h/$\sigma$become large øx, øy, øfp approach to 1 and øs, øfs to 0 asymptotically regardless of kurtosis. øx, øy, øfp increase with increasing kurtosis in the mixed lubrication regime. øs, øfs is associated with an additional flow transport due to the combined effect of sliding and roughness. As h/$\sigma$ decreases øs, øfs increase up to a certain point, and then decrease toward zero. This behavior can be attributed to the increasing number of contacts in the mixed lubrication regime. øx in the presence of elastic deformation on the surface is larger than øx in the absence of it because local film thickness( h$\_$T/) increases by elastic deformation.