• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.255-267
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• 2021

The analysis of simply supported single-cell skew-curved reinforced concrete (RC) box-girder bridges is carried out using a finite element based CsiBridge software. The behaviour of skew-curved box-girder bridges can not be anticipated simply by superimposing the individual effects of skewness and curvature, so it becomes important to examine the behaviour of such bridges considering the combined effects of skewness and curvature. A comprehensive parametric study is performed wherein the combined influence of the skew and curve angles is considered to determine the maximum bending moment, maximum shear force, maximum torsional moment and maximum vertical deflection of the bridge girders. The skew angle is varied from 0° to 60° at an interval of 10°, and the curve angle is varied from 0° to 60° at an interval of 12°. The scantly available literature on such bridges focuses mainly on the analysis of skew-curved bridges under dead and point loads. But, the effects of actual loadings may be different, thus, it is considered in the present study. It is found that the performance of these bridges having more curvature can be improved by introducing the skewness. Finally, several equations are deduced in the non-dimensional form for estimating the forces and deflection in the girders of simply supported skew-curved RC box-girder bridges, based upon the results of the straight one. The developed equations may be helpful to the designers in proportioning, analysing, and designing such bridges, as the correlation coefficient is about 0.99.

• Journal of Information Processing Systems
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• v.6 no.4
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• pp.511-520
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• 2010

This paper proposes two new methods to detect the fingerprints of different persons based on one-dimensional and two-dimensional discrete wavelet transformations (DWTs). Recent literature shows that fingerprint detection based on DWT requires less memory space compared to pattern recognition and moment-based image recognition techniques. In this study four statistical parameters - cross correlation co-efficient, skewness, kurtosis and convolution of the approximate coefficient of one-dimensional DWTs are used to evaluate the two methods involving fingerprints of the same person and those of different persons. Within the contexts of all statistical parameters in detection of fingerprints, our second method shows better results than that of the first method.

• Journal of the Korean Data and Information Science Society
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• v.24 no.4
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• pp.901-909
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• 2013

A skewed double power function distribution is defined by a double power function distribution. We shall evaluate the coefficient of the skewness of a skewed double power function distribution. We shall obtain an approximate maximum likelihood estimator (MLE) and a moment estimator (MME) of the skew parameter in the skewed double power function distribution, and compare simulated mean squared errors for those estimators. And we shall compare simulated MSEs of two proposed reliability estimators in two independent skewed double power function distributions with different skew parameters.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.272-277
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• 2001

In order to evaluate variation of fatigue data of the LP steam turbine blade steel, it is important to estimate P - S - N curves to accurately define the probability distributions. In this study, new procedure is introduced to determine the expression of P - S - N curves. For this purpose, 3-parameter Weibull distribution was found to be most appropriate among assumed distributions when the probability distributions of the fatigue life were examined by the proposed analysis. Furthermore, parameter estimation for P - S - N curves was performed using various optimization to maximize the correlation coefficient. As a result of this, sequential linear programing method is used for estimation of P - S - N curves.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.262-267
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• 2001

In this study, the Reliability of degraded steam turbine blade was evaluated using the limited fatigue data. The statistical estimation of limited fatigue data implies that some unknown uncertainties which may be involved in fatigue reliability analysis. Therefore, an appropriate distribution in the fatigue strength was determined by the characteristic distribution - linear correlation coefficient, fatigue physics, error parameter. 3-parameter Weibull distribution is the most appropriate distribution to assume for infinite region. The load applied on the blade is mainly tensile. The maximum Von-Mises stress is 219.4 MPa at the steady state service condition. The failure probability($F_p$) derived from the strength-stress interference model using Monte carlo simulation under variable service condition is 0.25% at the 99.99% confidence level.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.573-584
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• 2007

We define a skew-symmetric Laplace distribution by a symmetric Laplace distribution and evaluate its coefficient of skewness. And we derive an approximate maximum likelihood estimator(AME) and a moment estimator(MME) of a skewed parameter in a skew-symmetric Laplace distribution, and hence compare simulated mean squared errors of those estimators. We compare asymptotic mean squared errors of two defined estimators of reliability in two independent skew-symmetric distributions.

• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.859-870
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• 2009

We obtain a skewed double Weibull distribution by a double Weibull distribution, and evaluate its coefficient of skewness. And we obtain the approximate maximum likelihood estimator(AML) and moment estimator of skew parameter in the skewed double Weibull distribution, and hence compare simulated mean squared errors(MSE) of those estimators. We compare simulated MSE of two proposed reliability estimators in two independent skewed double Weibull distributions each with different skew parameters. Finally we introduce a skewed double Weibull distribution generated by a uniform kernel.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2792-2799
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• 2015

In this paper, we utilize source mel-frequency cepstral coefficients (SMFCCs), skewness, and kurtosis extracted in glottal flow signals to improve speaker recognition performance. Generally, because the high band magnitude response of glottal flow signals is somewhat flat, the SMFCCs are extracted using the response below the predefined cutoff frequency. The extracted SMFCC, skewness, and kurtosis are concatenated with conventional feature parameters. Then, dimensional reduction by the principal component analysis (PCA) and the linear discriminat analysis (LDA) is followed to compare performances with conventional systems under equivalent conditions. The proposed recognition system outperformed the conventional system for large scale speaker recognition experiments. Especially, the performance improvement was more noticeable for small Gaussan mixtures.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05c
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• pp.170-176
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• 2002

In this papers, we proposed the feature extraction method due to partial discharge type of transformers. For wavelet transform, Daubechie's filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion, skewness, kurtosis) about acoustic emission signal generated from each partial discharge type. The defects which could occur in a transformer were simulated by using needle-plane electrode, IEC electrode and Void electrode. Also, these coefficients are used to identify signal of partial discharge type electrode fault in transformer. As a result, from compare of acoustic emission amplitude and acoustic average value, we are obtained results of IEC electrode> Void electrode> Needle-Plane electrode. otherwise, In case of skewness and kurtosis, we are obtained results of Needle-Plane electrode electrode> Void electrode> IEC electrode.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.07b
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• pp.894-899
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• 2002

In this papers, we proposed the new method in order to diagnosis partial discharge type of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion, skewness, kurtosis) about high frequency current signal per 3-electrode type (needle-plane electrode, IEC electrode and Void electrode.). Also. these coefficients are used to identify Signal of internal partial discharge in transformer. As a result. from compare of high frequency current signal amplitude and average value. we are obtained results of IEC electrode> Void electrode> Needle-Plane electrode. otherwise. In case of skewness and kurtosis, we are obtained results of Void electrode> IEC electrode > Needle-Plane electrode. As Improved method in order to diagnosis partial discharge type of transformers, we use neural network.