• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.49-56
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• 1999

This paper present a study on the application of sound pressure and vibration signals to detect the presence of defects in a rolling element bearing using a statistical analysis method. The well established statistical parameters such as the crest factor and the distribution of moments including kurtosis and skewless are utilized in this study. In addition, other statistical parameters derived from the beta distribution function are also used. A comparison study on the performance of the different types of parameter used is also performed. The statistical analysis is used because of its simplicity and quick computation. Under ideal conditions, the statistical method can be used to identify the different types of defect present in the bearing. In addition, the results also reveal that there is no significant advantages in using the beta function parameters when compared to using kurtosis and the crest factor for detecting and identifying defects in rolling element bearings from both sound and vibration signals.

• Proceedings of the KSPS conference
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• 2006.11a
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• pp.92-95
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• 2006

The purpose of this study is to find the acoustic parameters on frequency domain to distinguish the Korean nasals, /m, n, ng/ from each other. Since it is not easy to characterize the antiformant on frequency domain, we suggest the new parameters that are calculated by LTAS(Long term average spectrum). Maximum energy value and its frequency and minimum energy and its frequency of zero are obtained from the spectrum respectively. In addition, slope1, slope2, total energy value, centroid, skewness, and kurtosis are suggested as new parameters as well. The parameters that are revealed as to be statistically signigicant difference are roughly peak1_a, zero_f, slope_1, slope_2, highENG, zero_ENG, and centroid.

• Journal of Information Processing Systems
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• v.8 no.3
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• pp.421-436
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• 2012

Different types of fingerprint detection algorithms that are based on extraction of minutiae points are prevalent in recent literature. In this paper, we propose a new algorithm to locate the virtual core point/centroid of an image. The Euclidean distance between the virtual core point and the minutiae points is taken as a random variable. The mean, variance, skewness, and kurtosis of the random variable are taken as the statistical parameters of the image to observe the similarities or dissimilarities among fingerprints from the same or different persons. Finally, we verified our observations with a moment parameter-based analysis of some previous works.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.6-9
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• 1996

In this paper, we attempted to analyze the contraction patterns of elbow flexor muscle during isometric, concentric and eccentric contraction. The analysis parameters are consisted of Sequency domain parameters (mean frequency, median frequency, skewness, kurtosis) and time domain parameters (zero crossing, positive maxima, integrated EMG). As a results, the analysis parameters have specific trends for muscles, muscle contraction patterns, muscle contraction angles. Especially, at the time domain analysis, IEMG is a dominant parameter for analysis of activation patterns, and the skewness, kurtosis are useful parameters for functional recognition.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.68-74
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• 2005

Among many dimensional and dimensionless amplitude parameters, kurtosis (4-th normalized moment of probability density function) is generally regarded as a sensitive good parameter for machine diagnosis. However, higher order moment may be supposed to be much more sensitive. Bicoherence is an absolute deterioration factor whose range is 1 to 0. The theoretical value of n-th moment divided by n-th moment calculated by measured data would behave in the same way. We propose a simplified calculation method for an absolute index of n-th moment and name this as simplified absolute index of n-th moment. Some favorable results are obtained.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.50 no.3
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• pp.121-129
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• 2001

In this paper, a new efficient feature extraction method based on Daubechies discrete wavelet transform is presented. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of aging(the early period, the middle period, the last period)

• 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.

• Journal of Navigation and Port Research
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• v.31 no.6
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• pp.471-478
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• 2007

This study is carried out the investigation of nonlinear characteristics of the field wave observation data acquired in the western sea area in Jeju island during one year. It is aimed to offer the fundamental data for Freak wave forecasting in real sea. For this, the nonlinear parameters of ocean waves, which are Skewness, Atiltness, Kurtosis and Spectrum band width parameter et al., are introduced, and the parameters are compared and discussed with some characteristics wave components, ie, significant wave height, maximum wave height, and so on. As a results, we know that the parameters describe nonlinear characteristics of observed wave spectrum broadly, are feebly related with occurrence of abnormal maximum wave height, namely freak event, however the Kurtosis, $K_t$ which is a degree of peakness of mode of surface elevation distribution, has better relationship than others.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.6
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• pp.306-315
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• 2017

This study introduces a software for real-time assessment of combustion stability for utility gas turbines. The software was written with LabView, and implemented the time-domain kurtosis as a parameter to proactively access the instantaneous combustion stability during operation of the industrial gas turbine. The simple time-domain assessment algorithm incorporated in the software is advantageous over conventional frequency-domain signal processing of dynamic pressure signal since it reduces the computational cost, thereby making the algorithm more appropriate for real-time monitoring of combustion stability. Benchmark data obtained from a model gas turbine combustor were used for the reproducibility test of the software. The assessment obtained from the software agreed well with previously published results, indicating that incorporation of the software could enhance the performance of systems monitoring the combustion stability for gas turbines during power generation.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.685-696
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• 2012

These days the interest of quality leads to the necessity of control charts for monitoring the process in various fields of practical applications. The $\overline{X}$ chart is one of the most widely used tools for quality control that also performs well under the normality of quality characteristics. However, quality characteristics tend to have nonnormal properties in real applications. Numerous recent studies have tried to find and explore the performance of $\overline{X}$ chart due to non-normality; however previous studies numerically examined the effects of non-normality and did not provide any theoretical justification. Moreover, numerical studies are restricted to specific type of distributions such as Burr or gamma distribution that are known to be flexible but can hardly replace other general distributions. In this paper, we approximate the false alarm rate(FAR) of the $\overline{X}$ chart using the Edgeworth expansion up to 1/n-order with the fourth cumulant. This allows us to examine the theoretical effects of nonnormality, as measured by the skewness and kurtosis, on $\overline{X}$ chart. In addition, we investigate the effect of skewness and kurtosis on $\overline{X}$ chart in numerical studies. We use a skewed-normal distribution with a skew parameter to comprehensively investigate the effect of skewness.