• 한국정밀공학회지
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• 제13권4호
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• pp.136-142
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• 1996

Automatic monitoring of cutting process is one of the most important technology in machining. AE sensing technology has been applied to monitoring process and proved to be effective in detecting tool abnor- malities such as tool wear and fracture. In this experimental study. AE signals were detected from the tool holder for continuous and interrupted cutting, which obtained from changing workpice material configuration, under control of constant cutting speed from CNC lathe. From statistical and frequency analysis, the AE signals were analyzed to obtaining the characteristics of continuous and interrupted cutting conditions and tool failure. The Kurtosis values decreased but RMS voltages increased as the cutting speed increased, in both continuous and interrupted cutting. RMS voltage is suddenly increased but Kurtosis value is suddenly decreased when tool failure condition. Power spectrum density of AE signals when tool failure reaches extreme value around 0.065 cycles/ .mu. m.

• 한국안전학회지
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• 제15권1호
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• pp.100-105
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• 2000

Correlation between propagation of bush-type electrical tree and internal partial discharges(PD) was discussed. We used specimens with needle-plane electrode system made of LDPE(Low Density Polyethylene), observed inception and propagation of electrical tree by optical microscope interfaced with computer and investigated characteristics of phase resolved PD pulses accompanying with propagation of electrical tree. Electrical tree generally growed bush-type tree. PD data detected and analyzed were average discharge current and statistical operator of q-n, $\psi$- $q_{avg}$, $\psi$-n, ${\psi}-q_{max}$ distribution. Parameters used were skewness, kurtosis, average discharge phase, cross-correlation factor, asymmetry and etc. In generally, average discharge current had good linear relationship with propagation of bush type electrical tree on this experiment. Peak discharge magnitude and repetition rate were increased, but they had not good linear relationship.p.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1999년도 추계학술대회 논문집 - 한국공작기계학회
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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.

• 한국멀티미디어학회논문지
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• 제21권7호
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• pp.737-744
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• 2018

This paper presents a study on the performance of the higher-order moments for speech/music classification. For a successful speech/music classifier, extracting features that allow direct access to the relevant speech or music specific information is crucial. In addition to the conventional variance-based features, we utilize the higher-order moments of features, such as skewness and kurtosis. Moreover, we investigate the subband decomposition parameters in extracting features, which improves classification accuracy. Experiments on two speech/music datasets, which are publicly available, were performed and show that the higher-order moment features can improve classification accuracy when combined with the conventional variance-based features.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2006년도 추계학술대회 발표논문집
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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.

• Communications for Statistical Applications and Methods
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• 제18권2호
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• pp.155-164
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• 2011

This paper suggests two ratio-cum-product estimators of finite population mean using known coefficient of variation and co-efficient of kurtosis of auxiliary characters. The bias and mean squared error of the proposed estimators with large sample approximation are derived. It has been shown that the estimators suggested by Upadhyaya and Singh (1999) are particular case of the suggested estimators. Almost ratio-cum product estimators of suggested estimators have also been obtained using Jackknife technique given by Quenouille (1956). An empirical study is also carried out to demonstrate the performance of the suggested estimators.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2003년도 학술대회지
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• pp.201-209
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• 2003

In the mixed lubrication regime, the roughness effects are very 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 mixed lubrication. In this paper, the pressure flow factors and shear stress factor for Gaussian and non-Gaussian surfaces are evaluated in terms of kurtosis and skewness. particularly, the elastic deformation of the surface is considered.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2014년도 추계학술대회 논문집
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• pp.745-750
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• 2014

The chatter vibration in the machining process plays bad role in machining quality such as high roughness as well as tool life and machine failure. And the grinding process under this risk in the fully automated factory is exposed to the unexpected mass machining quality problem. Studying the vibration signal of the hub bearing grinding process, the reason of chatter vibration was explained with the specific machining pattern of chatter. And this study suggests the chatter detecting method in the production line, which is monitoring the peak acceleration level around the natural frequencies of the specimen, and calculating kurtosis value by assuming the chatter is related to the resonance of the specimen. The suggested method was applied to the vehicle hub bearing grinding process and proved good to detecting the chatter induced machining quality problem.

• 한국기계연구소 소보
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• 통권19호
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• pp.61-68
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• 1989

As the system monitoring technology become required in order to improve the system performance and the productivity, we’ve studied to the detection for the tool wear and the tool breakage using AE sensors that is able to detection of generated high frequency stress pulse at cutting. The detection system is consist of a sensing part, a amplifier part, a signal processing part, and a analysis & output part. The moment (a rms and a kurtosis) of statistical method is used for analysis of AE singnal. The experiment are carried out in a CNC lathe. In this study, we achieved that the amplitude level of the AE signal and statistical moments was largely changed as the tool failure. The change rate of Kurtosis was especially large, but the change rate of the rms was small.

• 전기학회논문지
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• 제62권6호
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• pp.825-832
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• 2013

In this study, Independent Component Analysis (ICA) algorithms are suggested to extract the original ECG part from the mixed signal contaminated with the unwanted frequency components and especially 60Hz power line disturbances. With this aim, we implement a novel method to suppress the baseline-wandering disturbances and power line artefacts contained in patch-electrodes sensory ECG data by separating the unmixed signal with finding the optimal weight W based on Kurtosis value. With applying brutal force and gradient ascent searching algorithm to find W, we can conclude that the unwanted frequency components especially in the ambulatory ECG data can be eliminated by Independent Component Analysis.