• Journal of the Korean Society of Manufacturing Process Engineers
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• v.3 no.2
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• pp.18-24
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• 2004

The effect of surface characterization parameters, such as surface roughness, skewness and kurtosis, on sliding friction and wear was studied experimentally. The friction coefficient was examined under the various parameters and sliding speed, normal load and type of lubricant with ball-on-disk type tribo-meter. The surface of the lower skewness in negative or the higher kurtosis between the same arithmetic mean value tends to indicate low friction.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.10
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• pp.53-60
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• 2001

Surface roughness plays a significant role in friction, wear, and lubrication in machine components. Most engineering surfaces have the nongaussian height distribution. So, in this study, contact simulations are conducted for not only gaussian surfaces but also nongaussian surfaces. Nongaussian rough surface considering 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.

• Journal of the Korean Society for Precision Engineering
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• v.29 no.12
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• pp.1313-1320
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• 2012

Condition monitoring of the reciprocating compressor is important task. As a traditional method, health monitoring system of refrigerator depends on decision of a skilled person based on his experience. However, the skilled person cannot monitor all the compressors completely. If a sampled compressor is faulty, thousands of compressors manufactured at that place are regarded as faulty compressors. Therefore it is necessary to monitor all compressors in the production line. In the paper real time health monitoring system is developed based on high order time frequency method and artificial neural network. The system is installed in the mass production line. The result of the application has been very successful, and currently the system is working very well on the production line.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.687-692
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• 2006

In a conventional ICA(Independent Component Analysis) based face recognition method, PCA(Principal Component Analysis) first is used for feature extraction, ICA learning method then is applied for feature enhancement in the reduced dimension. It is not considered that a necessary component can be located in the discarded feature space. In the new ICA(NICA), learning extracts features using the magnitude of kurtosis (4-th order central moment or cumulant). But, the pure ICA method can not discard noise effectively. The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. Namely, PCA does whitening and noise filtering. ICA performs feature extraction. Experiment results show the effectiveness of the new ICA method compared to the conventional ICA approach.