• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.1083-1087
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• 1997

End milling is available for machining the variable shape of products and has brrn widely applied in many Manufacturing industries. The surface finish of machined parts determines quality and functionality of products. Surface roughness causes friction,noise,fracture, glossiness and seizure, so many research had been performed to precisely. In particular an experimental analysis was carried out to investigate the influence ofsurface roughness on the fractal dimension. This parameter was assumed to contain not only information of roughness but also extra meaning. Experiments which were performed under various cutting conditions to compare fractal dimension with surface roughness R /sab a/ show fractal dimension to be useful parameter for determining of roughness.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1998.10a
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• pp.115-123
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• 1998

The morphological analysis of wear particle is a very effective means for machine condition monitoring and fault diagnosis. In order to describe morphology of various wear particle, the wear test was carried out under different experimental conditions. And fractal descriptors was applied to boundary and surface of wear particle with image processing system. These descriptors to analyze shape and surface wear particle are shape fractal dimension and surface fractal dimension. The shape fractal dimension can be derived from the boundary profile and surface fractal dimension can be determined by sum of intensity difference of surface pixel. The morphology of wear particles can be effectively obtained by two fractal dimensions.

• Wind and Structures
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• v.31 no.4
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• pp.363-371
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• 2020

Proper understanding of offshore wind speed variability is of essential importance in practice, which provides useful information to a wide range of coastal and marine activities. In this paper, long-term wind speed data recorded at various offshore stations are analyzed in the framework of fractal dimension analysis. Fractal analysis is a well-established data analysis tool, which is particularly suitable to determine the complexity in time series from a quantitative point of view. The fractal dimension is estimated using the conventional box-counting method. The results suggest that the wind speed data are generally fractals, which are likely to exhibit a persistent nature. The mean fractal dimension varies from 1.31 at an offshore weather station to 1.43 at an urban station, which is mainly associated with surface roughness condition. Monthly variability of fractal dimension at offshore stations is well-defined, which often possess larger values during hotter months and lower values during winter. This is partly attributed to the effect of thermal instability. In addition, with an increase in measurement interval, the mean and minimum fractal dimension decrease, whereas the maximum and coefficient of variation increase in parallel.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1844-1847
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• 1997

Assuming that EEG(electroencephalogram), which is generated by a nonlinear electrical of billions of neurons in the brain, has chaotic characteristics, it is confirmend by frequency spectrum analysis, log frequency spectrum analysis, correlation dimension analysis and Lyapunov exponents analysis. Some chaotic characteristics are related to the degree of brain activity. The slope of log frequency spectrum increases and the correlation dimension decreasess with respect to the activities, while the largest Lyapunov exponent has only a rough correlation.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.567-576
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• 2006

In this paper, we applied the multiblock dimension reduction methods to the classification of tumor based on microarray gene expressions data. This procedure involves clustering selected genes, multiblock dimension reduction and classification using linear discrimination analysis and quadratic discrimination analysis.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.6
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• pp.43-50
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• 2011

The fractal method has recently been applied to a model for determining soil grain size distribution. The objective of this study is to review the applicability of the fractal method for a analysis of submarine sedimentary environments by comparing fractal constants with grain size statistical analysis for the soil samples of Pohang (PH) and Namhae (NH). The y-interception of log (grain size)-log (passing) equation was also used because grain size distribution couldn't be expressed with fractal dimension only. The result of comparison between fractal constants (dimension, y-interception) and grain size statistical indices, the fractal dimension was directly proportional to the mean and the sorting. And the y-interception showed high correlation with the mean. The fractal dimension and y-interception didn't show significant correlation with the skewness and the kurtosis. Thus regression equations between fractal constants and two statistical indices (mean, sorting) were derived. All classifications of the mean and the sorting could be determined using the regression equation based on the fractal dimension and y-interception. Therefore, fractal constants could be used as an alternative index representing the sedimentary environments instead of the mean and sorting.

• Journal of Ocean Engineering and Technology
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• v.10 no.4
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• pp.84-91
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• 1996

A fractal analysis on fracture surface of aluminium-particulate SiC composites was attempted. As the volume fraction of SiC in composites increases, the fractal dimension tends to increase. However, no correlation between the fractal dimension and the fracture toughness in terms of critical energy release rate was observed. Since the fractal dimension represents the roughness of fracture surface, the fracture toughness would be a function of not only fracture surface roughness but also additional parameters. Thus the applicability of fractal analysis to the estimation of fracture toughness must depend on the proper choice and interpretation of additioal paramerters. In this paper, the size of characteristic strctural unit for fracture was considered as an additional parameter. As a result, the size appeared to be a function of only volume fraction of SiC. Finally, a master curve for fracture toughness of aluminium-particulate SiC composites was proposed as a function of fractal dimension and volume fraction of SiC.

• Journal of the Korean Home Economics Association
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• v.36 no.3
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• pp.47-60
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• 1998

This Pupose of this study was to identify the type of attitudes toward money to investigate the relationship between the attitued and consumption expenditure pattern. Data were obtained from 398 household living in Seoul. Factor analysis was used for examining dimensions of attitudes toward money and cluster analysis for classifying the households by money attitudes. This study found five money attitude dimensions, i.e., the Means of Success dimension, the Means of Pleasure dimension, the Means of Security dimension, the Symbol of Anxiety dimension, and the Parsimony dimension, Based on the variation in the dimensions, five money types were identified, ie., the Means of Success, the Means of Pleasure, the Means of Security, the Symbol of Anxiety, and the Parsimony.

• Tribology and Lubricants
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• v.18 no.2
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• pp.147-152
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• 2002

The morphological analysis of wear particle is a very effective means fur machine condition monitoring and fault diagnosis. In order to describe morphology of various wear particle, the wear test was carried out under friction experimental conditions. And fractal descriptors was applied to boundary and surface of wear particle with image processing. These descriptors to analyze shape and surface of wear particle are shape fractal dimension and surface fractal dimension. The boundary fractal dimension can be derived from the boundary profile and surface fractal dimension can be determined by sum of intensity difference of surface pixel. The morphology of wear particles can be effectively obtained by two fractal parameter.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.857-860
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• 1993

In this paper, we apply the self generating neuro fuzzy model (SGNFM) to the dimension analysis of the chaotic time series. Firstly, we formulate a nonlinear time series identification problem with nonlinear autoregressive (NARMAX) model. Secondly, we propose an identification algorithm using SGNFM. We apply this method to the estimation of embedding dimension for chaotic time series, since the embedding dimension plays an essential role for the identification and the prediction of chaotic time series. In this estimation method, identification problems with gradually increasing embedding dimension are solved, and the identified result is used for computing correlation coefficients between the predicted time series and the observed one. We apply this method to the dimension estimation of a chaotic pulsation in a finger's capillary vessels.