• Journal of the Korean Statistical Society
• /
• v.31 no.2
• /
• pp.163-175
• /
• 2002

An extended version of the minimax eccentricity factor estimation for multiple set case is proposed. In addition, two more simple methods for multiple set factor analysis exploiting the concept of generalized canonical correlation analysis is suggested. Finally, a certain connection between the generalized canonical correlation analysis and the multiple set factor analysis is derived which helps us clarify the relationship.

• Journal of Environmental Science International
• /
• v.14 no.2
• /
• pp.251-258
• /
• 2005

Statistical analysis between operating parameters and effluent quality on advanced wastewater treatment plant was performed. Through factor analysis four factors derived varimax rotation were selected each plant. Four components explained $80\%,\;82\%$ of the total variance of the process, respectively. The components on MLE plant were identified in the following order: 1) HRT increase and BOD load decrease by influent decrease, 2) Biomass, 3) SVI increase by internal return increase, 4) Microbial diversity by SRT increase. On $A_2O$ plant, we defined them as follows: factor 1, high MLSS by return rate increase, HRT increase by influent decrease; factor 2, biomass; factor 3, BOD of influent; factor 4 was relate to DO.

• Journal of the Korean Statistical Society
• /
• v.9 no.1
• /
• pp.13-29
• /
• 1980

The statistical theory of factor analysis is briefly reviewed with emphasis on the maximum-likelihood method. A modified version of Joreskog(1975) is used for the implementation of the maximum-likelihood method. For the minimization of the conditional minimum function, an adaptive Newton-Raphson method is applied.

• Journal of Environmental Science International
• /
• v.17 no.4
• /
• pp.405-412
• /
• 2008

Statistical analysis technique was applied to operating parameters and removal efficiency data sets obtained from advanced wastewater treatment plant during 1 year. Through factor analysis three factors derived varimax rotation were selected each plant. Three components explained 96%, 87% of the total variance of the process, respectively. The components on $A_2O$ Plant were identified in the following order : 1) Shortening the SRT during high-flow period, 2) Keeping biomass high on winter 3) factor was related to DO. On DNR plant, we defined them as follows: factor 1, Prolonged the SRT during high-flow period; factor 2 was related to sludge return; factor 3, Influent BOD during low-DO period. This technique was believed to assist operators in identifying priorities to improve operation efficiency.

• Journal of Advanced Marine Engineering and Technology
• /
• v.24 no.4
• /
• pp.405-413
• /
• 2000

This paper present 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 skew 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.

• Communications for Statistical Applications and Methods
• /
• v.6 no.1
• /
• pp.33-42
• /
• 1999

A method of detecting influential observations in principal factor analysis is suggested. it is based on a perturbation of the empirical distribution function and an adoption of the local influence method. An illustrative example is given.

• Journal of the Korean Statistical Society
• /
• v.25 no.1
• /
• pp.67-80
• /
• 1996

Factor analysis is a multivariate technique for describing the in-terrelationship among many variables in terms of a few underlying but unobservable random variables called factors. There are various approaches for this factor analysis. In particular, principal factor analysis is one of the most popular methods. This follows the mathematical algorithm of the principal component analysis based on the singular value decomposition. But it is known that the singular value decomposition is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, using the resistant singular value decomposition of Choi and Huh (1994), we derive a resistant principal factor analysis relatively little influenced by notable observations.

• Journal of the Korean Ceramic Society
• /
• v.36 no.10
• /
• pp.1069-1074
• /
• 1999

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1999.10a
• /
• pp.49-56
• /
• 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 Korean Society for Noise and Vibration Engineering Conference
• /
• 2003.05a
• /
• pp.797-801
• /
• 2003

The Statistical Energy Analysis is based on the power flow and the energy conservation between sub-systems, which enable the prediction of acoustic and structural vibration behavior in mid-high frequency ranges. This paper discusses the identification of SEA coupling loss factor parameters from experimental measurements of small reverberation chamber sound pressure levels and structural accelerations. As structural subsystems, steel plates with and without damping treatment are considered. Calculated CLFs were verified by both transmission loss values for air-borne CLF case and running SEA commercial software As a result, CLFs have shown a good agreement with those computed by software. Acoustical behavior of air-borne noise and structure-borne noise has been examined. which shows reasonable results, too.