• Proceedings of the Korean Geotechical Society Conference
• /
• 2010.09a
• /
• pp.416-422
• /
• 2010

Estimating condition of geotechnical structures are difficult because of nonlinear time dependency and seasonal effects. Measuring data of structure failure is highly variable in time and space, and a unique approach cannot be defined to model structure movements. Characteristics of movements are obtained by using a statistical method called Principal Component Analysis(PCA). The PCA is a non-parametric method to separate unknown, statistically uncorrelated source processes from observed mixed processes. Instead, since the "best" mathematical relationship is estimated for given data sets of the input and output measured from target systems. As a consequence, this method is advantageous in modeling systems whose geomechanical properties are unknown or difficult to be measured.

• Communications for Statistical Applications and Methods
• /
• v.13 no.2
• /
• pp.233-241
• /
• 2006

Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS (least median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimmed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven optimization algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.145-148
• /
• 2019

Principal Component Analysis (PCA) is a powerful technique in data analysis and widely used to detect anomalies in Wireless Sensor Networks. However, the performance of conventional PCA is not high on time-series data collected by sensors. In this paper, we propose a Joint Exponential Smoothing and Trend-based Principal Component Analysis (JES-TBPCA) for Anomaly Detection which is based on conventional PCA. Experimental results on a real dataset show a remarkably higher performance of JES-TBPCA comparing to conventional PCA model in detection of stuck-at and offset anomalies.

• Communications for Statistical Applications and Methods
• /
• v.29 no.2
• /
• pp.203-223
• /
• 2022

We propose a Bayesian approach to cumulative logistic regression model for the ordinal response based on the orthogonal principal components via singular value decomposition considering the multicollinearity among predictors. The advantage of the suggested method is considering dimension reduction and parameter estimation simultaneously. To evaluate the performance of the proposed model we conduct a simulation study with considering a high-dimensional and highly correlated explanatory matrix. Also, we fit the suggested method to a real data concerning sprout- and scab-damaged kernels of wheat and compare it to EM based proportional-odds logistic regression model. Compared to EM based methods, we argue that the proposed model works better for the highly correlated high-dimensional data with providing parameter estimates and provides good predictions.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.22 no.3
• /
• pp.595-602
• /
• 1998

Specimens of 304 stainless steel were tested to failure at elevated temperatures under multiaxial stress states, uniaxial tension using smooth bar specimens, biaxial shearing using double shear bar specimens, and triaxial tension using notched bar specimens. Rupture times are compared for uniaxial, biaxial, and triaxial stress states with respect to the maximum principal stress, the von Mises effective stress, and the principal facet stress. The results indicate that the principal facet stress gives the best correlation for the material investigated, and this parameter can predict creep life data under multiaxial stress states with rupture data obtained with specimens under uniaxial stresses. The results also suggest that grain boundary cavitation, coupled with localized deformation processes such as grain boudary sliding, controls the lifetimes of the specimens.

• Journal of the Korean Society of Safety
• /
• v.31 no.4
• /
• pp.35-41
• /
• 2016

In order to guarantee the process safety and prevent accidents, the deviations from normal operating conditions should be monitored and their root causes have to be identified as soon as possible. The statistical theories-based method among various fault diagnosis methods has been gaining popularity, due to simplicity and quickness. However, according to fault magnitudes, the scalar value generated by statistical methods can be changed and this point can lead to produce wrong information. To solve this difficulty, this work employs PCA (Principal Component Analysis) based method with qualitative information. In the case study of our previous study, the number of assumed faults is much smaller than that of process variables. In the case study of this study, the number of predefined faults is 19, while that of process variables is 6. It means that a fault diagnosis becomes more difficult and it is really hard to isolate a single fault with a small number of variables. The PCA model is constructed under normal operation data in order to get a loading vector and the data set of assumed faulty conditions is applied with PCA model. The significant changes on PC (Principal Components) axes are monitored with CUSUM (Cumulative Sum Control Chart) and recorded to make the information, which can be used to identify the types of fault.

• Journal of Korea Multimedia Society
• /
• v.20 no.2
• /
• pp.134-143
• /
• 2017

Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.

• Korean Journal of Human Ecology
• /
• v.18 no.4
• /
• pp.857-867
• /
• 2009

The purpose of this study was to examine the relationships among principal's transformational and transactional leadership, subjective quality of life of teacher, and organizational commitment of teacher in kindergarten and day care center based on the survey data from 203 teachers working in kindergarten and day care center in Kyoungbuk province. The collected data were analyzed by Simple Regression, Multiple Regression in SPSS Win program(15.0 version). The main results of this study were as follows. First, principal's transformational and transactional leadership each exerted positive effects on teacher's subjective quality of life and organizational commitment. Second, teacher's subjective quality of life had a positive influence on organizational commitment. Finally, each effect of principal's transformational and transactional leadership on teacher's organizational commitment was mediated by teacher's subjective quality of life.

• The Korean Journal of Applied Statistics
• /
• v.18 no.1
• /
• pp.129-135
• /
• 2005

Representative observations are useful to express explicitly the distributional variation of the data by a few selected observations corresponding to the quantiles in the univariate situation. Jones and Rice(1992) extended it to the multidimensional case by the principal component based method. This study introduces a modified version of Jones and Rice exploiting the self-consistency of principal components in expressing the chosen observation vectors. Compared to that of Jones and Rice, the suggested method tends to provide with less susceptible representative observations to the sampling variation of the data and the resulted vectors benefits from the self-consistency.

• Journal of Mechanical Science and Technology
• /
• v.19 no.7
• /
• pp.1432-1440
• /
• 2005

High-temperature rupture behavior of 5083-Al alloy was tested for failure at 548K under multiaxial stress conditions: uniaxial tension using smooth bar specimens, biaxial shearing using double shear bar specimens, and triaxial tension using notched bar specimens. Rupture times were compared for uniaxial, biaxial, and triaxial stress conditions with respect to the maximum principal stress, the von Mises effective stress, and the principal facet stress. The results indicate that the von Mises effective and principal facet stresses give good correlation for the material investigated, and these parameters can predict creep life data under the multiaxial stress states with the rupture data obtained from specimens under the uniaxial stress. The results suggest that the creep rupture of this alloy under the testing condition is controlled by cavitation coupled with highly localized deformation process, such as grain boundary sliding. It is also conceivable that strain softening controls the highly localized deformation modes which result in cavitation damage in controlling rupture time of this alloy.