• Journal of the Korean Statistical Society
• /
• v.30 no.4
• /
• pp.585-595
• /
• 2001

A second-order response surface model is often used to approximate the relationship between a response factor and a set of explanatory factors. In this article, we deal with canonical analysis in response surface models. For the interpretation of the geometry of second-order response surface model, standard errors and confidence intervals for the eigenvalues of the second-order coefficient matrix play an important role. If the confidence interval for some eigenvalue includes 0 or the estimate of some eigenvalue is very small (near to 0) with respect to other eigenvalues, then we are able to delete the corresponding canonical factor. We propose a formulation of criterion which can be used to select canonical factors. This criterion is based on the IMSE(=Integrated Mean Squared Error). As a result of this method, we may approximately write the canonical factors as a set of some important explanatory factors.

• Journal of the Korean Statistical Society
• /
• v.25 no.4
• /
• pp.589-601
• /
• 1996

Geometric approach to extend the classical two-set theory of canonical correlation analysis to three or more sets is considered. It provides statistical graphs to represent the data in a low dimensional space. Procedures are developed for computing the canonical variables and the corresponding properties are investigated. The solution is equivalent to that of the usual problem in the case of two sets. Goodness-of-fit of the proposed plots is studied and a numerical example is included.

• Smart Structures and Systems
• /
• v.17 no.6
• /
• pp.1031-1053
• /
• 2016

The health conditions of in-service civil infrastructures can be evaluated by employing structural health monitoring technology. A reliable health evaluation result depends heavily on the quality of the data collected from the structural monitoring sensor network. Hence, the problem of sensor fault diagnosis has gained considerable attention in recent years. In this paper, an innovative sensor fault diagnosis method that focuses on fault detection and isolation stages has been proposed. The dynamic or auto-regressive characteristic is firstly utilized to build a multivariable statistical model that measures the correlations of the currently collected structural responses and the future possible ones in combination with the canonical correlation analysis. Two different fault detection statistics are then defined based on the above multivariable statistical model for deciding whether a fault or failure occurred in the sensor network. After that, two corresponding fault isolation indices are deduced through the contribution analysis methodology to identify the faulty sensor. Case studies, using a benchmark structure developed for bridge health monitoring, are considered in the research and demonstrate the superiority of the new proposed sensor fault diagnosis method over the traditional principal component analysis-based and the dynamic principal component analysis-based methods.

• The Journal of the Acoustical Society of Korea
• /
• v.36 no.4
• /
• pp.279-284
• /
• 2017

The localization of sources has a numerous number of applications. To estimate the position of sources, the relative time delay between two or more received signals for the direct signal must be determined. Although the GCC (Generalized Cross-Correlation) method is the most popular technique, an approach based on CCA (Canonical Correlation Analysis) was also proposed for the TDE (Time Delay Estimation). In this paper, we propose a new adaptive algorithm based on CCA in order to utilized the sparsity in the eigenvector of CCA based time delay estimator. The proposed algorithm uses the eigenvector corresponding to the maximum eigenvalue with log-sum regularization in order to utilize the sparsity in the eigenvector. We have performed simulations for several SNR(signal to noise ratio)s, showing that the new CCA based algorithm can estimate the time delays more accurately than the conventional CCA and GCC based TDE algorithms.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.63-68
• /
• 2005

Transcription factors regulate gene expression by binding to gene upstream region. Each transcription factor has the specific binding site in promoter region. So the analysis of gene upstream sequence is necessary for understanding regulatory mechanism of genes, under a plausible idea that assumption that DNA sequence motif profiles are closely related to gene expression behaviors of the corresponding genes. Here, we present an effective approach to the analysis of the relation between gene expression profiles and gene upstream sequences on the basis of kernel canonical correlation analysis (kernel CCA). Kernel CCA is a useful method for finding relationships underlying between two different data sets. In the application to a yeast cell cycle data set, it is shown that gene upstream sequence profile is closely related to gene expression patterns in terms of canonical correlation scores. By the further analysis of the contributing values or weights of sequence motifs in the construction of a pair of sequence motif profiles and expression profiles, we show that the proposed method can identify significant DNA sequence motifs involved with some specific gene expression patterns, including some well known motifs and those putative, in the process of the yeast cell cycle.

• ALGAE
• /
• v.18 no.1
• /
• pp.49-58
• /
• 2003

The short-term dynamics of the summer phytoplankton community structure were investigated in coastal waters around Namhae Island, the Southern Sea of Korea. The study was based on a comprehensive survey constituting 39 collections from 13 stations on July 18-22, August 1-2, 14-16 and 27-30, respectively. The community structure was analysed using cluster analysis and important environmental correlates of the assemblage structure were identified with canonical correspondence analysis (CCA). Water temperature, salinity, NO₂, NO₃, NH₄, PO₄, chlorophyll a and transparency were measured as physico-chemical environmental factors which may be associated with the phytoplankton community structure. Variations of salinity and concentrations of NO₃ and chlorophyll a were not significant. In addition to warmer water temperature, concentrations of NO₂, NO₄and PO₄ decreased at the beginning of August. And transparency was deeper and water column became very unstable after the middle of August. A wide taxonomic diversity was encountered during the survey, including a total of 121 taxa which was composed of 72 diatoms, 48 dinoflagellates and 1 euglenoid species. Cluster analysis showed that the Phytoplankton community could be divided into 4 distinct groups, indicating rapid changes of the community in the short course of this survey. These phytoplankton groups also showed distinctive dispersion patterns in 2-dimensional canonical space, indicating distinct groupings for stations at each survey. Dominant taxa of diatoms (Chaetoceros curvisetus, Chaetoceros spp., Leptocylindrus danicus, Leptocylindrus mediteraneus, Skeletonema costanum, and Pseudo-nitzschia pungen) clustered in region of CCA space corresponding to stations surveyed at the middle of July. Dominant taxa of dinoflagellates were tightly associated with stations surveyed at the middle (Karenia breve) and end (Cochlodinium polykrikoides and Polykrikos schwartzii) of August. The CCA also showed that the phytoplankton community compositions were highly associated with water temperature, transparency, NO₂, NH₄ and PO₄, suggesting that gradients in physical and nutrient conditions affect short-term changes in phytoplankton composition.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.06a
• /
• pp.602-606
• /
• 2005

A systematic procedure of probability analysis for general distributions is developed based on the first four moments estimated from polynomial interpolation of the system response function and the Pearson system. The function approximation is based on a specially selected experimental region for accuracy and the number of function evaluations is taken equal to that of the unknown coefficient for efficiency. For this purpose, three error-minimizing conditions are proposed and corresponding canonical experimental regions are formed for popular probability. This approach is applied to study the stochastic properties of the performance functions of a MEMS structure, which has quite large fabrication errors compared to other structures. Especially, the vibratory micro-gyroscope is studied using the statistical moments and probability density function (PDF) of the performance function to be the difference between resonant frequencies corresponding to sensing and driving mode. The results show that it is very sensitive to the fabrication errors and that the types of PDF of each variable also affect the stochastic properties of the performance function although they have same the mean and variance.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2009.05a
• /
• pp.227-228
• /
• 2009

This study proceeded to find the optimum mixing rate of a high strength concrete with 80MPa of the contribution and composite effect on the resistance to fire of the fibers were analyzed and the corresponding results were exploited to derive practical mix proportions. Also proceeded to propriety examination of limit value for optimum operating condition.

• The Bulletin of The Korean Astronomical Society
• /
• v.43 no.1
• /
• pp.55.1-55.1
• /
• 2018

Studies on rotation-powered pulsars with strong surface magnetic field may help us clarify the unclear link between magnetars and canonical radio pulsars because the magnetar-like emission is expected to be observed. PSR J1119-6127 associated with SNR G292.2-0.5 has a high magnetic field of $4.1{\times}1013$ gauss, and a young characteristic age of ~1700 years can be served as the good candidate to compare with magnetars and rotation-powered pulsars. The glitch accompanied by the radiative changes detected in 2007 is the first case we observed for a rotationally powered radio pulsar. This pulsar experienced magnetar-like outbursts in mid. 2016, similar to the 2006 transition occurred on the other radio-quiet rotation-powered pulsar with strong surface magnetic field, PSR J1846-0258. In this talk, I'll report the investigation with X-ray and gamma-ray data of this magnetar-like pulsar. A sudden decrease in the gamma-ray emission at the GeV band was detected immediately after the X-ray outburst. Accompanying with the disappearance of the radio pulsation, the gamma-ray pulsation cannot be resolved as well after the outburst. We tried to derive the timing behavior and some intriguing features of this pulsar in this work corresponding to the outburst using the Swift data, NuSTAR and XMM observations.

• The Journal of the Acoustical Society of Korea
• /
• v.32 no.6
• /
• pp.548-555
• /
• 2013

The localization of sources has a numerous number of applications. To estimate the position of sources, the relative delay between two or more received signals for the direct signal must be determined. Although the generalized cross-correlation method is the most popular technique, an approach based on eigenvalue decomposition (EVD) is also popular one, which utilizes an eigenvector of the minimum eigenvalue. The performance of the eigenvalue decomposition (EVD) based method degrades in the low SNR and the correlated environments, because it is difficult to select a single eigenvector for the minimum eigenvalue. In this paper, we propose a new adaptive algorithm based on Canonical Correlation Analysis (CCA) in order to extend the operation range to the lower SNR and the correlation environments. The proposed algorithm uses the eigenvector corresponding to the maximum eigenvalue in the generalized eigenvalue decomposition (GEVD). The estimated eigenvector contains all the information that we need for time delay estimation. We have performed simulations with uncorrelated and correlated noise for several SNRs, showing that the CCA based algorithm can estimate the time delays more accurately than the adaptive EVD algorithm.