• Earthquakes and Structures
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• 제10권1호
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• pp.211-238
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• 2016

The Maximum seismic responses of steel buildings with perimeter moment resisting frames (MRF), modeled as complex MDOF systems, are estimated for several incidence angles of the horizontal components and the critical one is identified. The accuracy of the existing rules to combine the effects of the individual components is also studied. Two and three components are considered. The critical response does not occur for principal components and the corresponding incidence angle varies from one earthquake to another. The critical response can be estimated as 1.40 and 1.10 times that of the principal components, for axial load and interstory shears, respectively. The rules underestimate the axial load but reasonably overestimate the shears. The rules are not always inaccurate in the estimation of the combined response for correlated components. On the other hand, totally uncorrelated (principal) components are not always related to an accurate estimation. The correlation of the individual effects (${\rho}$) may be significant, even for principal components. The rules are not always associated to an inaccurate estimation for large values of ${\rho}$, and small values of ${\rho}$ are not always related to an accurate estimation. Only for perfectly uncorrelated harmonic excitations and elastic analysis of SDOF systems, the individual effects of the components are uncorrelated and the rules accurately estimate the combined response. The degree of correlation of the components, the type of structural system, the response parameter under consideration, the location of the structural member and the level of structural deformation must be considered while estimating the level of underestimation or overestimation.

• Journal of Pharmaceutical Investigation
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• 제37권6호
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• pp.377-395
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• 2007

Near infrared(NIR) spectral data have been used for the noninvasive analysis of various biological samples. Nonetheless, absorption bands of NIR region are overlapped extensively. It is very difficult to select the proper wavelengths of spectral data, which give the best PCR(principal component regression) models for the analysis of constituents of biological samples. The NIR data were used after polynomial smoothing and differentiation of 1st order, using Savitzky-Golay filters. To find the best PCR models, all-possible combinations of available principal components from the given NIR spectral data were derived by in-house programs written in MATLAB codes. All of the extensively generated PCR models were compared in terms of SEC(standard error of calibration), $R^2$, SEP(standard error of prediction) and SECP(standard error of calibration and prediction) to find the best combination of principal components of the initial PCR models. The initial PCR models were found by SEC or Malinowski's indicator function and a priori selection of spectral points were examined in terms of correlation coefficients between NIR data at each wavelength and corresponding concentrations. For the test of the developed program, aqueous solutions of BSA(bovine serum albumin) and glucose were prepared and analyzed. As a result, the best PCR models were found using a priori selection of spectral points and the final model selection by SEP or SECP.

• 산업공학
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• 제24권1호
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• pp.8-14
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• 2011

Principal Component Analysis (PCA) reduces the dimensionality of the process by creating a new set of variables, Principal components (PCs), which attempt to reflect the true underlying process dimension. However, for highly nonlinear processes, this form of monitoring may not be efficient since the process dimensionality can't be represented by a small number of PCs. Examples include the process of semiconductors, pharmaceuticals and chemicals. Nonlinear correlated process variables can be reduced to a set of nonlinear principal components, through the application of Kernel Principal Component Analysis (KPCA). Support Vector Data Description (SVDD) which has roots in a supervised learning theory is a training algorithm based on structural risk minimization. Its control limit does not depend on the distribution, but adapts to the real data. So, in this paper proposes a non-linear process monitoring technique based on supervised learning methods and KPCA. Through simulated examples, it has been shown that the proposed monitoring chart is more effective than $T^2$ chart for nonlinear processes.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.854-859
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• 1994

As powertrain noise is better and better controlled, road noise inputs become more important. The interior road noise of a car is mainly induced by the wheels rolling over the road surface. Each of the four wheels act as an independent and uncorrelated excitation input. To rank the energy transfer form each input to the interior, a Transfer Path Analysis (TPA) needs to be made-which requires operational vibration measurements. However due to the multiple uncorrelated inputs, phase relations vary continuously. It is therefore necessary to separate the operational data into set of "independent phenomena" by means of a Principal Component Analysis (PCA). A TPA can then be carried out for each independent phenomenon. Operational deflection shapes referenced to these principal components share the physical phenomena. The details of the methodology are discussed and a discussion of the results on a car shows that the method gives accurate results for full vehicle testing.e testing.

• 한국멀티미디어학회논문지
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• 제23권12호
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• pp.1586-1593
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• 2020

This study was conducted on the face ratio of mammals. It can also be applied to character automation by checking factors about the difference between animal and human face shapes. This paper used the face and face area data generated for Deep Learning learning. In detail, the proportion factors of the area comprising the faces of 20 species of animals and humans were defined and the average ratio was calculated. Next, the proportion of each animal was analyzed using the Principal Component Analysis (PCA). Through this, we would like to propose the golden ratio of mammals.

• 한국콘텐츠학회논문지
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• 제15권1호
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• pp.475-481
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• 2015

종속변수와 설명변수 사이의 관계가 선형이 아닌 경우에는 비선형 관계를 반영할 수 있는 다항회귀분석을 이용하여 회귀분석을 수행한다. 한편, 다항회귀분석에는 설명변수의 거듭제곱항들이 설명변수에 추가되므로 설명변수들 사이에 상관관계가 발생하여 다항회귀모형의 성능 저하 문제가 발생할 수 있다. 본 논문에서는 PGF 수치역변환 문제를 사례로 하여 주성분회귀분석을 통해 다항회귀분석의 성능을 극적으로 향상시킬 수 있음을 보인다. 본 논문에서는 PGF의 정의를 이용하여 PGF를 다항회귀분석으로 모형화한다. 다항회귀분석을 이용하여 PGF 전개식의 회귀계수를 추정하면 회귀계수의 추정 자체가 불가능하거나 계수 추정의 정확성이 저하되는 문제가 발생한다. 이 경우 다항회귀분석에 주성분회귀분석을 적용하면 계수 추정의 정확도가 극적으로 향상되어 다항회귀분석의 계수 추정 시 발생하는 문제를 해결할 수 있음을 밝힌다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2005년도 추계학술대회논문집
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• pp.127-130
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• 2005

There are many methods in feature extraction have been developed. Recently, principal components analysis (PCA) and independent components analysis (ICA) is introduced for doing feature extraction. PCA and ICA linearly transform the original input into new uncorrelated and independent features space respectively In this paper, the feasibility of using nonlinear feature extraction will be studied. This method will employ the PCA and ICA procedure and adopt the kernel trick to nonlinearly map the data into a feature space. The goal of this study is to seek effectively useful feature for faults classification.

• International Journal of Contents
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• 제14권4호
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• pp.24-29
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• 2018

EEG-based brain-computer interfaces has focused on explicitly expressed intentions to assist physically impaired patients. For EEG-based-computer interfaces to function effectively, it should be able to understand users' implicit information. Since it is hard to gather EEG signals of human brains, we do not have enough training data which are essential for proper classification performance of implicit intention. In this paper, we improve the subject independent classification of implicit intention through the generation of additional training data. In the first stage, we perform the PCA (principal component analysis) of training data in a bid to remove redundant components in the components within the input data. After the dimension reduction by PCA, we train ICA (independent component analysis) network whose outputs are statistically independent. We can get additional training data by adding Gaussian noises to ICA outputs and projecting them to input data domain. Through simulations with EEG data provided by CNSL, KAIST, we improve the classification performance from 65.05% to 66.69% with Gamma components. The proposed sample generation method can be applied to any machine learning problem with fewer samples.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 추계 학술발표회 논문집
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• pp.69-72
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• 2003

The paper considers partial least squares (PLS) as a new dimension reduction technique for the feature vector to overcome the small sample size problem in face recognition. Principal component analysis (PCA), a conventional dimension reduction method, selects the components with maximum variability, irrespective of the class information. So PCA does not necessarily extract features that are important for the discrimination of classes. PLS, on the other hand, constructs the components so that the correlation between the class variable and themselves is maximized. Therefore PLS components are more predictive than PCA components in classification. The experimental results on Manchester and ORL databases show that PLS is to be preferred over PCA when classification is the goal and dimension reduction is needed.

• 패션비즈니스
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• 제17권4호
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• pp.177-190
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• 2013

The purpose of this study is 1) to investigate the types of upscale fashion department stores' Facebook page contents, 2) to compare the types of Facebook page contents with the department stores, and 3) to explore the dimensions of the Facebook page components and their relations. For the study, three preeminent department stores in social media marketing were chosen: Bergdorf Goodman, Barneys New York, and Saks Fifth Avenue. Three hundred sixty five contents of these department stores' pages were investigated, which were uploaded from February 1st to March 31st of 2013. Content analysis, correspondence analysis, and categorical principal component analysis were used for the research. The result showed that there are four important types of contents in pages: product-related contents, fashion-related contents, department stores-related contents, and the contents of communicating with users. And these components of contents were related with department stores distinctively. The two dimensions of the page components were revealed: the basic components (contents, 'like', 'share', and 'comments') and the additional components (links and photos). Among contents, the introduction of products was appealed but news and events were not liked by users; the contents without a photo were not linked to additional information either.