• Journal of Korea Multimedia Society
• /
• v.9 no.11
• /
• pp.1435-1444
• /
• 2006

We present an effective and real-time face detection method based on Principal Component Analysis(PCA) and Support Vector Machines(SVMs). We extract simple Haar-like features from training images that consist of face and non-face images, reinterpret the features with PCA, and select useful ones from the large number of extracted features. With the selected features, we construct a face detector using an SVM appropriate for binary classification. The face detector is not affected by the size of a training data set in a significant way, so that it showed 90.1 % detection rates with a small quantity of training data. it can process 8 frames per second for $320{\times}240$ pixel images. This is an acceptable processing time for a real-time system.

• Smart Structures and Systems
• /
• v.12 no.3_4
• /
• pp.327-344
• /
• 2013

Beam string structure (BSS) is introduced as a new type of hybrid prestressed string structures. The composition and mechanics features of BSS are discussed. The main principles of wavelet packet transform (WPT), principal component analysis (PCA) and support vector machine (SVM) have been reviewed. WPT is applied to the structural response signals, and feature vectors are obtained by feature extraction and PCA. The feature vectors are used for training and classification as the inputs of the support vector machine. The method is used to a single one-way arched beam string structure for damage detection. The cable prestress loss and web members damage experiment for a beam string structure is carried through. Different prestressing forces are applied on the cable to simulate cable prestress loss, the prestressing forces are calculated by the frequencies which are solved by Fourier transform or wavelet transform under impulse excitation. Test results verify this method is accurate and convenient. The damage cases of web members on the beam are tested to validate the efficiency of the method presented in this study. Wavelet packet decomposition is applied to the structural response signals under ambient vibration, feature vectors are obtained by feature extraction method. The feature vectors are used for training and classification as the inputs of the support vector machine. The structural damage position and degree can be identified and classified, and the test result is highly accurate especially combined with principle component analysis.

• Journal of information and communication convergence engineering
• /
• v.15 no.3
• /
• pp.165-169
• /
• 2017

Altmetrics is an emergent research area whereby social media is applied as a source of metrics to evaluate scientific impact. Recently, the interest in altmetrics has been growing. Traditional scientific impact evaluation indictors are based on the number of publications, citation counts and peer reviews of a researcher. As research publications were increasingly placed online, usage metrics as well as webometrics appeared. This paper explores the potential benefits of altmetrics and the deep relationship between each metrics. Firstly, we found a weak-to-medium correlation among the 11 altmetrics and visualized such correlation. Secondly, we conducted principal component analysis and exploratory factor analysis on altmetrics of social media, divided the 11 altmetrics into four feature sets, confirming the dispersion and relative concentration of altmetrics groups and developed the altmetrics evaluation model. We can use this model to evaluate the scientific impact of articles on social media.

• Journal of KSNVE
• /
• v.10 no.4
• /
• pp.686-691
• /
• 2000

In this paper a computer aided analysis method is proposed for durability assessment in the early design stages using dynamic analysis, stress analysis and fatigue life prediction method. From dynamic analysis of a vehicle suspension system, dynamic load time histories of a suspension component are calculated. From the dynamic load time histories and the stress of the suspension component, a dynamic stress time history at the critical location is produced using the superposition principle. Using linear damage law and cycle counting method, fatigue life cycle is calculated. The predicted fatigue life cycle is verified by experimental durability tests.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2000.06a
• /
• pp.1883-1888
• /
• 2000

In this paper a computer aided analysis method is proposed for durability assessment in the early design stages using dynamic analysis, stress analysis and fatigue life prediction method. From dynamic analysis of a vehicle suspension system, dynamic load time histories of a suspension component are calculated. From the dynamic load time histories and the stress of the suspension component, a dynamic stress time history at the critical location is produced using the superposition principle. Using linear damage law and cycle counting method, fatigue life cycle is calculated. The predicted fatigue life cycle is verified by experimental durability tests.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 1996.10a
• /
• pp.118-123
• /
• 1996

The purpose of this study is to develop acoustic substructure synthesis method that can be applied to acoustic modal analysis of complex acoustic systems. Acoustic modal analysis method to be introduced here is a method that analyze acoustic natural mode shape of the complex acoustic system by the principle of CMS(component mode synthesis method). This paper describes the acoustic modal analysis of the acoustic finite element model of simple expansion pipe by acoustic substructure synthesis method. The results of acoustic modal analysis analyzed by Acoustic substructure synthesis method and the results, by FEM(finite element method) shows good agreement.

• Earthquakes and Structures
• /
• v.23 no.2
• /
• pp.169-181
• /
• 2022

To research the dynamic response of the high-rise structure under the rocking ground motion, which we believed that the effect cannot be ignored, especially accompanied by vertical ground motion. Theoretical analysis and shaking table seismic simulation tests were used to study the response of a high-rise structure to excitation of a H-V-R ground motion that included horizontal, vertical, and rocking components. The use of a wavelet analysis filtering technique to extract the rocking component from data for the primary horizontal component in the first part, based on the principle of horizontal pendulum seismogram and the use of a wavelet analysis filtering technique. The dynamic equation of motion for a high-rise structure under H-V-R ground motion was developed in the second part, with extra P-△ effect due to ground rocking displacement was included in the external load excitation terms of the equation of motion, and the influence of the vertical component on the high-rise structure P-△ effect was also included. Shaking table tests were performed for H-V-R ground motion using a scale model of a high-rise TV tower structure in the third part, while the results of the shaking table tests and theoretical calculation were compared in the last part, and the following conclusions were made. The results of the shaking table test were consistent with the theoretical calculation results, which verified the accuracy of the theoretical analysis. The rocking component of ground motion significantly increased the displacement of the structure and caused an asymmetric displacement of the structure. Thus, the seismic design of an engineering structure should consider the additional P-△ effect due to the rocking component. Moreover, introducing the vertical component caused the geometric stiffness of the structure to change with time, and the influence of the rocking component on the structure was amplified due to this effect.

• Journal of Korean Society of Rural Planning
• /
• v.21 no.2
• /
• pp.103-114
• /
• 2015

The comprehensive rural village development projects (CRVDP) have been carried out as the core one of the rural development schemes in Korea since 2004. CRVDP included the various rural experience programs to increase rural income and in order to promote rural community development in the project area. This study analyzed the operating management conditions, types and characteristics of the rural experience programs targeting the 168 CRVDPs have been completed so that the recommendations and lessons which were found the usefulness, challenges and improvements to the CRVDP can be provided to be better the same kinds of rural development projects. We identified the relationships between performances such as increasing village income and utilization of rural amenity resources to the CRVDP and operational management types of the rural experience programs as well. Employing principle component analysis and cluster analysis technique, this study found 5 clusters of rural experience programs among 168 CRVDPs. The results of analysis of variance indicated that there were significant the mean differences between clusters such as the utilization of rural amenity resources(0.01), income of rural experience programs(0.1). According to the result of the Chi-squire test, there was very significant differences between internet homepage operation and clusters(0.01). Finally, the analysis of covariance about the income of rural experience programs showed that there were significant the mean differences between clusters(0.05).

• Communications for Statistical Applications and Methods
• /
• v.24 no.2
• /
• pp.143-154
• /
• 2017

A variable selection method based on probabilistic principal component analysis (PCA) using penalized likelihood method is proposed. The proposed method is a two-step variable reduction method. The first step is based on the probabilistic principal component idea to identify principle components. The penalty function is used to identify important variables in each component. We then build a model on the original data space instead of building on the rotated data space through latent variables (principal components) because the proposed method achieves the goal of dimension reduction through identifying important observed variables. Consequently, the proposed method is of more practical use. The proposed estimators perform as the oracle procedure and are root-n consistent with a proper choice of regularization parameters. The proposed method can be successfully applied to high-dimensional PCA problems with a relatively large portion of irrelevant variables included in the data set. It is straightforward to extend our likelihood method in handling problems with missing observations using EM algorithms. Further, it could be effectively applied in cases where some data vectors exhibit one or more missing values at random.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.283-284
• /
• 2006

In this paper, we propose a novel segmentation and classification method using texture features for the document image. First, we extract the local entropy and then segment the document image to separate the background and the foreground using the Otsu's method. Finally, we classify the segmented regions into each component using PCA(principle component analysis) algorithm based on the texture features that are extracted from the co-occurrence matrix for the entropy image. The entropy-based segmentation is robust to not only noise and the change of light, but also skew and rotation. Texture features are not restricted from any form of the document image and have a superior discrimination for each component. In addition, PCA algorithm used for the classifier can classify the components more robustly than neural network.