• Title/Summary/Keyword: PCA(Principal Component Analysis

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Electromechanical impedance-based long-term SHM for jacket-type tidal current power plant structure

  • Min, Jiyoung;Yi, Jin-Hak;Yun, Chung-Bang
    • Smart Structures and Systems
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    • v.15 no.2
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    • pp.283-297
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    • 2015
  • Jacket-type offshore structures are always exposed to severe environmental conditions such as salt, high speed of current, wave, and wind compared with other onshore structures. In spite of the importance of maintaining the structural integrity for an offshore structure, there are few cases to apply a structural health monitoring (SHM) system in practice. The impedance-based SHM is a kind of local SHM techniques and to date, numerous techniques and algorithms have been proposed for local SHM of real-scale structures. However, it still requires a significant challenge for practical applications to compensate unknown environmental effects and to extract only damage features from impedance signals. In this study, the impedance-based SHM was carried out on a 1/20-scaled model of an Uldolmok current power plant structure in Korea under changes in temperature and transverse loadings. Principal component analysis (PCA)-based approach was applied with a conventional damage index to eliminate environmental changes by removing principal components sensitive to them. Experimental results showed that the proposed approach is an effective tool for long-term SHM under significant environmental changes.

Morphological Analysis on the Kalopanax pictus (Araliaceae) of Korean Populations (한국 음나무(두릅과) 집단의 형태적 분석)

  • Jung, Sang-Duk;Hong, Jung-Hee;Bang, Kyung-Hwan;Huh, Man-Kyu
    • Journal of Life Science
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    • v.14 no.3
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    • pp.400-405
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    • 2004
  • Morphological characteristics of Kalopanox pictus Nakai were studied to examine population differentiation of this species. Based on a phenogram of using 23 morphological characteristics, differentiation of regions were distinct. Collections of 138 specimens from nine populations served as operational taxonomic unit (OTU's) were examined for phenotypic similarity and morphological variation using clustering (Ward's minimum variance method) and principal component analysis (PCA). The first three principal components were responsible for 77.0% of the total variance. Principal component 1 explained 52% of the total variance and was contributed to by the number of palmately parted, the number of pinnately lobed, and width between two lateral lobe apex.

Qualitative and Quantitative Analysis of Space Minerals using Laser-Induced Breakdown Spectroscopy and Raman Spectroscopy (레이저 유도 분해 분광법과 라만 분광법을 이용한 우주 광물의 정성 및 정량 분석 기법)

  • Kim, Dongyoung;Yoh, Jack J.
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.46 no.6
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    • pp.519-526
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    • 2018
  • In order to analyze space resources, it had to be brought to earth. However, using laser-induced breakdown spectroscopy(LIBS) and Raman spectroscopy, it is possible to analyze qualitative and quantitative analysis of space minerals in real time. LIBS is a spectroscopic method in which a high energy laser is concentrated on a material surface to generate a plasma, and the emitted light is acquired through a spectroscope to analyze the atomic composition. Raman spectroscopy is a spectroscopic method that analyzes the molecular structure by measuring scattered light. These two spectroscopic methods are complementary spectroscopic methods for analyzing the atoms and molecules of unknown minerals and have an advantage as space payloads. In this study, data were analyzed qualitatively by using principal component analysis(PCA). In addition, a mixture of two minerals was prepared and a quantitative analysis was performed to predict the concentration of the material.

Assessment of Water Quality Characteristics in the Middle and Upper Watershed of the Geumho River Using Multivariate Statistical Analysis and Watershed Environmental Model (다변량통계분석 및 유역환경모델을 이용한 금호강 중·상류 유역의 수질특성평가)

  • Seo, Youngmin;Kwon, Kooho;Choi, Yun Young;Lee, Byung Joon
    • Journal of Korean Society on Water Environment
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    • v.37 no.6
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    • pp.520-530
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    • 2021
  • Multivariate statistical analysis and an environmental hydrological model were applied for investigating the causes of water pollution and providing best management practices for water quality improvement in urban and agricultural watersheds. Principal component analysis (PCA) and cluster analysis (CA) for water quality time series data show that chemical oxygen demand (COD), total organic carbon (TOC), suspended solids (SS) and total phosphorus (T-P) are classified as non-point source pollutants that are highly correlated with river discharge. Total nitrogen (T-N), which has no correlation with river discharge and inverse relationship with water temperature, behaves like a point source with slow and consistent release. Biochemical oxygen demand (BOD) shows intermediate characteristics between point and non-point source pollutants. The results of the PCA and CA for the spatial water quality data indicate that the cluster 1 of the watersheds was characterized as upstream watersheds with good water quality and high proportion of forest. The cluster 3 shows however indicates the most polluted watersheds with substantial discharge of BOD and nutrients from urban sewage, agricultural and industrial activities. The cluster 2 shows intermediate characteristics between the clusters 1 and 3. The results of hydrological simulation program-Fortran (HSPF) model simulation indicated that the seasonal patterns of BOD, T-N and T-P are affected substantially by agricultural and livestock farming activities, untreated wastewater, and environmental flow. The spatial analysis on the model results indicates that the highly-populated watersheds are the prior contributors to the water quality degradation of the river.

Hybrid Pattern Recognition Using a Combination of Different Features

  • Choi, Sang-Il
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.11
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    • pp.9-16
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    • 2015
  • We propose a hybrid pattern recognition method that effectively combines two different features for improving data classification. We first extract the PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) features, both of which are widely used in pattern recognition, to construct a set of basic features, and then evaluate the separability of each basic feature. According to the results of evaluation, we select only the basic features that contain a large amount of discriminative information for construction of the combined features. The experimental results for the various data sets in the UCI machine learning repository show that using the proposed combined features give better recognition rates than when solely using the PCA or LDA features.

Development of the Hippocampal Learning Algorithm Using Associate Memory and Modulator of Neural Weight (연상기억과 뉴런 연결강도 모듈레이터를 이용한 해마 학습 알고리즘 개발)

  • Oh Sun-Moon;Kang Dae-Seong
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.43 no.4 s.310
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    • pp.37-45
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    • 2006
  • In this paper, we propose the development of MHLA(Modulatory Hippocampus Learning Algorithm) which remodel a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 3 steps system(DG, CA3, CAl) and improve speed of learning by addition of modulator to long-term memory learning. In hippocampal system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labelled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CAI region, convergence of connection weight which is used long-term memory is learned fast by neural networks which is applied modulator. To measure performance of MHLA, PCA(Principal Component Analysis) is applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by MHLA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

Tree-Dependent Components of Gene Expression Data for Clustering (유전자발현데이터의 군집분석을 위한 나무 의존 성분 분석)

  • Kim Jong-Kyoung;Choi Seung-Jin
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.06a
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    • pp.4-6
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    • 2006
  • Tree-dependent component analysis (TCA) is a generalization of independent component analysis (ICA), the goal of which is to model the multivariate data by a linear transformation of latent variables, while latent variables fit by a tree-structured graphical model. In contrast to ICA, TCA allows dependent structure of latent variables and also consider non-spanning trees (forests). In this paper, we present a TCA-based method of clustering gene expression data. Empirical study with yeast cell cycle-related data, yeast metaboiic shift data, and yeast sporulation data, shows that TCA is more suitable for gene clustering, compared to principal component analysis (PCA) as well as ICA.

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Chemometrics Approach For Species Identification of Pinus densiflora Sieb. et Zucc. and Pinus densiflora for. erecta Uyeki - Species Classification Using Near-Infrared Spectroscopy in combination with Multivariate Analysis - (소나무와 금강송의 수종식별을 위한 화학계량학적 접근 - 근적외선 분광법과 다변량분석을 이용한 수종 분류 -)

  • Hwang, Sung-Wook;Lee, Won-Hee;Horikawa, Yoshiki;Sugiyama, Junji
    • Journal of the Korean Wood Science and Technology
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    • v.43 no.6
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    • pp.701-713
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    • 2015
  • A model was designed to identify wood species between Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. using the near-infrared (NIR) spectroscopy in combination with principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). In the PCA using all of the spectra, Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. could not be classified. In the PCA using the spectrum that has been measured in sapwood, however, Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. could be identified. In particular, it was clearly classified by sapwood in radial section. And more, these two species could be perfectly identified using PLS-DA prediction model. The best performance in species identification was obtained when the second derivative spectra was used; the prediction accuracy was 100%. For prediction model, the $R_p{^2}$ value was 0.86 and the RMSEP was 0.38 in second derivative spectra. It was verified that the model designed by NIR spectroscopy with PLS-DA is suitable for species identification between Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc.

Seasonal Variation and Statistical Analysis of Particulate Pollutants in Urban Air (도시대기립자상물질중 오염성분의 계절적 변동 및 통계적 해석)

  • 이승일
    • Journal of environmental and Sanitary engineering
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    • v.9 no.2
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    • pp.8-23
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    • 1994
  • During the period from Mar., 1991 to Feb., 1992 66 tSP samples were collected by Hi volume air sampler at 1 sampling site in Seoul and the amount of concentration of 21 components(SO$_{4}$$^{2-}$, NO$_{3}$$^{-}$, NH$_{4}$$^{+}$, Cl$^{-}$, Al, Ba, Ca, Cd, Cr, Cu, Fe, It Mg, Mn, Na, Ni, Pt Si, Ti, Zn, Zr ) were measured. And monthly and seasonal variation were surveyed and the principal component analysis( PCA ) were carried out with respect to these amount of pollutants, minimum of visibility and radiation on a horizontal surface. The total amount of soluble ion in water was high in order o(SO$_{4}$$^{2-}$> NO$_{3}$$^{-}$> N%'>Cl$^{-}$ and metal ion was high in order of Na> Ca>Si> Fe> Al> K> Mg> Zn> Pb> Cu>Ti> Mn > Ba> Cr> Zr> Ni> Cd. There was Seasonal variation in concentration for SO$_{4}$$^{2-}$, NH$_{4}$$^{+}$, Cl$^{-}$, Na, Al, Ca, Bt Mg, Fe and Si. It was assumed that the components of the highest concentration on April were depend on yellow sand and the frequency of wind velocity and direction. As the results of PCA, the amount of pollution components was able to characterized with two principal components(Z$_{1}$, Z$_{2}$ ). The first principal components Z$_{1}$ was considered to be a factor indicating the pollutants originated from natural generation and The second principal components Z$_{2}$ was considered to be a factor indicating the pollutants originated from human work. The monthly concentration of pollutants in ISP, minimum of visibility and radiation on a horizontal surface was possible to evaluate by the use of these two principal components Z$_{1}$ and Z$_{2}$ .

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The Provenance and Characteristic Classification of the White Porcelain in the Gyeongsangnam-do by Neutron Activation Analysis (중성자방사화분석을 활용한 경상남도 백자의 산지 및 특성 분류)

  • Kim, Na-Young;Kim, Gyu-Ho
    • Journal of Conservation Science
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    • v.21
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    • pp.89-100
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    • 2007
  • This study analyze concentration of minor and trace elements on 47 white porcelains excavated from Dudong-ri, Baekryeon-ri, Sachon-ri kilns in Gyeonsangnam-do by NAA(neutron activation analysis) and try to classify the provenance and characteristics according to the analytical result. Each kilns are divided into the group by PCA(principal component analysis) and LDA(linear discrimination analysis) using 17 elements; Ba Ce, Co, Cr, Cs, Dy, Eu, Hf, La Lu, Rb, Sc, Sm, Ta, Th, V, Yb. The contribution elements are Dy, Sm, La, Ce, Lu, Sc. And soft and hard white porcelains are similar with the chemical composition of the use materials therefore the difference of the chemical composition not confirmed a cause. The analytical results of the fine(I) and poor(II) quality white porcelains presume the difference of the povenance of clay materials or the poduction process such as difference purify and additive materials.

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