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

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Analysis of Vehicle Demand by Fuel Types including Hydrogen Vehicles (수소차를 포함한 연료유형에 따른 자동차 수요 분석)

  • Yuhyeon Bak;Jee Young Kim;Yoon Lee
    • Environmental and Resource Economics Review
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    • v.32 no.3
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    • pp.167-190
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    • 2023
  • This study analyzes the potential demand for automobiles based on fuel type using survey data in Korea. The dependent variable of the model is the future desired fuel type, including gasoline, diesel, hybrid, electricity, and hydrogen. The main explanatory variables are the respondent demographic characteristics, key reasons for choosing vehicle fuel type and environmental awareness extracted via principal component analysis (PCA). Using a multinomial logit (MNL) model, we find that respondents who consider fuel economy and infrastructure increase the demand for a hybrid car but decrease the demand for electric and hydrogen vehicles. The denial-types increase the demand for gasoline (petrol) and diesel (light oil), and decrease the demand for electric vehicles. The anxiety-types increase the demand of hybrid vehicles, and decrease the demand for electric vehicles. In contrast, in the case of pro-types, the demand for diesel (light oil) hydrogen vehicles decreased.

Selecting marker substances of main producing area of Codonopsis lanceolata in Korea using UPLC-QTOF-MS analysis (UPLC-QTOF-MS분석를 이용한 국내산 더덕 주산지의 표지물질 선정)

  • An, Young Min;Jang, Hyun-Jae;Kim, Doo-Young;Baek, Nam-In;Oh, Sei-Ryang;Lee, Dae Young;Ryu, Hyung Won
    • Journal of Applied Biological Chemistry
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    • v.64 no.3
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    • pp.245-251
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    • 2021
  • Codonopsis lanceolata (Deoduk) was grown in East Asia, including Korea, China, Japan, and Russia, and the roots of C. lanceolata have been used as functional foods and traditional medicine to treat symptoms of cough, bronchitis, asthma, tuberculosis, and dyspepsia. The phytochemicals of C. lanceolata have been reported such as phenylpropanoids, polyacetylenes, saponins, and flavonoids that are involved in pharmacological effects such as anti-obesity, anti-inflammation, anti-tumor, anti-oxidant, and anti-microbial activities. Selecting marker substances of the main producing area by MS-based metabolomics analysis is important to ensure the beneficial effect of C. lanceolata without side-effects because differences in cultivated areas of plants were related not only to the safety of medicinal plants but also to changes in chemical composition and biological efficacy. In our present study, ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry combined with multivariate statistical analysis was applied to recognize the main producing area of C. lanceolata in South Korea. As a result of Principal Component Analysis and loading plot analysis of three groups, Inje (Kangwon-do), Hoengseong (Kangwon-do), and Muju (Jeonlabuk-do), several secondary metabolites of C. lanceolata including tangshenoside I, lancemaside A, and lancemaside G, were suggested as potential marker substances to distinguish the place of main producing area of C. lanceolata.

BEEF MEAT TRACEABILITY. CAN NIRS COULD HELP\ulcorner

  • Cozzolino, D.
    • Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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    • 2001.06a
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    • pp.1246-1246
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    • 2001
  • The quality of meat is highly variable in many properties. This variability originates from both animal production and meat processing. At the pre-slaughter stage, animal factors such as breed, sex, age contribute to this variability. Environmental factors include feeding, rearing, transport and conditions just before slaughter (Hildrum et al., 1995). Meat can be presented in a variety of forms, each offering different opportunities for adulteration and contamination. This has imposed great pressure on the food manufacturing industry to guarantee the safety of meat. Tissue and muscle speciation of flesh foods, as well as speciation of animal derived by-products fed to all classes of domestic animals, are now perhaps the most important uncertainty which the food industry must resolve to allay consumer concern. Recently, there is a demand for rapid and low cost methods of direct quality measurements in both food and food ingredients (including high performance liquid chromatography (HPLC), thin layer chromatography (TLC), enzymatic and inmunological tests (e.g. ELISA test) and physical tests) to establish their authenticity and hence guarantee the quality of products manufactured for consumers (Holland et al., 1998). The use of Near Infrared Reflectance Spectroscopy (NIRS) for the rapid, precise and non-destructive analysis of a wide range of organic materials has been comprehensively documented (Osborne et at., 1993). Most of the established methods have involved the development of NIRS calibrations for the quantitative prediction of composition in meat (Ben-Gera and Norris, 1968; Lanza, 1983; Clark and Short, 1994). This was a rational strategy to pursue during the initial stages of its application, given the type of equipment available, the state of development of the emerging discipline of chemometrics and the overwhelming commercial interest in solving such problems (Downey, 1994). One of the advantages of NIRS technology is not only to assess chemical structures through the analysis of the molecular bonds in the near infrared spectrum, but also to build an optical model characteristic of the sample which behaves like the “finger print” of the sample. This opens the possibility of using spectra to determine complex attributes of organic structures, which are related to molecular chromophores, organoleptic scores and sensory characteristics (Hildrum et al., 1994, 1995; Park et al., 1998). In addition, the application of statistical packages like principal component or discriminant analysis provides the possibility to understand the optical properties of the sample and make a classification without the chemical information. The objectives of this present work were: (1) to examine two methods of sample presentation to the instrument (intact and minced) and (2) to explore the use of principal component analysis (PCA) and Soft Independent Modelling of class Analogy (SIMCA) to classify muscles by quality attributes. Seventy-eight (n: 78) beef muscles (m. longissimus dorsi) from Hereford breed of cattle were used. The samples were scanned in a NIRS monochromator instrument (NIR Systems 6500, Silver Spring, MD, USA) in reflectance mode (log 1/R). Both intact and minced presentation to the instrument were explored. Qualitative analysis of optical information through PCA and SIMCA analysis showed differences in muscles resulting from two different feeding systems.

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The analysis of physical features and affective words on facial types of Korean females in twenties (얼굴의 물리적 특징 분석 및 얼굴 관련 감성 어휘 분석 - 20대 한국인 여성 얼굴을 대상으로 -)

  • 박수진;한재현;정찬섭
    • Korean Journal of Cognitive Science
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    • v.13 no.3
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    • pp.1-10
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    • 2002
  • This study was performed to analyze the physical attributes of the faces and affective words on the fares. For analyzing physical attributes inside of a face, 36 facial features were selected and almost of them were the lengths or distance values. For analyzing facial contour 14 points were selected and the lengths from nose-end to them were measured. The values of these features except ratio values normalized by facial vortical length or facial horizontal length because the face size of each person is different. The principal component analysis (PCA) was performed and four major factors were extracted: 'facial contour' component, 'vortical length of eye' component, 'facial width' component, 'eyebrow region' component. We supposed the five-dimensional imaginary space of faces using factor scores of PCA, and selected representative faces evenly in this space. On the other hand, the affective words on faces were collected from magazines and through surveys. The factor analysis and multidimensional scaling method were performed and two orthogonal dimensions for the affections on faces were suggested: babyish-mature and sharp-soft.

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Hydrogeochemical Characterization of Groundwater in Jeju Island using Principal Component Analysis and Geostatistics (주성분분석과 지구통계법을 이용한 제주도 지하수의 수리지화학 특성 연구)

  • Ko Kyung-Seok;Kim Yongie;Koh Dong-Chan;Lee Kwang-Sik;Lee Seung-Gu;Kang Cheol-Hee;Seong Hyun-Jeong;Park Won-Bae
    • Economic and Environmental Geology
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    • v.38 no.4 s.173
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    • pp.435-450
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    • 2005
  • The purpose of the study is to analyze the hydrogeochemical characteristics by multivariate statistical method, to interpret the hydrogeochemical processes for the new variables calculated from principal components analysis (PCA), and to infer the groundwater flow and circulation mechanism by applying the geostatistical methods for each element and principal component. Chloride and nitrate are the most influencing components for groundwater quality, and the contents of $NO_3$ increased by the input of agricultural activities show the largest variation. The results of PCA, a multivariate statistical method, show that the first three principal components explain $73.9\%$ of the total variance. PC1 indicates the increase of dissolved ions, PC2 is related with the dissolution of carbonate minerals and nitrate contamination, and PC3 shows the effect of cation exchange process and silicate mineral dissolution. From the results of experimental semivariogram, the components of groundwater are divided into two groups: one group includes electrical conductivity (EC), Cl, Na, and $NO_3$, and the other includes $HCO_3,\;SiO_2,$ Ca, and Sr. The results for spatial distribution of groundwater components showed that EC, Cl, and Na increased with approaching the coastal line and nitrate has close relationship with the presence of agricultural land. These components are also correlated with the topographic features reflecting the groundwater recharge effect. The kriging analysis by using principal components shows that PC 1 has the different spatial distribution of Cl, Na, and EC, possibly due to the influence of pH, Ca, Sr, and $HCO_3$ for PC1. It was considered that the linear anomaly zone of PC2 in western area was caused by the dissolution of carbonate mineral. Consequently, the application of multivariate and geostatistical methods for groundwater in the study area is very useful for determining the quantitative analysis of water quality data and the characteristics of spatial distribution.

Component Based Face Detection for PC Camera (PC카메라 환경을 위한 컴포넌트 기반 얼굴 검출)

  • Cho, Chi-Young;Kim, Soo-Hwan
    • 한국HCI학회:학술대회논문집
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    • 2006.02a
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    • pp.988-992
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    • 2006
  • 본 논문은 PC카메라 환경에서 명암왜곡에 강인한 얼굴검출을 위한 컴포넌트 기반 얼굴검출 기법을 제시한다. 영상 내의 얼굴검출을 위해 에지(edge) 분석, 색상 분석, 형판정합(template matching), 신경망(Neural Network), PCA(Principal Component Analysis), LDA(Linear Discriminant Analysis) 등의 기법들이 사용되고 있고, 영상의 왜곡을 보정하기 위해 히스토그램 분석(평활화, 명세화), gamma correction, log transform 등의 영상 보정 방법이 사용되고 있다. 그러나 기존의 얼굴검출 방법과 영상보정 방법은 검출대상 객체의 부분적인 잡음 및 조명의 왜곡에 대처하기가 어려운 단점이 있다. 특히 PC카메라 환경에서 획득된 이미지와 같이 전면과 후면, 상하좌우에서 비추어지는 조명에 의해 검출 대상 객체의 일부분이 왜곡되는 상황이 발생될 경우 기존의 방법으로는 높은 얼굴 검출 성능을 기대할 수 없는 상황이 발생된다. 본 논문에서는 기울어진 얼굴 및 부분적으로 명암 왜곡된 얼굴을 효율적으로 검출할 수 있도록 얼굴의 좌우 대칭성을 고려한 가로방향의 대칭평균화로 얼굴검출을 위한 모델을 생성하여 얼굴검출에 사용한다. 이 방법은 부분적으로 명암왜곡된 얼굴이미지를 기존의 영상 보정기법을 적용한 것 보다 잘 표현하며, 얼굴이 아닌 후보는 비얼굴 이미지의 형상을 가지게 하는 특성이 있다.

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Face Detection Using Support Vector Domain Description in Color Images (컬러 영상에서 Support Vector Domain Description을 이용한 얼굴 검출)

  • Seo Jin;Ko Hanseok
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.42 no.1
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    • pp.25-31
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    • 2005
  • In this paper, we present a face detection system using the Support Vector Domain Description (SVDD) in color images. Conventional face detection algorithms require a training procedure using both face and non-face images. In SVDD however we employ only face images for training. We can detect faces in color images from the radius and center pairs of SVDD. We also use Entropic Threshold for extracting the facial feature and sliding window for improved performance while saving processing time. The experimental results indicate the effectiveness and efficiency of the proposed algorithm compared to conventional PCA (Principal Component Analysis)-based methods.

A Study on Real Time Pitch Alteration of Speech Signal (음성신호의 실시간 피치변경에 관한 연구)

  • 김종국;박형빈;배명진
    • The Journal of the Acoustical Society of Korea
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    • v.23 no.1
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    • pp.82-89
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    • 2004
  • This paper describes how to reduce the effect of an occupation threshold by that the transform of mixture components of HMM parameters is controlled in hierarchical tree structure to prevent from over-adaptation. To reduce correlations between data elements and to remove elements with less variance, we employ PCA (principal component analysis) and ICA (independent component analysis) that would give as good a representation as possible, and decline the effect of over-adaptation. When we set lower occupation threshold and increase the number of transformation function, ordinary WLLR adaptation algorithm represents lower recognition rate than SI models, whereas the proposed MLLR adaptation algorithm represents the improvement of over 2% for the word recognition rate as compared to performance of SI models.

Appearance-based Object Recognition Using Higher Order Local Auto Correlation Feature Information (고차 국소 자동 상관 특징 정보를 이용한 외관 기반 객체 인식)

  • Kang, Myung-A
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.15 no.7
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    • pp.1439-1446
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    • 2011
  • This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the higher correlation feature information and Principle Component Analysis. Since the suggested method doesn't require a lot of computation than the method using existing geometric information or stereo image, the fact that it is very suitable for building the real-time system has been proved through the experiment. In addition, since the existing point to point method which is a simple distance calculation has many errors, in this paper to improve recognition rate the recognition error could be reduced by using several successive input images as a unit of recognition with K-Nearest Neighbor which is the improved Class to Class method.

Application of Principle Component Analysis and Measurement of Ultra wideband PD signal for Identification of PD sources in Air (기중부분방전원 식별을 위한 광대역 부분방전신호의 측정 및 주성분분석기법의 적용)

  • Lee, K.W.;Kim, M.Y.;Park, D.W.;Shim, J.B.;Chang, S.H.
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 2006.06a
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    • pp.505-506
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    • 2006
  • PD(partial discharge) occurred from variable PD sources in air may be the cause of breakdown in high voltage equipment which affect huge outage in power system. Identification and localization of PD sources is very important for engineer to cope with huge accident beforhand. PD phenomena can be detected by acoustic emission sensor or electromagnetic sensor like antenna. This paper has investigated the identification method using PCA(principal component analysis) for the PD signals from variable PD sources, for which the electric field distribution and PD inception voltages were simulated by using commercial FEM program. PD signals was detected by ultra wideband antenna. Their own features were extracted as the frequency coefficients transformed with FFT(fast fourier transform) and used to obtain independent pincipal components of each PD signals.

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