• Title/Summary/Keyword: Principal Components Factor

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County-Based Vulnerability Evaluation to Agricultural Drought Using Principal Component Analysis - The case of Gyeonggi-do - (주성분 분석법을 이용한 시군단위별 농업가뭄에 대한 취약성 분석에 관한 연구 - 경기도를 중심으로 -)

  • Jang, Min-Won
    • Journal of Korean Society of Rural Planning
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    • v.12 no.1 s.30
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    • pp.37-48
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    • 2006
  • The objectives of this study were to develop an evaluation method of regional vulnerability to agricultural drought and to classify the vulnerability patterns. In order to test the method, 24 city or county areas of Gyeonggi-do were chose. First, statistic data and digital maps referred for agricultural drought were defined, and the input data of 31 items were set up from 5 categories: land use factor, water resource factor, climate factor, topographic and soil factor, and agricultural production foundation factor. Second, for simplification of the factors, principal component analysis was carried out, and eventually 4 principal components which explain about 80.8% of total variance were extracted. Each of the principal components was explained into the vulnerability components of scale factor, geographical factor, weather factor and agricultural production foundation factor. Next, DVIP (Drought Vulnerability Index for Paddy), was calculated using factor scores from principal components. Last, by means of statistical cluster analysis on the DVIP, the study area was classified as 5 patterns from A to E. The cluster A corresponds to the area where the agricultural industry is insignificant and the agricultural foundation is little equipped, and the cluster B includes typical agricultural areas where the cultivation areas are large but irrigation facilities are still insufficient. As for the cluster C, the corresponding areas are vulnerable to the climate change, and the D cluster applies to the area with extensive forests and high elevation farmlands. The last cluster I indicates the areas where the farmlands are small but most of them are irrigated as much.

The Study of Korean Manufacturing Industry Wage : Principal Components Regression Analysis (한국 제조업의 임금결정에 대한 연구 : 외환위기 전·후를 중심으로)

  • Oh, Yu-Jin;Park, Sung-Joon;Kim, Yu-Seop
    • Journal of Labour Economics
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    • v.28 no.1
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    • pp.61-82
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    • 2005
  • We investigate wage differentials in Korea in the manufacturing industry, as well as factors affecting structural change in wage determination for the pre- and post-financial crisis regimes. We use the 1995 and 1999 data from the Survey Report on the Wage Structure (SRWS) from the Ministry of Labor. Principal components regression analysis is used to tackle multicollinearity. We employ factor analysis to reduce a set of variables to a smaller number, which contain observed and latent variables. Our empirical investigation provide evidences for changes in wages structure between 1995 and 1999. In 1995, the job quality factor is the most critical in the determination of wages, while in 1999, the industry attributes factor impacts greatly on the wages.

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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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Sequential Registration of the Face Recognition candidate using SKL Algorithm (SKL 알고리즘을 이용한 얼굴인식 후보의 점진적 등록)

  • Han, Hag-Yong;Lee, Sung-Mok;Kwak, Boo-Dong;Choi, Won-Tae;Kang, Bong-Soon
    • Journal of the Institute of Convergence Signal Processing
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    • v.11 no.4
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    • pp.320-325
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    • 2010
  • This paper is about the method and procedure to register the candidate sequentially in the face recognition system using the PCA(Principal Components Analysis). We use the method to update the principal components sequentially with the SKL algorithm which is improved R-SVD algorithm. This algorithm enable us to solve the re-training problem of the increase the candidates number sequentially in the face recognition using the PCA. Also this algorithm can use in robust tracking system with the bright change based to the principal components. This paper proposes the procedure in the face recognition system which sequentially updates the principal components using the SKL algorithm. Then we compared the face recognition performance with the batch procedure for calculating the principal components using the standard KL algorithm and confirms the effects of the forgetting factor in the SKL algorithm experimentally.

International Inflation Synchronization and Implications

  • CHON, SORA
    • KDI Journal of Economic Policy
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    • v.42 no.2
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    • pp.57-84
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    • 2020
  • This study analyzes global inflation synchronization and derives policy implications for the Korean economy. Unlike previous studies that assume a single global inflation factor, this study investigates if inflation in Korea can be explained further by other global inflation factors. Our principal component analysis provides three principal components for global inflation that are linked to the Korea inflation rate - the first component is closely related to OECD inflation, and the second and third components reflect China's inflation. This study empirically demonstrates via in-sample fitting and out-of-sample forecasting that the three principal components of global inflation play a significant role in explaining and predicting Korean inflation in the short-term, while their role is limited in the mid-term. Domestic macroeconomic variables are found to be more important for the mid-term movements of the Korean inflation rate. The empirical results here suggest that the Bank of Korea should focus more on domestic economic conditions than on global inflation when implementing monetary policy because global factors are likely to be already reflected in domestic macro-variables in the mid-term.

A Study on the Classification of Islands by PCA ( I ) (PCA에 의한 도서분류에 관한 연구( I ))

  • 이강우
    • The Journal of Fisheries Business Administration
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    • v.14 no.2
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    • pp.1-14
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    • 1983
  • This paper considers a classification of the 88 islands located at Kyong-nam area in Korea, using by examples of 12 components of the islands. By means of principal component analysis 2 principle components were extracted, which explained a total of 73.7% of the variance. Using an eigen variable criterion (λ>1), no further principle components were discussed. Principal component 1 and 2 explained 63.4% and 10.3% of the total variance respectively, The representation of the unrelated factor scores along the first and second principal axes produced a new information with respect to the classification of the islands. Based upon the representation, 88 islands were classified into 6 groups i. e. A, B, C, D, E, and F according to similarity of the components among them in this paper. The "Group F" belongs to a miscellaneous assortment that does not fit into the logical category. category.

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Characterization of Methanol-Water and Acetonitrile-Water Mixtures Using Iterative Target Transform Factor Analysis on Near Infrared Absorption Spectra (근적외선흡광스픽트럼에 대한 반복목표변환인자분석에 의한 메탄올-물 혼합액 및 아세토니트릴 -물 혼합액의 특성 확인)

  • 박영주;조정환
    • YAKHAK HOEJI
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    • v.48 no.1
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    • pp.6-12
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    • 2004
  • Near-infrared spectra of methanol-water mixtures and acetonitrile-water mixtures were acquired to find interactions between solvents widely used for reverse-phase liquid chromatography. Mixtures were prepared to give a series of increasing mole fractions of methanol or acetonitrile in water. Data matrices of acquired spectra were analyzed to determine the proper number of principal components of each mixture system using Malinowski's factor indicator function. Initial guess of score matrix and loading matrix were calculated by nonlinear iterative partial least squares (NIPALS) algorithm for faster computation. Iterative target transform factor analysis (ITTFA) was applied to convert the initial estimation of score matrix to true concentration profile and loading matrix to pure spectra of pure components of the mixtures. In case of methanol-water the number of principal components was found to be 4 and those initial guess of factors were converted to the pure spectra of water methanol and two kinds of complexes. In case of acetonitrile-water the number of pure components of the mixtures was found to be 3 and the pure spectrum of acetonitrile-water complex was found. The nonlinear characteristics of concentration profiles of complexes in the solvent mixtures may give a good criteria in understanding their elution characteristics in reverse-phase liquid chromatogrsphy.

Development of a Rapeseed Reaping Equipment Attachable to a Conventional Combine (Ill) - Analysis of Principal Factor for Loss Reduction of Rapeseed Mechanical Harvesting - (보통형 콤바인 부착용 유채 예취장치 개발 (III) - 유채 기계 수확 손실 절감을 위한 요인 구명 -)

  • Lee, C.K.;Choi, Y.;Jun, H.J.;Lee, S.K.;Moon, S.D.;Kim, S.S.
    • Journal of Biosystems Engineering
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    • v.34 no.2
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    • pp.114-119
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    • 2009
  • Field test was conducted to investigate primary factors reducing rapeseed harvesting using a reciprocating cutter-bar of combine. The results showed that the correlation between crop moisture content and yield loss had a U-type, which indicated that the yield reduction increased at too high and too low crop moisture contents. The proper ranges of crop moisture contents were 27${\sim}$35%, 21${\sim}$56%, and 62${\sim}$73% in case of grain, pod and stem, respectively. Crop moisture content was negatively correlated with header loss, but positively correlated with threshing loss. In contrary, stem moisture content showed positive correlations with total loss, threshing loss and separation loss. Working speed was positively correlated with header loss. Total flow rate, pod flow rate and stem flow rate were highly correlated with threshing loss and separation loss. However, grain flow rate did not show any correlation with total loss. According to the principal component analysis, two principal components were derived as components with eigenvalues greater than 1.0. The contribution rates of the first and the second components were 52.7% and 38.9%, which accounted for 91.6% of total variance. As a contributive factor influencing total loss of rapeseed mechanical harvesting, a crop moisture content factor was greater than a crop flow rate factor. The stepwise multiple regression analysis for total loss was conducted using crop moisture content factor, crop flow rate factor and coefficient. However, the model did not show any correlation among independent and dependent factors ($R^2$=0.060).

PCA-SVM Based Vehicle Color Recognition (PCA-SVM 기법을 이용한 차량의 색상 인식)

  • Park, Sun-Mi;Kim, Ku-Jin
    • The KIPS Transactions:PartB
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    • v.15B no.4
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    • pp.285-292
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    • 2008
  • Color histograms have been used as feature vectors to characterize the color features of given images, but they have a limitation in efficiency by generating high-dimensional feature vectors. In this paper, we present a method to reduce the dimension of the feature vectors by applying PCA (principal components analysis) to the color histogram of a given vehicle image. With SVM (support vector machine) method, the dimension-reduced feature vectors are used to recognize the colors of vehicles. After reducing the dimension of the feature vector by a factor of 32, the successful recognition rate is reduced only 1.42% compared to the case when we use original feature vectors. Moreover, the computation time for the color recognition is reduced by a factor of 31, so we could recognize the colors efficiently.

Comparison of Stability Evaluation Methods using ASD and LRFD Codes for Girders and Towers of Steel Cable-Stayed Bridges (사장교 거더와 주탑의 안정성 검토를 위한 ASD와 LRFD 설계법 비교)

  • Choi Dong-Ho;Yoo Hoon
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2006.04a
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    • pp.1001-1008
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    • 2006
  • The main objective of this paper is to compare economical effectiveness of typical methods for checking stability in principal components of steel cable-stayed bridges. Elastic and inelastic buckling analyses are carried out for frame-like numerical models of cable-stayed bridges. The axial-flexural interaction equations prescribed in AASHTO Allowable Stress Design (ASD) and AASHTO Load and Resistance Factor Design (LRFD) are used in order to check the stability of principal components. Parametric studies are performed for numerical models which have the center span length of 300m, 600m, 900m and l200m with different girder depths. Peak values of the interaction equations are calculated at the intersection point between girders and towers. These peak values are considered as a major factor to design of principal components of cable-stayed bridges. As a result, more economical design for girders and towers can be feasible using the inelastic buckling analysis. In addition, LRFD codes are more economical about 20% on the average than ASD codes for all numerical models of cable-stayed bridges.

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