• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.33-36
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• 2003

Principal component analysis (PCA) was used to improve image classification by the unsupervised classification techniques, the K-means. To do this, I selected a Landsat TM scene of Jeju Island, Korea and proposed two methods for PCA： unstandardized PCA (UPCA) and standardized PCA (SPCA). The estimated accuracy of the image classification of Jeju area was computed by error matrix. The error matrix was derived from three unsupervised classification methods. Error matrices indicated that classifications done on the first three principal components for UPCA and SPCA of the scene were more accurate than those done on the seven bands of TM data and that also the results of UPCA and SPCA were better than those of the raw Landsat TM data. The classification of TM data by the K-means algorithm was particularly poor at distinguishing different land covers on the island. From the classification results, we also found that the principal component based classifications had characteristics independent of the unsupervised techniques (numerical algorithms) while the TM data based classifications were very dependent upon the techniques. This means that PCA data has uniform characteristics for image classification that are less affected by choice of classification scheme. In the results, we also found that UPCA results are better than SPCA since UPCA has wider range of digital number of an image.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.385-392
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• 2015

Principal component analysis (PCA), a dimension-reduction technique, is usually implemented after the variables are standardized when the measurement unit of variables are different. To standardize a variable we divide it by its standard deviation. But there is another way to transform a variable to be independent of its measurement unit. It is to divide it by its mean rather than standard deviation. Implementing PCA on standardized variables is equivalent to implementing PCA with a correlation matrix of original variables. Similarly, implementing PCA on the transformed variables divided by their means is equivalent to implementing PCA with a matrix related to the coefficients of variation of the original variables. We explain why we need to implement PCA on the variables transformed by their means.

• Journal of Plant Biology
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• v.25 no.2
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• pp.83-99
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• 1982

In this study, thirty-two stands in Kwangnung forest located in the central part of Korea were preferentially selected. In each stand, all stems for trees and shrubs were recorded by species and their girths were measured down to 5cm. In addition, several enviromental factors such as field soil pH, field soil moisture, soil compressibility, depth of soil, thickness of litter layer, elevation and basal area were measured. Three soil cores were sampled and various physical and chemcial properties was determined. The vegetational data were subjected to three kinds of multivariate ordination(PO, PCA, RA). The results suggested that Kwangnung forest was consisted of three forest types: coniferous, mixed and broad leaved forest communities. The relation between the stand scores of ordination and several environmental factors were investigated in terms of correlation analysis in order to examine the relationships between the vegetation and certain environmental factors. As a result of this analysis, the amount of sand content in A1 horizon decreased frm the coniferous to broad leaved forest, while maximum field capacity, pore space, exchangeable cations, loss on ignition, soil pH nad the amount of total nitrogen had a tendancy to increase significantly. However, easily soluble phosphorus appeared to have little to do with the forest types. The result of species ordination of centered-standardized PCA suggested that the major successional pathway in Kwangnung forest was; Pinus densifloralongrightarrowQuercus mongolica, Q. serrata, Q. alienalongrightarrowCarpinus laxifloralongrightarrowC. erosa in sequence. This trend is in good agreement with the past studies. In three kinds of ordination (centered PCA, centered-standardized PCA and RA) based on nineteen species and twenty-five stands, the total variances accounted for the first three axes were 77%, 46% and 63% respectively. The estimated beta diversity in Kwangnung forest assumed as a coenocline, was 1.5~1.8 HC. Increasing the effect of the sampling errors on ordination perfermance, this low heterogeneity seems to cause the poor concentration of the total variance. The results from the four kinds of ordination were in good agreement with each other, especially between PO, centered-standardized PCA and RA appeared robust. It seems to be worthy of applying multivariate method for analyzing other forest communities in Korea.

• The Journal of Korean Academic Society of Nursing Education
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• v.24 no.1
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• pp.5-15
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• 2018

Purpose: The purpose of this study was to investigate the knowledge and practice of patient-controlled analgesia use and management (PCA-UM) among nurses. Methods: Data were collected from 182 nurses employed by four general hospitals having more than 300 beds in Daejeon. The data were collected using self-report questionnaires from November 4 to November 20, 2015. Collected data were analyzed using descriptive statistics, t-test, and ANOVA. Results: The average nurses' knowledge about PCA-UM was 14.8 points out of 20. PCA-UM knowledge was significantly higher for nurses with experience in PCA education (t=3.55, p<.001). Most participants (91.2%) wanted to get PCA training, 86.8% of them provided PCA education to patients after surgery. Approximately 62% of participants regularly evaluated the level of consciousness of patients with PCA. Conclusion: Findings indicate that the knowledge and practice of PCA-UM among nurses were insufficient to provide safe and effective pain management to postoperative patients with PCA. Therefore, it is concluded that it is necessary to develop standardized PCA education programs for nurses to provide safe and effective pain management to postoperative patients with PCA.

• The Korean Journal of Pain
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• v.9 no.2
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• pp.354-362
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• 1996

Background: We started postoperative pain management service using an intravenous patient-controlled analgesia (IV-PCA, PCA), which is known as convenient and effective analgesic method. In this report, we describe the efficacy and safety of PCA and the experience of developing an acute pain service to treat postoperative pain using a PCA. Methods: Practices of an acute pain service were started at a ward for general surgery after preparation of the standardized protocols for PCA. In each patient, PCA was connected following administration of initial loading doses of analgesics at recovery room after operation. All patients were checked by acute pain service team once or twice daily. The scope of acute pain service was gradually spread to other departments such as orthopedic, thoracic, obstetric and gynecologic departments by requests of patients or surgeons. We managed 1,590 patients during first 22 months. among them, nine hundred seventy two cases were prospectively evaluated for their analgesis efficacy and side effects of PCA. Results: The number of patients was increased day by day. the most common type of operation was gastrectomy (21.6%). Commonly used analgesics were nalbuphine (59%) and morphine (37%). The mean duration of PCA attachment was 3.3 days. The degree of analgesia on operation day was good in 44.8% and tolerable in 52.6% of patients. Only 3.9% of patients complained severe pain during their postoperative periods. One elderly patient experienced respiratory depression (0.06%) owing to accidental misuse of PCA by his relatives. Overall patient's satisfaction was over 93%. Conclusion: According to our experiences, we conclude that PCA is an effective, relatively safe and highly satisfactory method for postoperative pain management. Because of these advantages of PCA, the creation of our acute pain service using a PCA was successful and expanded rapidly.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.167-170
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• 2003

본 연구는 무감독영상해석(Unsupervised Classification)에서 주성분 분석법(Principal Component Analysis)의 응용성을 연구하기 위하여, 주성분 분석법을 K-means, ISODATA 두가지 무감독분류법에 적용하였다. 적용대상지역은 제주도이다. 본 연구에서 주성분 분석 방법중에서 비정규형 주성분 분석방법 (Unstandardized PCA)과 정규형 주성분 분석방법(Standardized PCA) 두가지 경우로 나누어서 각각 연구하였다. 이를 위하여 제주도의 Landsat TM영상과 국토연구원에서 조사한 제주도 식생분류 조사자료와 현장조사 자료 그리고 1/25,000 수치지도를 이용하였다. 그리고 분석된 자료의 정확도를 평가하기 위하여 오차행렬(Error Matrix)을 도입하여 계산하였다. 우선 비정규형 주성분 분석법으로 구한 주성분 영상과 Landsat TM 원래 영상을 오차행렬을 이용하여 제주도의 식생 분류에 각각 적용하였다. 그 결과, K-means 무감독분류법에서는 Landsat TM 자료를 직접 이용한 경우에는 바다와 육상의 분류가 잘 되지 않았으며, 또한 전반적인 영상분류결과가 관측치와 많은 차이를 보였다. 그러나, 주성분 분석법으로 계산된 주성분 영상으로 K-means방법으로 분류 한 결과는 관측치와 잘 일치를 하였다. ISODATA의 경우, Landsat TM 원래영상을 계산하면, K-means으로 분류한 결과보다는 좋은 값을 나타냈으나, 주성분 분석법으로 구한 영상의 계산결과와 비교하면, 주성분 영상으로 구한 분류결과의 정확도가 약 15%정도 높게 나타났다. 정규형 주성분 분석법의 경우를 보면 K-means에서는 Landsat TM원래 자료보다 우수한 결과를 보여주었으나, 비정규형 주성분 분석법으로 계산된 결과보다는 정확도가 다소 떨어지는 단점이 있었고, ISODATA의 경우도 Landsat TM원래 자료보다 약 7%정도의 높은 정확도를 보였으나, 비정규형 영상보다는 약8%정도 낮은 정확도를 보였다. 본 연구에서 주성분 분석법으로 계산된 결과에서 주목되는 것은, 주성분 분석법으로 구한 주성분 영상은 분류방법(K-means, ISODATA, artificial neural networks)에 따라 분류된 결과값이 비슷하게 나타난 반면, Landsat TM원래 자료는 분류방법에 따라 결과값이 많은 차이를 보여 주었다. 그리고 주성분 분석 방법 중에서도 비정규형 주성분 분석법(Unstandardized PCA)이 정규형 주성분 분석법(Standardized PCA)보다 영상분석에서 더 좋은 결과를 보여주는 것으로 나타났다.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.359-359
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• 2020

현재 우리나라의 가뭄감시 정보는 기상학적/농업적/수문학적 가뭄이 별도의 지수로 개발되어 다양한 형태의 정보를 생산·제공되고 있다. 각각의 가뭄 지수들 기준 및 특성에 따라 분석되고 있기 때문에 가뭄전문가의 입장에서는 매우 정밀한 가뭄정보를 제공받는 장점이 있는 반면에, 일반 국민들이 가뭄 정보를 받아들이고 이해하는데 어려움이 있어 이를 한눈에 알아볼 수 있는 통합가뭄지도가 필요하며, 통합가뭄도를 제작하기 위해서는 통합가뭄지수가 개발되어야 한다. 본 연구에서는 원격탐사자료를 활용하여 농업적 가뭄지수인 Agricultural Dry Condition Index (ADCI)와 수문학적 가뭄지수인 Water Budget-based Drought Index (WBDI)를 개발하였으며, 기상학적 가뭄지수인 Standardized Precipitation Index (SPI)를 포함하여 기상-농업-수문학적 가뭄지수를 결합한 통합가뭄지수를 산정하였다. 다양한 가뭄지수를 활용하여 개발되었기 때문에 다변량 통계 모형 중 선형 모형인 Principal Component Analysis (PCA)기법과 비선형 모형인 Kernel Entropy PCA, Kernel PCA를 적용하였다. 또한 과거 가뭄사상을 활용하여 산정된 통합가뭄지수 검증을 위해 과거 가뭄사상에 대한 가뭄 발생시기, 심도, 쇠퇴패턴이 양상 평가 및 Intentionally Biased Bootstrap Resampling (IBBR)을 활용한 지수별 민감도 분석을 통해 통합가뭄지수 적용성 평가를 진행하였다.

• Animal Bioscience
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• v.34 no.1
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• pp.12-19
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• 2021

Objective: The objectives of the current study were to study the main environmental factors affecting birth weight (BW) and weaning weight (WW), estimate variance components, genetic parameters and genetic trend and to evaluate the variability and relationships among breeding value of BW and WW using principal components analysis (PCA). Methods: A total of 16,370 records were collected from 8,271 buffalo calves. Genetic parameters and breeding values were estimated using a bivariate animal model which includes direct, maternal and permanent maternal effects. These estimates were standardized and used in PCA. Results: The direct heritability estimates were 0.06 and 0.41 for BW and WW, respectively whereas direct maternal heritability values were 0.03 and 0.14, respectively. Proportions of variance due to permanent environmental effects of dam were 0.455 and 0.280 for BW and WW respectively. The genetic correlation between BW and WWs was weak approaching zero, but the maternal correlation was 0.26. The first two principal components (PC1 and PC2) were estimated utilizing the standardized breeding values according to Kaiser method. The total variance explained by the first two PCs was 71.17% in which 45.91% and 25.25% were explained by PC1 and PC2, respectively. The direct breeding values of BW were related to PC2 but those of WW and maternal breeding values of BW and WWs were associated with PC1. Conclusion: The results of genetic parameters and PCA indicate that BW and WWs were not genetically correlated and improving growth traits of Egyptian buffaloes could be achieved using WW without any adverse effect by BW.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.151-156
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• 2003

본 연구에서는 2000년 LANDSAT ETM+ 수도권 영상을 이용하여 도시지역 10개소, 식생지역 10개소를 선정해서 각각에 대해 표준화 주성분 분석을 적용하여 두 지역간의 고유벡터 매트릭스를 비교ㆍ분석해보았다. 도시 지역과 식생 지역각각에 대해 총 6개의 주성분이 생성되었으며 PC-2와 고유벡터 부호가 변한 밴드(band2, band7)를 RGB로 조합하여 수원지역을 대상으로 분류(Classification)한 결과의 정확도를 분광서명 분별 분석(Signature Separability Analysis)통해 얻은 밴드조합(band1, band3, band5) 영상의 분류결과와 비교해 보았다. 수원지역 2000년 IKONOS 영상의 다중분광 밴드(4×4m)와 전정색 밴드(1x1m)를 융합한 영상이 분류 정확도를 판단하는 기준으로 사용되었다. 비교결과 분류 전체 정확도는 각각 87.7%, 77.29% Khat 지수는 0.83, 0.68로 나타나 PC-2, 밴드2, 밴드7을 이용했을 때 분류 정확도를 높일 수 있다는 결과를 얻었다.

• 한국HCI학회:학술대회논문집
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• 2008.02c
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• pp.172-177
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• 2008

Biometrics is essential for person identification because of its uniqueness from each individuals. Face recognition technology has advantage over other biometrics because of its convenience and non-intrusive characteristics. In this paper, we will present a overview of face recognition technology including face detection, feature extraction, and face recognition system. For face detection, we will describe template based method and face component based approach. PCA and LDA approach will be discussed for feature extraction, and nearest neighbor classifiers -will be covered for matching. Large database and the standardized performance evaluation methodology is essential in order to support state-of-the-art face recognition system. Also, 3D based face recognition technology is the key solution for the pose, lighting and expression variations in many applications.