• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.28 no.3
• /
• pp.361-367
• /
• 2010

We produced continuous vertical time series of 14 permanent GPS stations operated by National Geographic Information Institute by processing about five years of data. Then we computed the height velocities by using a linear regression fitting of those time series, and did principal component analysis to understand the overall characteristics of the series. The prominent signal obtained as the first mode of PCA results showed an average of 4.2 mm/yr vertical velocity. The values of the first mode eigenvectors were consistent at all sites. Thus, we concluded that all the 14 stations are uplifting nearly at the same velocity for the test period. Then changes of precision before and after removing the first mode signal from the 14 height time series were analyzed. As a result, the precision improved 34.8% on average.

• Proceedings of the Korea Contents Association Conference
• /
• 2018.05a
• /
• pp.475-476
• /
• 2018

생물통계학에 기반한 사용자 인증의 새로운 방법으로 안구 운동 정보가 새롭게 각광받고 있다. 이 논문에서는 안구운동정보가 사용자 인증 문제에 왜 좋은 지를 설명하고, 인증의 정확도를 향상시키기 위한 방안으로 주성분분석에 의한 방법을 제안한다. 주성분 분석은 데이터에서 변동이 가장 큰 방향을 찾아주기에 이를 활용하여 안구운동 데이터의 특징을 추출하면 인증 성능이 향상될 수 있을 것이다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.05a
• /
• pp.515-518
• /
• 2017

I used Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA) to utilize biometric technology for security. I used 14 korean consonants(ㄱ to ㅎ). And It has both information of gestures for each consonants and identity of user. So this experiment is set for this two aspects. I used database including 20 people's images. Each person did 140 action for every consonant with 10 trials. PCA and LDA must be applied on self-collected database using MATLAB programming. Equal Error Rate (EER) is used for evaluate performance of this analysis.

• The Korean Journal of Applied Statistics
• /
• v.30 no.1
• /
• pp.135-145
• /
• 2017

Principal component analysis (PCA) describes the variation of multivariate data in terms of a set of uncorrelated variables. Since each principal component is a linear combination of all variables and the loadings are typically non-zero, it is difficult to interpret the derived principal components. Sparse principal component analysis (SPCA) is a specialized technique using the elastic net penalty function to produce sparse loadings in principal component analysis. When data are structured by groups of variables, it is desirable to select variables in a grouped manner. In this paper, we propose a new PCA method to improve variable selection performance when variables are grouped, which not only selects important groups but also removes unimportant variables within identified groups. To incorporate group information into model fitting, we consider a hierarchical lasso penalty instead of the elastic net penalty in SPCA. Real data analyses demonstrate the performance and usefulness of the proposed method.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2016.05a
• /
• pp.583-583
• /
• 2016

다양한 평가지표가 반영된 복합 지수(Composite Index)는 물관리 정책의 우선순위 결정 및 정책성과의 모니터링에 유용한 도구로 사용되고 있다. 각 지표별 중요도를 나타내는 가중치는 최종 지수의 산정에 영향을 미칠 수 있으며, 그 결정방법도 Data Envelopment Analysis(DEA), Benefit of doubt Approach(BOD), Unobserved Component Model(UCM), Budget Allocation Process(BAP), Analytic Hierarchy Process(AHP), Conjoint Analysis(CA) 등 다양하다. 본 연구에서는 여러 가지 가중치 결정방법 중 통계적 방법인 주성분 분석(Principal Component Analysis, PCA)을 사용하여 Park et al.(2016)이 제시한 물관리 탄력성 지수(Water Management Resilience Index, WMRI)에 대한 가중치를 산정하여 동일 가중치를 적용한 기존 결과와 비교하였다. 물관리 탄력성 지수는 자연조건상 물관리 취약성(Vulnerability), 기존 수자원 인프라의 견고성(Robustness), 물위기 적응전략의 다양성(Redundancy)의 3가지 부지수(sub-index)는 각각 13개, 11개, 7개의 지표(Indicator)로 구성되어 있으며, 117개 중권역을 다목적댐 하류 본류유역(범주 1), 용수공급 및 유량조절이 불가능한 지류(범주 2)와 가능한 지류(범주 3)로 분류하여 적용되었다. 각 부지수별로 추출된 3개, 5개, 3개의 주성분이 전체 자료의 76.4%, 71.2%, 63.2%를 설명하는 것으로 분석되었으며 부지수별 주성분의 고유벡터(Eigenvector)와 고유값(Eigenvalue)를 계산하고 각 지표의 가중치를 산정하였다. 주성분 분석에 의한 가중치와 동일 가중치를 적용하였을 경우와 비교해보면 취약성 부지수 1.9%, 견고성 부지수 1.9%, 다양성 부지수 2.1%의 차이가 나타나며 물관리 탄력성 지수는 0.4%의 차이를 보임에 따라 Park et al.이 제시한 연구결과의 적정성을 확인할 수 있었다. 주성분 분석은 객관적인 가중치 설정을 위한 통계적 접근방법의 하나로써 다양한 물관리 정책지수 산정시 활용될 수 있을 것이며, 향후 다른 가중치 산정방법을 적용함으로써 각 방법에 따른 지수 결과의 민감도 및 장단점을 분석할 수 있을 것으로 판단된다.

• The Korean Journal of Applied Statistics
• /
• v.30 no.4
• /
• pp.555-566
• /
• 2017

For multivariate datasets with large number of variables, classical dimensional reduction methods such as principal component analysis may not be effective for data visualization. The underlying reason is that the dimensionality of the space of variables is often larger than two or three, while the visualization to the human eye is most effective with two or three dimensions. This paper proposes a working procedure which first partitions the variables into several "latent" clusters, explores individual data subsets, and finally integrates findings. We use R pakacage "ClustOfVar" for partitioning variables around latent dimensions and the principal component biplot method to visualize within-cluster patterns. Additionally, we use the technique for embedding supplementary variables to figure out the relationships between within-cluster variables and outside variables.

• The KIPS Transactions:PartD
• /
• v.9D no.1
• /
• pp.75-80
• /
• 2002

Putnam develops SLIM (Software LIfecycle Management) model based upon the assumption that the manpower utilization during software project development is followed by a Rayleigh distribution. To obtain the manpower distribution, we have to be estimate the total development effort and difficulty ratio parameter. We need a way to accurately estimate these parameters early in the requirements and specification phase before investment decisions have to be made. Statistical tests show that system attributes are highly correlation (redundant) so that Putnam discards one and get a parameter estimator from the other attributes. But, different statistical method has different system attributes and presents different performance. To select the principle system attributes, this paper uses the principle component analysis (PCA) instead of Putnam's method. The PCA's results improve a 9.85 percent performance more than the Putnam's result. Also, this model seems to be simple and easily realize.

• Korean Journal of Plant Taxonomy
• /
• v.38 no.2
• /
• pp.93-109
• /
• 2008

The morphological variation was analysed to examine previous hypotheses on the taxonomy of B. amurensis complex which includes B. amurensis Rupr. var. amurensis, B. amurensis var. quelpaertensis (Nakai) Nakai and B. amurensis var. latifolia Nakai. The results from the univariational and principal components analyses employing 22 putatively diagnostic characters indicate that B. amurensis var. quelpaertensis is distinct from var. amurensis in the length and width of leaves, angle of leaf apex, distance between spinose teeth, length of internode, number of flowers per inflorescence, whereas B. amurensis var. latifolia is different from other varieties in the angle of leaf apex and leaf length/width ratio. In principal component analysis, the characters of the leaf including leaf width and length were the main characteristics to distinguish those three taxa. The evidence both from the principal components analyses and current geographical distribution pattern suggest that retaining the varietal status for the two taxa, B. amurensis var. latifolia and B. amurensis var. quelpaertensis is reasonable.

• The KIPS Transactions:PartB
• /
• v.17B no.2
• /
• pp.177-182
• /
• 2010

This paper proposes a new document clustering method using PCA and fuzzy association. The proposed method can represent an inherent structure of document clusters better since it select the cluster label and terms of representing cluster by semantic features based on PCA. Also it can improve the quality of document clustering because the clustered documents by using fuzzy association values distinguish well dissimilar documents in clusters. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2011.04a
• /
• pp.32-35
• /
• 2011

전기역학적 임피던스(electromechanical impedance)를 이용한 구조물 건전성 모니터링(structural health monitoring; SHM) 기술은 구조물의 주요 부재에 압전센서를 부착하여 이로부터 획득한 임피던스 신호의 변화를 관찰함으로써 구조물의 국부적 상태를 실시간으로 진단하는 것이다. 임피던스는 손상뿐만 아니라 외부 온도에도 민감하게 반응하기 때문에 구조물 진단 결과에 상당한 오차를 유발할 수 있으므로 이에 대한 보상을 수행해야 한다. 따라서 본 논문에서는 온도변화가 임피던스 기반 진단 결과에 미치는 영향을 PZT 센서를 사용하여 실험적으로 연구하였다. 리액턴스(reactance)의 주성분 분석(Principal Component Analysis; PCA)을 통해 도출된 첫번째 주성분과 저항(resistance)으로부터 계산된 손상지수 사이의 관계를 분석함으로써, 온도변화에 의해 구별되지 않았던 손상을 보다 확연하게 구별 할 수 있음을 확인하였다.