• 한국지반공학회논문집
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• 제26권10호
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• pp.69-79
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• 2010

본 연구에서는 사면의 이상 거동 및 붕괴 감지를 위해 실제 계측시스템 설치 후 이상보고가 있었던 사변을 대상으로 비모수적 통계방법인 주성분분석 (PCA : Principal Component Analysis)을 적용하였다. 분석결과, 사면의 이상거동여부를 나타내는 척도인 주성분점수는 이상징후 발생시 정상상태에 비해 상대적으로 크거나 낮은 값을 나타내어 변화량에 큰 차이를 보였다. 이를 통해 주성분 분석을 이용하여 사면의 이상 거동 및 붕괴를 감지할 수 있는 것을 확인하였다. 주성분분석을 활용하여 정량적인 사면거동 및 이상징후의 예측이 가능할 것으로 판단된다.

• 수산해양기술연구
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• 제35권3호
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• pp.243-249
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• 1999

Specificity of catches has been analyzed to japanese tuna purse seine A principle component analysis was used to improve the efficiency of fishing and increase sustainable production and productivity of Korean tuna purse seine.The result are as follows;From the principal component analysis of the fish catches, the first principal component(Z1) to promote principal component score was skipjack Kastsuwonus Pelamis, LINNAEUS and yellowfin tuna Thunnus Albacares, BONNATERRE (Small : smaller than 10kg) and proportion was 86.8% of total. The second principal component(Z2) to increase principal component score was yellowfin tuna (Large : larger than 10kg) and proportion was 9.5%.On the other hand, fish operating that have caught skipjack and yellowfin tuna (Small and Larger) was not so much. Fish catches for one species raised volume of the catches while catches for multi-species decreased it since principal composition score for one species and both species together has been increased.Fish school could be divided into three groups of schools each of which was associated with drift objects, payaho and ship, school associated with shark, whale and porpoise and school of breezing, feeding and jumping from proportion of principal component analysis for fish catches of school types. However, the biological pattern is different among school associated with ship, payaho and school associated with drift objects for analysis eigen vector. School associated with ship, payaho and school associated with drifting object associated is judged as school which be assembled to vessel and drifted log temporary.

• 한국환경과학회지
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• 제7권2호
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• pp.171-176
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• 1998

This study was conducted to evaluate water quality utilizing principal component analysis in the Nakdong River Estuary. From the results of analysis, water quality in the Nakdong River Estuary could be explained up to 65.3 Percente by three factors which were Included In river loadlnwastes from the Nakdong River and rainfalls : 39.1%1, sediment resuspension(13.7BS) and metabolism(12.5%). In the eastern part of estuary In flowing the Nakdong River, river loading factor score(factor 1 Pas higher than that In western part. Sediment resuspension factor score(factor 2) was high in shallow water, while metabolism factor score(factor 3) was high in deeper water. For seasonal variations of factors score, factor 1 was h19h- 1y related to rainfall season.

• Communications for Statistical Applications and Methods
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• 제28권2호
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• pp.135-150
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• 2021

We propose principal differential analysis based classification methods. Computations of squared multiple correlation function (RSQ) and principal differential analysis (PDA) scores are reviewed; in addition, we combine principal differential analysis results with the logistic regression for binary classification. In the numerical study, we compare the principal differential analysis based classification methods with functional principal component analysis based classification. Various scenarios are considered in a simulation study, and principal differential analysis based classification methods classify the functional data well. Gene expression data is considered for real data analysis. We observe that the PDA score based method also performs well.

• Plant Resources
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• 제2권2호
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• pp.75-80
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• 1999

Twenty two varieties of pecan including wild types were classified based on 6 characters measured by principal component analysis score distance. The results are summarized as fellow. Twenty two varieties were classified into 5 groups based in PCA score distance. Five groups were distinctly characterized by many morphological characters. Total variation could be explained by 51%, 95%, 99% with first, third and fifth principal components respectively. Varimax rotation of the factor loading of the first factors indicated that the first component was highly loaded with leaf characters, the second component with fruit characters, but fruit length was negative loaded. The second, the third and the fourths groups of cultivars had very close genetic parentage similarity.

• 응용통계연구
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• 제34권6호
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• pp.945-956
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• 2021

본 연구에서는 가중주성분분석으로부터 정준대응분석을 유도하는 Legendre와 Legendre (2012)의 알고리즘을 고찰하였다. 그리고, 가중주성분분석에 기반한 Legendre와 Legendre (2012)의 정준대응분석이 가우시안 반응모형에 기초한 Ter Braak (1986)의 정준대응분석과 동일함을 다루었다. 생태학에서 종의 발현 정도를 잘 설명할 수 있는 가우시안 반응곡선에서 도출된 Ter Braak (1986)의 정준대응분석은 종 패킹 모형(species packing model)이라는 기본 가정을 사용한 후 일반화선형모형과 정준상관분석을 결합시키는 방법으로 도출된다. 그런데 Legendre와 Legendre (2012)의 알고리즘은 이러한 가정없이 Benzecri의 대응분석과 상당히 유사한 방법으로 계산되는 특징을 지닌다. 그러므로 가중주성분석에 기초한 정준대응분석을 사용하면, 결과물 활용에 약간의 유연성을 지닐 수 있게 된다. 결론적으로 본 연구에서는 서로 다른 모형에서 출발한 두 방법이 장소점수(site score), 종 점수(species score) 그리고 환경변수와의 상관관계가 서로 동일함을 보인다.

• 한국잠사곤충학회지
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• 제32권1호
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• pp.17-30
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• 1990

주성분분석에 의하여 분류된 148개 보존잠품종중에서 제일주성분 score에 따라 10개의 교배모품종을 선발한 후 (정등, 1989), 이들로부터 Top-교잡에 의해 24개 교배조합을 작성하고 조합능력검정을 행함과 동시에 주성분과 교배조합능력과의 상호관계를 분석하였다. 1. 선발된 육종모품종중에서 일본종계 N$_{39}$ 및 중국종계 $C_{46}$ 이 대부분의 형질에서 일반조합능력이 높았다. 2. 특정조합능력은 형질 또는 교배조합에 따라 차이가 심하였다. 3. 육종모품종의 제1주성분 score는 5영경과일수, 전령경과일수, 수견량, 전견중, 견층중, 견층비율, 견사장, 견사량, 해서사장, 생사량비율, 소절등의 일반조합능력과 고도의 정의 상관관계가 있었다. 4. 유사도거리(D$^2$)는 수견양, 해서사장, 해서사량, 해서율, 생사량비율, 소절등의 특정조합능력과 정의 상관이 있었다. 따라서 교배조합의 양친이 원연일수록 이들 5개형질에 대한 특정조합능력은 높게 나타났다. 5. 육종모품종의 특성을 이용한 주성분분석의 제1주성분 score에 의해 일반조합능력의 예측이 가능하였다.

• 한국작물학회지
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• 제31권2호
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• pp.173-178
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• 1986

1979년에 조신력, 팔달, 진흥, 통일, 밀양2003등 5품종을 공시하여 질소수준을 0kg, 12kg, 18kg/10a에서 재배하여 15회에 걸쳐 조사한 엽후, 엽면적지수, 주당건물중과 수양 및 수양구성요소와의 관계를 주성분분석을 이용하여 분석한 결과는 다음과 같다. 1. 엽후, 엽면적지수, 주당건물중의 시계열데이타의 주성분분석에서 제 1주성분은 전기간의 평균적 크기의 대소를 표시하는 특성으로, 제 2주성분은 엽후, 엽면적지수에서는 변대의 위치, 주당건물중에서는 생육초기의 특성을 표시하는 종합특성치로 도출할 수 있었다. 2. 생장특성을 표시하는 종합특성치(주성분스코아)의 엽후는 품종간 차리가 엽면적지수 및 주당건물중은 질소수준간 차이가 인정되있다. 3. 수양 및 수량구성요소와의 관계에서 임실비율과 주당 수수가 엽면적 및 주당건물중의 스코아와 관계가 있었고 엽후와는 거의 관계가 없었으며 수양에 미치는 영향도 임실비율과 주당수수를 통해서라는 것을 알 수 있었다.

• Journal of the Korean Data and Information Science Society
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• 제19권3호
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• pp.751-759
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• 2008

Mixtures of factor analyzers(MFA) is useful to model the distribution of high-dimensional data on much lower dimensional space where the number of observations is very large relative to their dimension. Mixtures of common factor analyzers(MCFA) can reduce further the number of parameters in the specification of the component covariance matrices as the number of classes is not small. Moreover, the factor scores of MCFA can be displayed in low-dimensional space to distinguish the groups. We propose the factor scores of MCFA as new low-dimensional features for classification of high-dimensional data. Compared with the conventional dimension reduction methods such as principal component analysis(PCA) and canonical covariates(CV), the proposed factor score was shown to have higher correct classification rates for three real data sets when it was used in parametric and nonparametric classifiers.

• Biotechnology and Bioprocess Engineering:BBE
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• 제11권5호
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• pp.432-441
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• 2006

2D fluorescence sensors produce a great deal of spectral data during fermentation processes, which can be analyzed using a variety of statistical techniques. Principal component analysis (PCA) and a self-organizing map (SOM) were used to analyze these 2D fluorescence spectra and to extract useful information from them. PCA resulted in scores and loadings that were visualized in the score-loading plots and used to monitor various fermentation processes with recombinant Escherichia coli and Saccharomyces cerevisiae. The SOM was found to be a useful and interpretative method of classifying the entire gamut of 2D fluorescence spectra and of selecting some significant combinations of excitation and emission wavelengths. The results, including the normalized weights and variances, indicated that the SOM network is capable of being used to interpret the fermentation processes monitored by a 2D fluorescence sensor.