• Journal of the Korean Geotechnical Society
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• v.26 no.10
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• pp.69-79
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• 2010

To detect abnormal events in slopes, Principal Component Analysis (PCA) is applied to the slope that was collapsed during monitoring. Principal component analysis is a kind of statical methods and is called non-parametric modeling. In this analysis, principal component score indicates an abnormal behavior of slope. In an abnormal event, principal component score is relatively higher or lower compared to a normal situation so that there is a big score change in the case of abnormal. The results confirm that the abnormal events and collapses of slope were detected by using principal component analysis. It could be possible to predict quantitatively the slope behavior and abnormal events using principal component analysis.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.35 no.3
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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.

• Journal of Environmental Science International
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• v.7 no.2
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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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• v.28 no.2
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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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• v.2 no.2
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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.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.945-956
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• 2021

In this study, we considered the algorithm of Legendre and Legendre (2012), which derives canonical correspondence analysis from weighted principal component analysis. And, it was proved that the canonical correspondence analysis based on the weighted principal component analysis is exactly the same as Ter Braak's (1986) canonical correspondence analysis based on the Gaussian response model. Ter Braak (1986)'s canonical correspondence analysis derived from a Gaussian response curve that can explain the abundance of species in ecology well uses the basic assumption of the species packing model and then conducts generalized linear model and canonical correlation analysis. It is derived by way of binding. However, the algorithm of Legendre and Legendre (2012) is calculated in a method quite similar to Benzecri's correspondence analysis without such assumptions. Therefore, if canonical correspondence analysis based on weighted principal component analysis is used, it is possible to have some flexibility in using the results. In conclusion, this study shows that the two methods starting from different models have the same site scores, species scores, and species-environment correlations.

• Journal of Sericultural and Entomological Science
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• v.32 no.1
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• pp.17-30
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• 1990

A 6${\times}$4 top-cross set was made from the ten silkworm parental lines selected by the first principal component scores. They were also analysed for the relationship between the combining ability and the first principal component score. The highest general combining ability effects were detected in the parental lines of Japanese, N39 and chinese, C46, for the most quantitative characters in the study. The first principal component score of factors related to silk productivity in the parents was significantly and positively correlated to the general combining ability of the twelve characters such as cocoon yield, cocoon weight, cocoon shell weight, cocoon shell percentage, duration of the 5th instar larvae, total larval period, length of a bave, weight of a have, non-breaking length of a bave, non-breaking weight of a have, raw silk percentage, and neatness. Similarity distance (D$^2$) was related to the specific combining ability of the characters such as cocoon yield, non-breaking length of a bave, non-breaking weight of a have, non-breaking ratio of a bave, raw silk percentage, neatness. From the results, it is possible to predict the general combining ability effects by the principal component scores for the 12 characters of the parents related to silk productivity.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.31 no.2
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• pp.173-178
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• 1986

Principal component analysis was used for ana1zing growth data to know the relationship between growth characteristics and yield as well as its components. The first principal component accounted for average time of the specific leaf area sampled, leaf area index, and dry weight, and the second component for the position of the changing point of growth characteristics. The component scores were more affected by the nitrogen level than variety. Yield were affected by fertility ratio and number of spikelets per hill which have close relation with the component score of leaf area index and dry weight per hill.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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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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• v.11 no.5
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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.