• 산업경영시스템학회지
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• 제36권4호
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• pp.59-63
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• 2013

In biomass gasification, efficiency of energy quantification is a difficult part without finishing the process. In this article, a radial basis function neural network (RBFN) is proposed to predict biomass efficiency before gasification. RBFN will be compared with a principal component regression (PCR) and a multilayer perceptron neural network (MLPN). Due to the high dimensionality of data, principal component transform is first used in PCR and afterwards, ordinary regression is applied to selected principal components for modeling. Multilayer perceptron neural network (MLPN) is also used without any preprocessing. For this research, 3 wood samples and 3 other feedstock are used and they are near infrared (NIR) spectrum data with high-dimensionality. Ash and char are used as response variables. The comparison results of two responses will be shown.

• 대한의용생체공학회:의공학회지
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• 제27권2호
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• pp.59-63
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• 2006

Magnetocardiogram (MCG) topography is a useful diagnostic technique that employs multi-channel magnetocardiograms. Measurement of artifact-free MCG signals is essenctial to obtain MCG topography or map for a diagnosis of human heart. Principal component analysis (PCA) combined with an artificial neural network (ANN) is proposed to remove a pulse-type artifact in the MCG signals. The algorithm is composed of a PCA module which decomposes the obtained signal into its principal components, followed by an ANN module for the classification of the components automatically. In the experiments with volunteer subjects, 97% of the decisions that were made by the ANN were identical to those by the human experts. Using the proposed technique, the MCG topography was successfully obtained without the artifact.

• Journal of the Korean Data and Information Science Society
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• 제21권3호
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• pp.589-595
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• 2010

In discriminant analysis, we consider an intraclass correlation pattern by principal component analysis. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider two procedures, i.e., the test and proportion procedures, for selecting the principal components in classifica-tion. We compare the regular classification method and the proposed two procedures. We consider two methods for estimating error rate, i.e., the leave-one-out method and the bootstrap method.

• KDI Journal of Economic Policy
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• 제42권2호
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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.

• 대한원격탐사학회지
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• 제4권1호
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• pp.49-60
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• 1988

Principal Component Analysis (PCA) was applied for regional geological mapping to a multivariate data set of the Landsat TM data in the heavily vegetated and topographically rugged Chungju area. The multivariate data set selection was made by statistical analysis based on the magnitude of regression of squares in multiple regression, and it includes R1/2/R3/4, R2/3, R5/7/R4/3, R1/2, R3/4. R4/3. AND R4/5. As a result of application of PCA, some of later principal components (in this study PC 3 and PC 5) are geologically more significant than earlier major components, PC 1 and PC 2 herein. The earlier two major components which comprise 96% of the total information of the data set, mainly represent reflectance of vegetation and topographic effects, while though the rest represent 3% of the total information which statistically indicates the information unstable, geological significance of PC3 and PC5 in the study implies that application of the technique in more favorable areas should lead to much better results.

• 대한토목학회논문집
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• 제33권5호
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• pp.1981-1987
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• 2013

차량의 $CO_2$ 배출량은 통행속도에 따라 다르게 나타난다. 또한, 차량의 통행속도는 도로의 종류나 위치, 시간대, 교통량 등에 따라 다르게 나타난다. 본 논문에서는 주성분분석(PCA : Principal Component Analysis)을 이용하여 간선도로 구간 별 시간대 별로 차량 당 $CO_2$ 다량 배출구간을 판별하여 평가하였다. 분석 결과, 주성분분석 결과 제 1주성분과 제 2주성분으로 성분이 구분되는 것을 알 수 있었고 시간대가 각 주성분을 설명할 수 있는 주요 성분임을 알 수 있었다. 제 1주성분의 경우 새벽시간대와 오후시간대로 주성분을 설명할 수 있었다. 제 2주성분의 경우 오전, 오후 첨두시 시간대로 주성분을 설명할 수 있었다. 그리고 주성분 점수를 산출하여 분석한 결과 제 1주성분의 경우 새벽시간대에도 정체현상이 지속되는 잠원IC~한남대교 구간이 타 구간에 비해 주성분 점수가 높게 나타났고 제 2주성분의 경우 오전,오후 첨두시의 정체현상이 극심한 서울시 접속부와의 이격이 가까운 구간에서 주성분 점수가 높게 나타났다. 결과적으로 주성분 점수를 통하여 차량 당 $CO_2$ 다량 배출 구간을 판별할 수 있었다.

• Genomics & Informatics
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• 제18권1호
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• pp.11.1-11.3
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• 2020

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 추계학술대회 논문집
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• pp.241-242
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• 2012

• Asian-Australasian Journal of Animal Sciences
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• 제4권3호
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• pp.245-250
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• 1991

Analysis of the characteristics and the grouping of the species of sown and weeds in artificial pastures was studied applying the principal component analysis method. Presency and coverage of six sown species and fifteen weed species which occurred in pastures of under-grazing and optimumgrazing were subject to analysis. From field survey, species were divided into three groups: the group A included five species such as Festuca arundinacea, Lolium perenne and Dactylis glomerata, etc., the group B included eleven species such as Polygonum longisetum, Agrostis alba and Rumex obtusifolius, etc., and the group C included five species such as Miscanthus sinensis, Rubus palmatus and Artemisia princeps, etc. The group A species corresponded to good pasture conditions and management. On the contrary, the group C species occurred in poor pasture conditions with inadequate management. The group B species corresponded to intermediate pasture conditions and management. Interrelated pair species co-existing and species non-co-existing were discovered. Factor loading as negative for the group A species. positive for the group C species and positive but lower than the group C species for the group B species. From these results it is concluded that the principal component analysis seems to one of the useful tools for the analysis of characteristics of species and the diagnosis of sown pasture vegetation, although further studies are required to get more general information about species characteristics.