• Food Science of Animal Resources
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• v.36 no.6
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• pp.719-728
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• 2016

The aim of this study was to explore the volatile compounds of hind leg, foreleg, abdomen and Longissimus dorsi in both male and female Hyla rabbit meat by solid phase microextraction tandem with gas chromatography mass spectrometry, and to seek out the key odorants via calculating the odor activity value and principal component analysis. Cluster analysis is used to study the flavor pattern differences in four edible parts. Sixty three volatile compounds were detected, including 23 aldehydes, 4 alcohols, 5 ketones, 11 esters, 5 aromatics, 8 acids and 7 hydrocarbons. Among them, 6 aldehydes and 3 acids were identified as the potential key odorants according to the ratio of concentration and threshold. The contents of volatile compounds in male Hyla rabbit meat were significantly higher than those in female one (p<0.05). The results of principal component analysis showed that the first two principal component cumulative variance contributions reach 87.69%; Hexanal, octanal, 2-nonenal, 2-decenal and decanal were regard as the key odorants of Hyla rabbit meat by combining odor activity value and principal component analysis. Therefore volatile compounds of rabbit meat can be effectively characterized. Cluster analysis indicated that volatile chemical compounds of Longissimus dorsi were significantly different from other three parts, which provide reliable information for rabbit processing industry and for possible future sale.

• 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.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.1
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• pp.29-43
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• 2024

The present study concerns a feasibility study for applying principal component analysis to ship dynamics in maneuver. Using the four degrees of freedom standard modular model for ship dynamics maneuver simulations of large angle zigzag tests with rudder deflection angle variations are conducted. The datasets of ship motion, hydrodynamic force, and moment during the maneuver are acquired to identify the principal modes. The covariance matrix of obtained ship dynamics variables shows a strong linear correlation between the motion, hydrodynamic force, and moment, except the surge force. Four eigenvectors of the covariance matrix are selected as the principal modes of ship dynamics. Using the principal modes, ship motion in turning circle and zigzag tests is reconstructed, showing good agreement with the original data.

• Journal of Korean Port Research
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• v.13 no.1
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• pp.11-26
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• 1999

The subject of port efficiency is one of the important issues facing port authorities and policy makers today. A number of studies have been undertaken which compare ports in terms of their efficiency. But any port comparison can only be valid and meaningful if a port’s efficiency is compared with a similar port. The main objective of this paper is to introduce a systematic approach to identifying similar ports based on the technique of principal component analysis and cluster analysis. And it seeks to identify the most important factors underlying the port classification. Lack of awareness of which factors differentiate ports has resulted in an unnecessary collection of data which are of limited use in port classification. This paper has identified five groupings of similar ports within which port comparision can be justifiably made. This approach can be used for any future port comparision.

• Korean Journal of Plant Resources
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• v.11 no.2
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• pp.210-216
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• 1998

Taxonomic studies in external morphology, principal component analysis and cluster analysis were conducted to understand the intraspecific relationship among three habitats (Jirisan, Odaesan and Ulleungdo) of allium voctorialis var. platyphyllum. External morphology such as bulb color, leaf blade length and width, petiole length, total leaf length, peduncle length, perianth lobe length and width, length of anther and filament were useful characters for identification of poplations in three habitats. The results obtained based on the principal component (Pc) analysis of treated 72 OTUs(included outgroup) were divided into three groups by the PC 1 ,2,3 and the sums of contributions for the total variance were 84.1%(PC1 51.0%, PC2 24.9% and PC3 8.2%, respectively). In cluster analysis by the UPGMA and Ward's methods , there was similarities in the compostion of clustered taxa, and only Ulleungdo population was distinctly identified from population of other two habitats.

• Structural Engineering and Mechanics
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• v.44 no.2
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• pp.179-196
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• 2012

An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.61-64
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• 2011

There are many applications that require robust and accurate eye tracking, such as human-computer interface(HCI). In this paper, a novel approach for eye tracking with a principal component analysis on rotated face. In the process of iris detection, intensity information is used. First, for select eye region using principal component analysis. Finally, for eye detection using eye region's intensity. The experimental results show good performance in detecting eye from FERET image include rotate face.

• Journal of Soil and Groundwater Environment
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• v.27 no.6
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• pp.22-36
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• 2022

Water resources in Jeju Island are dependent virtually entirely on groundwater. For groundwater resources, drought damage can cause environmental and economic losses because it progresses slowly and occurs for a long time in a large area. Therefore, this study quantitatively evaluated groundwater level fluctuations using principal component and cluster analyses for 42 monitoring wells in Jeju Island, and further identified the types of groundwater fluctuations caused by drought. As a result of principal component analysis for the monthly average groundwater level during 2005-2019 and the daily average groundwater level during the dry season, it was found that the first three principal components account for most of the variance 74.5-93.5% of the total data. In the cluster analysis using these three principal components, most of wells belong to Cluster 1, and seasonal characteristics have a significant impact on groundwater fluctuations. However, wells belonging to Cluster 2 with high factor loadings of components 2 and 3 affected by groundwater pumping, tide levels, and nearby surface water are mainly distributed on the west coast. Based on these results, it is expected that groundwater in the western area will be more vulnerable to saltwater intrusion and groundwater depletion caused by drought.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1981-1987
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• 2013

$CO_2$ emissions of vehicles vary with vehicle's speeds. In addition, the speeds vary with road type, location, time and traffic volume. In this paper, the section in which a large quantity of $CO_2$ emissions per vehicle is exhausted is determined and analyzed with principal component analysis(PCA). In results of analysis, the principal components analysis were divided into two principal components. It had been identified that the main component was the time zone one which is able to explain each components' role. The first principal component could explain the role of a major component on $CO_2$ emissions per vehicle in the early morning and afternoon hour, respectively. The second principal component could explain the role of the component on $CO_2$ emissions per vehicle in the morning and afternoon peak hours, respectively. Therefore, the section in which a large quantity of $CO_2$ emissions per vehicle could be deterimined by PCA scores.

• The Korean Journal of Applied Statistics
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• v.27 no.1
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• pp.31-41
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• 2014

Biplot is a useful graphical method to simultaneously explore the rows and columns of a two-way data matrix. In particular, principal component factor biplot is a graphical method to describe the interrelationship among many variables in terms of a few underlying but unobservable random variables called factors. If we consider the unobservable variables (which are mutually independent and also non-Gaussian), we can apply the independent component analysis decomposing a mixture of non-Gaussian in its independent components. In this case, if we apply the principal component factor analysis, we cannot clearly describe the interrelationship among many variables. Therefore, in this study, we apply the independent component analysis of Jutten and Herault (1991) decomposing a mixture of non-Gaussian in its independent components. We suggest an independent component biplot to interpret the independent component analysis graphically.