• Journal of Plant Biotechnology
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• v.42 no.4
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• pp.401-408
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• 2015

Horticultural traits and genetic relationship were evaluated for 83 melon (Cucumis melo L.) cultivars. Survey of a total of 36 characteristics for seedling, leaf, stem, flower, fruit, and seed and subsequent multiple analysis of variance (MANOVA) were conducted. Principal component analysis (PCA) showed that 8 principle components including fruit weight, fruit length, fruit diameter, cotyledon length, seed diameter, and seed length accounted for 76.3% of the total variance. Cluster analysis of the 83 melon cultivars using average linkage method resulted in 5 clusters at coefficient of 0.7. Cluster I consisted of cultivars with high values for fruit-related traits, Cluster II for soluble solid content, and Cluster V for high ripening rate. Genotyping of the 83 cultivars was conducted using 15 expressed-sequence tagged-simple sequence repeat (EST-SSR) from the Cucurbit Genomics Initiative (ICuGI) database. Analysis of genetic relatedness by UPGMA resulted in 6 clusters. Mantel test indicated that correlation between morphological and genetic distance was very low (r = -0.11).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.54-60
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• 2010

The local appearance-based method is one of the face recognition methods that divides face image into small areas and extracts features from each area of face image using statistical analysis. It collects classification results of each area and decides identity of a face image using a voting scheme by integrating classification results of each area of a face image. The conventional local appearance-based method divides face images into small pieces and uses all the pieces in recognition process. In this paper, we propose a local appearance-based method that makes use of only the relatively important facial components. The proposed method detects the facial components such as eyes, nose and mouth that differs much from person to person. In doing so, the proposed method detects exact locations of facial components using support vector machines (SVM). Based on the detected facial components, a number of small images that contain the facial parts are constructed. Then it extracts features from each facial component image using principal components analysis (PCA). We compared the performance of the proposed method to those of the conventional methods. The results show that the proposed method outperforms the conventional local appearance-based method while preserving the advantages of the conventional local appearance-based method.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1067-1076
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• 2020

In this paper, an unsupervised saliency guided change detection method using UAV and aerial imagery is proposed. Regions that are more different from other areas are salient, which make them more distinct. The existence of the substantial difference between two images makes saliency proper for guiding the change detection process. Change Vector Analysis (CVA), which has the capability of extracting of overall magnitude and direction of change from multi-spectral and temporal remote sensing data, is used for generating an initial difference image. Combined with an unsupervised CVA and the saliency, Principal Component Analysis(PCA), which is possible to implemented as the guide for change detection method, is proposed for UAV and aerial images. By implementing the saliency generation on the difference map extracted via the CVA, potentially changed areas obtained, and by thresholding the saliency map, most of the interest areas correctly extracted. Finally, the PCA method is implemented to extract features, and K-means clustering is applied to detect changed and unchanged map on the extracted areas. This proposed method is applied to the image sets over the flooded and typhoon-damaged area and is resulted in 95 percent better than the PCA approach compared with manually extracted ground truth for all the data sets. Finally, we compared our approach with the PCA K-means method to show the effectiveness of the method.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.4
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• pp.467-476
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• 2019

Objective: This research was conducted to study the genetic diversity in several Indonesian cattle breeds using microsatellite markers to classify the Indonesian cattle breeds. Methods: A total of 229 DNA samples from of 10 cattle breeds were used in this study. The polymerase chain reaction process was conducted using 12 labeled primers. The size of allele was generated using the multiplex DNA fragment analysis. The POPGEN and CERVUS programs were used to obtain the observed number of alleles, effective number of alleles, observed heterozygosity value, expected heterozygosity value, allele frequency, genetic differentiation, the global heterozygote deficit among breeds, and the heterozygote deficit within the breed, gene flow, Hardy-Weinberg equilibrium, and polymorphism information content values. The MEGA program was used to generate a dendrogram that illustrates the relationship among cattle population. Bayesian clustering assignments were analyzed using STRUCTURE program. The GENETIX program was used to perform the correspondence factorial analysis (CFA). The GENALEX program was used to perform the principal coordinates analysis (PCoA) and analysis of molecular variance. The principal component analysis (PCA) was performed using adegenet package of R program. Results: A total of 862 alleles were detected in this study. The INRA23 allele 205 is a specific allele candidate for the Sumba Ongole cattle, while the allele 219 is a specific allele candidate for Ongole Grade. This study revealed a very close genetic relationship between the Ongole Grade and Sumba Ongole cattle and between the Madura and Pasundan cattle. The results from the CFA, PCoA, and PCA analysis in this study provide scientific evidence regarding the genetic relationship between Banteng and Bali cattle. According to the genetic relationship, the Pesisir cattle were classified as Bos indicus cattle. Conclusion: All identified alleles in this study were able to classify the cattle population into three clusters i.e. Bos taurus cluster (Simmental Purebred, Simmental Crossbred, and Holstein Friesian cattle); Bos indicus cluster (Sumba Ongole, Ongole Grade, Madura, Pasundan, and Pesisir cattle); and Bos javanicus cluster (Banteng and Bali cattle).

• The KIPS Transactions:PartD
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• v.10D no.2
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• pp.277-282
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• 2003

The knowledge discovery from web has been studied in many researches. There are some difficulties using web log for training data on efficient information predictive models. In this paper, we studied on the method to eliminate sparseness from web log data and to perform web user clustering. Using missing value imputation by Bayesian inference of MCMC, the sparseness of web data is removed. And web user clustering is performed using self organizing maps based on 3-D plot by principal component. Finally, using KDD Cup data, our experimental results were shown the problem solving process and the performance evaluation.

• Safety and Health at Work
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• v.6 no.2
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• pp.77-84
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• 2015

Background: A unique framework for performance optimization of generation companies (GENCOs) based on health, safety, environment, and ergonomics (HSEE) indicators is presented. Methods: To rank this sector of industry, the combination of data envelopment analysis (DEA), principal component analysis (PCA), and Taguchi are used for all branches of GENCOs. These methods are applied in an integrated manner to measure the performance of GENCO. The preferred model between DEA, PCA, and Taguchi is selected based on sensitivity analysis and maximum correlation between rankings. To achieve the stated objectives, noise is introduced into input data. Results: The results show that Taguchi outperforms other methods. Moreover, a comprehensive experiment is carried out to identify the most influential factor for ranking GENCOs. Conclusion: The approach developed in this study could be used for continuous assessment and improvement of GENCO's performance in supplying energy with respect to HSEE factors. The results of such studies would help managers to have better understanding of weak and strong points in terms of HSEE factors.

• Journal of the Korean Magnetic Resonance Society
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• v.15 no.1
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• pp.54-68
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• 2011

$^1H$ nuclear magnetic resonance (NMR) spectroscopy of biological samples has been proven to be an effective and nondestructive approach to probe drug toxicity within an organism. In this study, ketoprofen toxicity was investigated using $^1H$-NMR spectroscopy coupled with multivariate statistical analysis. Histopathologic test of ketoprofen-induced acute gastrointestinal damage in rats demonstrated a significant dose-dependent effect. Furthermore, principal component analysis (PCA) derived from $^1H$-NMR spectra of urinary samples showed clear separation between the vehicle-treated control and ketoprofen-treated groups. Moreover, PCA derived from endogenous metabolite concentrations through targeted profiling revealed a dose-dependent metabolic shift between the vehicle-treated control, low-dose ketoprofen-treated (10 mg/kg body weight), and high-dose ketoprofen-treated (50 mg/kg) groups coinciding with their gastric damage scores after ketoprofen administration. The resultant metabolic profiles demonstrated that the ketoprofen-induced gastric damage exhibited energy metabolism perturbations that increased urinary levels of citrate, cis-aconitate, succinate, and phosphocreatine. In addition, ketoprofen administration induced an enhancement of xenobiotic activity in fatty oxidation, which caused increase levels of N-isovalerylglycine, adipate, phenylacetylglycine, dimethylamine, betaine, hippurate, 3-indoxylsulfate, N,N-dimethylglycine, trimethyl-N-oxide, and glycine. These findings demonstrate that $^1H$-NMR-based urinary metabolic profiling can be used for noninvasive and rapid way to diagnose adverse drug effects and is suitable for explaining the possible biological pathways perturbed by nonsteroidal anti-inflammatory drug toxicity.

• Korean Journal of Plant Resources
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• v.31 no.6
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• pp.641-651
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• 2018

Floral scents and metabolites from cut flowers of 14 peony cultivars (Paeonia lactiflora Pall.) were analyzed to discriminate different cultivars and to compare the Korean cultivar with the other cut peonies imported to Korea using electronic nose (E-nose) and Fourier transform infrared (FT-IR) spectroscopy combined with multivariate analysis, respectively. Principal component analysis (PCA) and discriminant function analysis (DFA) dendrogram of peony floral scents were not precisely same but there were 3 groups including same cultivars. PCA and partial least squares-discriminant analysis (PLS-DA) dendrograms of peony metabolites showed that different cut peony cultivars were clustered into two major groups including same cultivars. Fragrance pattern of Korean 'Taebaek' was classified to same group with 'Jubilee' on the PCA and DFA results and its metabolite pattern was clearly discriminated by the PCA and PLS-DA compared to the other cultivars. These results show that the 14 peony cut flowers could be discriminated corresponding to their chemical relationship and the metabolic profile of Korean 'Taebaek' has distinctive characteristics. Furthermore, we suggest that these results could be used as the preliminary data for breeding new cut peony cultivars and for improving the availability of Korean cut peony in cosmetic industry.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.587-598
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• 2006

The number of variables exceeds the number of samples in microarray data. We propose a nonparametric local linear logistic classification procedure using orthogonal components for classifying high-dimensional microarray data. The proposed method is based on the local likelihood and can be applied to multi-class classification. We applied the local linear logistic classification method using PCA, PLS, and factor analysis components as new features to Leukemia data and colon data, and compare the performance of the proposed method with the conventional statistical classification procedures. The proposed method outperforms the conventional ones for each component, and PLS has shown best performance when it is embedded in the proposed method among the three orthogonal components.

• Journal of Environmental Health Sciences
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• v.35 no.6
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• pp.517-525
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• 2009

The water quality of Lake Shihwa had been rapidly deteriorating since 1994 due to wastewater input from the watersheds, limited water circulation and the lack of a wastewater treatment policy. In 2000, the government decided to open the tidal embankment and make a comprehensive management plan to improve the water quality, especially inflowing stream water around Shihwa and Banwol industrial complex. However, the water quality and microbial community have not as yet been fully evaluated. The purpose of this study is to investigate the influent water quality around the industrial area based on chemical and biological analysis, and collected surface water sample from the Siheung Stream, up-stream to down-stream through the industrial complex, Samples were collected in July 2009. The results show that the downstream site near the industrial complex had higher concentrations of heavy metals (Cu, Mn, Fe, Mg, and Zn) and organic matter than upstream sites. A combination of DGGE (Denaturing Gradient Gel Electrophoresis) gels, lists of K-WQI (Korean Water Quality Index), cluster analysis, MDS (Multi-Dimensional Scaling) and PCA (Principal Component Analysis) has demonstrated clear clustering between Siheung stream 3 and 4 and with a high similarity and detected metal reducing bacteria (Shewanella spp.) and biodegrading bacteria (Acinetobacter spp.). These results suggest that use of both chemical and microbiological marker would be useful to fully evaluate the water quality.