• Journal of Ecology and Environment
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• v.36 no.3
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• pp.205-210
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• 2013

Cicuta virosa L. (Apiaceae) is a perennial emergent plant designated as an endangered species in South Korea. According to the former records, only four natural habitats remain in South Korea. A former study suggested that three of four populations (Pyeongchang: PC, Hoengseong: HS, Gunsan: GS) would be classified as different ecotypes based on their different morphological characteristics and life cycle under different environmental conditions. To evaluate this suggestion, we estimated genetic diversity in each population and distance among three populations by random amplification of polymorphic DNA. Seven random primers generated a total of 61 different banding positions, 36 (59%) of them were polymorphic. Nei's gene diversity and the Shannon diversity index increased in the order of PC < HS < GS, which is the same order of population size. In the two-dimensional (2D) plot of first two principal components in principal component analysis with the presence of 61 loci, individuals could be grouped as three populations easily (proportion of variance = 0.6125). Nei's genetic distance for the three populations showed the same tendency with the geographical distance within three populations. And it is also similar to the result of discriminant analysis with the morphological or life-cycle factors from the previous study. From the results, we concluded that three different populations of C. virosa should be classified as ecotypes based on not only morphology and phenology but genetic differences in terms of diversity and distance as well.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Proceedings of the Polymer Society of Korea Conference
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• 2006.10a
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• pp.355-355
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• 2006

In this study, we have applied principal component analysis-based 2D (PCA2D) correlation spectroscopy to the temperature-dependent IR spectra of biodegradable poly(hydroxyalkanoate). PCA2D analysis reveals clearly that there are two components in crystalline band of C=O stretching mode without being hampered by noise. To better understand the thermal behavior of biodegradable poly(hydroxyalkanoate), eigenvalue manipulating transformation (EMT) technique was also employed. By uniformly lowering the power of a set of eigenvalues associated with the original data, the subtle contributions from minor eigenvectors are highlighted. Details of thermal behavior of biodegradable poly(hydroxyalkanoate) studied by PCA2D correlation spectroscopy with EMT will be discussed.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10573-10576
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• 2015

Background: Accuracy in feature extraction is an important factor in image classification and retrieval. In this paper, a breast tissue density classification and image retrieval model is introduced for breast cancer detection based on thermographic images. The new method of thermographic image analysis for automated detection of high tumor risk areas, based on two-directional two-dimensional principal component analysis technique for feature extraction, and a support vector machine for thermographic image retrieval was tested on 400 images. The sensitivity and specificity of the model are 100% and 98%, respectively.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1181-1181
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• 2001

Robinson with ${coworkers}^{1}$ have introduced two-state outer-neighbor bonding model to explain the anomalies of water. The studies on the properties of water as a function of temperature and pressure revealed that, unlike other ideas, all $H_2O$ molecules in liquid are tetrabonded. On the average they are forming two different bonding types. One type is the regular tetrahedral water-water bonding similar to that found in the ordinary ice Ih, whereas the other is a more dense nonregular tetrahedral bonding similar to that appearing in the ice II. The transformation between these two bonding forms is evidenced by FT-NIR experiment. The FT-NIR measurements were done for liquid water in the temperature range from $20^{\circ}C$ up to $80^{\circ}C$ in a wide extent of frequencies: 12 000 - 4000 $cm^{-1}$ /. Temperature dependent variations in the volume fraction of these two structures are directly related to the spectral changes. The absorbance variations are explored by means of the two-dimensional correlation spectroscopy (2DCOS), principal component analysis (PCA), curve fitting and second derivatives. The presence of the isosbestic points in a range of the combination and overtone transitions indicates that the experimental spectra are a superposition of two temperature independent components. One component of diminishing intensity with temperature increase, is assigned to a stronger hydrogen bonds occurred in the Ih type, whereas the second component showing an opposite behavior, one can attribute to a weaker H-bonds characteristic for the II type. The understanding of the hydrogen bonding network in the liquid water is very important in interpretation of the interaction between water and protein chain. The two-state model of water surrounding the protein surface could advance an understanding of the hydration process.

• Korean Journal of Optics and Photonics
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• v.31 no.3
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• pp.125-133
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• 2020

An innovative method for separating overlapping latent fingerprints, using laser-induced plasma spectroscopy (LIPS) combined with multivariate analysis, is reported in the current study. LIPS provides the capabilities of real-time analysis and high-speed scanning, as well as data regarding the chemical components of overlapping fingerprints. These spectra provide valuable chemical information for the forensic classification and reconstruction of overlapping latent fingerprints, by applying appropriate multivariate analysis. This study utilizes principal-component analysis (PCA) and partial-least-squares (PLS) techniques for the basis classification of four types of fingerprints from the LIPS spectra. The proposed method is successfully demonstrated through a classification example of four distinct latent fingerprints, using discrimination such as soft independent modeling of class analogy (SIMCA) and partial-least-squares discriminant analysis (PLS-DA). This demonstration develops an accuracy of more than 85% and is proven to be sufficiently robust. In addition, by laser-scanning analysis at a spatial interval of 125 ㎛, the overlapping fingerprints were separated as two-dimensional forms.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.39-45
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• 2010

As the value of environment is increasing, the water quality has been a matter of interest to the nation and people. Research on water quality has been widely studied, but focused on geographical characteristic and river characteristics like inflow, outflow, quantity and speed of water. In this paper, two approaches to measure the similarity of sampling sites by using water quality data are discussed and compared with two-years empirical data of Yongdam-Dam. The existing method has calculated their similarities with principal component scores. The proposed approach in this paper use correlation matrix of water quality related variables and MDS for measuring the similarity, which is shown to be better in the sense of being clustering which is identical to geographical clustering since it can consider the time series pattern of water quality.

• Korean Journal of Plant Taxonomy
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• v.41 no.3
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• pp.223-229
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• 2011

The taxonomy of the Isodon excisus complex has been ambiguous and problematic because the morphological characters, especially characters related to the leaf distinguishing subgroups of the complex in the original descriptions, are variable. To elucidate the taxonomic structure of the I. excisus complex in Korea, 34 characters were measured from 70 OTUs representing different locations and analyzed by principal component analysis (PCA). The analysis showed that principle component axis 1, 2, 3 (PC1, PC2, PC3) represents 52.0% of the total variance and characters showing high loading values for PC1 were leaf shape, density of non-glandular hairs on the lower surface of the leaf, and characters related to the teeth of the leaf. The length of apical tooth and the angle between two widest points of the leaf were highly correlated to PC2 and PC3, respectively. Three-dimensional scatter plotting of OTUs for PC1, PC2, and PC3 axis showed that the areas of previously recognized three subgroups of I. excisus completely overlapped. Our result supported that just one taxon, I. excisus var. excisus, should be recognized in the complex at the variety level.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.450-463
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• 2018

Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sounds being inevitably corrupted by random disturbance, the most important part of the diagnosis consists of discovering the hidden elements inside the data that can reveal the faulty patterns. This paper presents a novel feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by the drills. Using the Fourier analysis, the magnitude spectrum of the sounds are extracted, converted into two-dimensional vectors and uniformly normalized in such a way that they can be represented as 8-bit grayscale images. Histogram equalization is then performed over the obtained images in order to adjust their very poor contrast. The obtained contrast enhanced images will be used as the features of our diagnosis system. Finally, principal component analysis is performed over the image features for reducing their dimensions and a nonlinear classifier is adopted to produce the final response. Unlike the conventional features, the results demonstrate that the proposed feature extraction method manages to capture the hidden health patterns of the sound.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1614-1622
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• 2018

The research related to fault diagnosis in permanent magnet synchronous motors (PMSMs) has attracted considerable attention in recent years because various faults such as permanent magnet demagnetization and short-circuited turns can occur and result in unexpected failure of motor related system. Several conventional current and back electromotive force (BEMF) analysis techniques were proposed to detect certain faults in PMSMs; however, they generally deal with a single fault only. On the contrary, cases of multiple faults are common in PMSMs. We propose a fault diagnosis method for PMSMs with single and multiple combined faults. Our method uses three phase BEMF voltages based on the fast Fourier transform (FFT), support vector machine(SVM), and visualization tools for identifying fault types and severities in PMSMs. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are used to visualize the high-dimensional data into two-dimensional space. Experimental results show good visualization performance and high classification accuracy to identify fault types and severities for single and multiple faults in PMSMs.