• International Journal of Industrial Entomology and Biomaterials
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• v.14 no.1
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• pp.23-32
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• 2007

The nature of genetic variation and diversity among the 65 multivoltine silkworm genotypes was evaluated for 16 post cocoon characters. The components of genetic variation revealed higher PCV (60.487%) and GCV (44.56%) for evenness (variation 1) followed by cohesion (PCV=55.38%, GCV=40.36%) and non-broken filament length (PCV=32.05%, GCV=31.28%). The higher heritability ($h^2$ in broad sense) was observed for boil-off loss (95.6%) followed by non-broken filament length (95.22%). The both genotypic and phenotypic correlation indicated significant positive correlation of filament length with non-broken filament length, silk recovery, raw silk, neatness, and low neatness; and negative correlation with denier, renditta and silk waste. The principal component analysis (PCA) revealed 75.381 % of total variance from the five principal components extracted. On the basis of Mahalonobis' $D^2$ values (Ward's minimum variance), the sixty-five multivoltine silkworm genotypes were classified in to 9 clusters with substantial inter and intra cluster distances. Number of genotypes included in different clusters varied from 3 to 17. The results indicated that the optimum distance obtained in cluster VII (15.059) along with higher cluster mean values especially for filament length, non broken filament length, renditta, silk recovery, silk waste, and raw silk emphasized the utilization of these genotypes in the conventional silkworm breeding programme for improvement of multivoltine silk fibre quality. The possibility of exploiting genetic variation in post cocoon traits for efficient breeding programme is discussed.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.49 no.2
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• pp.224-233
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• 2016

The objective of this study was to develop an artificial neural network (ANN) model for the acoustic identification of commercially important fish species in Korea. A broadband echo acquisition and processing system operating over the frequency range of 85-225 kHz was used to collect and process species-specific, time-frequency feature images from six fish species: black rockfish Sebastes schlegeli, black scraper Thamnaconus modesutus [K], chub mackerel Scomber japonicus, goldeye rockfish Sebastes thompsoni, konoshiro gizzard shad Konosirus punctatus and large yellow croaker Larimichthys crocea. An ANN classifier was developed to identify fish species acoustically on the basis of only 100 dimension time-frequency features extracted by the principal components analysis (PCA). The overall mean identification rate for the six fish species was 88.5%, with individual identification rates of 76.6% for black rockfish, 82.8% for black scraper, 93.8% for chub mackerel, 90.6% for goldeye rockfish, 96.9% for konoshiro gizzard shad and 90.6% for large yellow croaker, respectively. These results demonstrate that individual live fish in well-controlled environments can be identified accurately by the proposed ANN model.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.16-22
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• 2012

In this paper, we proposed a method of baseline correction for analysis of Raman spectra of platelets from Alzheimer's disease (AD) transgenic mice. Measured Raman spectra include the meaningful information and unnecessary noise which is composed of baseline and additive noise. The Raman spectrum is divided into the local region including several peaks and the spectrum of the region is modeled by curve fitting using Gaussian model. The additive noise is clearly removed from the process of replacing the original spectrum with the fitted model. The baseline correction after interpolating the local minima of the fitted model with linear, piecewise cubic Hermite and cubic spline algorithm. The baseline corrected models extract the feature with principal component analysis (PCA). The classification result of support vector machine (SVM) and maximum $a$ posteriori probability (MAP) using linear interpolation method showed the good performance about overall number of principal components, especially SVM gave the best performance which is about 97.3% true classification average rate in case of piecewise cubic Hermite algorithm and 5 principal components. In addition, it confirmed that the proposed baseline correction method compared with the previous research result could be effectively applied in the analysis of the Raman spectra of platelet.

• Korean Journal of Cognitive Science
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• v.18 no.4
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• pp.445-455
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• 2007

We present a new biometric identification system based on multi-view features of teeth using principal components analysis(PCA). The multi-view features of teeth consist of the frontal view, the left side view and the right side view. In this paper, we try to stan the foundations of a dental biometrics for secure access in real life environment. We took the pictures of the three views teeth in the experimental environment designed specially and 42 principal components as the features for individual identification were developed. The classification for individual identification based on the nearest neighbor(NN) algorithm is created with the distance between the multi-view teeth and the multi-view teeth rotated. The identification performance after rotating two degree of test data is 95.2% on the left side view teeth and 91.3% on the right side view teeth as the average values.

• Korean Journal of Applied Biomechanics
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• v.27 no.3
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• pp.205-210
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• 2017

Objective: The aim of the study was to investigate the age-related ability of dynamic balance recovery through perturbation response during standing. Method: Six older and 6 younger adults participated in this study. External perturbation during standing as pulling force applied at the pelvic level in the anterior direction was provided to the subject. The margin of stability was quantified as a measure of postural stability or dynamic balance recovery, and using principal component analysis (PCA), the regularity of the margin of stability (MoS) was calculated. Results: Our results showed that in the older adult group, 60.99% and 28.63% of the total variance were captured using the first and second principal components (PCs), respectively, and in the younger adult group, 81.95% and 10.71% of the total variance were captured using the first and second PCs, respectively. Conclusion: Ninety percent of the total variance captured using the first two PCs indicates that the older adults had decreased regularity of the MoS than the younger adults. Thus, the results of the present study suggest that aging is associated with non-regularity of dynamic postural stability.

• Journal of the Korean Society of Tobacco Science
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• v.35 no.1
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• pp.20-27
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• 2013

An Electronic Nose(E-Nose) and Gas Chromatography/Mass Spectroscopy (GC/MS) are meanwhile conventional technique to analyze volatile materials in many industries (e.g., food, medicine, environment) and have broad acceptance in the analysis of tobacco products. In this study, an experiment where tin oxide gas sensor array responses and GC/MS profiles are used to characterize the volatile compounds of different cigarettes at the same time is performed and the measurements of two instruments are compared for cigarette samples with a known chemical information. E-Nose and GC/MS were employed to differentiate and match flavored cigarettes with commercial tobacco flavoring agents (lavender, vanilla, peppermint, orange, star anise). For verifying reliability of two systems, the analyses were conducted in terms of amount of flavors in each cigarettes using partial least squares (PLS) and with the principal components analysis (PCA). Various chemical sensors and GC/MS data was reduced into two principal factors (PC1, PC2) for being distinguished with visualized regions. Both systems provided adequate results for odor characteristics of cigarettes in this study with each instrument having its own advantages and disadvantages.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.3
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• pp.247-253
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• 2007

Determination of crack depth in filed using the self-calibrating surface wane transmission measurement and the cutting frequency in the transmission function (TRF) is very difficult due to variations of the measurement conditions. In this study, it is proposed to use the measured full TRF as a feature for crack depth assessment. A Principal component analysis (PCA) is employed to generate a basis of the measured TRFs for various crack cases. The measured TRFs are represented by their projections onto the most significant principal components. Then artificial neural networks (NNs) using the PCA-compressed TRFs is applied to assess the crack in concrete. Experimental study is carried out for five different crack cases to investigate the effectiveness of the proposed method. Results reveal that the proposed method can be effectively used for the crack depth assessment of concrete structures.

• Korean Journal of Agricultural Science
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• v.49 no.1
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• pp.129-136
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• 2022

In this study, we aimed to develop a maturity classification model for tomatoes using hyperspectral imaging in the range of 400 - 1,000 nm. Fifty-seven tomatoes harvested in August and November of 2021 were used as the sample set, and hyperspectral data was extracted from the surfaces of these tomatoes. A combined method of SNV (standard normal variate) and SG (Savitzky-Golay) methods was used for the pre-processing of the hyperspectral data. In addition, the hyperspectral data were analyzed for all maturity stages and considering bandwidths with different FWHM (full width at half maximum) values of 2, 25, and 50 nm. The PCA (principal component analysis) method was used to analyze the principal components related to maturity stages for the tomatoes. As a result, 500 - 550 nm and 650 - 700 nm bands were found to be related to the maturity stages of tomatoes. In addition, PC1 and PC2 explained approximately 97% of the variance at all FWHM conditions and thus were used as input data for classification model training based on the SVM (support vector machine). The SVM models were able to classify tomato maturity into five stages (Green, Turning, Pink, Light red, and Red) with over 95% accuracy regardless of the FWHM condition. Therefore, it was considered that hyperspectral data with 50 nm FWHM and SVM is feasible for use in the classification of tomato maturity into five stages.

• YAKHAK HOEJI
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• v.57 no.4
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• pp.272-281
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• 2013

In this study, we developed and validated the HPLC method using the isolated components from Erycibae caulis. Their structures were elucidated by spectroscopic methods including UV, $^1H$-NMR, $^{13}C$-NMR, FAB-Mass and ESI-Mass as Compound 1 (crypto-chlorogenic acid), Compound 2 (scopolin), Compound 3 (neochlorogenic acid) and Compound 4 (3,4-di-O-caffeoylquinic acid). Major three compounds and scopoletin were decided as representative components of Erycibae caulis. We established HPLC analytical method by using the representative components and 20 commercial samples which were collected considering to various cultivated area. The HPLC fingerprinting was successfully achieved with an AKZO NOBEL Kromasil 100-5C18 column. The mobile phase consisted of 0.5% acetic acid in water (A) and methanol (B) using gradient method of 85(A) to 50(A) for 35min. The fingerprints of chromatograms were recorded at an optimized wavelength of 330 nm. This developed analytical method was validated with specificity, selectivity, accuracy and precision. And it is suggested that scopolin, scopoletin, neochlorogenic acid, 3,4-di-O-caffeoylquinic acid were more than 0.162%, 0.133%, 0.057%, 0.044%, respectively. In addition, principal component analysis (PCA) was performed on the analytical data of 20 different Erycibae caulis samples in order to classify samples collected from different regions. We hope that this assay can be readily utilized as quality control method for Erycibae caulis.

• Agricultural and Biosystems Engineering
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• v.5 no.1
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• pp.25-29
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• 2004

Fluorescence spectroscopy was used to characterize chicken carcass diseases. Spectral signatures of three different disease categories of poultry carcasses (airsacculitis, cadaver and septicemia) were obtained from fluorescence emission measurements in the wavelength range of 360 to 600 nm with 330 nm excitation. Principal Component Analysis (PCA) was used to select the most significant wavelengths for the classification of poultry carcasses. These wavelengths were analyzed for pathologic correlation of poultry diseases. Using a Soft Independent Modeling of Class Analogy (SIMCA) of principal components with a Mahalanobis distance metric, poultry carcasses were individually classified into different classes with $97.9\%$ accuracy.