• Journal of the Korean Wood Science and Technology
• /
• v.48 no.5
• /
• pp.591-607
• /
• 2020

This study was aimed to investigate the changes of chemical composition of the volatile organic compounds (VOCs) emitted from red pine needles in the process of needle abscission or senescence. The VOCs in intact, senescent, and litter red pine needle samples were analyzed by headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS). And then, multivariate statistical interpretation of the processed data sets was conducted to investigate similarities and dissimilarities of the needle samples. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to investigate the dataset structure and discrimination between samples, respectively. From the data preview, the levels of major components of VOCs from needles were not significantly different between needle samples. By PCA investigation, the data reduction according to classification based on the chlorophyll a / chlorophyll b (Ca/Cb) ratio were found to be ideal for differentiating intact, senescent, and litter needles. The following OPLS-DA taking Ca/Cb ratio as y-variables showed that needle samples were well grouped on score plot and had the significant discriminant compounds, respectively. Several compounds had significantly correlated with Ca/Cb ratio in a bivariate correlation analysis. Notably, the litter needles had a higher content of oxidized compounds than the intact needles. In summary, we found that chemical compositions of VOCs between intact, senescent, and litter needles are different each other and several compounds reflect characteristic of needle.

• The Korean Journal of Malacology
• /
• v.10 no.1
• /
• pp.19-26
• /
• 1994

Eight species of the family Bithyniidae, Bithynia tentaculata, B. leachi, B. siaminsis, B manchourica, B misella, B. kiusiuensis, Gabbia australis and a Bithynia wp. collected from Nepal, were studied for their relationship between species. Total 20 characters were employed for the principal component analysis(PCA) and taxonomic distance. G. misella and B. kiusiuensis were closely related and similar to G. australis. B. manchourica, B. tentaculata and B. siamensis relatively closed group, and taxonomic distance of B. leachi was far from the other species.

• Transactions of The Korea Fluid Power Systems Society
• /
• v.7 no.4
• /
• pp.39-43
• /
• 2010

The flow regulators we widely use have some disadvantages. They have a constant flow within each regulator and an inaccuracy with extruding capillary. In this study, we have developed a new type of regulator which was made up of two different capillary tubes overlapped each other. The developed regulator can vary and control the amount of flow. The design parameters of the developed regulator are obtained by using the analytical software. We have proved that the developed regulator can control flow properly through making a trial product and experiment.

• Bulletin of the Korean Chemical Society
• /
• v.7 no.1
• /
• pp.73-77
• /
• 1986

Data on the concentration of Na, K, Sc, Cr, Fe, Co, Cu, Ga, Rb, Cs, Ba, La, Ce, Sm, Eu, Tb, Lu, Hf, Ta, and Th obtained by neutron activation analysis have been used to characterize Korean porcelainsherds by multivariate analysis. The mathematical approach employed is principal component analysis (PCA). PCA was found to be helpful for dimensionality reduction and for obtaining information regarding (a) the number of independent causal variables required to account for the variability in the overall data set, (b) the extent to which a given variable contributes to a component and (c) the number of causal variables required to explain the total variability of each measured variable.

• Journal of Biomedical Engineering Research
• /
• v.27 no.2
• /
• pp.59-63
• /
• 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.

• Korean Journal of Crystallography
• /
• v.13 no.3_4
• /
• pp.152-157
• /
• 2002

The molecular and crystal structure of Tricyclazole, C/sub9/H/sub7/N₃S, has been determined by single crystal x-ray diffraction study. Crystallographic data for title compound: Pca2₁, a=14.889(1) Å, b=7.444(1) Å, c=15.189(2) Å, V=1683.3(3) ų, Z= 8. The molecular structure model was solved by direct methods and refined by full-matrix least-squares. The final reliable factor, R, is 0.047 for 1533 independent reflections (F/sub o//sup 2/)). The asymmetry unit contains two molecules which are in plate conformation, parallel to each other and related by a pseudo four-fold screw on the b-direction.

• ETRI Journal
• /
• v.40 no.6
• /
• pp.802-812
• /
• 2018

Disease discrimination using an electronic nose is achieved by measuring the presence of a specific gas contained in the exhaled breath of patients. Many studies have reported the presence of acetone in the breath of diabetic patients. These studies suggest that acetone can be used as a biomarker of diabetes, enabling diagnoses to be made by measuring acetone levels in exhaled breath. In this study, we perform a chemical sensor array optimization to improve the performance of an electronic nose system using Wilks' lambda, sensor selection based on a principal component (B4), and a stepwise elimination (SE) technique to detect the presence of acetone gas in human breath. By applying five different temperatures to four sensors fabricated from different synthetic materials, a total of 20 sensing combinations are created, and three sensing combinations are selected for the sensor array using optimization techniques. The measurements and analyses of the exhaled breath using the electronic nose system together with the optimized sensor array show that diabetic patients and control groups can be easily differentiated. The results are confirmed using principal component analysis (PCA).

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.46 no.9
• /
• pp.1114-1121
• /
• 2017

Bacillus strains not producing harmful components were isolated from Korean traditional soybean products. Extracellular enzyme activities (amylase, protease, cellulase, and xylanase) of isolated Bacillus strains were measured, and Bacillus strains with high protease activity were selected. The selected 15 strains were identified as Bacillus amyloliquefaciens (10), Bacillus methylotrophicus (1), Bacillus velezensis (1), and Bacillus subtilis (3). Among them, B. subtilis JBG17019, B. amyloliquefaciens JBD17076, and B. amyloliquefaciens JBD17109 showed antimicrobial activities against food-borne microorganisms. The production abilities of glutamate, glutamine, and poly-${\gamma}$-glutamic acid (${\gamma}$-PGA) of the selected Bacillus strains were measured to analyze fermentation characteristics related to glutamic acid metabolism. The factor for multivariate was analyzed by the principal components analysis (PCA) method between fermentation characteristics and ${\gamma}$-PGA production. The three principal components were classified according to the PCA method: PC1 [enzyme activity (amylase, cellulase, and xylanase)], PC2 (${\gamma}$-PGA), and PC3 (protease, glutamate, and glutamine). As a result, B. amyloliquefaciens JBD17076 and B. subtilis JBG17019 strains were evaluated as having excellent enzyme activity and ${\gamma}$-PGA production.

• Journal of IKEEE
• /
• v.21 no.2
• /
• pp.136-141
• /
• 2017

In this paper, we propose an algorithm for memory optimization to apply deep learning to PC. The proposed algorithm minimizes the memory and computation processing time by reducing the amount of computation processing and data required in the conventional deep learning structure in a general PC. The algorithm proposed in this paper consists of three steps: a convolution layer configuration process using a random filter with discriminating power, a data reduction process using PCA, and a CNN structure creation using SVM. The learning process is not necessary in the convolution layer construction process using the discriminating random filter, thereby shortening the learning time of the overall deep learning. PCA reduces the amount of memory and computation throughput. The creation of the CNN structure using SVM maximizes the effect of reducing the amount of memory and computational throughput required. In order to evaluate the performance of the proposed algorithm, we experimented with Yale University's Extended Yale B face database. The results show that the algorithm proposed in this paper has a similar performance recognition rate compared with the existing CNN algorithm. And it was confirmed to be excellent. Based on the algorithm proposed in this paper, it is expected that a deep learning algorithm with many data and computation processes can be implemented in a general PC.

• Journal of the Korean Society of International Agriculture
• /
• v.31 no.4
• /
• pp.378-383
• /
• 2019

To determine whether FT-IR spectral analysis based on multivariate analysis for whole cell extracts can be used to discriminate papaya at metabolic level. FT-IR spectral data from leaves were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and 1,100-950 cm^-1, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins (1,700-1,500 cm^-1), phosphodiester groups from nucleic acid and phospholipid (1,500-1,300 cm^-1) and carbohydrate compounds (1,100-950 cm^-1). The result of PCA analysis showed that papaya leaves could be separated into clusters depending on different growth temperature. In this case, showed discrimination confirmed according to metabolite content of growth condition from papaya. And PLS-DA analysis also showed more clear discrimination pattern than PCA result. Furthermore, these metabolic discrimination systems could be applied for rapid selection and classification of useful papaya cultivars.