• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.875-890
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• 2021

In order to confirm the safety of a genetically modified organism (GMO), we assess its potential toxicity on non-target insects and spiders. In this study, the effects of GM soybean, a type of vitamin-A-enhanced transgenic soybean with tolerance to the herbicide glufosinate, were assessed under a field condition. The study compared this vitamin-A-enhanced transgenic soybean and a non-GM soybean (Gwangan) in a living modified organism (LMO) isolated field of Kyungpook National University (Gunwi) and the National Institute Agricultural Sciences (Jeonju) in the Republic of Korea in 2019 - 2020. In total, 207,760 individual insects and arachnids, representing 81 families and 13 orders, were collected during the study. From the two types of soybean fields, corresponding totals of 105,765 and 101,995 individuals from the vitamin-A-enhanced transgenic soybean and Gwangan samples areas were collected. An analysis of variance indicated no significant differences (p < 0.05). A multivariate analysis showed that the dominance and richness outcomes of plant-dwelling insects were similar. The data on insect species population densities were subjected to a principal component analysis (PCA) and an orthogonal partial least squares-discriminant analysis (OPLS-DA), which did not distinguish between the two varieties, i.e., the vitamin-A-enhanced transgenic soybean and the non-GM soybean in any cultivated field. However, the results of the PCA analysis could be divided overall into four groups based on the yearly survey areas. Therefore, there was no evidence for the different impact of vitamin A-enhanced transgenic soybean on the above-ground insects and spiders compared to non-GM soybean.

• Journal of the Korea Society of Computer and Information
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• v.18 no.4
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• pp.45-55
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• 2013

In this study, we propose a tongue diagnosis system which detects the tongue from face image and divides the tongue area into six areas, and finally generates tongue fur ratio of each area. To detect the tongue area from face image, we use ASM as one of the active shape models. Detected tongue area is divided into six areas and the distribution of tongue coating of six areas is examined by SVM. For SVM, we use a 3-dimensional vector calculated by PCA from a 12-dimensional vector consisting of RGB, HSV, Lab, and Luv. As a result, we stably detected the tongue area using ASM. Furthermore, we recognized that PCA and SVM helped to raise the ratio of tongue coating detection.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.17-23
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• 2005

In this paper, we propose face recognition method using embedded data in super states segmentalized that is specification region exist to face region, hair, forehead, eyes, ears, nose, mouth, and chin. Proposed method defines super states that is specification area in normalized size (92×112), and embedded data that is extract internal factor in super states segmentalized achieve face recognition by PCA algorithm. Proposed method can receive specification data that is less in proposed image's size (92×112) because do orignal image to learn embedded data not to do all loaming. And Showed face recognition rate in image of 92×112 size averagely 99.05%, step 1 99.05%, step 2 98.93%, step 3 98.54%, step 4 97.85%. Therefore, method that is proposed through an experiment showed that the processing speed improves as well as reduce existing face image's information.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.1-7
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• 2016

Structural MRI(sMRI) imaging is used to extract morphometric features after Grey Matter (GM), White Matter (WM) for several univariate and multivariate method, and Cerebro-spinal Fluid (CSF) segmentation. A new approach is applied for the diagnosis of very mild to mild AD. We propose the classification method of Alzheimer disease patients from normal controls by combining morphometric features and Gaussian Mixture Models parameters along with MMSE (Mini Mental State Examination) score. The combined features are fed into Multi-kernel SVM classifier after getting rid of curse of dimensionality using principal component analysis. The experimenral results of the proposed diagnosis method yield up to 96% stratification accuracy with Multi-kernel SVM along with high sensitivity and specificity above 90%.

• Journal of Environmental Health Sciences
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• v.41 no.1
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• pp.49-60
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• 2015

Objectives: Pine needles were used as a passive air sampler (PAS) of atmospheric persistent organic pollutants (POPs). This study was performed to investigate concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) deposited on pine needles near a waste incinerator and PCDD/Fs source contributions using principal component analysis (PCA). Methods: Two-year-old pine needles were sampled at 11 points with respect to distance and wind direction from the incinerator. PCDD/Fs deposited on pine needles were analyzed with HRGC/HRMS. The source contribution of PCA was calculated with SPSS. Results: The average concentration of PCDD/Fs deposited on pine needle was 0.79 (0.27-1.76) pg TEQ/g dry, PCDDs with 0.24 (0.01-0.95) pg TEQ/g dry and PCDFs with 0.56 (0.27-0.82) pg TEQ/g dry, respectively. The average concentration fraction of PCDDs was 29.7%, that of PCDFs was 70.3%, and PCDFs were more prevalent than PCDDs. The contributions of PCDD/Fs sources were estimated as incineration at 58.3% and automobiles at 28.4%. However, a relation and regulation between PCDD/Fs concentrations deposited on pine needles and distance from incinerator or wind direction was not shown. Conclusion: It was concluded that atmospheric PCDD/Fs concentrations near an industrial complex with a waste incinerator were affected by multiple sources. However, PCDD/Fs concentrations were lower than in other inland cities with the exception of background area.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.232-237
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• 2013

A newly developed fall detection algorithm using the HMM (Hidden Markov Model) extracted from the video is introduced. To distinguish between the fall from personal difference fall pattern or the normal activities of daily living (ADL), HMM machine learning algorithm is used. For getting fall feature vector of video, the motion vector from the optical flow is applied to the PCA (Principal Component Analysis). The combination of the angle, ratio of long-short axis, velocity from results of PCA make the new fall feature parameters. These parameters were applied to the HMM and the results were compared and analyzed. Among the newly proposed various kinds of fall parameters, the angle of movement showed the best results. The results show that this parameter can distinguish various types of fall from ADLs with 91.5% sensitivity and 88.01% specificity.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.425-430
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• 2015

This paper is a study to improve the classification efficiency of rotating objects by using deep neural networks to which a deep learning algorithm was applied. For the classification experiment of rotating objects, COIL-20 is used as data and total 3 types of classifiers are compared and analyzed. 3 types of classifiers used in the study include PCA classifier to derive a feature value while reducing the dimension of data by using Principal Component Analysis and classify by using euclidean distance, MLP classifier of the way of reducing the error energy by using error back-propagation algorithm and finally, deep learning applied DBN classifier of the way of increasing the probability of observing learning data through pre-training and reducing the error energy through fine-tuning. In order to identify the structure-specific error rate of the deep neural networks, the experiment is carried out while changing the number of hidden layers and number of hidden neurons. The classifier using DBN showed the lowest error rate. Its structure of deep neural networks with 2 hidden layers showed a high recognition rate by moving parameters to a location helpful for recognition.

• Journal of Applied Biological Chemistry
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• v.61 no.4
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• pp.357-365
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• 2018

The quality of Korean doenjang, which was traditionally made for this study, was monitored for physicochemical properties, antioxidant capacity, and sensory properties at six months intervals for three years. The collected data were comprehensively analyzed using the k-means clustering via principal component analysis (PCA) to determine the optimal intake duration and sensory factors associated with acceptance. Doenjang samples were classified with every year interval based on PCA, and then the classified doenjang samples were further grouped into cluster one, two, and three based on the k-means clustering. In Cluster three, doenjang that was aged for thirty and thirty-six months, respectively, showed high total phenolic content, antioxidant capacity, superoxide dismutase like activity, and 2,2-diphenyl-1-picryl-hydrazyl radical scavenging capacity. Interestingly, along with acceptance, the levels of free amino acids and organic acids were higher in Cluster 3. The sensory factors found to be associated with acceptance included umami taste and brown color. In conclusion, this study proposes the intake of doenjang aged for thirty months based on its antioxidant activity and sensory properties although doenjang is usually ready after twelve months of aging.

• Animal Bioscience
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• v.35 no.1
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• pp.105-114
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• 2022

Objective: This work was carried out to evaluate the effect of pasture (PA) feeding on buffalo meat quality compared with buffaloes reared intensively with the use of corn silage as a forage base or alternatively with polyphite meadow hay (PH). Methods: Thirty Mediterranean bull buffaloes were distributed into three experimental diet groups: maize silage (MS), PH, and PA. The animals were slaughtered at a live weight of 250 kg, and carcass and meat quality were evaluated. After 7 days of ageing, physical and chemical parameters of longissimus thoracis muscle were determined. To evaluate lipid oxidation the thiobarbituric acid reactive substances was tested at 7 and 14 days, and also the fatty acid profile was recorded by gas chromatography. Results: The PA group, even if it showed carcass parameters lower than those of the silage maize group, reported a good meat percentage (60.59% vs 58.46%, respectively) and lower fat percentage (p<0.001). PA-fed animals showed meat redness, and even if only on raw meat, shear force was higher than the others. Low values of conjugate linoleic acid, polyunsaturated fatty acids, and n-3 were reported in the silage maize group. Principal component analysis (PCA) clearly showed the influence of different diets on meat quality, and PCA1 and PCA2 explained 82% of the variability. Conclusion: Buffaloes reared on PA had meat with high nutritional value even if they showed poor carcass performance compared to the animals fed on MS. Buffaloes fed on polyphite hay were in an intermediate position, similar to grazing animals, according to the same nutritional determinations.

• YAKHAK HOEJI
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• v.60 no.1
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• pp.29-35
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• 2016

Traditional Korean medicines may be managed more scientifically, through the development of logical criterion to verify their cultivation region. It contributes to advance the industry of traditional herbal medicines. Volatile compounds were obtained from 14 samples of domestic Taeksa and 30 samples of Chinese Taeksa by steam distillation. The metabolites were identified by NIST mass spectral library in the obtained gas chromatography/mass spectrometer (GC/MS) data of 35 training samples. The multivariate statistical analysis, such as Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), were performed based on the qualitative and quantitative data. Finally trans-(2,3-diphenylcyclopropyl)methyl phenyl sulfoxide (47.265 min), 1,2,3,4-tetrahydro-1-phenyl-naphthalene (47.781 min), spiro[4-oxatricyclo[5.3.0.0.(2,6)]decan-3-one-5,2'-cyclohexane] (54.62 min), 6-[7-nitrobenzofurazan-4-yl]amino-morphinan-4,5-epoxy (54.86 min), p-hydroxynorephedrine (55.14 min) were determined as marker metabolites to verify candidates for the origin of Taeksa. The statistical model was well established to determine the origin of Taeksa. The cultivation areas of test samples, each 3 domestic and 6 Chinese Taeksa were predicted by the established OPLS-DA model and it was confirmed that all 9 samples were precisely classified.