• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.889-899
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• 2017

Independent component analysis is a multivariate approach to separate mixed signals into original signals. It is the most widely used method of blind source separation technique. ICA uses linear transformations such as principal component analysis and factor analysis, but differs in that ICA requires statistical independence and non-Gaussian assumptions of original signals. PCA have a natural ordering based on cumulative proportion of explained variance; howerver, ICA algorithms cannot identify the unique optimal ordering of the components. It is meaningful to set order because major components can be used for further analysis such as clustering and low-dimensional graphs. In this paper, we compare the performance of several criteria to determine the order of the components. Kurtosis, absolute value of kurtosis, negentropy, Kolmogorov-Smirnov statistic and sum of squared coefficients are considered. The criteria are evaluated by their ability to classify known groups. Two types of data are analyzed for illustration.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.53-62
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• 2006

Object recognition techniques using principal component analysis are disposed to be decreased recognition rate when lighting change of image happens. The purpose of this thesis is to propose an object recognition technique using new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. And the object recognition algorithm proposed here represents more enhanced recognition rate using improved k-Nearest Neighbor. In this thesis, we proposed an object recognition algorithm which creates object space by pre-processing and being learned image using histogram equalization and median filter. By spreading histogram of test image using histogram equalization, the effect to change of illumination is reduced. This method is stronger to change of illumination than basic PCA method and normalization, and almost removes effect of illumination, therefore almost maintains constant good recognition rate. And, it compares ingredient projected test image into object space with distance of representative value and recognizes after representative value of each object in model image is made. Each model images is used in recognition unit about some continual input image using improved k-Nearest Neighbor in this thesis because existing method have many errors about distance calculation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.55-65
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• 2011

In this paper, a novel method of marker detection robust against marker occlusion in augmented reality is proposed. the proposed method consists of four steps. In the first step, in order to effectively detect an occluded marker, we first utilize the Affine-SIFT (ASIFT, Affine-Scale Invariant Features Transform) for detecting matching points between an enrolled marker and an input images with an occluded marker. In the second step, we apply the Principal Component Analysis (PCA) for eliminating outlier of the matching points in the enrolled marker. And then matching points are projected to the first and second axis for longest value and the shortest value of an ellipse are determined by average distance between the projected points and a center of the points. In the third step, Convex-hull vertices including matching points are considered as polygon vertices for estimating a geometric affine transformation. In the final step, by estimating the geometric affine transformation of the points, a marker robust against a marker occlusion is detected. Experimental results have shown that the proposed method effectively detects occlude markers.

• Korean Journal of Human Ecology
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• v.24 no.5
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• pp.725-739
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• 2015

The purpose of this study was to investigate the quality characteristics and descriptive analysis of Yanggaeng prepared with Lycii fructus extract (LD). LD were added in ratios (w/w) of 0 (C), 1.5 (LY1), 3.0 (LY2), 4.5 (LY3), and 6% (LY4), and then proximate compositions, physicochemical properties, and sensory evaluations of the Yanggaeng were measured LY1~LY4 samples showed higher contents of crude lipid, crude protein and crude ash as well as $^{\circ}Brix$ compared to control (p<0.001). pH and lightness (L) of samples decreased as the LD increased. With regard to the texture of Yanggaeng samples, the scores of hardness, adhesiveness, springness, and cohesiveness was significantly increased by the Addition of LD (p<0.05, p<0.01). For the descriptive analysis, ten panelist generated and evaluated 29 sensory attributes for the Yanggaeng, and there were significant differences among the samples for all 26 sensory attributes. For the descriptive data, principal component analysis (PCA) was performed to summarize the sensory characteristics of the Yanggaeng. The results of PCA showed that the positive attributes, e.g. savoury, were closely in relationship with LY2 and LY3. Form the findings, this study suggests that 3~4.5% addition of LD was effective for preparation of Yanggaeng in the aspects of the consumer acceptability.

• 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.