• Journal of Information Processing Systems
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• v.12 no.3
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• pp.468-488
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• 2016

The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four-Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.1
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• pp.25-31
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• 2004

This paper is research about the improvement of recognition rate of the biometrics user identification system using the data previously stored in the non contact Ic smart card. The proposed system identifies the user by analyzing the iris pattern his or her us. First, after extracting the area of the iris from the image of the iris of an eye which is taken by CCD camera, and then we save PCA Coefficient using GHA(Generalized Hebbian Algorithm) into the Smart Card. When we confirmed the users, we compared the imformation of the biometrics of users with that of smart card. In case two kinds of information was the same, we classified the data by using SVM(Support Vector Machine). The Experimental result showed that this system outperformed the previous developed system.

• Journal of Environmental Science International
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• v.14 no.7
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• pp.629-637
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• 2005

Polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) were investigated in soil and pine needle samples taken from 16 sites of industrial and residential areas in Changwon, Korea to assess their distribution levels and to suggest the influence of industrial activities, PCDDs/DFs levels in the soil samples ranged from 0.57 to 20.79 pg I-TEQ/g dry weight with a mean value of 4.20 pg I-TEQ/g dry weight. PCDDs/DFs levels in the pine needle samples ranged from 0.39 to 8.75 pg I-TEQ/g dry weight with a mean value of 4.09 pg I-TEQ/g dry weight. In both soil and pine needle samples, the PCDDs/DFs concentrations in the industrial area sites were higher than those in the residential area sites. Homologue profiles in pine needle samples showed different patterns compared with soil samples. Based on the results of principal component analysis (PCA), it was confirmed that pine needles reflected a direct influence from local potential sources of PCDDs/DFs, showing a much higher degree of reflection than in soils. Pine needles are very useful as an indicator for monitoring or estimating the contamination of PCDDs/DFs in other areas which have been impacted by point pollution sources.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.625-630
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• 2014

The amount of information is increasing rapidly with the development of the internet and the computer. Since these enormous information is managed by the document forms, it is necessary to search and process them efficiently. The document clustering technique which clusters the related documents through the similarity between the documents help to classify, search, and process the large amount of documents automatically. This paper proposes a method to find the initial seed points through principal component analysis when the documents represented by vectors in the feature vector space are clustered by K-means algorithm in order to increase clustering performance. The experiment shows that our method has a better performance than the traditional K-means algorithm.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2786-2788
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• 2003

본 논문에서는 생체자기신호의 잡음제거 기법 중 PCA(Principal Component Analysis) 알고리즘을 사용하여 효과적으로 노이즈를 제거하기 위한 방법을 제안하였다. 61 채널 SQUID 시스템을 이용하여 심자도 신호를 측정하였고, 40 채널 SQUID 시스템을 이용하여 뇌자도 신호를 측정하였다. 그리고, 측정한 신호 성분들을 제안한 방법을 이용하여 주성분들을 분리하였고, 이들 중에서 노이즈 성분을 추정하여 측정한 신호에서 제거하였다. 이러한 방법을 이용한 결과, 심자도 신호에 존재하는 펄스 노이즈로 인하여 왜곡된 생체 자기 신호의 노이즈를 감소 시킬 수 있었으며, 뇌자도 신호에 존재하는 외부 노이즈 성분을 제거하여 임상 진단에 유용한 데이터를 얻을 수 있었다.

• Journal of Intelligence and Information Systems
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• v.10 no.3
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• pp.105-114
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• 2004

In the past year, the increasing concern about the biometric recognition makes the great activities on the security fields, such as the entrance control or user authentication. In particular, although the features of face recognition, such as user friendly and non-contact made it to be used widely, unhappily it has some disadvantages of low accuracy or low Re-attempts Rates. For this reason, I suggest the new approach to re-classify the classified data of recognition result data to solve the problems. For this study, I will use the typical appearance-based, PCA(Principal Component Analysis) algorithm and verify the performance improvement by adopting the re-classification approach using 200 peoples (10 pictures per one person).

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.528-534
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• 2015

Green manure crops have been well recognized as the alternative for chemical fertilizer, especially N fertilizer, because of its positive effect on soil and the environment. Hairy vetch and green barley are the most popular crops for cultivation of rice in paddy field. This study was conducted to evaluate effects of hairy vetch and green barley on soil microbial community and chemical properties during short-term application (three years). For this study, treatments were composed of hairy vetch (Hv), green barley (Gb), hairy vetch + green barley (Hv+Gb), and chemical fertilizer without green manure crops (Con.). Hv+Gb treatment showed the highest microbial biomass among treatments. Principal component analysis (PCA) showed that PC1 (73.0 %) was affected by microbial biomass and PC2 (21.5 %) was affected by fungi, cy19:0/18:$1{\omega}7c$ (stress indicator). Combined treatment with hairy vetch and green barley could be more efficient than green manure crop treatment as well as chemical fertilizer treatment for improvement of soil microorganisms.

• The Korean Journal of Food And Nutrition
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• v.34 no.1
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• pp.1-14
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• 2021

This study evaluated the antioxidant activity and quality characteristics of rice cookie with added butterbur powder in a ratio of 0, 5, 10, 15, and 20% to confirm the possibility of butterbur as a functional food. The moisture content, spread factor, leavening rate, and hardness of rice cookies increased with an increase in the amount of butterbur powder; whereas a decrease in the pH and baking loss rate was observed. The L and b values decreased as the amount of butterbur powder increased, but the value was the lowest when 5% of butterbur powder was added. The sensory liking score showed the highest preference for 10% butterbur powder regarding appearance, flavor, taste, texture, and overall preference. In the principal component analysis (PCA), the addition of 10% butterbur powder positively affected the measure of food acceptance in terms of organoleptic properties of butterbur. Besides, as the amount of added butterbur powder increased, the antioxidant activity of rice cookies increased. Based on these results, it appears that the addition of butterbur powder to rice cookies in a 10% ratio can produce rice cookies with excellent antioxidant activity, overall quality, and high preference.

• Annual Conference of KIPS
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• 2010.11a
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• pp.705-708
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• 2010

An eigen color co-occurrence approach is proposed that exploits the correlation between color channels to identify the degree of image similarity. This method is based on traditional co-occurrence matrix method and histogram equalization. On the purpose of feature extraction, eigen color co-occurrence matrices are computed for extracting the statistical relationships embedded in color images by applying Principal Component Analysis (PCA) on a set of color co-occurrence matrices, which are computed on the histogram equalized images. That eigen space is created with a set of orthogonal axes to gain the essential structures of color co-occurrence matrices, which is used to identify the degree of similarity to classify an input image to be tested for various purposes. In this paper RGB, Gaussian color space are compared with grayscale image in terms of PCA eigen features embedded in histogram equalized co-occurrence features. The experimental results are presented.

• Journal of Plant Biotechnology
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• v.45 no.3
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• pp.242-249
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• 2018

This study was conducted to develop an SNP set that can be useful for marker-assisted breeding (MAB) in watermelon (Citrullus. lanatus L) using Genotyping-by-sequencing (GBS) analysis of 20 commercial elite watermelon inbreds. The result of GBS showed that 77% of approximately 1.1 billion raw reads were mapped on the watermelon genome with an average mapping region of about 4,000 Kb, which indicated genome coverage of 2.3%. After the filtering process, a total of 2,670 SNPs with an average depth of 31.57 and the PIC (Polymorphic Information Content) value of 0.1~0.38 for 20 elite inbreds were obtained. Among those SNPs, 55 SNPs (5 SNPs per chromosome that are equally distributed on each chromosome) were selected. For the understanding genetic relationship of 20 elite inbreds, PCA (Principal Component Analysis) was carried out with 55 SNPs, which resulted in the classification of inbreds into 4 groups based on PC1 (52%) and PC2 (11%), thus causing differentiation between the inbreds. A similar classification pattern for PCA was observed from hierarchical clustering analysis. The SNP set developed in this study has the potential for application to cultivar identification, F1 seed purity test, and marker-assisted backcross (MABC) not only for 20 elite inbreds but also for diverse resources for watermelon breeding.