• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.29-30
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• 2011

본 논문에서는 스마트폰 얼굴인식을 통해 출입을 관리하는 시스템을 설계하고 구현한다. 이를 위해 스마트폰에서 얼굴인식을 위한 사용가능한 다양한 알고리즘을 조사하였다. 얼굴 인식의 첫 단계는 얼굴검출이며 다음 단계는 얼굴인식이다. 얼굴 검출을 위해서는 컬러 세그멘테이션, 템플릿매칭 등의 알고리즘을 적용하였으며, 얼굴 인식을 위해서는 PCA(Principal Component Analysis)에 기반을 둔 Eigenface와 LDA(Linear Discriminant Analysis)에 기반을 둔 Fisherface를 비교하여 구현하고 적용하였다. 스마트 폰의 제한된 하드웨어에서 얼굴인식 시스템을 구현하는 관계로 알고리즘의 정확도와 알고리즘의 계산 복잡도 사이에서 적절한 조절이 필요하였다.

• Annual Conference of KIPS
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• 2013.05a
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• pp.342-345
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• 2013

본 논문은 모바일 환경에서의 다중생체인식을 통한 개인인증 시스템을 제안한다. 다중생체인식을 위하여 얼굴인식과 화자인식을 선택하였으며, 시스템의 인식 시나리오는 다음을 따른다. 얼굴인식을 위하여 Modified census transform (MCT) 기반의 얼굴검출과 k-means 클러스터 분석 (cluster analysis) 알고리즘 기반의 눈 검출을 통해 얼굴영역 전처리를 수행하고, principal component analysis (PCA) 기반의 얼굴인증 시스템을 구현한다. 화자인식을 위하여 음성의 끝점 추출과 Mel frequency cepstral coefficient(MFCC) 특징을 추출하고, dynamic time warping (DTW) 기반의 화자 인증 시스템을 구현한다. 그리고 각각의 생체인식을 본 논문에서 제안된 방법을 기반으로 융합하여 인식률을 향상시킨다.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.6
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• pp.522-531
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• 2016

In this study, we propose an object identification method for bin-picking developed for industrial robots. We identify the grasp posture and the associated geometric parameters of grasp objects using the joint data of a robotic gripper. Prior to grasp identification, we analyze the grasping motion in a low-dimensional space using principle component analysis (PCA) to reduce the dimensions. We collected the joint data from a human hand to demonstrate the grasp-identification algorithm. For data acquisition of the human grasp data, we conducted additional research on the motion characteristics of a human hand. We explain the method for using the algorithm of grasp identification for bin-picking. Finally, we present a subject for future research using our proposed algorithm of grasp model and identification.

• Journal of Microbiology and Biotechnology
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• v.23 no.2
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• pp.177-183
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• 2013

Bacterial blight, an important and potentially destructive bacterial disease in rice caused by Xanthomonas oryzae pv. oryzae (Xoo), has recently developed resistance to the available antibiotics. In this study, mass spectrometry (MS)-based metabolite profiling and multivariate analysis were employed to investigate the correlation between timedependent metabolite changes and antimicrobial activities against Xoo over the course of Phomopsis longicolla S1B4 fermentation. Metabolites were clearly differentiated based on fermentation time into phase 1 (days 4-8) and phase 2 (days 10-20) in the principal component analysis (PCA) plot. The multivariate statistical analysis showed that the metabolites contributing significantly for phases 1 and 2 were deacetylphomoxanthone B, monodeacetylphomoxanthone B, fusaristatin A, and dicerandrols A, B, and C as identified by liquid chromatography-mass spectrometry (LC-MS), and dimethylglycine, isobutyric acid, pyruvic acid, ribofuranose, galactofuranose, fructose, arabinose, hexitol, myristic acid, and propylstearic acid were identified by gas chromatography-mass spectrometry (GC-MS)-based metabolite profiling. The most significantly different secondary metabolites, especially deacetylphomoxanthone B, monodeacetylphomoxanthone B, and dicerandrol A, B and C, were positively correlated with antibacterial activity against Xoo during fermentation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.7 no.2
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• pp.85-92
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• 1989

The aim of this study is to find out the combination of effective transformed data, applying Remote Sensing techniques, as to the classification and particular objects by transforming the MSS data and TM data of the satellite LANDSAT into several linearly transformed data. Since one of the problems in the processing of the LANDSAT data is the vastness of the data, the Linear Transformation could be a method to perform analysis of those vast data, more efficiently and economically. This method is carried out as follows : (1) offering the simplicity over complex data, (2) selectional processing over redundant data and removing unnecessary data, (3) emphasizing on the object of the study ; by transforming multispectral data through linear calculation and statistical transformation. In this study, the analysis and transformation of the data have been performed by means of Band Ratioing and Principal Component Analysis. As the classificatory consequence, Infrared/RED Ratioing which expands the characterization of green vegetation, has been useful for a distinctive classification among other classes. For the Principal Component Analysis, band 1,2,7 are efficient in the classification of the green vegetation.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.161-169
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• 2007

A study was conducted in the Hong Kong with the aim of deriving an algorithm for the retrieval of suspended sediment (SS) and sea surface salinity (SSS) concentrations from Aqua/MODIS level 1B reflectance data with 250m and 500m spatial resolutions. 'In-situ' measurements of SS and SSS were also compared with coincident MODIS spectral reflectance measurements over the ocean surface. This is the first study of SSS modeling in Southeast Asia using earth observation satellite images. Three analysis techniques such as multiple regression, linear regression, and principal component analysis (PCA) were performed on the MODIS data and the 'in-situ' measurement datasets of the SS and SSS. Correlation coefficients by each analysis method shows that the best correlation results are multiple regression from the 500m spatial resolution MODIS images, $R^2$= 0.82 for SS and $R^2$ = 0.81 for SSS. The Root Mean Square Error (RMSE) between satellite and 'in-situ' data are 0.92mg/L for SS and 1.63psu for SSS, respectively. These suggest that 500m spatial resolution MODIS data are suitable for water quality modeling in the study area. Furthermore, the application of these models to MODIS images of the Hong Kong and Pearl River Delta (PRO) Region are able to accurately reproduce the spatial distribution map of the high turbidity with realistic SS concentrations.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5A
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• pp.351-360
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• 2011

Jacket-type offshore structures are always exposed to severe environmental conditions such as salt, high speed of current, wave, and wind compared with other onshore structures. In spite of the importance of maintaining the structural integrity for offshore structure, there are few cases to apply structural health monitoring (SHM) system in practice. The impedance-based SHM is a kind of local SHM techniques and to date, numerous techniques and algorithms have been proposed for local SHM of real-scale structures. However, it still requires a significant challenge for practical applications to compensate unknown environmental effects and to extract only damage features from impedance signals. In this study, the impedance-based SHM was carried out on a 1/20-scaled model of an Uldolmok current power plant structure under changes in temperature and transverse loadings. Principal component analysis (PCA) was applied using conventional damage index to eliminate principal components sensitive to environmental change. It was found that the proposed PCA-base approach is an effective tool for long-term SHM under significant environmental changes.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.4
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• pp.21-26
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• 2011

Feature ranking is useful to gain knowledge of data and identify relevant features. In this study, we proposed a use of feature ranking for classification of neuro-degeneration and vascular dementia in micro-Raman spectra of platelet. The entire region of the spectrum is divided into local region including several peaks, followed by Gaussian curve fitting method in the region to be modeled. Local minima select from the subregion and then remove the background based on the position by using interpolation method. After preprocessing steps, significant features were selected by feature ranking method to improve the classification accuracy and the computational complexity of classification system. PCA (principal component analysis) transform the selected features and the overall features that is used classification with the number of principal components. These were classified as MAP (maximum a posteriori) and it compared with classification result using overall features. In all experiments, the computational complexity of the classification system was remarkably reduced and the classification accuracy was partially increased. Particularly, the proposed method increased the classification accuracy in the experiment classifying the Parkinson's disease and normal with the average 1.7 %. From the result, it confirmed that proposed method could be efficiently used in the classification system of the neuro-degenerative disease and vascular dementia of platelet.

• Journal of Korean Society of Environmental Engineers
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• v.29 no.5
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• pp.521-527
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• 2007

To investigate the relationship between PCB sources and concentration level in soil, PCBs concentration of 8 soil samples around Shiwa industrial complex were measured. The concentration of PCBs in soil samples were ranged from 2.43 to 274 ng/g dry (0.116 to 60.5 pg WHO-TEQ/g dry) md off-gas were ranged from 48.6 to $2872ng/m^3(0.00150\sim15.2ng\;WHO-TEQ/m^3)$; these are similar levels with results of previous study in Korea. The homologue patterns in soil samples were varied from sample to sample, but isomer patterns were very similar with each other. The two principal components were extracted by Principal Component Analysis(PCA) of 8 soil samples and cumulative factor loading was 95.7%. As the result of PCA, it could be expected that PCBs in soil samples of this study were more affected by PCB products than combustion process and mostly affected by already-known sources.

• Korean Journal of Food Science and Technology
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• v.33 no.1
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• pp.24-27
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• 2001

This study was performed to analyse aroma patterns of Nagaimo, Ichoimo and Tsukuneimo by the electronic nose with 32 conducting polymer sensors. Response by the electronic nose was analysed by the principal component analysis(PCA). Sensory evaluation also for organoleptic taste and odor of Nagaimo, Ichoimo and Tsukuneimo was performed. Nagaimo was very crunchy and sweet. Tsukuneimo was roasted nutty, hard, viscid taste and sticky. Ichoimo had intensive unique yam flavor and moderate hardness between Nagaimo and Ichoimo. Intensity of Ichoimo for unique yam flavor by the electronic nose was the strongest. The quality factor(QF) of PCA for normalized pattern by thirty two sensors showed less than 2, and so aroma pattern of three yam cultivars had no difference. But when the PCA was performed for normalized pattern by eight selected sensitive sensors, the QF for Nagaimo and Tsukuneimo is 2.057. Thus aroma pattern between Nagaimo and Tsukuneimo could be distinguished.