• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.370-373
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• 2005

We have developed Hybrid algorithm for yellow sand detection. Hybrid algorithm is composed of three methods using infrared bands. The first method used the differential absorption in brightness temperature difference between $11\mu m\;and\;12\mu m$ (BID _1), through which help distinguish the yellow sand from various meteorological clouds. The second method uses the brightness temperature difference between $3.7\mu m\;and\;11\mu m$ (BID_2). The technique would be most sensitive to dust loading during the day when the BID _2 is enhanced by reflection of $3.7\mu m$ solar radiation. The third one is a newly developed algorithm from our research, the so-called surface temperature variation method (STY). We have applied the three methods to MODIS for derivation of the yellow sand dust and in conjunction with the Principle Component Analysis (PCA), a form of eigenvector statistical analysis. PCI shows better results for yellow sand detection in comparison with the results from individual method. The comparison between PCI and MODIS aerosols optical depth (AOD) shows remarkable good correlations during daytime and relatively good correlations over the land.

• 대한원격탐사학회지
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• 제22권5호
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• pp.403-406
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands. This algorithm is a hybrid algorithm that has used two methods combined. The first method used the differential absorption in brightness temperature difference between $11{\mu}m\;and\;12{\mu}m\;(BTD1)$. The radiation at $11{\mu}m$ is absorbed more than at $12{\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m\;and\;11{\mu}m(BTD2)$. This technique is sensitive to dust loading, which the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. First the Principle Component Analysis (PCA), a form of eigenvector statistical analysis from the two methods, is performed and the aerosol pixel with the lowest 10% of the eigenvalue is eliminated. Then the aerosol index (AI) from the combination of BTD 1 and 2 is derived. We applied this method to Multi-functional Transport Satellite-l Replacement (MTSAT-1R) data and obtained that the derived AI showed remarkably good agreements with Ozone Mapping Instrument (OMI) AI and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth.

• Near Infrared Analysis
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• 제1권2호
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• pp.21-27
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• 2000

Temperature-dependent changes in near-infrared (NIR) spectra have been measured for oleic acid, and nonanoic acid in the pure liquid state. Particular attention has been paid to the 5400-4800 cm$\^$-1/ region where a number of combination bands appear. The NIR spectra of oleic acid show that a band at 5303 cm$\^$-1/ increases with temperature while that at 5270 cm/sup-1/ decreases. It ha been found from their second derivative spectra that these spectral changes take place stepwisely with two break points at 30 and 53$\^{C}$, which correspond to the phase transition temperatures oleic acid reported previously. Principle component analysis (PCA) has been carried out for the NIR spectra of oleic acid in the 5400-4800 cm$\^$-1/ region measured over a temperature range of 15-80$\^{C}$. core plots of the first and second principal components (PCs) show that the NIR spectra are classified into three groups; the spectra measured in the temperature range of 15-30$\^{C}$, those in the range of 31-53$\^{C}$, and those in the range of 54-80$\^{C}$. These temperature ranges correspond to those for quasi-smectic liquid crystal, disordered liquid crystal, and isotropic liquid of oleic acid in the pure liquid state. In other words, PCA provides unambiguous evidence for the phase transitions. similar studies have been carried out for petroselinic acid and nonanoic acid in the pure liquid states, but they do not show any evidence for phase transitions.

• 한국식품저장유통학회지
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• 제30권4호
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• pp.602-616
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• 2023

Fifteen types of rice collected from Kurdistan region-Iraq were investigated by principal component analysis (PCA) in terms of physical properties and cooking characteristics. The dimensions of evaluated grains correspond to 5.05-8.75 mm for length, 1.54-2.47 mm for width, and 1.37-1.95 for thickness. The equivalent diameter was in the range of 5.23-10.03 mm, and the area took 13.30-28.25 mm². The sphericity analysis values varied from 0.32 to 0.56, the aspect ratio from 0.17 to 0.39, and the volume of the grain was measured in the range from 4.48 to 17.74 mm³, hectoliter weight values were 730-820 kg/m³, and true density from 0.6 to 0.96 g/cm³. The broken grain ratio was 1.5-18.3%, thousand kernel weight corresponded to 15.88 to 22.42 g. The water uptake ratios for 30 min of soaking were increased at 60℃ compared to 30 and 45℃. The PCA was used to study the correlation of the most effective factors. Results of PCA showed that the first (PC1) and second (PC2) components retained 63.4% and 34.8% of the total variance, which PC1 was mostly related to hectoliter, broken ratio, and moisture content characteristics while PC2 was mostly concerned with hardness and true density. For cooking properties, the PC1 and PC2 retained 88.5% and 9.3% of the total variance, respectively. PC1 was mostly related to viscosity, spring value, and hardness after cooking, while PC2 was mostly concerned with spring value, hardness before cooking, and hardness after cooking.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권3호
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• pp.194-197
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• 2005

Digital display products are gradually becoming diversified and pursuing high-quality image display. Digital TV supports various video signal formats from conventional SD to digital HD because the format conversion of video image is required. Traditional format conversion of the video image is achieved by a 1-dimensional linear interpolator applying both horizontal and vertical direction. Jagging artifact can be expressed as the linkage of line segments in several directions. In this paper, we present the method that removes jagging artifact effectively using PCA (Principle Component Analysis) and reserve the detail in a given image.

• International Journal of Fluid Machinery and Systems
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• 제3권4호
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• pp.369-378
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• 2010

The creation of the hydraulic turbine flow factor map will undoubtedly benefit its design by decreasing both the design cycle time and product cost. In this paper, the geometry and flow variables, which effectively affect the flow factor, are proposed, analyzed and determined. These flow variables are further used to create the operating condition maps by using different model approaches categorized into Response Surface Method (RSM) and Artificial Neural Network (ANN). The accuracies of models created by different approaches are compared and the performances of model approaches are analyzed. The influences of chosen variables and the combination of Principle Component Analysis (PCA) and model approaches are also studied. The comparison results between predicted and actual flow factors suggest that two-hidden-layer Feed-forward Neural Network (FFNN), and one.hidden-layer FFNN with PCA has the best performance on forming this mapping, and are accurate sufficiently for hydraulic turbine design.

• 전기학회논문지
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• 제61권1호
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• pp.125-129
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• 2012

In this paper, we propose an intelligent method to surveil moving objects and to judge an unusual-behavior by using network cameras. To surveil moving objects, the Scale Invariant Feature Transform (SIFT) algorithm is used to characterize the feature information of objects. To judge unusual-behaviors, the virtual human skeleton is used to extract the feature points of a human in input images. In this procedure, the Principal Component Analysis (PCA) improves the accuracy of the feature vector and the fuzzy classifier provides the judgement principle of unusual-behaviors. Finally, the experiment results show the effectiveness and the feasibility of the proposed method.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2003년도 춘계학술발표대회논문집
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• pp.435-440
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• 2003

본 논문에서는 실시간 출입통제시스템에 적용이 가긍한 복잡한 배경에서의 다중 얼굴 영역 검출과 추적을 통한 얼굴 인식 알고리즘을 제안하였다. 제안된 알고리즘에서는 배경영상과 입력된 연속적인 프레임간의 차영상을 적용함으로써 물체의 움직임을 감지한 후. IISI컬러 좌표모델을 이용하여 얼굴의 1차 후보 영역을 검출하고, 잡음제거를 위해 모폴로지 연산을 수행하였다 또한 Line Projection을 이용한 객체 분할법(Object Segmentation)으로 객체를 분할함으로써 다중 얼굴 영역을 추출하였다. 또한 추출된 얼굴영역에서 눈 영역 검출을 통해 각각의 얼굴 영역들을 검증하였으며 검증된 얼굴들의 최외각 4개의 좌표를 이용하여 얼굴 추적율을 높였다. 마지막으로 얼굴 인식은 추출된 얼굴 영역으로부터 주성분 분석(PCA ： Principle Component Analysis)방법을 이용함으로써 97～98％의 높은 인식율을 보였다.

• 약학회지
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• 제48권3호
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• pp.177-181
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• 2004

Non-invasive blood glucose measurement from mouse tail was performed by near-infrared (NIR) spectroscopy. Three groups; normal, type I diabetes (insulin dependent diabetes mellitus, IDDM), type II diabetes (non-insulin dependent diabetes mellitus, NIDDM) group, were studied over a 10 weeks period with the collection of near-infrared (NIR) spectra. Spectral variations from long-term measurement (10 weeks) from dramatic and nonlinear changes in the optical properties of the live tissue sample were compensated by chemometrics techniques such as principle component analysis (PCA) and partial least squares (PLS) regression. The effect from mouse body temperature changes on NIR spectral data was also considered. This study showed that the compensation of variations from long-term measurement and temperature changes improved calibration accuracy of non-invasive blood glucose measurement.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 춘계학술발표대회
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• pp.333-335
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• 2012

고명암 대비(High Dynamic Range)영상을 일반 디스플레이 장치로 표현하기 위한 톤 매핑 기법을 제안한다. 제안하는 방식은 주성분분석(Principle Component Analysis)을 통해 구한 휘도채널을 양방향필터를 이용하여 기본 영상과 디테일 영상으로 분리한다. 기본영상은 동적영역분할과 재분배를 수행하고, 기본영상의 밝기값과 향상된 밝기값을 이용하여 후광현상을 제거한다. 실험 결과에서 제안하는 기법은 저명암대비 영상에서 명암비 향상과 동시에 디테일이 보존되는 것을 확인할 수 있다.