• Journal of the Korea Society of Computer and Information
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• v.8 no.2
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• pp.31-36
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• 2003

This paper proposes an acceleration method of PCA(Principal Component Analysis) based feature detection. The feature detection method makes decision whether the target feature is included in a given image, and if included, calculates the position and extent of the target feature. The position and scale of the target feature or face is not known previously, all the possible locations should be tested for various scales to detect the target. This is a search Problem in huge search space. This Paper proposes a fast face and feature detection method by reducing the search space using the multi-stage prediction map and contour Prediction map. A Proposed method compared to the existing whole search way, and it was able to reduce a computational complexity below 10％ by experiment.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.913-915
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• 2005

임의의 영상에서 얼굴을 검출하는 것은 얼굴을 인식하는데 있어서 선행되어야 할 필수과정이다. 본 논문은 조명의 변화가 심한 컬러영상에서 얼굴을 검출하는 것을 목적으로 한다. 본 논문은 기존의 기하학적 밝기분포 마스크만을 사용한 방법이 조명 변화에 취약한 단점을 보완하는데 중점을 두었다. 히스토그램 평활화(Histogram Equalization : HE)와 감마 크기 보정 (Gamma Intensity Correction : GIC) 방법을 이용해서 조명에 대한 간섭을 줄인 후, 영상 전체에서 피부 영역을 추출하고 이어서 눈 후보들을 검출한다. 검출된 눈 후보들로부터 기하학적 밝기분포 마스크를 적용하여 효과적으로 얼굴 후보들을 찾을 수 있고, 이렇게 찾아진 얼굴 후보들은 주성분분석법(Principal Component Analysis : PCA)를 이용해서 얼굴인지 여부를 판별하게 된다. 본 알고리즘은 조명 밝기 등으로 인해 검출률이 떨어졌던 단점을 보완할 수 있었고, 향후 얼굴 검출 분야에 있어서도 활용 가치가 있을 것으로 생각된다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.304-306
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• 2006

본 논문에서는 GWT(Gabor Wavelet Transform) 계수 에너지와 원 영상간의 영상 결합을 수행한 영상을 주성분 분석법(Principal Component Analysis)에 적용하여 얼굴 인식을 하는 방법을 제안한다. GWT는 가버 함수의 크기 변화와 방향 변화에 의해 생성된다. 따라서 GWT는 다양한 크기 변화와 방향 변화를 가지는 변환으로 특정 주파수 성분과 방향성을 가지는 영상 구조가 어디에 있는지의 지역적 정보를 효과적으로 표현할 수 있는 변환으로 알려져 있다. GWT를 통해 나온 계수 에너지를 추출하고 원 영상에 더하여 지역적 특성을 크게 만든 후에 통계적 방법 중 가장 많이 사용되어지고 검증을 받은 PCA를 사용하여 인식한다. GWT 계수의 에너지는 얼굴 윤곽선, 눈과 입, 얼굴과 머리의 경계 등 색감의 급격한 변화를 나타내는 곳의 정보를 표현을 해주기 때문에 특징점 추출에 사용되고 있지만 이를 전역적으로 이용하여 인식하는 방법에 관한 연구가 이루어지지 않고 있다. 본 논문에서는 에너지 값만으로 전체 얼굴 영상의 세부적 표현을 할 수 없기 때문에 원 영상과의 l:l 비율의 영상 결항을 한 후 얼굴 인식 처리에 사용한다. 이 영상을 얼굴인식에 사용하였을 때원본 영상을 사용하였을 때보다 오인식이 줄었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.416-416
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• 2019

육상 담수는 대기중 이산화탄소($CO_2$) 배출의 중요한 발생원으로 주목되고 있다. 하천 및 강에서 대기중으로 배출되는 $CO_2$는 전 세계 탄소순환의 핵심요소이며, 대부분의 하천과 강은 $CO_2$로 과포화 되어있다. 세계적으로 하천 및 강의 $CO_2$ 배출량은 호수 및 저수지의 배출량보다 약 5배 많은 것으로 보고되고 있으나, 국내연구에서는 연구사례가 드물다. 따라서 본 연구의 목적은 낙동강 중하류에 위치해있는 강정고령보(GGW), 달성보(DSW), 합천창녕보(HCW), 창녕함안보(CHW)에서 발생되는 순 대기 배출 플럭스(Net Atmospheric Flux, NAF)의 동적 변동 특성을 분석하고, 데이터마이닝 기법을 적용하여 쉽게 수집할 수 있는 물리적 및 수질 변수로 $CO_2$ NAF를 추정하는데 사용할 수 있는 간략한 예측 모델을 개발하는데 있다. $CO_2$ NAF는 대기-수면 경계면에서의 $CO_2$ 부분압($pCO_2$)의 차에 기체전달속도를 곱하여 산정하였으며, 기체전달속도는 Cole and Caraco(1998)가 제안한 식을 사용하였다. 담수와 해수의 탄산염 시스템에서 열역학적 화학평형을 모두 고려한 $CO_2$SYS 프로그램을 사용하여 수중의 $pCO_2$를 산정하였고, $CO_2$ NAF는 Henry의 법칙과 Fick의 1차 확산법칙을 사용하여 계산하였다. $CO_2$ NAF의 시간적 변동성에 영향을 미치는 환경요인을 평가하기 위해서 상관분석, 주성분분석(Principal Component Analysis; PCA), 단계적다중회귀모델(Step-wise Multiple Linear Regression; SMLR), 랜덤포레스트(Random Forest; RF)방법을 사용하였다. SMLR 모델은 R package인 olsrr, RF 모델은 R package인 caret, randomForest를 이용하여 분석하였다. 연구 결과, 4개 보 상류 하천구간은 조류의 성장이 활발한 일부 기간을 제외한 대부분의 기간에서 $CO_2$를 대기로 배출하는 종속영양시스템(Heterotrophic system)을 보였다. $CO_2$ NAF의 중위값은 HCW에서 최소 $391.5mg-CO_2/m^2day$, DSW에서 최대 $1472.7mg-CO_2/m^2day$였다. 모든 보에서 NAF는 pH와 강한 음의 상관관계를 보였으며, $pCO_2$와 Chl-a도 음의 상관관계를 보였다. 이는 조류가 수중에서 $CO_2$를 소비하고 pH를 증가시키기 때문이다. PCA 분석 결과, NAF와 $pCO_2$가 높은 공분산을 보였으며, pH와 Chl-a는 반대 방향으로 군집되어 상관분석과 동일한 결과를 보였다. 이 연구를 통해 개발된 SMLR 모델과 RF 모델의 Adj. $R^2$ 값은 모든 보에서 0.77 이상으로 나왔으며, $pCO_2$ 측정 데이터가 없더라도 하천의 $CO_2$ NAF를 추정하는 방법으로 사용될 수 있을 것으로 평가된다.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.8
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• pp.971-978
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• 2017

Danggui leaf (Angelica acutiloba Kitagawa) has numerous dietary fiber and healthy functional properties such as anti-inflammatory activity. However, it is usually discarded after its roots are harvested. For wide application of danggui leaf, squeezed danggui leaf juice was used for making pan bread. Treatments included imported wheat flour (IMWF) and domestic wheat flour (DOWF) as a control, 1% soluble solid contents of squeezed danggui leaf juice instead of wheat flour (1% SDLJ), 2% SDLJ, 3% SDLJ, and 4% SDLJ. In the fermentation expansion, dough volume was not significantly different between DOWF and 1% SDLJ, whereas it decreased according to increased amount of squeezed danggui leaf juice. Regarding physical properties, springiness and cohesiveness decreased according to increased amount of squeezed danggui leaf juice and longer storage period. Gumminess, brittleness, and hardness increased according to increased amount of squeezed danggui leaf juice, although these were not statistically different among IMWF, DOWF, and 1% SDLJ after making pan bread. Principal component analysis (PCA) was performed to assess the correlation between storage period and physical properties. In the PCA, variance proportion of principal component (PC) 1 was 0.87 while that of PC 2 was 0.10. Further, 1% SDLJ showed similar results as DOWF and INWF after making pan bread and after 3 days of storage. In conclusion, use of 1% SDLJ is desirable for making squeezed danggui leaf juice.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.3900-3914
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• 2012

This study examined the background of the recent global financial crisis and the concept of one of the financial derivatives such as the credit default swap(CDS) or synthetic CDO(collateral debt obligations), given the rapid growing and changing the over-the-counter derivative markets in their volume and structures. In comparison with the previous literature such as the study of Park & Kim (2011), this research empirically performed more thorough and comprehensive investigations to find any financial characteristics or attributes to determine the CDS spreads. Regarding the results obtained from the multiple regression models, the explanatory variables such as STYIELD3, SLOPE, INASSETS, and VOLATILITY, showed their statistically significant effects on all the tested dependent variables(DVs). Another procedure such as the principle component analysis(PCA), was also performed to account for additional IDVs as possible determinants of the dependent variables. Subsequent to this analysis, larger coefficients of each corresponding eigenvector such as BETA, PFT2, GROWTH, STD, and BLEVERAGE were found to be possible financial determinants. For robustness, all the IDVs were employed to be tested in the 'full' regression model with stepwise procedure. As a result, STYIELD3, SLOPE, and VOLATILITY, and BETA showed their statistically significant relationship with all the dependent variables of the CDS spreads.

• Conservation Science in Museum
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• v.2
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• pp.69-76
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• 2000

This study, a part of scientific analysis of the 23 Neolithic red earthenwares excavated from southeastern coast and islands. We analyzed earthenware through statistical method and hardness analysis of clay, following the analysis of clay composition, its firing temperature, pigment composition and its thickness. Clay composition was analyzed by using ICP-AES and XRF, and then Principal Component Analysis (one of multivariate methods) was used for classification. As a result, clay of the earthenware was classified into 3 groups(Bumbang, Youkjido and other sites). In addition, hardness analysis of pigment and clay based on the depth of earthenware surface showed that pigment layer containing lots of Fe₂O₃ had higher hardness than clay part, which can be interpreted that Fe₂O₃ contributes to raising hardness in case of high temperature firing. The fact that pigment hardness is higher than that of clay part implies that pigment was applied before firing.

• The Journal of Bigdata
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• v.8 no.2
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• pp.45-54
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• 2023

Seoul's public bicycle system, 'Ttareungyi,' introduced in 2015, has achieved an annual ridership of 40 million in 2022. Similarly, electric scooters, a type of personal mobility device, surpassed one million riders in 2020 due to various sharing platforms. However, the major roadways for these new transportation, bicycle lanes, are notably insufficient compared to other forms of transport. Hence, this study proposes an optimal location selection method for bicycle lanes in Seoul to prevent accidents and enhance bicycle ride safety. The location selection process prioritizes road safety concerning bicycle accident risk. Using regression models, high-risk areas for bicycle accidents are identified. Cluster analysis categorizes these areas into six clusters, each suggesting suitable types of bicycle lanes based on cluster-specific characteristics. We hope that this study will contribute to the improvement of Seoul's transportation environment, including the expansion of dedicated bicycle lanes and lanes for personal mobility devices.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.1-11
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• 2019

With the development of remote sensing sensor technology, it has become possible to acquire satellite images with various spectral information. In particular, since the hyperspectral image is composed of continuous and narrow spectral wavelength, it can be effectively used in various fields such as land cover classification, target detection, and environment monitoring. Change detection techniques using remote sensing data are generally performed through differences of data with same dimensions. Therefore, it has a disadvantage that it is difficult to apply to heterogeneous sensors having different dimensions. In this study, we have developed a change detection method applicable to hyperspectral image and high spat ial resolution satellite image with different dimensions, and confirmed the applicability of the change detection method between heterogeneous images. For the application of the change detection method, the dimension of hyperspectral image was reduced by using correlation analysis and principal component analysis, and the change detection algorithm used CVA. The ROC curve and the AUC were calculated using the reference data for the evaluation of change detection performance. Experimental results show that the change detection performance is higher when using the image generated by adequate dimensionality reduction than the case using the original hyperspectral image.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1733-1741
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• 2020

This study analyzed the effect of meteorological factors on the concentration of PM10 (particulate matter 10) in the atmosphere and the variation of rainwater quality using multivariate statistical analysis. The concentration of PM10 in the atmosphere was continuously measured during eleven precipitation events with a custom-built PM sensor node. A total of 183 rainwater samples were analyzed for pH, EC (electrical conductivity), and water-soluble cations (Na+, Mg2+, K+, Ca2+, NH4+) and anions (Cl-, NO3-, SO42-). The data has been analyzed using two multivariate statistical techniques (principal component analysis, PCA, and Pearson correlation analysis) to identify relationships among PM10 concentrations in the atmosphere, meteorological factors, and rainwater quality factors. When the rainfall intensity was relatively strong (> 5 mm/h, rainfall type 1), the PM10 concentration in the atmosphere showed a negative correlation (r = -0.55, p < 0.05) with cumulative rainfall. The PM10 concentration increased the concentration of water-soluble ions (r = 0.25) and EC (r = 0.4), and decreased the pH (r = -0.7) of rainwater samples. However, for rainfall type 2 (< 5 mm/h), there was no negative correlation between the PM10 concentration in the atmosphere and cumulative rainfall and no statistically significant correlation between the PM10 concentration in the atmosphere and rainwater quality.