• 센서학회지
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• 제30권5호
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• pp.314-319
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• 2021

In this study, biomarkers were analyzed and segmented using tunable infrared gas sensors after performing the principal component analysis. The free spectral range of the device under test (DUT) was around 30 nm and DUT-5580 yielded the highest output voltage property among the others. The biomarkers (isoprophyl alcohol, ethanol, methanol, and acetone solutions) were sequentially mixed with deionized water and their mists were carried into the gas chamber using high-purity nitrogen gas. A total of 17 different mixed gases were tested with three tunable infrared gas sensors, namely DUT-3144, DUT-5580, and DUT-8010. DUT-8010 resolved the infrared absorption spectra of whole mixed gases. Based on the principal component analysis with each DUT and their combinations, each mixed gas and the trends in increasing gas concentration could be well analyzed when the contributions of the eigenvalues of the first and second were higher than 70% and 10%, respectively, and their sum was greater than 90%.

• 한국ITS학회 논문지
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• 제19권4호
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• pp.81-99
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• 2020

기종점 데이터는 수요 분석 및 서비스 설계를 위해서 대중교통, 도로운영 등 다양한 분야에서 저장 및 활용되고 있다. 최근 빅데이터의 활용성이 증대되면서 기종점 데이터의 분석 및 활용에 대한 수요도 함께 증가하고 있다. 기존의 일반적인 교통 정보 데이터가 수집장비 수(n)에 비례하여 데이터양이 증가(α·n)하는 것과는 다르게, 기종점 데이터는 수집지점 수(n)의 증가에 따라 수집 데이터의 양이 기하급수적으로 증가(α·n²)하는 경향이 있다. 이로 인하여 기종점 데이터를 원시 데이터의 형태로 장기간 저장하고 빅데이터 분석에 활용하는 것은 대용량의 저장 공간이 필요하다는 것을 고려할 때 실용적 대안으로 여겨지지 않고 있다. 이와 함께 기종점 데이터는 0~10 사이의 작은 수요 부분에 패턴화된 형태와 무작위 적인 형태의 데이터가 섞여있어 작은 수요가 그룹화되어 발생하는 주요 패턴을 추출하기에 어려움이 있다. 이러한 기종점 데이터의 저장용량의 한계와 패턴화 분석의 한계를 극복하고자 본 연구에서는 주성분 분석을 활용한 대중교통 기종점 데이터의 압축 및 분석 방법을 제안하였다. 본 연구에서는 서울시와 세종시의 대중교통 이용 데이터를 활용하여 모빌리티 데이터를 분석하고, 모빌리티 기종점 데이터에 포함된 무작위 성향이 높은 데이터를 제거하기 위해 주성분분석 기반의 데이터 압축 및 복원에 관한 연구를 수행하였다. 주성분분석으로 분해된 기종점 데이터와 원데이터를 비교하여 주요한 수요 패턴을 찾고 이를 통해 압축률과 복원율을 높일 수 있는 주성분 범위를 제안하였다. 본 연구에서 분석한 결과, 서울시 기준 1~80, 세종시 기준 1~60까지의 주성분을 사용할 경우 주요 이동 데이터의 손실 없이 기종점 데이터에 포함되어있는 노이즈를 제거하고 데이터를 압축 및 복원이 가능하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.694-698
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• 2008

In hydrology, it is a difficult task to deal with multivariate time series such as modeling streamflows of an entire complex river system. Normal distribution based model such as MARMA (Multivariate Autorgressive Moving average) has been a major approach for modeling the multivariate time series. There are some limitations for the normal based models. One of them might be the unfavorable data-transformation forcing that the data follow the normal distribution. Furthermore, the high dimension multivariate model requires the very large parameter matrix. As an alternative, one might be decomposing the multivariate data into independent components and modeling it individually. In 1985, Lins used Principal Component Analysis (PCA). The five scores, the decomposed data from the original data, were taken and were formulated individually. The one of the five scores were modeled with AR-2 while the others are modeled with AR-1 model. From the time series analysis using the scores of the five components, he noted "principal component time series might provide a relatively simple and meaningful alternative to conventional large MARMA models". This study is inspired from the researcher's quote to develop a multivariate simulation model. The multivariate simulation model is suggested here using Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Three modeling step is applied for simulation. (1) PCA is used to decompose the correlated multivariate data into the uncorrelated data while ICA decomposes the data into independent components. Here, the autocorrelation structure of the decomposed data is still dominant, which is inherited from the data of the original domain. (2) Each component is resampled by block bootstrapping or K-nearest neighbor. (3) The resampled components bring back to original domain. From using the suggested approach one might expect that a) the simulated data are different with the historical data, b) no data transformation is required (in case of ICA), c) a complex system can be decomposed into independent component and modeled individually. The model with PCA and ICA are compared with the various statistics such as the basic statistics (mean, standard deviation, skewness, autocorrelation), and reservoir-related statistics, kernel density estimate.

• 한국약용작물학회지
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• 제16권4호
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• pp.218-224
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• 2008

국내자생 쑥속의 식물을 약용작물 및 산업화에 활용하기 위한 기초자료를 얻고자 본 연구를 수행하였다. 그 결과, 수집된 쑥속 식물들은 사철쑥 (A. capillaris)을 포함한 20종 1아종 2변종 24분류군으로 분류 되었으며, 이를 바탕으로 25개의 화기형질을 이용하여 주성분 분석과 군집분석을 수행하였다. 주성분 분석결과 제1주성분은 전체 분산의 44.73%, 제2주성분은 16.86%, 제 3주성분은 8.88%, 제4주성분은 7.07%의 기여율을 보였으며, 상위 제4주성분까지의 누적 기여율이 77.56%였다. 군집분석 결과, 자방의 퇴화, 아관목, 두화의 크기 등의 주요형질에 의해 크게 3개의 군으로 구분되어졌으며, 화기구조의 식별형질로는 기발표된 Dracunculus, Abrotanum, Absinthium 3절과 완전히 일치하지는 않았으나 국내 자생쑥의 분류형질로 활용이 가능하였다.

• 한국실내디자인학회논문집
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• 제21권4호
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• pp.163-171
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• 2012

The objective of this study was to analyze the characteristics of traditionality expressions at modernized Chinese restaurant in Hong Kong. As a case study, the study examined 12 modernized Chinese restaurants in Hong Kong. The gathered data were categorized and examined according to the ways of traditionality expressions, which included reproduction, transformation, and reinterpretation of traditional components. Each of the components was measured for the amount of traditional or modernity expression on a five-point scale. The five-point scoring system put an emphasis on heritage; 1 point was given to principal modernity(modernity: 90-100% + tradition: 0-10%), 2 points were given to principal modernity + auxiliary tradition(modernity: 70-90% + tradition: 10-30%), 3 points were given to the same ratio between tradition and modernity(modernity: 40-60% + tradition: 40-60%), 4 points were given to principal tradition + auxiliary modernity(modernity: 10-30% + tradition: 70-90%), and 5 points were given to principal tradition(modernity: 0-10% + tradition: 90-100%). The analysis performed according to those criteria and methodologies led to the following findings and conclusions: Traditional components were most reproduced in the ornaments placed all over the restaurant and applied to the chirography of the restaurant logos, walls, and windows/doors in a big number. The methodology of transforming tradition was evenly applied to each of the spatial components. With the most transformations occurring to the lattices, there were many different ways to transform tradition including the partition, chirography, pattern, red lantern, furniture and ornament, and traditional materials that were turned into modern ones. Few examples of reinterpreting tradition were observed in the restaurant titles, inside floors, and ceilings, but plenty of examples were found in the walls, windows/doors, lighting, and furniture in a range of ways. Most of them reinterpreted the traditional forms and added altered patterns to them to remind customers of tradition. In short, all of the three ways of expressing tradition were actively applied to each component in an array of ways.

• 응용통계연구
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• 제18권1호
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• pp.129-135
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• 2005

일변량 자료의 경우 대표관찰치는 사분위수 등에 기초하여 자료의 분포와 변이를 함축적으로 표현하기 위한 목적으로 사용되는 소수 개의 관찰치이다. Jones와 Rice(1992)는 다변량 자료에 대한 대표관찰치를 선택하기 위해 주성분분석에 근거한 방법을 제시한 바 있다. 이 연구에서는 주성분의 자기일치성을 이용하여 대표관찰치를 선택하고, 이를 표현하는 방안을 고찰한다. 기존의 방법에 의한 대표관찰치가 자료의 표본변이에 민감한 한편, 여기에서 제안되는 방법의 결과는 자기일치성을 가진다.

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2003년도 International Symposium on Clean Environment
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• pp.33-36
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• 2003

Principal component analysis (PCA) was used to improve image classification by the unsupervised classification techniques, the K-means. To do this, I selected a Landsat TM scene of Jeju Island, Korea and proposed two methods for PCA： unstandardized PCA (UPCA) and standardized PCA (SPCA). The estimated accuracy of the image classification of Jeju area was computed by error matrix. The error matrix was derived from three unsupervised classification methods. Error matrices indicated that classifications done on the first three principal components for UPCA and SPCA of the scene were more accurate than those done on the seven bands of TM data and that also the results of UPCA and SPCA were better than those of the raw Landsat TM data. The classification of TM data by the K-means algorithm was particularly poor at distinguishing different land covers on the island. From the classification results, we also found that the principal component based classifications had characteristics independent of the unsupervised techniques (numerical algorithms) while the TM data based classifications were very dependent upon the techniques. This means that PCA data has uniform characteristics for image classification that are less affected by choice of classification scheme. In the results, we also found that UPCA results are better than SPCA since UPCA has wider range of digital number of an image.

• Communications for Statistical Applications and Methods
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• 제20권2호
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• pp.97-105
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• 2013

Stratification (among other applications) is a popular technique used in survey practice to improve the accuracy of estimators. Its full potential benefit can be gained by the effective use of auxiliary variables in stratification related to survey variables. This paper focuses on the problem of stratum formation when multiple stratification variables are available. We first review a variance reduction strategy in the case of univariate stratification. We then discuss its use for multivariate situations in convenient and efficient ways using three methods: compromised measures of size, principal components analysis and a K-means clustering algorithm. We also consider three types of compromising factors to data when using these three methods. Finally, we compare their efficiency using data from MU281 Swedish municipality population.

• 한국정보보호학회:학술대회논문집
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• 한국정보보호학회 2006년도 하계학술대회
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• pp.649-653
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• 2006

In this paper, we propose a hybrid feature extraction method in which Principal Components Analysis is combined with optimized k-Means clustering technique. Our approach hierarchically reduces the redundancy of features with high explanation in principal components analysis for choosing a good subset of features critical to improve the performance of classifiers. Based on this result, we evaluate the performance of intrusion detection by using Support Vector Machine and a nonparametric approach based on k-Nearest Neighbor over data sets with reduced features. The Experiment results with KDD Cup 1999 dataset show several advantages in terms of computational complexity and our method achieves significant detection rate which shows possibility of detecting successfully attacks.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2015년도 하계학술대회
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• pp.205-207
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• 2015

본 논문에서는 먼저 주성분 분석 (Principal components analysis, PCA) 을 활용한 Non-local means (NLM) 을 소개하고, 주성분 분석을 하기 위해 필수적인 공분산 행렬 계산을 효율적으로 하는 방법을 제안한다. NLM 에서의 Neighborhood patch 의 크기를 $S{\times}S=S^2$, 이미지 전체의 픽셀 수를 ${\mathcal{Q}}$ 일 때 공분한 행렬을 계산 하기 위해서는 $S^2{\times}{\mathcal{Q}}$ 크기를 가지는 행렬간의 곱 연산이 필요하다. 결론적으로 본 논문에서는 이 행렬의 크기를 줄임으로써 PSNR (Peak signal-to-noise ratio) 의 손실 없이 NLM 의 복잡도를 줄일 수 있음을 보여준다.