• 한국안전학회지
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• 제31권4호
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• pp.35-41
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• 2016

In order to guarantee the process safety and prevent accidents, the deviations from normal operating conditions should be monitored and their root causes have to be identified as soon as possible. The statistical theories-based method among various fault diagnosis methods has been gaining popularity, due to simplicity and quickness. However, according to fault magnitudes, the scalar value generated by statistical methods can be changed and this point can lead to produce wrong information. To solve this difficulty, this work employs PCA (Principal Component Analysis) based method with qualitative information. In the case study of our previous study, the number of assumed faults is much smaller than that of process variables. In the case study of this study, the number of predefined faults is 19, while that of process variables is 6. It means that a fault diagnosis becomes more difficult and it is really hard to isolate a single fault with a small number of variables. The PCA model is constructed under normal operation data in order to get a loading vector and the data set of assumed faulty conditions is applied with PCA model. The significant changes on PC (Principal Components) axes are monitored with CUSUM (Cumulative Sum Control Chart) and recorded to make the information, which can be used to identify the types of fault.

• 상하수도학회지
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• 제27권3호
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• pp.351-361
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• 2013

In this study the Principal Component Analysis (PCA) was applied to flow data in a water distribution pipe system to analyze the relevance between the flow observation dates, which have the outliers of observed night flows, and the maintenance records. The data was obtained from four small size water distribution blocks to which 13 maintenance records such as pipe leak and water meter leak belong. The flow data during four months were used for the analysis. The analysis was carried out to identify an appropriate analysis period for a PCA model for a water distribution block. To facilitate the analyses a computational algorithm was developed. MATLAB was utilized to realize the algorithm as a computer program. As a result, an appropriate PCA period for each of the case study small size water distribution blocks was identified.

• 응용통계연구
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• 제28권3호
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• pp.385-392
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• 2015

주성분분석은 차원축소를 위한 대표적 기법이다. 주성분분석에서 변수들이 측정단위가 다르거나 분산의 불균형이 심할 경우 흔히 변수를 표준화한 다음 분석할 것이 권장된다. 표준화 변환은 표준편차를 나누어주는 변환인데, 측정단위에 무관하게 만들기 위해서라면 평균을 나누어주는 변환도 고려해볼 수 있다. 표준화 변환을 한 다음 주성분분석하는 것은 상관행렬로 주성분분석하는 것과 같은데, 평균을 나누어주는 변환을 한 후 주성분분석하는 것은 변동계수와 관련된 행렬로 주성분분석하는 것과 같음을 보이고, 그렇게 변환을 한 다음 주성분분석을 실시하는 것이 왜 필요한가를 설명하였다.

• 전기학회논문지
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• 제58권12호
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.113-118
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• 2024

Pursuance Sentiment Analysis on Twitter is difficult then performance it's used for great review. The present be for the reason to the tweet is extremely small with mostly contain slang, emoticon, and hash tag with other tweet words. A feature extraction stands every technique concerning structure and aspect point beginning particular tweets. The subdivision in a aspect vector is an integer that has a commitment on ascribing a supposition class to a tweet. The cycle of feature extraction is to eradicate the exact quality to get better the accurateness of the classifications models. In this manuscript we proposed Term Frequency-Inverse Document Frequency (TF-IDF) method is to secure Principal Component Analysis (PCA) with Naïve Bayes Classifiers. As the classifications process, the work proposed can produce different aspects from wildly valued feature commencing a Twitter dataset.

• 한국게임학회 논문지
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• 제11권1호
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• pp.139-146
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• 2011

본 논문에서는 주성분 분석을 사용한 포토모자이크 생성 기법을 제안한다. 후보 이미지 집합의 주성분 분석 결과인 주성분과 계수를 사용하여 후보 이미지 검색을 보다 빠르고 정확하게 포토모자이크를 생성한다. 두 이미지를 하나의 주성분으로 투영해서 계산된 두 계수가 유사하면 두 이미지의 본래 정보 역시 유사하기 때문에, 본 논문에서 제안하는 주성분 분석을 사용하는 계수 비교 방법은 이미지의 색상 정보와 위치 정보를 동시에 비교할 수 있다. 계수 비교 방법은 모든 색상 비교 방법보다 빠르고, 평균 색상 비교 방법보다 정확하게 포토모자이크를 생성한다. 본 논문에서 제안하는 포토모자이크 알고리즘은 그래픽스 하드웨어의 가속을 받아 수행되므로 실시간에 입력 영상을 처리할 수 있다.

• 인지과학
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• 제16권4호
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• pp.323-331
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• 2005

본 논문은 조명 변화에 강인하며 중립 표정과 같은 표정 측정의 기준이 되는 단서 없이 다양한 내적상태 안에서 얼굴표정을 인식할 수 있는 개선된 시스템을 제안한다. 표정정보를 추출하기 위한 전처리 작업으로, 백색화(whitening) 단계가 적용되었다. 백색화 단계는 영상데이터들의 평균값이 0이며 단위분산 값으로 균일한 분포를 갖도록 하여 조명 변화에 대한 민감도를 줄인다. 백색화 단계 수행 후 제 1 주성분이 제외된 나머지 주성분들로 이루어진 PCA표상을 표정정보로 사용함으로써 중립 표정에 대한 단서 없이 얼굴표정의 특징추출을 가능하게 한다. 본 실험 결과는 또한 83개의 내적상태와 일치되는 다양한 얼굴표정들에서 임의로 선택된 표정영상들을 내적상태의 차원모델에 기반한 얼굴표정 인식을 수행함으로써 다양하고 자연스런 얼굴 표정 인식을 가능하게 하였다.

• Journal of Communications and Networks
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• 제12권2호
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• pp.191-197
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• 2010

In this paper, we propose a novel methodology to identify border gateway protocol (BGP) updates associated with major events - affecting network reachability to multiple ASes - and separate them (statistically) from those attributable to minor events, which individually generate few updates, but collectively form the persistent background noise observed at BGP vantage points. Our methodology is based on principal component analysis, which enables us to transform and reduce the BGP updates into different AS clusters that are likely affected by distinct major events. We demonstrate the accuracy and effectiveness of our methodology through simulations and real BGP data.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2002년도 총회 및 춘계학술발표회
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• pp.110-113
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• 2002

Statistical- based, principal component analysis (PCA) was applied to chemical data from two underground storage systems containing LPG to assess the usefulness of such technique at the initial stage (Pyeongtaek) or middle stage (Ulsan) of hydrochemical studies. For the first case, both natural and anthropogenic contamination characterize regional groundwater. Saline water buffered by Namyang lake affects as a natural factor, whereas cement grouting influence as an artificial factor. For the second study area, contaminations due to operation of LPG caverns, such as disinfection activity and cement grouting effect, deteriorate groundwater quality. This study indicates that principal component analysis would be particularly useful for summarizing large data set for the purpose of subsurface characterization, assessing their vulnerability to contamination and protecting recharge zones.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2010년도 추계 학술발표회
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• pp.416-422
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• 2010

Estimating condition of geotechnical structures are difficult because of nonlinear time dependency and seasonal effects. Measuring data of structure failure is highly variable in time and space, and a unique approach cannot be defined to model structure movements. Characteristics of movements are obtained by using a statistical method called Principal Component Analysis(PCA). The PCA is a non-parametric method to separate unknown, statistically uncorrelated source processes from observed mixed processes. Instead, since the "best" mathematical relationship is estimated for given data sets of the input and output measured from target systems. As a consequence, this method is advantageous in modeling systems whose geomechanical properties are unknown or difficult to be measured.