• 한국안전학회지
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• 제29권4호
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• pp.73-77
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• 2014

In the field of fault diagnosis, the deviations from normal operating conditions are monitored to identify the type of faults and find their root causes. One of the most representative methods is the statistical approaches, due to a large amount of advantages. However, ambiguous diagnosis results can be generated according to fault magnitudes, even if the same fault occurs. To tackle this issue, this work proposes principal component analysis (PCA) based method with qualitative information. The PCA model is constructed under normal operation data and the residuals from faulty conditions are calculated. The significant changes of these residuals are recorded to make the information for identifying the types of fault. This model can be employed easily and the tasks for building are smaller than these of other common approaches. The efficacy of the proposed model is illustrated in Tennessee Eastman process.

• Nuclear Engineering and Technology
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• 제32권5호
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• pp.485-494
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• 2000

In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique . Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.276-276
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• 2018

In this paper the potential of the Principle Component Analysis(PCA) technique that can be used to detect leaks in water pipe network blocks was evaluated. For this purpose the PCA was conducted to evaluate the relevance of the calculated outliers of a PCA model utilizing the recorded pipe flows and the recorded pipe leak incidents of a case study water distribution system. The PCA technique was enhanced by applying the computational algorithms developed in this study. The algorithms were designed to extract a partial set of flow data from the original 24 hour flow data so that the variability of the flows in the determined partial data set are minimal. The relevance of the calculated outliers of a PCA model and the recorded pipe leak incidents was analyzed. The results showed that the effectiveness of detecting leaks may improve by applying the developed algorithm. However, the analysis suggested that further development on the algorithm is needed to enhance the applicability of the PCA in detecting leaks in real-world water pipe networks.

• Journal of Ecology and Environment
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• 제38권2호
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• pp.145-156
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• 2015

This study was based on water quality data of the Lake Doam watershed, monitored from 2010 to 2013 at eight different sites with multiple physiochemical parameters. The dataset was divided into two sub-datasets, namely, non-rainy and rainy. Principal component analysis (PCA) and factor analysis (FA) techniques were applied to evaluate seasonal correlations of water quality parameters and extract the most significant parameters influencing stream water quality. The first five principal components identified by PCA techniques explained greater than 80% of the total variance for both datasets. PCA and FA results indicated that total nitrogen, nitrate nitrogen, total phosphorus, and dissolved inorganic phosphorus were the most significant parameters under the non-rainy condition. This indicates that organic and inorganic pollutants loads in the streams can be related to discharges from point sources (domestic discharges) and non-point sources (agriculture, forest) of pollution. During the rainy period, turbidity, suspended solids, nitrate nitrogen, and dissolved inorganic phosphorus were identified as the most significant parameters. Physical parameters, suspended solids, and turbidity, are related to soil erosion and runoff from the basin. Organic and inorganic pollutants during the rainy period can be linked to decayed matters, manure, and inorganic fertilizers used in farming. Thus, the results of this study suggest that principal component analysis techniques are useful for analysis and interpretation of data and identification of pollution factors, which are valuable for understanding seasonal variations in water quality for effective management.

• KSBB Journal
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• 제16권4호
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• pp.381-388
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• 2001

DNA칩의 유전자 발현 데이터의 통합적 분석을 위하여 매트랩을 기반으로 한 통합분석 프로그램을 구축하였다. 이 프로그램은 유전자 발현 분석을 위해 일반적으로 많이 쓰는 방법인 Hierarchical clustering(HC), K-means, Self-organizing map(SOM), Principal component analysis(PCA)를 지원하며, 이외에 Fuzzy c-means방법과 최근에 발표된 Singular value decomposition(SVD) 분석 방법도 지원하고 있다. 통합분석프로그램의 성능을 알아보기 위하여 효모의 포자형성(sporulation)과 정의 유전자발현 데이터를 사용하였으며, 각 분석 방법에 따른 분석 결과를 제시하였으며, 이 프로그램이 유전자 발현데이타의 통합적인 분석을 위해 효과적으로 사용될 수 있음을 제시하였다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.305-309
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• 2000

In this paper, we presents a new methodology for face recognition after analysing conventional ICA(Independent Component Analysis) based approach. In the literature we found that ICA based methods have followed the same procedure without any exception, first PCA(Principal Component Analysis) has been used for feature extraction, next ICA learning method has been applied for feature enhancement in the reduced dimension. However, it is contradiction that features are extracted using higher order moments depend on variance, the second order statistics. It is not considered that a necessary component can be located in the discarded feature space. In the new methodology, features are extracted using the magnitude of kurtosis(4-th order central moment or cumulant). This corresponds to the PCA based feature extraction using eigenvalue(2nd order central moment or variance). The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. ICA methodology is analysed using SVD(Singular Value Decomposition). PCA does whitening and noise reduction. ICA performs the feature extraction. Simulation results show the effectiveness of the methodology compared to the conventional ICA approach.

• 핵의학기술
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• 제19권1호
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• pp.12-16
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• 2015

주성분분석(Principal component analysis, PCA)은 고차원 변수들 사이의 복잡한 상관성 구조를 더 낮은 차원으로 단순화하여 상관성의 구조를 쉽게 설명하기 위한 다변량분석기법으로 뇌영상 분석에서 자주 사용되는 방법이다. 주성분분석의 기본개념은 서로 직교하는 변수들의 선형결합을 통해서 원래의 뇌영상 자료가 가지고 있는 전체정보를 최대한 설명할 수 있는 서로 독립적인 새로운 변수들을 유도하는 것이다. 뇌영상분석에서 주성분분석의 효율성과 유용성을 알아보기 위해서 C[11]-PIB 영상을 이용하여 분석하였다. 대상 및 방법으로는 평균나이가 같은 9명의 정상인, 10명의 알츠하이머/경도인지장애환자들의 C[11]-PIB 영상을 이용하였다. PET-CT 장비로는 Biograph 6 Hi-Rez (Siemens-CTI, Knoxville, TN)를 영상을 획득하였고 9.6 MBq/kg C[11]-PIB를 정맥주사 한 후 40분 후에 20분 동안 3D acquisition mode로 방출영상을 얻었다. Attenuation map은 X-ray CT scan을 이용하여 재구성하였다(130 kVp, 240 mA). PIB template을 만들기 위해서 정상인에서 3T MRI T1-weighted 영상을 동시에 얻었다. 주성분분석을 위한전처리과정으로서 공간정규화 및 공간편평화를 SPM8을 이용하여 실시하였고 주성분분석은 Matlab2012b를 이용하여 분석하다. 결과는 주성분분석을 통해서 서로 독립적인 주성분영상들을 얻을 수 있었다. 주성분분석을 통해서 얻어진주성분영상은 C[11]-PIB brain PET 영상의 패턴을 몇 개의 주성분으로 단순화 할 수 있었으며 주로는 neocortex를 변동 나타내는 영상, white matter의 변동을 나타내는 영상 그리고 pons등 deep brain의 변동을 나타내는 영상 등으로 단순화되었다. 결론으로는 주성분분석은 C[11]-PIB brain 영상을 단순화하여 영상의 패턴을 해석하는데 매우 유용하였다. 이러한 주성분분석은C[11]-PIB영상 분석뿐만 아니라 뇌의 포도당 대사를 측정하는 FDG-PET 또는 뇌기능영상등의 다변량분석 방법으로서 그 적용범위가 클 것으로 기대된다.

• 한국항공우주학회지
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• 제43권1호
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• pp.23-32
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• 2015

전투기 조종사 모델링은 국방 M&S(Modeling & Simulation)를 활용한 전쟁 모의 및 전투 실험의 기초 기술로 국방 M&S의 중요성이 대두됨에 따라 연구의 필요성이 높아지고 있다. 특히, 최근 전투 로그의 축적으로 통계적 학습 기법을 활용한 모델링의 적용이 가능해졌으며 전투 로그의 시계열적 특성을 반영할 수 있는 HMM(Hidden Markov Model)이 적합하다. 하지만 HMM은 이산형 혹은 연속형 중 한 형태의 변수만을 통해 학습되므로 이형 변수로 구성된 전투 로그에 적용을 위해서는 형변환 과정이 필요하다. 따라서 본 논문에서는 형변환을 위한 dPCA(Discrete Principal Component Analysis)와 HMM을 접목한 dPCA-HMM 기반 조종사 모델링 방법을 제안한다. 국방과학연구소 관급 시뮬레이터로부터 생성된 전투 로그를 이용한 비교 실험을 통해 제안하는 방법론의 성능을 평가하였으며, 만족스러운 성능을 나타내었다.

• 한국지하수토양환경학회지:지하수토양환경
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• 제27권spc호
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• pp.34-50
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• 2022

Principal component analysis (PCA) was conducted using hydrochemical data in four testbeds (A to D) built for the development of site characterization technologies to assess the hydrochemical processes controlling the hydrochemistry in each site. The PCA results indicated the nitrogen loading to deep bedrock aquifers through permeable fractures in Testbed A, the chemical weathering enhanced with the biodegradation of petroleum hydrocarbons in Testbed B, the reductive dechlorination in Testbed C, and the different hydrochemistry depending on the depth to bedrock in Testbed D, consistent with the characteristics of each site. In Testbeds B and D, outliers seemed to affect the PCA result probably due to the small number of samples, whereas the PCA result was still consistent with site characteristics. This study result indicates that the PCA is widely applicable to hydrochemical data for the assessment of major hydrochemical processes in contamination sites, which is useful for site characterization when combined with other site characterization technologies, e.g., geological survey, geophysical investigation, borehole logging. It is suggested that PCA is applied in contaminated sites to interpret hydrochemical data not only for the distribution of contamination levels but also for the assessment of major hydrochemical processes and contamination sources.

• International Journal of Aeronautical and Space Sciences
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• 제13권3호
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• pp.398-404
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• 2012

A fault detection process is necessary for high integrity systems like satellites, missiles and aircrafts. Especially, the satellite has to be expected to detect faults autonomously because it cannot be fixed by an expert in the space. Faults can cause critical errors to the entire system and the satellite does not have sufficient computation power to operate a large scale fault management system. Thus, a fault detection method, which has less computational burden, is required. In this paper, we proposed a modified PCA (Principal Component Analysis) as a powerful fault detection method of redundant IMU (Inertial Measurement Unit). The proposed method combines PCA with the parity space approach and it is much more efficient than the others. The proposed fault detection algorithm, modified PCA, is shown to outperform fault detection through a simulation example.