• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.302-305
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• 2003

카메라를 이용하여 얼굴을 인식하는 방법은 현재까지 털러 가지 접근 방법들이 제시되어 왔지만, 제약 조건 없고 안정적인 인식 방법은 아직 도출되지 않은 상태이다. 본 연구에서는 얼굴영역을 몇 개의 주성분 변수로 변환하여 영상의 명암, 얼굴위치와 무관하게 얼굴의 영역을 추출할 수 있는 시스템을 연구하였고, 10명 이내의 소규모 집단과 실내 환경을 전제 조건으로 하여 응용하였다.

• Journal of Construction Engineering and Project Management
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• v.7 no.4
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• pp.20-28
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• 2017

In almost every construction project, delay is an inevitable yet controllable phenomenon. The Indian construction industry encounters an enormous amount of delays in projects. Delay affects both time and money in the forms of schedule and cost overruns, respectively. Due to impressive and dynamic growth in the Indian construction sector, planned efforts are essential to limit these undesirable delays. On account of the surge in the rate of residential building construction, the task of identification and analysis of the delays in residential projects in India has been attempted by the authors. A questionnaire survey was conducted involving 100 stakeholders. Further analysis included an Importance Index to rank the identified delays, Principle Component Analysis for advanced statistical analysis, and Correlation Analysis to check the extent of agreement amongst stakeholders. Conclusions drawn with reference to the analysed data eventually reflected finance-related issues, as well as labour related problems as the dominating causes of delays. The aim of the research is to provide insight to the construction stakeholders and researchers, on an international scale, with the obtained results.

• Horticultural Science & Technology
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• v.29 no.4
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• pp.382-387
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• 2011

The chemical composition of essential oils obtained from aerial parts in spearmint, apple mint and chocolate mint, was investigated by gas chromatography/mass spectrometry analyses. (-)-Carvone (33.0%) was quantitatively major compound in spearmint, followed by R-(+)-limonene (11.7%) and ${\beta}$-phellandrene (9.7%); (-)-carvone (37.4%) and germacrene D (11.9%) in apple mint; and (-)-menthol (34.3%), p-menthone (18.4%) and menthofuran (9.8%) in chocolate mint. Hierarchical cluster analysis and principle components analysis showed the clear difference in chemical composition of the three mint oils.

• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.167-175
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• 2016

The volatile component analysis of 82 commercialized liquors(44 samples of single malt whisky, 20 samples of blended whisky and 18 samples of brandy) was carried out by gas chromatography after liquid-liquid extraction with dichloromethane. Pattern recognition techniques such as principle component analysis(PCA), cluster analysis(CA), linear discriminant analysis(LDA) and partial least square discriminant analysis(PLSDA) were applied for the discrimination of different liquor categories. Classification rules were validated by considering sensitivity and specificity of each class. Both techniques, LDA and PLSDA, gave 100% sensitivity and specificity for all of the categories. These results suggested that the common characteristics and identities as typification of whiskies and brandys was founded by using multivariate data analysis method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.5
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• pp.33-40
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• 2004

In this paper, we constructs face database by using wavelet comparison, and compare face recognition rate by using principle component analysis (Principal Component Analysis : PCA) algorithm. General face recognition method constructs database, and do face recognition by using normalized size. Proposed method changes image of normalized size (92${\times}$112) to 1 step, 2 step, 3 steps to wavelet compression and construct database. Input image did compression by wavelet and a face recognition experiment by PCA algorithm. As well as method that is proposed through an experiment reduces existing face image's information, the processing speed improved. Also, original image of proposed method showed recognition rate about 99.05%, 1 step 99.05%, 2 step 98.93%, 3 steps 98.54%, and showed that is possible to do face recognition constructing face database of large quantity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3884-3910
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• 2016

Intrusion Detection System (IDS) in general considers a big amount of data that are highly redundant and irrelevant. This trait causes slow instruction, assessment procedures, high resource consumption and poor detection rate. Due to their expensive computational requirements during both training and detection, IDSs are mostly ineffective for real-time anomaly detection. This paper proposes a dimensionality reduction technique that is able to enhance the performance of IDSs up to constant time O(1) based on the Principle Component Analysis (PCA). Furthermore, the present study offers a feature selection approach for identifying major components in real time. The PCA algorithm transforms high-dimensional feature vectors into a low-dimensional feature space, which is used to determine the optimum volume of factors. The proposed approach was assessed using HTTP packet payload of ISCX 2012 IDS and DARPA 1999 dataset. The experimental outcome demonstrated that our proposed anomaly detection achieved promising results with 97% detection rate with 1.2% false positive rate for ISCX 2012 dataset and 100% detection rate with 0.06% false positive rate for DARPA 1999 dataset. Our proposed anomaly detection also achieved comparable performance in terms of computational complexity when compared to three state-of-the-art anomaly detection systems.

• Journal of KIISE:Software and Applications
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• v.30 no.5_6
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• pp.454-460
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• 2003

In this paper, we propose an efficient method for recognizing human lips. Based on Point Distribution Model, a lip shape is represented as a set of points. We calculate a lip model and the distribution of shape parameters using Principle Component Analysis and Gaussian mixture, respectively. The Expectation Maximization algorithm is used to determine the maximum likelihood parameter of Gaussian mixture. The lip contour model is derived by using the gray value changes at each point and in regions around the point and used to search the lip shape in a image. The experiments have been performed for many images, and show very encouraging result.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.4
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• pp.62-71
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• 2009

In this paper, we propose a fault diagnosis algorithm for BLDC motors by principle component analysis (PCA) and implement a real-time fault diagnosis system for BLDC motors. To verify the proposed diagnosis algorithm, various faulty data are acquired by Lab VIEW program from experimental system. We extract a fault feature using principle component analysis after preprocessing and then finally the fault diagnosis is performed by Euclidean similarity. Also, we embed the PCA algorithm and k-NN classification algorithm into a digital signal processor. From various experiments, we found that the proposed algorithm can be used as a powerful technique to classify the several fault signals acquired from BLDC motors.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.878-882
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• 2004

In this paper, we propose a hierarchical hybrid neural network for detecting faults of induction motor. Implementing the classifier based on the input and output data, we apply appropriate transform and classification method at each step. In the proposed method, after obtaining the current of state of motor for each period, we transform it by Principle Component Analysis(PCA) to reduce its dimension. Before the training process, we use the conditional Fuzzy C-means(FCM) for obtaining the initial parameters of neural network for more effective learning procedure. From the various simulations, we find that the proposed method shows better performance to detect and diagnosis of induction motor and compare than other methods.