• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2003년도 추계학술발표대회(상)
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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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• 제7권4호
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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.

• 원예과학기술지
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• 제29권4호
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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.

• 한국컴퓨터정보학회논문지
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• 제21권5호
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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.

• 전자공학회논문지CI
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• 제41권5호
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• pp.33-40
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• 2004

본 논문에서는 웨이블릿 압축을 이용하여 얼굴 데이터베이스를 구축하고, 주성분 분석(Principal Component Analysis : PCA) 알고리듬을 이용하여 얼굴 인식률을 비교한다. 일반적인 얼굴인식 방법은 정규화된 크기를 이용하여 데이터베이스를 구축하고, 얼굴 인식을 한다. 제안된 방법은 정규화된 크기(92×112)의 영상을 웨이블릿 압축으로 1단계, 2단계, 3단계로 변환하고 데이터베이스를 구축한다. 입력 영상도 웨이블릿으로 압축하고 PCA 알고리듬으로 얼굴인식 실험을 하였다 실험을 통하여 제안된 방법은 기존 얼굴영상의 정보를 축소할 뿐만 아니라 처리속도도 향상되었다. 또한 제안된 방법은 원본 영상이 99.05%, 1단계 99.05%, 2단계 98.93%, 3단계 98.54% 정도의 인식률을 보였으며, 대량의 얼굴 데이터베이스를 구축하여 얼굴인식을 하는데 가능함을 보였다.

• 소음진동
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• 제7권5호
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• pp.737-746
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• 1997

The substructure synthesis method is used for making it easy to analyze vibration systems generally in vibration field. In the past, this method has been to be used mainly because of shortage of computer memory and CPU time. But recently this method is used for analyzing complex structure or identifying the characteristics of systems precisely. The purpose of this study is to develop acoustic substructure synthesis method that can be applied to acoustic modal analysis of complex acoustic systems. Acoustic modal analysis method to be introduced here is a method that analyze acoustic natural mode shape of the complex acoustic system by the principle of CMS(component mode synthesis method). This paper describes the acoustic modal analysis of the acoustic finite element model of simple expansion pipe by acoustic substructure synthesis method. The resutls of acoustic modal analysis analyzed by Acoustic substructure synthesis method and the results by FEM(finite element method) shows good agreement.

• 한국식품과학회지
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• 제37권6호
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• pp.1048-1064
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• 2005

Detection of specific compounds influencing food flavor quality is not easy. Electronic nose, comprised of electronic chemical sensors with partial specificity and appropriate pattern recognition system, is capable of recognizing simple and complex volatiles. It provides fast analysis with simple and straightforward results and is best suited for quality control and process monitoring of flavor in food industry. This review examines application of electronic nose in food analysis with brief explanation of its principle. Characteristics of different sensors and sensor drift. and solutions to related problems are reviewed. Applications of electronic nose in food industry include monitoring of fermentation process and lipid oxidation, prediction of shelf life, identification of irradiated volatile compounds, discrimination of food material origin, and quality control of food and processing by principal component analysis and neural network analysis. Electronic nose could be useful for quality control in food industry when correlating analytical instrumental data with sensory evaluation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권8호
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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.

• 한국정보과학회논문지:소프트웨어및응용
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• 제30권5_6호
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• pp.454-460
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• 2003

이 논문은 입술의 형태를 효과적으로 인식하는 방법을 제안하였다. 입술은 PDM(Point Distribution Model)을 기반으로 점들의 집합으로 표현하였다. 주성분 분석법을 적용하여 입술 모델을 구하고 모델에서 사용하는 형태계수의 분포를 GMM(Gaussian Mixture Model)을 이용하여 구하였다. 이 과정에서 계수를 정하기 위하여 EM(Expectation Maximization) 알고리듬을 사용하였다. 입술 경계선 모델은 입술을 구성하는 각 점과 주변 영역에서의 화소간 변화를 이용하여 구성하였으며 입술 탐색시 사용되었다. 여러 영상을 대상으로 실험한 결과 좋은 결과를 얻었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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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.