• Korean Journal Plant Pathology
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• v.13 no.1
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• pp.37-45
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• 1997

Disease incidence (DI), pre-emergence damping-off (PDO), days until the first symptom appeared (DUS), disease progress curve (DPC), and area under disease progress curve (AUDPC) were investigated in vivo after sowing ginseng seeds in each of 37 ginseng-cultivated soils which were sampled from 4 regions in Korea. Non linear fitting parameters, A, B, K and M, were estimated from the Richards' function, one of the disease progress models, by using the DI at each day from the bioassay. Inter- and intra-relationships between disease variables and stand-missing rate (SMR) in fields were investigated by using the simple correlation analysis. Disease variables of the root rot were divided into two groups: variables related to disease incidence, e.g., DI, AUDPC and A parameter, and variables related to disease progress, e.g., B, K and M parameters. DI, AUDPC, and DUS had significant correlations with SMR in ginseng fields, and then it showed that the disease development in vivo corresponded with that in fields. Soil samples could be separated into 3 and 4 groups, respectively, on the basis of the principal component 1 (PC1) and the principal component 2 (PC2), which were derived from the principal component analysis (PCA) of Richards' parameters, A, B, K and M. PC1 accounted for B, K and M parameters, and PC2 accounted for A parameter.

• ETRI Journal
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• v.40 no.6
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• pp.802-812
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• 2018

Disease discrimination using an electronic nose is achieved by measuring the presence of a specific gas contained in the exhaled breath of patients. Many studies have reported the presence of acetone in the breath of diabetic patients. These studies suggest that acetone can be used as a biomarker of diabetes, enabling diagnoses to be made by measuring acetone levels in exhaled breath. In this study, we perform a chemical sensor array optimization to improve the performance of an electronic nose system using Wilks' lambda, sensor selection based on a principal component (B4), and a stepwise elimination (SE) technique to detect the presence of acetone gas in human breath. By applying five different temperatures to four sensors fabricated from different synthetic materials, a total of 20 sensing combinations are created, and three sensing combinations are selected for the sensor array using optimization techniques. The measurements and analyses of the exhaled breath using the electronic nose system together with the optimized sensor array show that diabetic patients and control groups can be easily differentiated. The results are confirmed using principal component analysis (PCA).

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.487-492
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• 2008

Facial expression recognition is an intensive research area for designing Human Computer Interfaces. In this work, we present a new facial expression recognition system utilizing Enhanced Independent Component Analysis (EICA) for feature extraction and discrete Hidden Markov Model (HMM) for recognition. Our proposed approach for the first time deals with sequential images of emotion-specific facial data analyzed with EICA and recognized with HMM. Performance of our proposed system has been compared to the conventional approaches where Principal and Independent Component Analysis are utilized for feature extraction. Our preliminary results show that our proposed algorithm produces improved recognition rates in comparison to previous works.

• Journal of Aerospace System Engineering
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• v.6 no.1
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• pp.13-18
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• 2012

This paper presents a structural health monitoring method of aircraft reciprocating engine using Principal Component Analysis (PCA) which analyzes vibration expressed by Averaged Normalized Power Spectral Density (ANPSD). Because ANPSD of the rotating shaft is sensitive to the rotating speed, this paper proposes to use a post-processing method of ANPSD is used to reduce the sensitivity. The PCA extracts compressed information from the post-processed ANPSDs and the information means the difference between current and normal cases of the engine. The experimental results demonstrate the feasibility and effectiveness of the proposed method to detect abnormal cases of the engine.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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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.

• Natural Product Sciences
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• v.16 no.3
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• pp.180-184
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• 2010

The simple and accurate method was established for the simultaneous determination of (-)-menthone and (-)-menthol in Menthae herba obtained from Korea and China. A quantitative analysis was performed with a gas chromatography-flame ionization detector and reference compounds were separated on a capillary HP-Innowax column (30 m $\times$ 0.23 mm, 0.50 ${\mu}m$, Agilent, MA, USA). The correlation coefficients of the compounds showed good linearity ($r^2$ > 0.9997) over the linear range. The precision, repeatability and stability showed less than 1.7% of relative standard deviation (RSD) values for two compounds. Recovery rates were within the range of 95.72 - 103.76%. The method was applied successfully to analyze 15 samples of Menthae herba and achieved sufficient and specific separation of reference compounds. The principal component analysis (PCA) exhibited the classification of 15 samples according to their locations of origin.

• Journal of Environmental Science International
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• v.7 no.2
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• pp.171-176
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• 1998

This study was conducted to evaluate water quality utilizing principal component analysis in the Nakdong River Estuary. From the results of analysis, water quality in the Nakdong River Estuary could be explained up to 65.3 Percente by three factors which were Included In river loadlnwastes from the Nakdong River and rainfalls : 39.1%1, sediment resuspension(13.7BS) and metabolism(12.5%). In the eastern part of estuary In flowing the Nakdong River, river loading factor score(factor 1 Pas higher than that In western part. Sediment resuspension factor score(factor 2) was high in shallow water, while metabolism factor score(factor 3) was high in deeper water. For seasonal variations of factors score, factor 1 was h19h- 1y related to rainfall season.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.2
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• pp.205-214
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• 2014

As a result of the rapid economic growth and birth control policy, China is experiencing low fertility rates and increasing life expectancy, which makes Chinese population aging very quickly and unprepared for their retired life. The reverse mortgage may be an attractive option for the elderly because it is a loan against a house that they do not have to pay back as long as they live there. In this paper, in order to introduce the reverse mortgage scheme in China the factors that could influence the demand of reverse mortgage are reviewed and the Chinese market environment is analyzed. Then the principal component analysis is performed in order to recommend the regions or cities that have higher potential for successful implementation of a reverse mortgage than any other ones in China.

• Annual Conference of KIPS
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• 2019.10a
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• pp.145-148
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• 2019

Principal Component Analysis (PCA) is a powerful technique in data analysis and widely used to detect anomalies in Wireless Sensor Networks. However, the performance of conventional PCA is not high on time-series data collected by sensors. In this paper, we propose a Joint Exponential Smoothing and Trend-based Principal Component Analysis (JES-TBPCA) for Anomaly Detection which is based on conventional PCA. Experimental results on a real dataset show a remarkably higher performance of JES-TBPCA comparing to conventional PCA model in detection of stuck-at and offset anomalies.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.21-26
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• 2018

As the large amount of data can be efficiently stored, the methods extracting meaningful features from big data has become important. Especially, the techniques of converting high- to low-dimensional data are crucial for the 'Data visualization'. In this study, principal component analysis (PCA; linear dimensionality reduction technique) and Isomap (non-linear dimensionality reduction technique) are introduced and applied to neural big data obtained by the functional magnetic resonance imaging (fMRI). First, we investigate how much the physical properties of stimuli are maintained after the dimensionality reduction processes. We moreover compared the amount of residual variance to quantitatively compare the amount of information that was not explained. As result, the dimensionality reduction using Isomap contains more information than the principal component analysis. Our results demonstrate that it is necessary to consider not only linear but also nonlinear characteristics in the big data analysis.