• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.1
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• pp.73-81
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• 2012

For planning and developing rural regions, it is very important to understand and utilize regional characteristics including social, demographic, and economic aspects. The purpose of this study is to find effective analysis techniques and provide a procedure design for mining regional characteristics in South Korea through reviewing and analyzing 41 related studies. The engaged research methods can be classified into five categories (PCA+CA, PCA, CA, GIS, and PCA+GIS) with the combination of three methodologies: principal component analysis (PCA), cluster analysis (CA), and geographical information system (GIS). The combination of PCA and CA occupied about 40 % of research methods used in related studies. The analysis tool of Korean Rural Information Supporting System (KRISS) is designed based on the outcomes of this study and applied to classify the regional capacity of agriculture using agricultural census data (2000) for evaluating its applicability.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.4
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• pp.613-617
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• 2011

This study was conducted to compare the quality of the brown rice (BR) and milled rice (MR) during storage. To assess quality, BR and MR were analysed by their fat acidity and flavor pattern using a SMart Nose$^{(R)}$. BR was stored for 30 days at $30^{\circ}C$, and analysed after 5, 15, 20, and 30 days of storage. MR produced in 2005, 2009, and 2010 were also tested. The fat acidity of both rice groups was increased with extended storage and the fat acidity of BR was more rapidly increased than that of MR in general. The flavor patterns from the SMart Nose$^{(R)}$ results were analyzed by the principal component analysis (PCA). The major groups of atomic mass unit (amu) for good discrimination contribution were from 41 to 85 amus. The PCA1 and PCA2 of BR were 95.64% and 2.78%, respectively when the samples were categorized by storage period. The PCA1 and PCA2 of MR were 81.18% and 13.85%, respectively when the samples were compared by production year. Both rice groups could be practically differentiated into flavor patterns by volatile properties for storage period. With regard to the correlation between fat acidity and flavor pattern, we could find that increasing storage period increased fat acidity value and changed flavor pattern from SMart Nose$^{(R)}$. Accordingly, SMart Nose$^{(R)}$ could be successfully used for easy screening and quality evaluation of stored rice.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.6
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• pp.816-822
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• 2012

Volatile components of acorn crude starches and gel powder created from them were analyzed by Gas Chromatograph-Ion Mobility Mass Spectrometry (GC-IMS). Crude starches were obtained from acorns harvested in South Korea (KAS), China (CAS), and North Korea (NAS). The principal component analysis (PCA) of each volatile component exhibited a significant contribution of PC 1 showing up to 60.5%. The acorn crude starch from KAS could be distinguished from crude starch from China by PC 1 (p<0.05). However, NAS and CAS could not be segregated statistically by the PC 1 component. PC 2, which exhibited 22.8% contribution, of KAS, also showed a meaningful difference (p<0.05) from those of CAS and NAS, making it possible to distinguish domestic acorn starch from imports.