• Journal of Korean Society for Atmospheric Environment
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• v.11 no.2
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• pp.179-184
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• 1995

This study was carried out to characterize sources of suspended particulate matter(SPM) in Mt. Soback area from January to Novembver in 1993. The collection and major water soluble ion analysis of SPM were conducted by using a High Volumn Air Sampler(HVAS; W&A Inc., PM-10) and ion chromatograph(DIONEX 4000i), respectively. The variations of SPM and major ion concentrations were found to be 9. sim. 156.mu.g/ $m^{3}$, $F^{-10}$ 0.00 .sim. 0.15.mu.g/ $m^{3}$, C $l^{-10}$ 0.06 .sim. 3.79.mu.g/ $m^{3}$, N $O_{3}$$^{-10}$ 0.90 .sim. 6.85.mu.g/ $m^{3}$, S $O_{4}$$^{2-}$ 1.99 .sim. 9.36.mu.g/ $m^{3}$ N $a^{+}$0.00 .sim. 0.27.mu.g/ $m^{3}$, N $H_{4}$ $^{+}$0.72 .sim. 5.77.mu.g/ $m^{3}$, $k^{+}$0.03 .sim. 0.88.mu.g/ $m^{3}$ and $Ca^{2+}$0.12 .sim. 2.76.mu.g/ $m^{3}$. Tree sources were identified by Principal Component Amalysis(PCA) using a SPSS/P $C^{+}$. The explanation ability of forst, second and third Principal Component were 60.8%, 13.6%, 8.2%, of total variance. The sources classfied by PCA were found to be secondary aerosol/fuel combustion, soil dust related cement production/yellow sand and aerosol related waste burning.related waste burning.g.

• Korean Journal of Plant Resources
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• v.19 no.1
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• pp.105-111
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• 2006

The objectives of this study, an analysis of fruit and leaf morphological characteristics among the five Zizyphus cultivars could be used for the investigation of cultivars classification and could provide information to make out the UPOV TG(Test Guidelines). ANOVA tests showed that there were statistically significant differences in all fruit and leaf morphological characteristics among the five Zizyphus cultivars at 1% level. But, for kernel characteristics, differences were statistically non-significant among the cultivars. Approximately, the Wolchul and Boeun cultivars showed larger and smaller values in overall characteristics and cultivars, respectively. The results of principal component analysis(PCA) for the fruit and leaf morphological characteristics showed that the first for principal components(PC's) explained about 65.3% of the total variation. The first PC was correlated with those characteristics that were mainly related to the terminal leaf length(TLL), leaf length(LL), fruit length(FL), terminal leaf width(TLW), and leaf petiole length(LPL). The second and third PC was mainly correlated with the terminal leaf morphological index(TLMI). Therefore, these characteristics were important to analysis of the fruit and leaf morphological characteristics and classification among the five Zizyphus cultivars. Cluster analysis using UPGMA method based on principal components showed that five Zizyphus cultivars could be clustered into two groups. Group I comprises Mudung, Wolchul, and Bokjo and Geumsung cultivars, Group II is Boeun cultivar. These results well similar to that of principal component analysis.

• Journal of Korea Multimedia Society
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• v.18 no.5
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• pp.610-619
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• 2015

*In this paper, we propose a real-time face tracking system using an adaptive face detector and a tracking algorithm. An image is divided into the regions of background and face candidate by a real-time updated skin color identifying system in order to accurately detect facial features. The facial characteristics are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted by Principal Component Analysis (PCA), and the interpreted principal components are processed by Support Vector Machine (SVM) that classifies into facial and non-facial areas. The movement of the face is traced by Kalman filter and Mean shift, which use the static information of the detected faces and the differences between previous and current frames. The proposed system identifies the initial skin color and updates it through a real-time color detecting system. A similar background color can be removed by updating the skin color. Also, the performance increases up to 20% when the background color is reduced in comparison to extracting features from the entire region. The increased detection rate and speed are acquired by the usage of Kalman filter and Mean shift.

• Food Science of Animal Resources
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• v.37 no.5
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• pp.626-634
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• 2017

This study set out to identify the changes in the nutrient contents during the chicken cooking process as basic data for the establishment of a national health nutrition policy. Samples were produced using 3 chicken parts (wing, breast, and leg) and 7 cooking methods (boiling, pan-cooking, pan-frying, deep-frying, steaming, roasting, and microwaving), and the essential amino acid contents, principal components, and retention rates were analyzed. Weight loss was observed in all chicken parts with all cooking methods. The protein and essential amino acid contents of the chicken samples differed significantly according to the part and the cooking method (p<0.01). The protein and essential amino acid contents (g/100 g) of raw and cooked chicken parts showed ranges of 16.81-32.36 and 0.44-2.45, respectively. The principal component analysis (PCA) clearly demonstrated that the cooking methods and chicken parts produced similar trends for the essential amino acid contents. The retention rates of the chicken parts varied with the cooking methods, yielding a minimum value of 83% for isoleucine in a roasted wing, 91% for protein in a steamed breast, and 77% for isoleucine and lysine in a roasted leg. Therefore, the protein and amino acid contents of the roasted breast were higher than those of the other cooked chicken parts.

• Asian Journal of Atmospheric Environment
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• v.4 no.1
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• pp.9-19
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• 2010

The size distribution of airborne particulate matter (PM) and the concentrations of associated metallic elements were investigated in a busy urban region of a typical Korean industrial city. The PM concentrations measured during the spring, except for those in the size range of 1.1 to 2.1 ${\mu}m$, were slightly higher than the PM concentrations in the summer. Coarse particles contributed greatly to the variation in PM concentrations in the spring, while fine and submicron particles contributed largely to the variation in PM concentrations in the summer. The difference in size modes of the PM concentrations between spring and summer may be explained by the Asian dust effect and its accompanying wind direction and speed. Extremely high enrichment factors (EFs) values (6,971 to 60,966) for all of the size distributions in PM were identified for cadmium (Cd). High EFs values (12 to 907) were also identified for other heavy metals including Cr, Cu, Ni, Pb, Zn and Mn. Low EF values (0.29 to 8.61) were identified for Ca, K, Mg and Na. These results support the common hypothesis that most heavy metals in ambient PM have anthropogenic sources and most light metals have crustal sources. The results of principal components analyses and cluster analyses for heavy metals indicate that the principal sources of PM and metals were emissions from non-ferrous metal smelters, oil combustion, incinerators, vehicular traffic and road dust.

• Journal of Information Technology Services
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• v.6 no.3
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• pp.241-249
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• 2007

This paper describes a real-time face tracking system using effective detector and Kalman filter. In the proposed system, an image is separated into a background and an object using a real-time updated face color for effective face detection. The face features are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. The moving face is traced with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. The proposed system sets up an initial skin color and updates a region of a skin color through a moving skin color in a real time. It is possible to remove a background which has a similar color with a skin through updating a skin color in a real time. Also, as reducing a potential-face region using a skin color, the performance is increased up to 50% when comparing to the case of extracting features from a whole region.

• Smart Structures and Systems
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• v.15 no.5
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• pp.1329-1344
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• 2015

The stiffness of a structure is one of several structural signals that are useful indicators of the amount of damage that has been done to the structure. To accurately estimate the stiffness, an equation of motion containing a stiffness parameter must first be established by expansion as a linear series model, a Taylor series model, or a power series model. The model is then used in multivariate autoregressive modeling to estimate the structural stiffness and compare it to the theoretical value. Stiffness assessment for modeling purposes typically involves the use of one of three statistical model refinement approaches, one of which is the efficient Akaike information criterion (AIC) proposed in this paper. If a newly added component of the model results in a decrease in the AIC value, compared to the value obtained with the previously added component(s), it is statistically justifiable to retain this new component; otherwise, it should be removed. This model refinement process is repeated until all of the components of the model are shown to be statistically justifiable. In this study, this model refinement approach was compared with the two other commonly used refinement approaches: principal component analysis (PCA) and principal component regression (PCR) combined with the AIC. The results indicate that the proposed AIC approach produces more accurate structural stiffness estimates than the other two approaches.

• Journal of Environmental Science International
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• v.12 no.6
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• pp.665-676
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• 2003

To investigate the chemical characteristics of PM$\_$2.5/ in Seoul, Korea, atmospheric particulate matters were collected using a PM$\_$10/ dichotomous sampler including PM$\_$10/ and PM$\_$2.5/ inlet during the period of October 2000 to September 2001. The Inductively Coupled Plasma-Mass Spectromety (ICP-MS), ion Chromatography (IC) methods were used to determine the concentration of both metal and ionic species. A statistical analysis was performed for the heavy metals data set using a principal component analysis (PCA) to derived important factors inherent in the interactions among the variables. The mean concentrations of ambient PM$\_$2.5/ and PM/sub10/ were 24.47 and 45.27 $\mu\textrm{g}$/㎥, respectively. PM$\_$2.5/ masses also showed temporal variations both yearly and seasonally. The ratios of PM$\_$2.5/PM$\_$10/ was 0.54, which similar to the value of 0.60 in North America. Soil-related chemical components (such as Al, Ca, Fe, Si, and Mn) were abundant in PM$\_$10/, while anthropogenic components (such as As, Cd, Cr, V, Zn and Pb) were abundant in PM2s. Total water soluble ions constituted 30∼50 % of PM$\_$2.5/ mass, and sulfate, nitrate and ammonium were main components in water soluble ions. Reactive farms of NH$_4$$\^$+/were considered as NH$_4$NO$_3$ and (NH$_4$)$_2$SO$_4$ during the sampling periods. In the results of PCA for PM$\_$2.5/, we identified three principal components. Major contribution to PM$\_$2.5/ seemed to be soil, oil combustion, unidentified source. Further study, the detailed interpretation of these data will need efforts in order to identify emission sources.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.889-899
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• 2017

Independent component analysis is a multivariate approach to separate mixed signals into original signals. It is the most widely used method of blind source separation technique. ICA uses linear transformations such as principal component analysis and factor analysis, but differs in that ICA requires statistical independence and non-Gaussian assumptions of original signals. PCA have a natural ordering based on cumulative proportion of explained variance; howerver, ICA algorithms cannot identify the unique optimal ordering of the components. It is meaningful to set order because major components can be used for further analysis such as clustering and low-dimensional graphs. In this paper, we compare the performance of several criteria to determine the order of the components. Kurtosis, absolute value of kurtosis, negentropy, Kolmogorov-Smirnov statistic and sum of squared coefficients are considered. The criteria are evaluated by their ability to classify known groups. Two types of data are analyzed for illustration.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.222-225
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• 2000

Human frequently communicate non-linguistic information with gesture. So, we must develop efficient and fast gesture recognition algorithms for more natural human-computer interaction. However, it is difficult to recognize gesture automatically because human's body is three dimensional object with very complex structure. In this paper, we suggest a method which is able to detect key frames and frame changes, and to classify image sequence into some gesture groups. Gesture is classifiable according to moving part of body. First, we detect some frames that motion areas are changed abruptly and save those frames as key frames, and then use the frames to classify sequences. We symbolize each image of classified sequence using Principal Component Analysis(PCA) and clustering algorithm since it is better to use fewer components for representation of gestures. Symbols are used as the input symbols for the Hidden Markov Model(HMM) and recognized as a gesture with probability calculation.