• The Korean Journal of Food And Nutrition
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• v.30 no.5
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• pp.1068-1079
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• 2017

This study was applied to the PCA (Primary Components Analysis) for the sixteen table setting at the 2017 Yeongju local food contest. In this contest, we have developed a seonbibansang and a temple one-dish meal. As a result of the correlation analysis, the applicability and composition were 0.7980, harmony and taste were 0.7747 and easiness and composition were 0.7435. In the Primary Component $Y_1$, all the variables $X_1{\cdots}X_{10}$ mean that the quality of the food had positive values greater than zero. The second Primary Component $Y_2$ has a large positive value while $X_4$, $X_5$, $X_6$, $X_7$, $X_9$ have negative values. $Y_2$ is a value representing the sanitation variable, and can be considered a traditional and characteristic table setting natural to the native food in Yeongju. In addition, we developed an-hyangbansang and seonmyoaecheong food content by applying PCA factors (the elements of harmony, ease and sanitation). Table setting of an-hyangbansang provided energy 61.5%, protein 20.0% and fat 18.5% and seonmyoaecheong provided energy 62.7%, protein 15.4% and fat 22.2%. This satisfied the necessary amount of caloric nutrient intake that could be provided in a meal. Especially through story-telling, a modern interpretation - or rebranding - of local and traditional foods could make these traditional food products familiar to consumers currently. The developed table setting is felt to be conductive to the possible commercialization and introduction of traditional food into the mainstream commercial food service industry.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.227-232
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• 2010

This paper proposes the real time lip reading method in the embedded environment. The embedded environment has the limited sources to use compared to existing PC environment, so it is hard to drive the lip reading system with existing PC environment in the embedded environment in real time. To solve the problem, this paper suggests detection methods of lip region, feature extraction of lips, and awareness methods of phonetic words suitable to the embedded environment. First, it detects the face region by using face color information to find out the accurate lip region and then detects the exact lip region by finding the position of both eyes from the detected face region and using the geometric relations. To detect strong features of lighting variables by the changing surroundings, histogram matching, lip folding, and RASTA filter were applied, and the properties extracted by using the principal component analysis(PCA) were used for recognition. The result of the test has shown the processing speed between 1.15 and 2.35 sec. according to vocalizations in the embedded environment of CPU 806Mhz, RAM 128MB specifications and obtained 77% of recognition as 139 among 180 words were recognized.

• Korean Journal of Optics and Photonics
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• v.19 no.4
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• pp.270-275
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• 2008

This paper addresses occluded object reconstruction and recognition with computational integral imaging (II). Integral imaging acquires and reconstructs target information in the three-dimensional (3D) space. The reconstruction is performed by averaging the intensities of the corresponding pixels. The distance to the object is estimated by minimizing the sum of the standard deviation of the pixels. We adopt principal component analysis (PCA) to classify occluded objects in the reconstruction space. The Euclidean distance is employed as a metric for decision making. Experimental and simulation results show that occluded targets are successfully classified by the proposed method.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.3
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• pp.371-374
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• 2011

The objectives of the present study evaluated aquatic animals on the water in a rice field. Field investigation was carried out in conventional tillage without rice straw or green manure crop treatment (CTFS, check plot), no-tillage without cover crops (NTNT), no-tillage amended with rape (NTRA), no-tillage amended with rye (NTRY), no-tillage amended with hairyvetch (NTHV), and no-tillage amended with Chinese milk vetch (NTCM). Total dense population of aquatic animals in HTHV was significantly higher than the other plots (p<0.05) on May 30. Dense populations of Daphniidae and Culicidae on June 20 were lowest in CTFS compared to no-tillage plots (p<0.05). Furthermore, in principal component analysis (PCA), PC1 explained 44.9% of variance, whereas PC2 explained 26%, for a cumulative total of 70.9% and the PC1 of the PCA separated the samples from NT treatments and CFS (p<0.05).

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.1
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• pp.71-79
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• 2009

Currently much research is being done on host based intrusion detection using system calls which is a portion of kernel based data. Sequence based and frequency based preprocessing methods are mostly used in research for intrusion detection using system calls. Due to the large amount of data and system call types, it requires a significant amount of preprocessing time. Therefore, it is difficult to implement real-time intrusion detection systems. Despite this disadvantage, the frequency based method which requires a relatively small amount of preprocessing time is usually used. This paper proposes an effective method for detecting denial of service attacks using the frequency based method. Principal Component Analysis(PCA) will be used to select the principle system calls and a bayesian network will be composed and the bayesian classifier will be used for the classification.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1373-1387
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• 2021

Forest fire poses a significant threat to the environment and society, affecting carbon cycle and surface energy balance, and resulting in socioeconomic losses. Widely used multi-spectral satellite image-based approaches for burned area detection have a problem in that they do not work under cloudy conditions. Therefore, in this study, Sentinel-1 Synthetic Aperture Radar (SAR) data from Europe Space Agency, which can be collected in all weather conditions, were used to identify forest fire damaged area based on a series of processes including Principal Component Analysis (PCA) and K-means clustering. Four forest fire cases, which occurred in Gangneung·Donghae and Goseong·Sokcho in Gangwon-do of South Korea and two areas in North Korea on April 4, 2019, were examined. The estimated burned areas were evaluated using fire reference data provided by the National Institute of Forest Science (NIFOS) for two forest fire cases in South Korea, and differenced normalized burn ratio (dNBR) for all four cases. The average accuracy using the NIFOS reference data was 86% for the Gangneung·Donghae and Goseong·Sokcho fires. Evaluation using dNBR showed an average accuracy of 84% for all four forest fire cases. It was also confirmed that the stronger the burned intensity, the higher detection the accuracy, and vice versa. Given the advantage of SAR remote sensing, the proposed statistical processing and K-means clustering-based approach can be used to quickly identify forest fire damaged area across the Korean Peninsula, where a cloud cover rate is high and small-scale forest fires frequently occur.

• Economic and Environmental Geology
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• v.53 no.5
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• pp.619-629
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• 2020

The National Groundwater Monitoring Network (NGMN) in South Korea has been implemented in alluvial/ bedrock aquifers for efficient management of groundwater resources. In this study, aquifer types were reclassified with unconfined and confined aquifers based on water-level fluctuation and water quality characteristics. Principal component analysis (PCA) of water-level data from paired monitoring wells of alluvial/bedrock aquifers results in the principal components of both aquifers showing similar water-level fluctuation pattern. There was no significant difference in the rate of water-level rises responding to precipitations and in the NO₃-N concentrations between the alluvial and bedrock aquifers. In contrast, in the results classified with the hydrogeological type, the principal components of water level were different between unconfined and confined conditions. The water-level rises to precipitation events were estimated to be 4.6 (R²=0.8) in the unconfined and 2.1 (R²=0.4) in the confined aquifers, respectively, indicating less impact of precipitation recharge to the confined aquifer. The confined aquifers have the average NO₃-N concentration below 3 mg/L, implying the natural background level protected from the sources at surface. In summary, reclassification of aquifers into hydrogeological types clearly shows the differences between unconfined and confined aquifers in the water-level fluctuation pattern and NO₃-N concentrations. The hydrogeologic condition of aquifer could improve groundwater resource management by providing critical information on groundwater quantity through recharge estimation and quality for protection from potential contamination sources.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.3
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• pp.289-295
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• 2010

Monitoring of the heavy metals at paddy rice agriculture is very important for environmental agriculture. A study was carried out of heavy metal concentrations in 260 paddy soil samples every four years from 1999 to 2007 in Gyeongnam Province. Heavy metals such as Cd, Cr, Cu, Ni, Pb, Zn, and As in paddy soils were analyzed. Average concentrations of heavy metal were Cd 0.426 (ranged 0.003-1.379) mg $kg^{-1}$ for Cd, 1.189 (0.003-3.264) mg $kg^{-1}$, for Cr, 9.68 (0.05-22.38) mg $kg^{-1}$ for Cu, 2.64 (0.01-7.36) mg $kg^{-1}$ for Ni, 23.7 (0.7-54.1) mg $kg^{-1}$ for Pb, 20.8 (0.7-131.2) mg $kg^{-1}$ for Zn, and 1.054 (0.001-2.110) mg $kg^{-1}$ for As, respectively. Long-term changes of heavy metals were showed that Cd, Ni, and Zn were significantly increased whereas Cr, Cu, and As were significantly decreased. Principle component analysis (PCA) of heavy metals in paddy soils was obtained with eigenvalues > 1 summing 34.3% of variance for PC1, 17.5% of variance for PC2, and 51.8% of the total variance in soil heavy metals.

• Journal of the Korean Society of Food Science and Nutrition
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• v.33 no.6
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• pp.1006-1012
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• 2004

We evaluated physicochemical and sensory characteristics of 25 commercial salt-fermented shrimps by kind (Oh Jeot, Yook Jeot and Chu Jeot) by manufacturer (traditional marketer (TS) ＆ company (CS)). Salinity and pH ranged 17.9∼28.7% and 7.82∼8.74, respectively, of which Chu Jeot was somewhat higher in salinity and pH, compared with those of the others. Amino nitrogen (AN), volatile basic nitrogen (VBN) and thiobarbituric acid reactive subjects (TBARS) showed great variation ranged with 21.41∼661.13 mg%, 263.2∼1180.2 mg% and 0.507∼1.322 $\mu\textrm{g}$/g, respectively. Hunter color of L value was 53.99∼67.45, a value, 4.98∼12.06 and b value, 4.45∼10.4. Physicochemical quality showed greater variations in Chu Jeot of TS than that of CS. Products of CS have higher salinity while lower VBN and AN than those of TS. Sensory results showed that mean scores of appearance, over-all taste, over-all flavor and over-all acceptability between TS and CS were not significantly different. The mean score of over-all acceptance was the highest in Yook Jeot. Physicochemical and sensory characteristics of salt-fermented shrimps in a PCA plot comprised of first principal component (68.36%) and second principal component (31.36%).

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.465-472
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• 2017

In the past, researchers mainly used the supervised learning technique of machine learning to analyze power data and investigated the identification of patterns through the data mining technique. Data analysis research, however, faces its limitations with the old data classification and analysis techniques today when the size of electric power data has increased with the possible real-time provision of data. This study thus set out to propose a clustering architecture to analyze large-sized electric power data. The clustering process proposed in the study supplements the K-means algorithm, an unsupervised learning technique, for its problems and is capable of automating the entire process from the collection of electric power data to their analysis. In the present study, power data were categorized and analyzed in total three levels, which include the row data level, clustering level, and user interface level. In addition, the investigator identified K, the ideal number of clusters, based on principal component analysis and normal distribution and proposed an altered K-means algorithm to reduce data that would be categorized as ideal points in order to increase the efficiency of clustering.