• Korean Journal of Medicinal Crop Science
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• v.21 no.4
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• pp.276-281
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• 2013

This study was conducted to compare the contents of ginsenoside according the water extract conditions of red ginseng. In method A, red ginseng extract was prepared at $75^{\circ}C$ for 18 hours by 1 time extraction, and method B, the preparation was done at $85^{\circ}C$ for 18 hours by 1 time extraction. In method C, the primary extract prepared at $75^{\circ}C$ for 9 hours was blended with the secondary extract prepared by re-extracting the red ginseng residue obtained after the primary extraction, at $85^{\circ}C$ for 9 hours. Method D was the same procedure as method C but the extraction temperature for the primary extraction was $85^{\circ}C$ and that for the secondary extraction was $95^{\circ}C$. The contents of total and $Rb_1$, $Rg_1$ and $Rg_3$ ginsenoside were highest in Method C. The content of prosapogenin (ginsenoside $Rg_2$, $Rg_3$, $Rb_1$ and $Rb_2$) was highest in Method B. There was no consistent tendency in Brix, pH, Hue value and absorbance among extraction methods.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.3
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• pp.85-93
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• 2015

This paper mainly introduces the methods of extracting landslide information using ALOS(Advanced Land Observing Satellite) images and GIS(Geographical Information System) technology. In this study, we classified images using three different methods which are the unsupervised the supervised and the PCA(Principal Components Analysis) for extracting landslide information based on characteristics of ALOS image. From the image classification results, we found out that the quality of classified image extracted with PCA supervised method was superior than the other images extracted with the other methods. But the accuracy of landslide information extracted from this image classification was still very low as the pixels were very similar between the landslide and safety regions. It means that it is really difficult to distinguish those areas with an image classification method alone because the values of pixels between the landslide and other areas were similar, particularly in a region where the landslide and other areas coexist. To solve this problem, we used the LSM(Landslide Susceptibility Map) created with ArcView software through weighted overlay GIS method in the areas. Finally, the developed LSM was applied to the image classification process using the ALOS images. The accuracy of the extracted landslide information was improved after adopting the PCA and LSM methods. Finally, we found that the landslide region in the study area can be calculated and the accuracy can also be improved with the LSM and PCA image classification methods using GIS tools.

• Journal of the Korea Computer Graphics Society
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• v.9 no.2
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• pp.11-18
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• 2003

For extracting geometric features in 3D meshes according to user-steering with effective interactions. this paper generalizes the 2D algorithms of snapping and wrapping that. respectively. moves a cursor to a nearby feature and constructs feature boundaries. First. we define approximate curvatures and move cost functions that are the numerical values measuring the geometric characteristics of the meshes, By exploiting the measuring values. the algorithms of geometric snapping and geometric wrapping are developed and implemented. We also visualize the results from applying the algorithms to extracting geometric features of general 3D mesh models such as a face model and a tooth model.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1425-1430
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• 2018

In this paper, we propose a CNN based deep learning algorithm for semantic segmentation of images. In order to improve the accuracy of semantic segmentation, we combined pixel level object classification and image level object classification. The image level object classification is used to accurately detect the characteristics of an image, and the pixel level object classification is used to indicate which object area is included in each pixel. The proposed network structure consists of three parts in total. A part for extracting the features of the image, a part for outputting the final result in the resolution size of the original image, and a part for performing the image level object classification. Loss functions exist for image level and pixel level classification, respectively. Image-level object classification uses KL-Divergence and pixel level object classification uses cross-entropy. In addition, it combines the layer of the resolution of the network extracting the features and the network of the resolution to secure the position information of the lost feature and the information of the boundary of the object due to the pooling operation.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2217-2220
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• 2003

It is known that an EEG is characterized by the unique and personal characteristics of an individual. Little research has been done to take into account these personal characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. These combinations are often unique like individual human beings and yet they have an underlying basic characteristics as well. We think that these combinations are the personal characteristics frequency components of the EEG. In this seminar, the EEG analysis method by using the Genetic Algorithms (GA), Factor Analysis (FA), and the Neural Networks (NN) is proposed. The GA is used for selecting the personal characteristic frequency components. The FA is used for extracting the characteristics data of the EEG. The NN is used for estimating the characteristics data of the EEG. Finally, in order to show the effectiveness of the proposed method, classifying the EEG pattern is carried out via computer simulations. The EEG pattern is evaluated under 4 conditions: listening to Rock music, Schmaltzy Japanese ballad music, Healing music, and Classical music. The results, when personal characteristics frequency components are NOT used, gave over 80 % accuracy versus a 95 % accuracy when personal characteristics frequency components are used. This result of our experiment shows the effectiveness of the proposed method.

• Journal of the Korean Institute of Telematics and Electronics
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• v.7 no.3
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• pp.5-11
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• 1970

Some of the coding method have been discussed by extracting characteristics from vowels and consonants of Korean characters. given letters were sampled through 3$\times$5 mesh and also constituted first matrix system which taken subpatterns of vertical Conponent as variables and then, characteristics of the letters are extracted from the second matrix system expresses by common characteristics which are combined-with first one. Single coding was obtained by scanning the characteristic pattern. a good agree between theoretical values and their measurements and the reproducing of all vowels and consonants of Korean chasacters about coding were certified on the display designed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.3
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• pp.185-190
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• 2014

The purpose of this research is to extract spectral reflectance characteristics of concretes through basic experiment on concrete specimens and site experiment on actual concrete structures using a field portable spectrometer and a VNIR hyperspectral sensor. A spectrometer (GER-3700) and a VNIR hyperspectral camera (AisaEagle VNIR Hyperspectral Camera) were utilized for extracting spectral characteristics of concrete specimens. Concretes normally show similar patterns that have correlation above 80%, while the high-strengthened concretes display very different results from the normal-strength concretes. We also made a certain conclusion in the laboratory experiment on concrete specimens that both the spectrometer and the VNIR camera vary in spectral reflectance depending on concrete strengths.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.655-657
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• 2003

This study is on the characteristics of discriminating of image using unique electric wave intensity value from satellite images data. Namely this study is on studying specific image characteristics by image spectrum and is on developing procedures discriminating of water, forest. narrow-leaved(coniferous) trees, broad-leaved(deciduous) trees, terrain, farmland, grassland, etc. using unique spectrum value in material. Finally applying this procedures, we design and implement discriminating system, IDEA(Information Discriminating Extracting Agent).

• Journal of the Korean Geographical Society
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• v.36 no.4
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• pp.458-473
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• 2001

The classified map of geomorphic surfaces is the most basic data for the geomorphological research. Up to recent days, the traditional methods extracting specific geomorphic surfaces are accomplished by analyzing the aerial photographs and topographical maps, and field works. Also it needs a lot of time and expertness. Furthermore it is difficult to gain the aerial photographs in Korea. Since digital maps in Korean Peninsula are almost completed recently, we tried to extract specific surfaces by analyzing the characteristics of marine terraces based on the level of paleoshoreline and slope analysis on the terrace surface using GIS. However, research used GIS was hardly found up to date, therefore many problems are not be solved yet. The aim of this study is to develop the more efficient and objective method for the extraction and classification of specific geomorphic surfaces by using GIS in Gampo-eup, Gyeongju city, Southeastem Coast in Korea, where a lot of traditional research has already accomplished. For this aim, we have designed the process of extracting specific geomorphic surfaces, chosen the factors that was Gyeongiu city, Southeastem Coast in Korea, where a lot of traditional research has already accomplished. For this aim, we have designed the process of extracting specific geomorphic surfaces, chosen the factors that was suitable for classification of specific geomorphic surface, and presented method of setting up optimum criteria of extraction. As last, effectiveness and problems of these methods were investigated through conincidence rate and error rate.

• Journal of KIISE:Software and Applications
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• v.34 no.2
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• pp.95-103
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• 2007

This paper describes an English-to-Korean automatic translation system for patent documents which is constructed by a method customizing from a general domain to a specific domain. The customizing method consists of following steps: 1) linguistically studying about characteristics of patent documents, 2) extracting unknown words from large patent documents and terminologically constructing, 3) customizing the target language words of existing terms, 4) extracting and constructing patent translation patterns peculiar to patent documents, 5) customizing existing translation engine modules according to linguistic study about characteristics of patent documents, 6) evaluation of automatic translation results. The English-to-Korean patent machine translation system implemented by these customization steps shows a translation accuracy of 81.03% and is improving.