• Journal of Ginseng Research
• /
• v.34 no.2
• /
• pp.93-97
• /
• 2010

To enhance the functionalities of ginseng, an acid impregnation pre-treatment was applied during red ginseng processing. Acetic, ascorbic, citric, malic, lactic, and oxalic acid were used for the acid impregnation treatment, and total and crude saponin concentrations and ginsenoside patterns were evaluated. Total and crude saponin contents of red ginseng pre-treated by acetic, ascorbic, and citric acid were similar to those of red ginseng without pre-treatment, whereas lactic, malic, and oxalic acid pre-treatment caused a reduction of total and crude saponin in red ginseng. From the high performance liquid chromatography analysis of ginsenosides, increased $Rg_3$ density was shown in red ginseng pre-treated by acetic, ascorbic, and citric acid impregnation. In the case of lactic, malic, and oxalic acid pre-treatment, increased $Rg_1$ density was observed in red ginseng. Increased $Rg_1$ and $Rg_3$ contents due to acid impregnation during red ginseng processing may contribute to improving bioactive functionalities of red ginseng.

• The Korean Journal of Physiology and Pharmacology
• /
• v.23 no.2
• /
• pp.131-139
• /
• 2019

Manually reviewing electroencephalograms (EEGs) is labor-intensive and demands automated seizure detection systems. To construct an efficient and robust event detector for experimental seizures from continuous EEG monitoring, we combined spectral analysis and deep neural networks. A deep neural network was trained to discriminate periodograms of 5-sec EEG segments from annotated convulsive seizures and the pre- and post-EEG segments. To use the entire EEG for training, a second network was trained with non-seizure EEGs that were misclassified as seizures by the first network. By sequentially applying the dual deep neural networks and simple pre- and post-processing, our autodetector identified all seizure events in 4,272 h of test EEG traces, with only 6 false positive events, corresponding to 100% sensitivity and 98% positive predictive value. Moreover, with pre-processing to reduce the computational burden, scanning and classifying 8,977 h of training and test EEG datasets took only 2.28 h with a personal computer. These results demonstrate that combining a basic feature extractor with dual deep neural networks and rule-based pre- and post-processing can detect convulsive seizures with great accuracy and low computational burden, highlighting the feasibility of our automated seizure detection algorithm.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.177-184
• /
• 2021

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2001.07a
• /
• pp.319-322
• /
• 2001

The proposed system was composed of pre-processor which was executing binary/high-pass filtering and post-processor which ranged from statistic data to prediction. In post-processor work, step one was filter process of image, step two was image recognition, and step three was destruction degree/time prediction. After these processing, we could predict image of the last destruction timestamp. This research was produced variation value according to growth of tree pattern. This result showed improved correction, when this research was applied image Processing. Pre-processing step of original image had good result binary work after high pass- filter execution. In the case of using partial discharge of the image, our research could predict the last destruction timestamp. By means of experimental data, this Prediction system was acquired ${\pm}$3.2% error range.

• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.532-535
• /
• 2005

These days, many image processing techniques have been studied for effective image compression. Among those, 2D image filtering is widely used for 2D image processing. The 2D image filtering can be implemented by performing ID linear filtering separately in the direction of horizontal and vertical. Efficiency of image compression depends on what filtering method is used. Generally, circular convolution is widely used in the 2D image filtering for image processing. However it doesn't consider correlations at the region of image boundary, therefore filtering can not be performed effectively. To solve this problem. I proposed new convolution technique using Symmetric-Mirroring convolution, satisfying the 'alias-free' and 'error-free' requirement in the reconstructed image. This method could provide more effective performance than former compression methods. Because it used very high correlative data when performed at the boundary region. In this paper, pre-processing filtering in H.264 codec was adopted to analyze efficiency of proposed filtering technique, and the simulator developed by Matlab language was used to examine the performance of the proposed method.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.11 no.2
• /
• pp.98-104
• /
• 2012

In this paper, precision processing is carried out for the pre hardened steel(HRC 54), which is one of injection mould materials. Processing characteristics are estimated according to the number of tool cutting blade and roundness is observed by the 3-Dimensional measuring machine. The surface roughness affected by the wire electric discharge machining are measured. Cutting component force of STAVOX is the highest in condition of 2F processing because load per a blade of cutting tool is high. Especially, the difference in Fz is over 20N by cutting load. The slower spindle rotation speed and tool feed rate are, the better cutting component force is. The roundness of hole processed in condition of 4F is good because feed rate is able to be fast. When rotation speed is increased, the surface roughness is decreased. The surface roughness acquired in condition of 2F processing is higher about 50% than 4F processing.

• Smart Structures and Systems
• /
• v.15 no.1
• /
• pp.209-225
• /
• 2015

Compared to ambient vibration testing, impact testing has the merit to extract not only structural modal parameters but also structural flexibility. Therefore, structural deflections under any static load can be predicted from the identified results of the impact test data. In this article, a signal processing procedure for structural flexibility identification is first presented. Especially, practical issues in applying the proposed procedure for structural flexibility identification are investigated, which include sensitivity analyses of three pre-defined parameters required in the data pre-processing stage to investigate how they affect the accuracy of the identified structural flexibility. Finally, multiple-reference impact test data of a three-span reinforced concrete T-beam bridge are simulated by the FE analysis, and they are used as a benchmark structure to investigate the practical issues in the proposed signal processing procedure for structural flexibility identification.

• Polymer(Korea)
• /
• v.37 no.3
• /
• pp.362-372
• /
• 2013

During extruder processing to manufacture cellulose fiber and film using cellulose-NMMO pre-dope produced by a new method, it seems to occur the changes of molecular weight and ${\alpha}$-cellulose content of cellulose upon thermal and mechanical degradation. In an extruder making cellulose solutions from the pre-dope obtained by high-speed mixer, the changes of cellulose molecular weight and ${\alpha}$-cellulose content resulted with the variations of processing temperature, concentration of cellulose, and residence time. The molecular weight and ${\alpha}$-cellulose content of cellulose decreased with decreasing cellulose concentration and increasing processing temperature. At 15% concentration and short residence time region, the change of ${\alpha}$-cellulose content was so high due to high-shear with an increase in temperature. From these processing conditions, the variations of ${\alpha}$-cellulose content and molecular weight showed different behaviors, and these processing conditions for making cellulose solution were found to be important factors.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.6 no.2
• /
• pp.65-79
• /
• 2003

In this study GIS based pre- and post-processing tool for the Visual MODFLOW that is specific software to model groundwater flow is developed. This tool not only makes input data scientifically but also manages input and output data in terms of the groundwater flow analysis. In addition it can storage project products systematically into Oracle database. The most characteristic figure of this processing tool is to provide the module, which automatically or semi automatically develops various grid cell sizes using GIS ArcView and also produces DXF files reflecting various boundary conditions in the modeling zone. In particular, eminences of this research are to create 3 dimensional hydrogeological structures with 2 dimensional GIS ArcView and to conduct pre- and post- processing along with same topology and data format of the MODFLOW.

• Journal of Environmental Impact Assessment
• /
• v.19 no.3
• /
• pp.315-321
• /
• 2010

ArcView and water network models have been integrated to develop the water network modeling system based upon GIS. To develop this system, pre, main, and post processing systems are required. GIS programming technique was adopted by using the ArcView's script language Avenue. The input data of models have been prepared by using the AutoCAD Map3D through the conversion of modeling input data to GIS data for A city. The modeling has been implemented by using EPANET, WaterCAD, InfoWorks. To develop the post processing system, the modeling results of the water network models have been analyzed by using GIS. During the application process of the developed system to B city with 300,000 population, main problems were found in the constructed GIS DB of that city. Thus, pilot study area of B city has been constructed, and pre-, main, and post-processing techniques were invented based upon GIS. Finally, the problems related to waterworks GIS projects in Korea were discussed and solutions were suggested.