• The Journal of Korean Medicine
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• v.34 no.3
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• pp.86-95
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• 2013

Objectives: Sprague-Dawley rats were fed with high fat diet, and atherogenic changes were seen in the aorta. However, when Sprague-Dawley rats were fed with a high fat diet and administered Youngyopaedoc-san together, these atherogenic changes were rarely seen. This study was aimed to find the inflammatory marker level changes in Sprague-Dawley rats with Youngyopaedoc-san-related anti-atherogenic effect. Methods: The extract from Youngyopaedoc-san was made by the pharmacy department of Kyung-hee Oriental Medical Hospital. The animals were divided into five groups: normal diet, high fat diet, high fat diet with Youngyopaedoc-san, high fat diet with Vytorin, and high fat diet with Youngyopaedoc-san and Vytorin. A light microscopic image of a cross section taken from the aorta of the Sprague-Dawley rat was analyzed. We compared inflammatory marker levels among the five groups. Results: The complex of Youngyopaedoc-san and Vytorin has more anti-atherogenic effects in the aorta of Sprague-Dawley rats fed with high fat diet than Vytorin alone. Youngyopaedoc-san has inhibitory effect on the increase of IFN-${\gamma}$ and IL-2 levels. The difference on eosinophil levels of each group was statistically significant, but the eosinophil level of each group was within normal limits, so the difference on eosinophil levels was not clinically significant. Conclusions: Youngyopaedoc-san-related anti-atherogenic effect could be a result of inhibitory mechanism on IFN-${\gamma}$ and IL-2.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6D
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• pp.849-856
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• 2011

This paper proposes a method for transforming unstructured text contents of engineering documents, which have complex hierarchical structure of subtitles with various heading symbols, into a semi-structured XML document according to the hierarchical subtitle structure. In order to extract the hierarchical structure from plain text information, this study employed a method of document structure extraction which is an analysis technique of the document structure. In addition, a method for processing enumerative text contents was developed to increase overall accuracy during extraction of the subtitles and construction of a hierarchical subtitle structure. An application module was developed based on the proposed method, and the performance of the module was evaluated with 40 test documents containing structural calculation records of bridges. The first test group of 20 documents related to the superstructure of steel girder bridges as applied in a previous study and they were used to verify the enhanced performance of the proposed method. The test results show that the new module guarantees an increase in accuracy and reliability in comparison with the test results of the previous study. The remaining 20 test documents were used to evaluate the applicability of the method. The final mean value of accuracy exceeded 99%, and the standard deviation was 1.52. The final results demonstrate that the proposed method can be applied to diverse heading symbols in various types of engineering documents to represent the hierarchical subtitle structure in a semi-structured XML document.

• Korean Journal of Food Science and Technology
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• v.50 no.4
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• pp.437-443
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• 2018

Activation of macrophages plays an important role in the host-immune system. In this study, we investigated the functional roles and related signaling mechanism of hot-water extracts of bee pollen (BPW) in RAW 264.7 macrophages. Since BPW did not exert cytotoxicity at concentrations ranging from 62.5 to $250{\mu}g/mL$ in macrophage cells, a concentration of $250{\mu}g/mL$ was used as the maximum dose of BPW throughout subsequent experiments. BPW increased inducible nitric oxide synthase-mediated nitric oxide production in a concentration-dependent manner. Additionally, BPW was found to induce macrophage activation by augmenting the expression of cell surface molecules (cluster of differentiation; CD80/86, and major histocompatibility complex; MHC class I/II) and production of pro-inflammatory cytokines (tumor necrosis $factor-{\alpha}$, interleukin-6, and $IL-1{\beta}$) through mitogen-activated protein kinase and nuclear $factor-{\kappa}B$ signaling pathways in RAW 264.7 macrophages. Taken together, our results indicate that BPW could potentially be used as an immunomodulatory agent.

• Korean Journal of Food Science and Technology
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• v.50 no.4
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• pp.444-450
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• 2018

Dendritic cells (DCs) are potent antigen-presenting cells that play a pivotal role in modulating both innate and adaptive immunity. This study examined the immunomodulatory activities of hot-water extracts of bee pollen (BPW) in bone-marrow derived DCs (BMDC) and mice splenocytes. BMDCs isolated from mice were treated with 250 and $500{\mu}g/mL$ BPW for 24 h. BPW, up to $500{\mu}g/mL$, did not display any cellular toxicity against BMDCs. In fact, it functionally induced BMDC activation via augmentation of CD80, CD86, and major histocompatibility complex (MHC) class I/II expression and pro-inflammatory cytokine (tumor necrosis factor; $TNF-{\alpha}$, interleukin; IL-6, and $IL-1{\beta}$) production. Interestingly, BPW treatment significantly increased the production of interferon $(IFN)-{\gamma}$ in splenocytes, suggesting its possible contribution to Th1 polarization in immune response. Taken together, these findings suggest that BPW may regulate innate and adaptive immunity via DC activation and Th1 polarization in immune responses.

• Journal of Life Science
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• v.21 no.5
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• pp.678-683
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• 2011

The objective of this study was to evaluate the skin inflammation effects of three herb mixture extracts, Ligularia fischeri, Solidago virga-aurea and Aruncus dioicus, which are from Ullung island in Korea. Regulatory mechanisms of cytokines and nitric oxide (NO) are involved in the immunological activity of Raw 264.7 cells. Tested cells were pretreated with 70% acetone extracts of Ligularia fischeri, Solidago virga-aurea and Aruncus dioicus (LSA-A) and further cultured for an appropriated time after lipopolyssacharide (LPS) addition. During the entire experimental period, 1, 10, and 100 ${\mu}g/ml$ of LSA-A had no cytotoxicity. In these concentrations, LSA-A inhibited the production of NO and prostaglandin $E_2$ ($PGE_2$), tumor necorsis factor-a (TNF-a), interleukin-1${\beta}$ (IL-1${\beta}$), interleukin-6 (IL-6) expression of inducible NO synthase (iNOS), and cyclooxygenase-2 (COX-2). LSA-A showed a 60% $PGE_2$ inhibition rate at 100 ${\mu}g/ml$. iNOS and COX-2 inhibition activities were 54%, and 65% at 100 ${\mu}g/ml$, respectively. In addition, LSA-A extract reduced the release of inflammatory cytokines including TNF-a, IL-1${\beta}$ and IL-6. These results suggest that LSA-A may have significant effects on inflammatory factors, and may be a potential anti-inflammatory therapeutic agent.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.960-966
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• 2009

A manifold is used to represent a relationship between high-dimensional data samples in low-dimensional space. In human pose estimation, it is created in low-dimensional space for processing image and 3D body configuration data. Manifold learning is to build a manifold. But it is vulnerable to silhouette variations. Such silhouette variations are occurred due to view-change, person-change, distance-change, and noises. Representing silhouette variations in a single manifold is impossible. In this paper, we focus a silhouette variation problem occurred by view-change. In previous view invariant pose estimation methods based on manifold learning, there were two ways. One is modeling manifolds for all view points. The other is to extract view factors from mapping functions. But these methods do not support one by one mapping for silhouettes and corresponding body configurations because of unsupervised learning. Modeling manifold and extracting view factors are very complex. So we propose a method based on triple manifolds. These are view manifold, pose manifold, and body configuration manifold. In order to build manifolds, we employ biased manifold learning. After building manifolds, we learn mapping functions among spaces (2D image space, pose manifold space, view manifold space, body configuration manifold space, 3D body configuration space). In our experiments, we could estimate various body poses from 24 view points.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Journal of Engineering Education Research
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• v.8 no.3
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• pp.36-48
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• 2005

Recently, developments of engineering technology are too fast change to educate the students in the college of engineering. The lag between the innovation of the technology and the education of the college of engineering becomes larger and larger. In this situation, the appropriate education update system is needed to change the curricula for the updated engineering education. In this paper, the job analysis method which is used in the technical service area is transformed to design the curriculum of the general higher education area. The job analysis method which is used in the technical service area derives the competence too detail and complex to used in the higher education area. for the higher education area, the social activity analysis of the alumni is needed to extract the representative jobs of the area. Also using this representative jobs, the job analysis and competence derivation is conducted. If needed, the regular expert meeting is held to converge the job-site opinions of the graduate and industry people. This curriculum changing process is provided as a part of the circular self-improve education system of the Electrical Engineering of the MyongJi University.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.420-438
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• 2004

Artificially or naturally contained texts in the natural images have significant and detailed information about the scenes. If we develop a method that can extract and recognize those texts in real-time, the method can be applied to many important applications. In this paper, we suggest a new method that extracts the text areas in the natural images using the low-level image features of color continuity. gray-level variation and color valiance and that verifies the extracted candidate regions by using the high-level text feature such as stroke. And the two level features are combined hierarchically. The color continuity is used since most of the characters in the same text lesion have the same color, and the gray-level variation is used since the text strokes are distinctive in their gray-values to the background. Also, the color variance is used since the text strokes are distinctive in their gray-values to the background, and this value is more sensitive than the gray-level variations. The text level stroke features are extracted using a multi-resolution wavelet transforms on the local image areas and the feature vectors are input to a SVM(Support Vector Machine) classifier for the verification. We have tested the proposed method using various kinds of the natural images and have confirmed that the extraction rates are very high even in complex background images.