• YAKHAK HOEJI
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• v.42 no.6
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• pp.613-620
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• 1998

Antidiabetic activity of Mori folium ethanol soluble fraction (MFESF) was examined in db/db mice, which is a spontaneously hyperglycemic, hyperinsulinemic and obese animal model . 500 and 1000mg/kg dose for MFFSF (designated by SY 500 and SY 1000, respectively) and 5mg/kg dose for acarbose were administered for 6 weeks. Body weight gain, fasting and non-fasting serum glucose, glycated hemoglobin and triglyceride were all reduced dose dependently when compared between db/db control group and MFESF treated group. At 11th and 13th week after birth, MFESF increased an insulin secretion which may result in lowering serum glucose level. Total activities of sucrase and maltase in SY 500 treated group were decreased when compared to db/db control. On the other hand, those in SY 1000 and acarbose treated groups were increased. This result may suggest that proteins for sucrase and maltase were compensatorily induced due to significant inhibition of glycosidase-catalyzed reaction at doses administered in this study.

• YAKHAK HOEJI
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• v.43 no.6
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• pp.818-826
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• 1999

Antidiabetic activity and mechanism of Sangbackpitang (SBPT) was examined in db/db mice, which is a spontaneously hyperglycemic, hyperinsulinemic and obese animal model. SBPT and acarbose were administered orally for 4 weeks. Fasting and non-fasting serum glucose, glycated hemoglobin and triglyceride were all reduced when compared between db/db control group and SBPT treated group. At 12th week after birth, SBPT increased an insulin secretion although statistic significance was not seen. Total activities of sucrase, maltase and lactase in SBPT treated group were all decreased when compared to db/db control. On the other hand, sucrase and maltase activities in acarbose treated groups were increased. Effect of SBPT on mRNA expression of glucose transporter(GLUT-4) was also examined. Quantitation of glucose transporter was performed by RT-PCR and in vitro transcription with co-amplification of rat-action gene as an internal standard. Muscular GLUT-4 mRNA expression in SBPT treated group was increased significantly. These results may suggest that SBPT lowered blood glucose ascribing to inhibition of glycosidase-catalyzed reaction and upregulation of muscular GLUT-4 mRNA expression.

• Journal of Animal Reproduction and Biotechnology
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• v.36 no.4
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• pp.203-211
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• 2021

Diabetic mellitus (DM) is a carbohydrate metabolic disorder that involves high blood sugar because insulin works abnormally. Type 2 diabetes accounts for most of them. However, diabetes treatments such as GLP-1 and DPP-4 inhibitors commonly caused side effects including gastrointestinal disorders. Grifola frondosa (G. frondosa) revealed various pharmacological effects in recent studies. It has a variety of anti-cancer polysaccharides through host-mediated mechanisms. D-fraction in G. frondosa has apoptotic effects, promoting myeloid cell proliferation and differentiation into granulocytes-macrophages. It has also been shown to reduce the survival rate of breast cancer cells. Though, no further study has been conducted on the specific effects of G. frondosa in the db/db mouse. Therefore, we would like to research the blood glucose improving effect of G. frondosa, a natural material, in type 2 diabetes model mouse, in this study. G. frondosa was administered to the disease model mouse (BKS.Cg-+Lepr^db/+Lepr^db/OlaHsd) for 8 weeks to monitor weight and blood glucose changes every week. And we evaluated anti-diabetes effects by checking biomarker changes shown through blood. Experiment did not show statistically significant weight differences, but control groups showed significantly higher weight gain than G. frondosa administered groups. We collected blood from the tail veins of the db/db mouse each week. As a result, the lowest blood sugar level was shown in the 500 mg/kg group of G. frondosa. Glucose in the blood was examined with HBA1c, and 7.8% was shown in the 500 mg/kg administration group, lower than in other groups. These results suggest the potential improvements of diabetes in G. frondosa.

• International Journal of Fluid Machinery and Systems
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• v.7 no.1
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• pp.16-27
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• 2014

Double-blade vertical axis wind turbines (DB-VAWTs) can improve the self-starting performance of lift-driven VAWTs. We here propose the quadruple-multiple streamtube model (QMS), based on the blade element momentum (BEM) theory, for simulating DB-VAWT performance. Model validity is investigated by comparison to computational fluid dynamics (CFD) prediction for two kinds of two-dimensional DB-VAWT rotors for two rotor scales with three inner-outer radius ratios: 0.25, 0.5, and 0.75. The BEM-QMS model does not consider the effects of an inner rotor on the flow speed in the upwind half of the rotor, so we introduce a correction factor for this flow speed. The maximum power coefficient predicted by the modified BEM-QMS model for a DB-VAWT is thus closer to the CFD prediction.

• Journal of Biosystems Engineering
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• v.30 no.2 s.109
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• pp.95-101
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• 2005

Adsorption and desorption experiments were carried out on rough rice and brown rice (Nampyung) at 5, 15, 25, 35, $45^{\circ}C$ for moisture contents between 8.7 and $25\%$ (db). The method employed was to measure the equilibrium relative humidity (ERH) of air in contact with the grain under static conditions, using an electronic hygrometer The effects of temperature and moisture contents were investigated, and the measured values were fitted to the modified Henderson, the modified Chung-Pfost, the modified Halsey and the modified Oswin model. The ERHs of rough rice and brown rice decreased with an decrease in moisture content and temperature, and the effects of temperature was no significant at moisture content of $25\%$ (db). Equilibrium moisture content (EMC) of brown rice was higher than rough rice at same temperature and relative humidity. Desorption EMC is higher than the adsorption, but there is no significant difference between desorption and adsorption EMC in moisture content near $25\%$ (db) at rough rice and near 9, 21 and $25\%$ (db) at brown rice. The modified Oswin model was the best in describing the adsorption EMC and the modified Chung-Pfost model was the best in describing the adsorption ERH of rough rice. The modified Oswin model was the best in describing the adsorption EMC/ERH of brown rice. The modified Chung-Pfost model was the best in describing the desorption EMC/ERH of rough rice and brown rice.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.55-66
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• 2021

In this paper, we propose a signal propagation modeling technique for generating a positioning fingerprint DB based on Long Term Evolution (LTE) signals. When a DB is created based on the location-based signal information collected in an urban area, gaps in the DB due to uncollected areas occur. The spatial interpolation method for filling the gaps has limitations. In addition, the existing gap filling technique through signal propagation modeling does not reflect the signal attenuation characteristics according to directions occurring in urban areas by considering only the signal attenuation characteristics according to distance. To solve this problem, this paper proposes a Deep Neural Network (DNN)-based signal propagation functionalization technique that considers distance and direction together. To verify the performance of this technique, an experiment was conducted in Seocho-gu, Seoul. Based on the acquired signals, signal propagation characteristics were modeled for each method, and Root Mean Squared Errors (RMSE) was calculated using the verification data to perform comparative analysis. As a result, it was shown that the proposed technique is improved by about 4.284 dBm compared to the existing signal propagation model. Through this, it can be confirmed that the DNN-based signal propagation model proposed in this paper is excellent in performance, and it is expected that the positioning performance will be improved based on the fingerprint DB generated through it.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.736-743
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• 2022

Construction workers experience a high rate of fatal incidents from mobile equipment in the industry. One of the major causes is the decline in the acoustic condition of workers due to the constant exposure to construction noise. Previous studies have proposed various ways in which audio sensing and machine learning techniques can be used to track equipment's movement on the construction site but not on the audibility of safety signals. This study develops a novel framework to help automate safety surveillance in the construction site. This is done by detecting the audio sound at a different signal-to-noise ratio of -10db, -5db, 0db, 5db, and 10db to notify the worker of imminent dangers of mobile equipment. The scope of this study is focused on developing a signal processing model to help improve the audible sense of mobile equipment for workers. This study includes three-phase: (a) collect audio data of construction equipment, (b) develop a novel audio-based machine learning model for automated detection of collision hazards to be integrated into intelligent hearing protection devices, and (c) conduct field experiments to investigate the system' efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.

• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.676-693
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• 2016

Although the methodology and models to assess the economic value of technology assets such as patents are being presented in various ways, there does not exist a structured assessment model which enables to objectively assess a database property's value, and thus there is a need to enhance the application feasibility of practical purposes such as licensing of DB assets, commercialization transfer, security, etc., through the establishment of the valuation model and the life-cycle decision logic. In this study, during the valuation process of DB assets, the size of customer demand group expected and the amount of demand, the size and importance of data sets, the approximate degree of database' contribution to the sales performance of a company, the life-cycle of database assets, etc. will be analyzed whether they are appropriate as input variables or not. As for most of DB assets, due to irregular updates there are hardly cases their life-cycle expires, and thus software package's persisting period, ie. 5 years, is often considered the standard. We herein propose the life-cycle estimation logic and valuation models of DB assets based on the concept of half life for DB usage frequency under the condition that DB assets' value decays and there occurs no data update over time.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.1
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• pp.95-117
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• 2006

The research purpose for this study is to evaluate the quality of the KOLIS-NET DB which builded bibliographic data of the holding collections of nationwide public libraries. The quality evaluation of the KOLIS-NET DB was inspired by the successful experience from researches precedent and tried to approach the case study focuses on six quality dimensions : coverage, duplication. currentness. accuracy, consistency completeness. The study verified comprehensively the quality of the KOLIS-NET DB through a quality evaluation model and analyzed the factors causing such inferior and substandard bibliographic records in the KOLIS-NET DB. Based on the results of the quality evaluation, the quality improvements of the KOLIS-NET DB was suggested.

• Convergence Security Journal
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• v.20 no.3
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• pp.115-123
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• 2020

Cyber Attack have evolved more and more in recent years. One of the best countermeasure to counter this advanced and sophisticated cyber threat is to predict cyber attacks in advance. It requires a lot of information and effort to predict cyber threats. If we use Open Source Intelligence(OSINT), the core of recent information acquisition, we can predict cyber threats more accurately. In order to predict cyber threats using OSINT, it is necessary to establish a Database(DB) for cyber attacks from OSINT and to select factors that can evaluate cyber threats from the established DB. We are based on previous researches that built a cyber attack DB using data mining and analyzed the importance of core factors among accumulated DG factors by AHP technique. In this research, we present a method for quantifying cyber threats and propose a cyber threats prediction model based on artificial neural networks.