소형 하이브리드 로켓 모터를 이용한 수중연소시험

김시진(Sijin Kim),김학철(Hakchul Kim),김계환(Kyehwan Kim),박영훈(Younghoon Park),박선정(Sunjung Park),이동근(Donggeun Lee),김진곤(JinKon Kim),문희장(Heejang Moon),유영준(Youngjun You),권민찬(Minchan Kwon) 한국추진공학회 2016 한국추진공학회 학술대회논문집 Vol.2016 No.5

본 연구는 하이브리드 로켓 모터의 수중추진기관 적용을 위한 초기연구로 Lab-scale 하이브리드 로켓 모터를 이용하여 수중연소시험을 수행하였다. 3 m x 1 m x 1 m 크기의 수조 내 바닥면에서 55 cm 위에 연소기를 위치하여 수중연소시험을 수행하였으며 아크릴판으로 제작된 파열판(rupture disk)을 이용하여 연소실 초기 기밀을 유지하였다. 산화제는 액체아산화질소(LN2O), 연료는 HDPE를 사용하였고 KNSB 추진제를 이용하여 점화하였으며 연소시험은 총 5초간 진행되었다. 수중연소시험 결과 설계 추력에 근접하는 추력을 얻을 수 있음을 확인하였다. In this study, underwater combustion test was performed using a lab-scale hybrid rocket motor. The purpose of this study is to find out the feasibility of underwater propulsion using hybrid rocket system. Combustor was located underwater above 55cm from the bottom of water tank where the size of water tank is 3m X 1m X 1m. The combustor was sealed by installing acrylic plate rupture disk at the nozzle exit. LN2O and HDPE were used as oxidizer and solid fuel respectively, while the KNSB propellant was used as the ignitor source. The combustion duration was set to 5 seconds, and it was confirmed that the measured thrust of underwater combustion test was close to the design thrust.

The inhibitory effect and the molecular mechanism of glabridin on RANKL-induced osteoclastogenesis in RAW264.7 cells.

Kim, Hyun-Sook,Suh, Kwang Sik,Sul, Donggeun,Kim, Byung-Jo,Lee, Seung Kwan,Jung, Woon-Won D.A. Spandidos 2012 INTERNATIONAL JOURNAL OF MOLECULAR MEDICINE Vol.29 No.2

<P>Osteoblastic bone formation and osteoclastic bone resorption are in balance to maintain a constant, homeostatically controlled amount of bone. Excessive bone resorption by osteoclasts is involved in the pathogenesis of bone-related disorders. In the present study, we evaluated the inhibitory effects of glabridin, a flavonoid purified from licorice root, on the receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclast differentiation and its molecular mechanisms in murine osteoclast progenitor RAW264.7 cells. Glabridin significantly inhibited RANKL-induced tartrate-resistant acid phosphatase (TRAP) activity, the formation of multinucleated osteoclasts and resorption-pit formation. In mechanistic studies of the anti-osteoclastogenic potential of glabridin, we found that glabridin inhibited RANKL-induced expression of c-Fos and subsequent expression of NFATc1, which is a master regulator of osteoclastogenesis. Interestingly, glabridin inhibited the RANKL-induced expression of signaling molecules (TRAF6, GAB2, ERK2, JNK1 and MKK7) and osteoclast survival-related signaling pathways such as c-Src, PI3K and Akt2. Glabridin also inhibited the bone resorptive activity of mature osteoclasts by inhibiting osteoclast-associated genes (cathepsin?K, MMP-9, CAII, TCIRG1, OSTM1 and CLCN7). Taken together, our data suggest that glabridin holds great promise for use in preventing osteoclastogenesis by inhibiting RANKL-induced activation of signaling molecules and subsequent transcription factors in osteoclast precursors and these findings may be useful for evaluating treatment options in bone-destructive diseases.</P>

DNA damage in T- and B-lymphocytes of rats exposed to benzene

Donggeun Sul,Doyoung Lee,Gyu-Chan Jo,Hosub Im,Hyungho Hong,Dukjin Jo,Chan-wha Kim,Hae-Joon Kim,Eunil Lee 한국환경성돌연변이발암원학회 2002 한국환경성돌연변이·발암원학회지 Vol.22 No.4

Single cell gel electrophoresis assay was carried out to evaluate DNA damage in T-and Blymphocytes from rats exposed to benzene and the correlation between DNA damage and the level of t,t-muconic<br/> acids, which are urinary benzene metabolites, was investigated. In control rats, the mean values of Olive tail moments in T- and B-lymphocytes were 1.507±0.187 and 1.579±0.206 respectively. DNA damages of T-lymphocytes in rats exposed for 4 weeks showed the highest Olive tail moments at each benzene concentration examined (2.72-4.351). However this DNA damage was decreased after 6 weeks of exposure (1.74-2.09). DNA damages of B-lymphocytes did not show such differences with exposure time or benzene concentration (1.49-2.07) except at 200 ppm at 4 weeks. T-lymphocytes show significantly more damages than B-lymphocyte upon acute exposure to benzene.

Prediction of Settling Velocity of Nonspherical Soil Particles Using Digital Image Processing

Kim, Donggeun,Son, Younghwan,Park, Jaesung Hindawi Limited 2018 Advances in civil engineering Vol.2018 No.-

<P>Digital image processing (DIP) is used to measure shape properties and settling velocity of soil particles. Particles with diameters of 1 to 10 mm are arbitrarily sampled for the test. The size of each particle is also measured by a Vernier caliper for comparison with the classification results using the shape classification table. The digital images were taken with a digital camera (Canon EOS 100d). Shape properties are calculated by image analysis software. Settling velocity of soil particles is calculated by displacement and time difference of images during settling. The fastest settling particles are spherical shaped. Shape factors well explain the difference of settling velocity by a particle shape. In particular, the aspect ratio has a high negative correlation with residual of settling velocity versus mean diameter. Especially, DIP has a higher applicability than classification using the shape classification table because it can measure a number of particles at once. The settling velocity of soil particles is expressed as a function of mean diameter and aspect ratio.</P>

Toward high-accuracy and high-applicability of a practical model to predict effective thermal conductivity of particle-reinforced composites

Kim, Jeonggeon,Goo, Yong-Rack,Choi, Indae,Kim, Songkil,Lee, Donggeun Elsevier 2019 INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER - Vol.131 No.-

<P><B>Abstract</B></P> <P>A particle-reinforced composite material is a matrix with thermally conductive particles that has a diverse range of applications from electronics to energy harvesting/storage systems. In the engineering design of a particle-reinforced composite material for application, it is crucial to accurately and practically predict its effective thermal conductivity. Here, we report the development of a simple analytical model for predictions with improved accuracy and applicability. Comprehensive evaluation of existing models was first conducted to clarify their limitations in prediction accuracy and applicability to various experimental conditions. To overcome the challenges of the existing models, our new model was derived to consider the effect of shape, particle aggregation, and mutual interaction of particles on effective thermal conductivity. Lattice Boltzmann simulations were conducted to obtain a quasi-universal coefficient representing interactions of particles, whereas a shape coefficient characterizing microstructures of aggregated particles was obtained from experimental data available from literature. As a result, our model prediction outperformed the existing models in its prediction accuracy, and it could be applicable to any experimental circumstances where previous model predictions are inappropriate to use.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Comprehensive evaluation showed that existing models had their conditional limitations. </LI> <LI> The models were unacceptably degraded under conditions for electronics applications. </LI> <LI> We developed a new model to accurately predict effective thermal conductivity. </LI> <LI> The new model was self-consistent and described asymptotic behaviors of existing models. </LI> <LI> With an additional correlation, the new model was reasonably applicable to any conditions. </LI> </UL> </P>

Artificial intelligence techniques for outcome prediction in marketing strategies and big data analytics for businesses

Donggeun Kim,Juyong Ko,Minho Sun,Jai Woo Lee 한국경영정보학회 2023 한국경영정보학회 학술대회논문집 Vol.2023 No.11

These days, business intelligence has witnessed the various challenges of big data analytics due to exponentially growing information with uncertainty existing in the market. To effectively analyze the large amount of data from various sources in business, algorithms of artificial intelligence techniques should be efficiently improved. We present a comprehensive and novel approach for the evaluation of outcome by using the interactions of features and then applying this approach to estimate the trend by jointly modelling features in management data. In terms of predictive accuracy, the proposed method outperformed machine learning methods such as regression, penalized regression, decision tree, random forest and k-nearest neighbors in the high-dimensional business data analysis. Data-preprocessing was used to curate the data for better prediction and network analysis was conducted to appropriately visualize and analyze the data analysis results. The business literature represents that investigating artificial intelligence techniques with theoretical ideas for big data analytics can have an impact on reducing costs and risks in management. Future directions have been devised to elucidate the gap between actual values in real-world data of business intelligence and predicted values by the proposed approach. Machine learning methods including features of demographic and strategic data can estimate the effect of marketing characteristics. Using the proposed method, businesses may better assess strongly correlated features with the target output in the similarly structured business data.

Evaluation of Calibration Method for Field Application of UAV-Based Soil Water Content Prediction Equation

Kim, Donggeun,Son, Younghwan,Park, Jaesung,Kim, Taejin,Jeon, Jihun Hindawi Limited 2019 Advances in civil engineering Vol.2019 No.-

<P>The objective of this study is to monitor the water content of soil quickly and accurately using a UAV. Because UAVs have higher spatial and temporal resolution than satellites, they are currently becoming more useful in remote sensing areas. We developed a water content estimation equation using the color of the soil and suggested a calibration method for field application. Since the resolution of the images taken by the UAV is different according to the altitude, the water content estimation formula is developed by using the images taken at each altitude. In order to calibrate the color difference according to lighting conditions, the calibration method using field data were proposed. The results of the study showed an altitude-specific estimation equation using RGB values of the UAV image through linear regression. The appropriate number of field data needed for calibration for site application of the estimation equation was found between 4 and 10. On-site application results of the proposed calibration method showed RMSE accuracy of 1.8 to 2.9%. Thus, the water content estimation and calibration method proposed in this study can be used in effectively monitoring the water content of soil using UAVs.</P>

A Big Data-Driven Business Data Analysis System : Applications of Artificial Intelligence Techniques in Problem Solving

Donggeun Kim,Sangjin Kim,Juyong Ko,Jai Woo Lee 한국경영정보학회 2023 한국경영정보학회 학술대회논문집 Vol.2023 No.06

Isotope-Coded Carbamidomethylation for Quantification of N-Glycoproteins with Online Microbore Hollow Fiber Enzyme Reactor-Nanoflow Liquid Chromatography-Tandem Mass Spectrometry

Kim, Jin Yong,Oh, Donggeun,Kim, Sook-Kyung,Kang, Dukjin,Moon, Myeong Hee American Chemical Society 2014 ANALYTICAL CHEMISTRY - Vol.86 No.15

<P>This paper introduces a simple, inexpensive, and robust quantitative proteomic method for quantifying N-linked glycoproteins based on isotope-coded carbamidomethylation (iCCM) incorporated into an online microbore hollow fiber enzyme reactor and nanoflow liquid chromatography-tandem mass spectrometry (mHFER-nLC-MS/MS). The iCCM quantitation uses carbamidomethylation (CM; a routine protection of thiol groups before proteolysis) of the Cys residue of proteins with iodoacetamide (IAA) or its isotope (IAA-<SUP>13</SUP>C<SUB>2</SUB>,D<SUB>2</SUB>: 4 Da difference). CM-/iCCM-labeled proteome samples are mixed for proteolysis; then, online enrichment of N-glycopeptides using lectin affinity is carried out in an mHFER before nLC-MS/MS for quantification using multiple reaction monitoring (MRM). Initial evaluation of the iCCM method varying the mixing ratio of CM-/iCCM-labeled bovine serum albumin (BSA) standards yielded successful quantification of 18 peptides with less than 2% variation in the calculated ratio of light/heavy-labeled peptides. The iCCM quantitation with mHFER-nLC-MS/MS was evaluated with three standard glycoproteins (α-1-acid glycoproteins, fetuin and transferrin) and then applied to serum glycoproteins from liver cancer patients and controls, resulting in successful quantification of 73 N-glycopeptides (from 49 N-glycoproteins), among which 19 N-glycopeptides from 14 N-glycoproteins showed more than a 2.5-fold aberrant change in liver cancer patients’ sera compared with the pooled control. Although iCCM quantitation with mHFER-nLC-MS/MS applies only to glycopeptides with Cys residue, the method can offer several advantages over other labeling methods when applied to targeted glycoproteins: The iCCM method does not require an additional labeling reaction under special conditions nor complicated procedures to purify labeled products using additional columns. Isotope labeling at the protein level can minimize potential uncertainty originating from unequal efficiencies in protein digestion in separate vials and retrieval of each labeled peptide when labeling takes place at the peptide level. In addition, the labeling reagents for the iCCM method are readily obtained at a reasonable cost, which can make protein quantification easily accessible.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/ancham/2014/ancham.2014.86.issue-15/ac501544r/production/images/medium/ac-2014-01544r_0001.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/ac501544r'>ACS Electronic Supporting Info</A></P>

Catalytic oxidation kinetics of iron-containing carbon particles generated by spraying ferrocene-mixed with diesel fuel into a hydrogen–air diffusion flame

Kim, Yong Ho,Kim, Yong-Tae,Kim, Soo Hyung,Lee, Donggeun Elsevier 2010 Carbon Vol.48 No.7

<P>Kinetic measurements of the catalytic oxidation of iron-containing soot particles were made for a better understanding of the role of catalytic particles in the initiation of soot oxidation. Carbon-based iron-containing soot particles were generated by spraying ferrocene-mixed with diesel fuel into an oxy-hydrogen flame. A commercial carbon black was used as a standard. Their oxidative kinetics and physico-chemical characteristics were measured by thermogravimetric analysis, secondary ion mass spectrometry, X-ray diffraction, gas-cell Fourier-transform infrared spectroscopy, induced coupled plasma-atomic emission spectroscopy, and high-resolution transmission electron microscopy. It was found that a tiny amount of ferrocene led to a significant reduction in both the on-set temperature and the activation energy of soot oxidation. Catalytic oxidation occurred in two consecutive steps, as temperature increased. The initiation of oxidation, even with an addition of ferrocene, was controlled mainly by surface oxygen complexes and partly by the long-range crystalline order of the carbon graphene layer. However, once catalytic oxidation began, the progress of the reaction was mainly determined by the amount of ferrocene that was added. (C) 2010 Elsevier Ltd. All rights reserved.</P>