• Journal of ILASS-Korea
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• v.22 no.1
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• pp.29-35
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• 2017

This paper describes the effects of spray breakup model constants on spray and combustion characteristics in single cylinder compression engine. KIVA-3V code coupled with a CHEMKIN chemistry solver was used for numerical analysis. In this study, spray simulations and combustion simulations are studied simultaneously. Spray simulation was conducted in constant volume to reduce the effects of air-flow as swirl or tumble. The model validation was conducted and there are little difference between experiments and simulation, this differences were reasonable. In spray simulation, the effects of model constants on spray tip penetration, spray patter and SMD were studied. Furthermore, the analysis of effects of breakup variables on combustion and emissions characteristics was conducted. The results show the KH-RT breakup model constants affects spray and combustion characteristics strongly. Increasing KH model variable (B1) and RT model constants ($C_{\tau}$, $C_{RT}$) induced slower breakup time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.454-456
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• 2017

Current and capacitance-voltage characteristics of tunnel field effect transistor (TFET) with various quantum models were investigated. Density gradient, Bohm quantum potential (BQP), and Vandort quantum correction are used with calibrating against Schrodinger-Poisson model. Drive-currents in all models. are decreased. When only BQP is used, SS and $V_{onset}$ are fixed but drive-current is decreased 3 times more than those of no quantum model. And When BQP with Vandort and density gradient are used, SS increased more than 40 mV./dec and $V_{onset}$ shifted as 0.07 eV.

• IMMUNE NETWORK
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• v.23 no.2
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• pp.19.1-19.10
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• 2023

Endemic human coronaviruses (HCoVs) have been evidenced to be cross-reactive to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although a correlation exists between the immunological memory to HCoVs and coronavirus disease 2019 (COVID-19) severity, there is little experimental evidence for the effects of HCoV memory on the efficacy of COVID-19 vaccines. Here, we investigated the Ag-specific immune response to COVID-19 vaccines in the presence or absence of immunological memory against HCoV spike Ags in a mouse model. Pre-existing immunity against HCoV did not affect the COVID-19 vaccine-mediated humoral response with regard to Ag-specific total IgG and neutralizing Ab levels. The specific T cell response to the COVID-19 vaccine Ag was also unaltered, regardless of pre-exposure to HCoV spike Ags. Taken together, our data suggest that COVID-19 vaccines elicit comparable immunity regardless of immunological memory to spike of endemic HCoVs in a mouse model.

• ETRI Journal
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• v.39 no.2
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• pp.213-221
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• 2017

The advent of wireless access in vehicular environments (WAVE) technology has improved the intelligence of transportation systems and enabled generic traffic problems to be solved automatically. Based on the IEEE 802.11p standard for vehicle-to-anything (V2X) communications, WAVE provides wireless links with latencies less than 100 ms to vehicles operating at speeds up to 200 km/h. To date, most research has been based on field test results. In contrast, this paper presents a numerical analysis of the V2X broadcast throughput limit using a path loss model. First, the maximum throughput and minimum delay limit were obtained from the MAC frame format of IEEE 802.11p. Second, the packet error probability was derived for additive white Gaussian noise and fading channel conditions. Finally, the maximum throughput limit of the system was derived from the packet error rate using a two-ray path loss model for a typical highway topology. The throughput was analyzed for each data rate, which allowed the performance at the different data rates to be compared. The analysis method can be easily applied to different topologies by substituting an appropriate target path loss model.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.371-380
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• 2013

The purpose of this study was to develop the equation of $\dot{V}O_{2max}$ by $sub_{max}imal$ PACER method for obese middle school boys. For this, $_{max}$imal test using Bruce protocol in lab was performed and then PACER $_{max}imal$ test with portable $\dot{V}O_{2max}$ equipment. To decide the level of submaximal test, during PACER with portable equipment, we found the section in which target hreat rate(over 75%$HR_{max}$) and then per section(75%,80%,85%,90%,95%) metabolic responses were recorded, with which we analyzed multiple regression by stepwise method. Model 1(at 90%$HR_{max}$): $\dot{V}O_{2max}$(ml/kg/min) = 142.721-0.275(repetition)-0.48(HR)+0.177(weight)-1.536(age)[%error 3.90ml/kg/min; performance until 2 stage(13 repetition)]. Model 2(at 95%$HR_{max}$): $\dot{V}O_{2max}$(ml/kg/min) = 182.851-0.103(repetition)-0.744(HR)+0.186(weight)-0.324(age)[%error 4.51ml/kg/min; performance until 3 stage(25 repetitions)]. estimated $\dot{V}O_{2max}$ from Model 1 was different about $3.25{\pm}6.32ml/kg/min$(%error=6.84%), otherwise model 2 was $3.16{\pm}4.54ml/kg/min$(%error=5.75%). considering %HRmax, as the submaximal test model 1 might be fit more than model 2 for obese middle school boys.

• Korean Journal of Food Science and Technology
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• v.36 no.2
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• pp.349-354
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• 2004

Predictive growth model of Vibrio parahaemolyticus in modified surimi-based imitation crab broth was investigated. Growth curves of V. parahaemolyticus were obtained by measuring cell concentration in culture broth under different conditions ($Initial\;cell\;level,\;1{\times}10^{2},\;1{\times}10^{3},\;and\;1{\times}10^{4}\;colony\;forming\;unit\;(CFU)/mL$; temperature, 15, 25 37, and $40^{\circ}C$; pH 6, 7, and 8) and applying them to Gompertz model. Microbial growth indicators, maximum specific growth rate (k), lag time (LT), and generation time (GT), were calculated from Gompertz model. Maximum specific growth rate (k) of V. parahaemolyticus increased with increasing temperature, reaching maximum rate at $37^{\circ}C$. LT and GT were also the shortest at $37^{\circ}C$. pH and initial cell number did not influence k, LT, and GT values significantly (p>0.05). Polynomial model, $k=a{\cdot}\exp(-0.5{\cdot}((T-T_{max}/b)^{2}+((pH-pH_{max)/c^{2}))$, and square root model, ${\sqrt{k}\;0.06(T-9.55)[1-\exp(0.07(T-49.98))]$, were developed to express combination effects of temperature and pH under each initial cell number using Gauss-Newton Algorism of Sigma plot 7.0 (SPSS Inc.). Relative coefficients between experimental k and k Predicted by polynomial model were 0.966, 0.979, and 0.965, respectively, at initial cell numbers of $1{\times}10^{2},\;1{\times}10^{3},\;and\;1{\times}10^{4}CFU/mL$, while that between experimental k and k Predicted by square root model was 0.977. Results revealed growth of V. parahaemolyticus was mainly affected by temperature, and square root model showing effect of temperature was more credible than polynomial model for prediction of V. parahaemolyticus growth.

• Structural Engineering and Mechanics
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• v.37 no.4
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• pp.385-399
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• 2011

The aim of this research is to model the behaviour of recently developed high force to volume (HF2V) passive energy dissipation devices using a simple finite element (FE) model. Thus, the end result will be suitable for use in a standard FE code to enable computationally fast and efficient analysis and design. Two models are developed. First, a detailed axial model that models an experimental setup is created to validate the approach versus experimental results. Second, a computationally and geometrically simpler equivalent rotational hinge element model is presented. Both models are created in ABAQUS, a standard nonlinear FE code. The elastic, plastic and damping properties of the elements used to model the HF2V devices are based on results from a series of quasi-static force-displacement loops and velocity based tests of these HF2V devices. Comparison of the FE model results with the experimental results from a half scale steel beam-column sub-assembly are within 10% error. The rotational model matches the output of the more complex and computationally expensive axial element model. The simpler model will allow computationally efficient non-linear analysis of large structures with many degrees of freedom, while the more complex and physically accurate axial model will allow detailed analysis of joint connection architecture. Their high correlation to experimental results helps better guarantee the fidelity of the results of such investigations.

• Journal of Biomedical Engineering Research
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• v.43 no.1
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• pp.45-51
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• 2022

The purpose of this study is to improve the classification accuracy compared to the existing InceptionV3 model by proposing a new model modified with the fully connected hierarchical structure of InceptionV3, which showed excellent performance in medical image classification. The data used for model training were trained after data augmentation on a total of 1026 chest X-ray images of patients diagnosed with normal heart and Cardiomegaly at Kyungpook National University Hospital. As a result of the experiment, the learning classification accuracy and loss of the InceptionV3 model were 99.57% and 1.42, and the accuracy and loss of the proposed model were 99.81% and 0.92. As a result of the classification performance evaluation for precision, recall, and F1 score of Inception V3, the precision of the normal heart was 78%, the recall rate was 100%, and the F1 score was 88. The classification accuracy for Cardiomegaly was 100%, the recall rate was 78%, and the F1 score was 88. On the other hand, in the case of the proposed model, the accuracy for a normal heart was 100%, the recall rate was 92%, and the F1 score was 96. The classification accuracy for Cardiomegaly was 95%, the recall rate was 100%, and the F1 score was 97. If the chest X-ray image for normal heart and Cardiomegaly can be classified using the model proposed based on the study results, better classification will be possible and the reliability of classification performance will gradually increase.

• Proceedings of the KIEE Conference
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• 1999.07a
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• pp.363-365
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• 1999

800kV GIS(Gas Insulated Switchgear) was first developed by Hyosung Corporation in Korea at Dec. 1998 and is going to be installed first in the Dangjin Thermal Plant. We intoned to verify the structural safety of 800kV GIS under seismic load. The modal analysis of 800kV GIS has been carried out to obtain the natural frequency and mode shape. PATRAN was used for mode)ing exactly 800kV GIS Geometry. 800kV GIS FE(Finite Element) model was constructed of shell elements for the enclosures and beam elements for the conductors and the support insulators NASTRAN was used for analyzing the normal modes of 800kV GIS FE model.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.455-461
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• 2021

Cardiomegaly is one of the most common diseases seen on chest X-rays, but if it is not detected early, it can cause serious complications. In view of this, in recent years, many researches on image analysis in which deep learning algorithms using artificial intelligence are applied to medical care have been conducted with the development of various science and technology fields. In this paper, we would like to evaluate whether the Inception V3 deep learning model is a useful model for the classification of Cardiomegaly using chest X-ray images. For the images used, a total of 1026 chest X-ray images of patients diagnosed with normal heart and those diagnosed with Cardiomegaly in Kyungpook National University Hospital were used. As a result of the experiment, the classification accuracy and loss of the Inception V3 deep learning model according to the presence or absence of Cardiomegaly were 96.0% and 0.22%, respectively. From the research results, it was found that the Inception V3 deep learning model is an excellent deep learning model for feature extraction and classification of chest image data. The Inception V3 deep learning model is considered to be a useful deep learning model for classification of chest diseases, and if such excellent research results are obtained by conducting research using a little more variety of medical image data, I think it will be great help for doctor's diagnosis in future.