• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.30-42
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• 2023

Various studies have been conducted using microtraffic simulation (VISSIM) to analyze the safety of traffic flow when introducing autonomous vehicles. However, no studies have analyzed traffic safety in mixed traffic while considering the driving behavior of general vehicles as a parameter in VISSIM. Therefore, the aim of this study was to optimize the input variables of VISSIM for non-autonomous vehicles through genetic algorithms to obtain realistic behavior. A traffic safety analysis was then performed according to the penetration rate of autonomous vehicles. In a 640 meter section of US highway I-101, the number of conflicts was analyzed when the trailing vehicle was a non-autonomous vehicle. The total number of conflicts increased until the proportion of autonomous vehicles exceeded 20%, and the number of conflicts decreased continuously after exceeding 20%. The number of conflicts between non-autonomous vehicles and autonomous vehicles increased with proportions of autonomous vehicles of up to 60%. However, there was a limitation in that the driving behavior of autonomous vehicles was based on the results of the literature and did not represent actual driving behavior. Therefore, for a more accurate analysis, future studies should reflect the actual driving behavior of autonomous vehicles.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.11 no.2
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• pp.82-91
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• 2006

We investigated the possibility of using effluents from a municipal sewage treatment plant (STP) as tracers a tracer for hydrologic studies of rivers. The possibility was checked in a 12-km long reach downstream of Jeonju Municipal Sewage Treatment Plant (JSTP). Time-series monitoring of the water chemistry reveals that chemical compositions of the effluent from the JSTP are fluctuating within a relatively wide range during the sampling period. In addition, the signals from the plant were observed at the downstream stations consecutively with increasing time lags, especially in concentrations of the conservative chemical parameters (concentrations f3r chloride and sulfate, total concentration of major cations, and electric conductivity). Based on this observation, we could estimate the stream flow (Q), velocity (v), and dispersion coefficient (D). A 1-D nonreactive solute-transport model with automated optimization schemes was used for this study. The values of Q, v, and D estimated from this study varied from 6.4 to $9.0m^3/sec$ (at the downstream end of the reach), from 0.06 to 0.10 m/sec, and from 0.7 to $6.4m^2/sec$, respectively. The results show that the effluent from a large-scaled municipal STP frequently provides good, multiple natural tracers far hydrologic studies.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.8 no.4
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• pp.599-604
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• 1998

We have investigated Pt and $RuO_2$ as a bottom electrode for ferroelectric capacitor applications. The bottom electrodes were prepared by using an RF magnetron sputtering method. Some of the investigated parameters were a substrate temperature, gas flow rate, RF power for the film growth, and post annealing effect. The substrate temperature strongly influenced the surface morphology and resistivity of the bottom electrodes as well as the film crystallographic structure. XRD results on Pt films showed a mixed phase of (111) and (200) peak for the substrate temperature ranged from RT to $200^{\circ}C$, and a preferred (111) orientation for $300^{\circ}C$. From the XRD and AFM results, we recommend the substrate temperature of $300^{\circ}C$ and RF power 80W for the Pt bottom electrode growth. With the variation of an oxygen partial pressure from 0 to 50%, we learned that only Ru metal was grown with 0~5% of $O_2$ gas, mixed phase of Ru and $RuO_2$ for $O_ 2$ partial pressure between 10~40%, and a pure $RuO_2$ phase with $O_2$ partial pressure of 50%. This result indicates that a double layer of $RuO_2/Ru$ can be grown in a process with the modulation of gas flow rate. Double layer structure is expected to reduce the fatigue problem while keeping a low electrical resistivity. As post anneal temperature was increased from RT to $700^{\circ}C$, the resistivity of Pt and $RuO_2$ was decreased linearly. This paper presents the optimized process conditions of the bottom electrodes for memory device applications.

• Korean Journal of Food Science and Technology
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• v.41 no.2
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• pp.173-178
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• 2009

Epidemiological studies showed that high trans-fat consumption is closely associated with getting the risks of cardiovascular disease. The objective of this study was to produce trans-free fat through lipase-catalyzed interesterification, as a substitute for the cream margarine commonly used in industry. Optimum conditions for interesterification in a packed bed enzyme bioreactor (PBEB) were determined using response surface methodology (RSM) based on central composite design. Three kinds of reaction variables were chosen, such as substrate flow rate (0.4-1.2 mL/min), reaction temperature (60-70$^{\circ}C$), and ratio of fully hydrogenated canola oil (FHCO, 35-45%) to evaluate their effects on the degree of interesterification. Optimum conditions from the standpoint of solid fat content (SFC) were found to be as follows: 0.4 mL/min flow rate, 64.7$^{\circ}C$ reaction temperate, and 42.8% (w/w) ratio of FHCO, respectively. The half-life of immobilized lipase in PBEB with two stages at 60$^{\circ}C$ ($1^{st}$ stage) and 55$^{\circ}C$ ($2^{nd}$ stage) was about more than 30 days as estimated by extrapolating the incubation time course of tristearoyl glycerol (TS) conversion, whereas the half-life of the enzyme in PBEB with single stage at 65$^{\circ}C$ was only about 15 days. Finally, the results from SFC analysis suggest that trans-free fat produced in this study seems to be a suitable substitute for the cream margarine commonly used in industry.

• Korean Journal of Food Science and Technology
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• v.30 no.1
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• pp.151-160
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• 1998

Tangerine peel is mostly discarded as waste in citrus processing. However, tangerine peel contains besides dietary fibers bioflavonoids such as naringin and hesperidin which act as antimicrobials and blood pressure depressants, respectively. A continuous membrane separation process was optimized for the production of bioflavonoids relative to feed flow rate, transmembrane pressure, temperature, and pH. The tangerine peel was blended with 7.5 times water volume and the extract was prefiltered through a prefiltration system. The prefiltered extract was ultrafiltered in a hollow fiber membrane system. The flux and feed flow rate didn't show any apparent correlation, but we could observe a mass-transfer controlled region of over 8 psi. When temperature increased from $9^{\circ}C\;to\;25^{\circ}C$, the flux increased about $10\;liters/m^2/min\;(LMH)$ but between $25^{\circ}C\;and\;33^{\circ}C$, the flux increased only 2 LMH. At every transmembrane pressure, the flux of pH 4.8 was the most highest and the flux at pH 3.0 was lower than that of pH 6.0, 7.0, or 9.0. Therefore, the optimum operating conditions were 49.3 L/hr. 10 psi, $25^{\circ}C$, and pH 4.8. Under the optimum conditions, the flux gradually decreased and finally reached a steady-state after 1 hr 50 min. The amount of dietary fibers in 1.0 g retentate in each separation step was analyzed and bioflavonoids concentration in each permeate was measured. The contents of total dietary fiber in the 170 mesh retentate and soluble dietary fiber in the prefiltered retentate were the highest. Naringin and hesperidin concentration in the permeate were $0.45{\sim}0.65\;mg/g\;and\;5.15{\sim}6.86\;mg/g$ respectively, being $15{\sim}22$ times and $79{\sim}93$ times higher than those in the tangerine peel. Therefore, it can be said that PM 10 hollow fiber membrane separation system may be a very effective method for the recovery of bioflavonoids from tangerine peel.

• Journal of Korea Water Resources Association
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• v.41 no.12
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• pp.1219-1230
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• 2008

Stream inflows induced by flood runoffs have a higher density than the ambient reservoir water because of a lower water temperature and elevated suspended sediment(SS) concentration. As the propagation of density currents that formed by density difference between inflow and ambient water affects reservoir water quality and ecosystem, an understanding of reservoir density current is essential for an optimization of filed monitoring, analysis and forecast of SS and nutrient transport, and their proper management and control. This study was aimed to quantify the characteristics of inflow density current including plunge depth($d_p$) and distance($X_p$), separation depth($d_s$), interflow thickness($h_i$), arrival time to dam($t_a$), reduction ratio(${\beta}$) of SS contained stream inflow for different flood magnitude in Daecheong Reservoir with a validated two-dimensional(2D) numerical model. 10 different flood scenarios corresponding to inflow densimetric Froude number($Fr_i$) range from 0.920 to 9.205 were set up based on the hydrograph obtained from June 13 to July 3, 2004. A fully developed stratification condition was assumed as an initial water temperature profile. Higher $Fr_i$(inertia-to-buoyancy ratio) resulted in a greater $d_p,\;X_p,\;d_s,\;h_i$, and faster propagation of interflow, while the effect of reservoir geometry on these characteristics was significant. The Hebbert equation that estimates $d_p$ assuming steady-state flow condition with triangular cross section substantially over-estimated the $d_p$ because it does not consider the spatial variation of reservoir geometry and water surface changes during flood events. The ${\beta}$ values between inflow and dam sites were decreased as $Fr_i$ increased, but reversed after $Fr_i$>9.0 because of turbulent mixing effect. The results provides a practical and effective prediction measures for reservoir operators to first capture the behavior of turbidity inflow.

• Journal of Life Science
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• v.12 no.3
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• pp.242-249
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• 2002

The optimal culture conditions in 500$m\ell$ flask suture, 5$\ell$ jar fermenter and 2,000 $\ell$ large stale culture were investigated to maximize the production of antibiotic on Rhizoctonia solani AC4, the causal agent of vegetables damping-off, by the strain Bacillus ehimensis YJ-37. Starch (1.5%) as a carbon source, peptone (0.6%) as a nitrogen source and MgC1$_2$(0.15%) as a metal ion in the medium containing N $a_2$HP $O_4$(0.3%) showed the highest production of the antibiotic(s) in a rotary shake (200 rpm). Optimal initial pH of the culture medium, culture temperature and culture time for the antibiotic(s) production were pH 8.0, 32$^{\circ}C$ and 54hrs, respertively. Under the optimal renditions in flask culture, cell growth and antifungal activity (clear zone size) were 1.42 $\times$ 10$^{8}$ cfu/$m\ell$ (21g-DCW/ $\ell$) and 13.9 mm, respectively. In 5 $\ell$ jar fermenter (medium 3 $\ell$), optimal air flow, agitation speed and culture time for the antibiotic(s) production were 2 vvm, 200 rpm and 48hrs, respectively. Under the optimal conditions in 5 $\ell$ jar fermenter, tell growth and antifungal activity were 2.06 $\times$ 10$^{8}$ cfu/$m\ell$ (30g-DCW/ $\ell$) and 13.4 mm, respectively. Under the culture conditions of air flow (30 vvm) and agitation speed (200 rpm) at 32$^{\circ}C$ for 10 days in 2,000 $\ell$ large scale culture (medium 1,800 $\ell$, pH 8.0), cell growth and antifungal activity were 0.81$\times$10$^{8}$ cfu/$m\ell$ (12g-DCW/ $\ell$) and 8.6 mm, respectively.

• The Korean Journal of Nuclear Medicine Technology
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• v.23 no.1
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• pp.75-79
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• 2019

Purpose Cadmium-zinc-telluride (CZT) camera with semiconductor detector is capable of dynamic myocardial perfusion SPECT for coronary flow reserve (CFR). Image acquisition with the heart positioned within 2 cm in the center of the quality field of view (QFOV) is recommended because the CZT detector based on focused multi-pinhole collimators and is stationary gantry without rotation. The aim of this study was to investigate the optimal method for measuring position of the heart within the center of the QFOV when performing dynamic myocardial perfusion SPECT with the Discovery NM 530c camera. Materials and Methods From June to September 2018, 45 patients were subject to dynamic myocardial perfusion SPECT with D530c. For accurate heart positioning, the patient's heart was scanned with a mobile ultrasound and marked at the top of the probe where the mitral valve (MV) was visible in the parasternal long-axis view (PLAX). And, the marked point on the patient's body matched with the reference point indicated CZT detector in dynamic stress. The heart was positioned to be in the center of the QFOV in rest. The coordinates of dynamic stress and rest were compared statistically. Results The coordinates of the dynamic stress using mobile ultrasound and those taken of the rest were recorded for comparative analysis with regard to the position of the couch and analyzed. There were no statistically significant differences in the coordinates of Table in & out, Table up & down, and Detector in & out (P > 0.05). The difference in distance between the 2 groups was measured at $0.25{\pm}1.00$, $0.24{\pm}0.96$ and $0.25{\pm}0.82cm$ respectively, with no difference greater than 2 cm in all categories. Conclusion The position of the heart taken using mobile ultrasound did not differ significantly from that of the center of the QFOV. Therefore, The use of mobile ultrasound in dynamic stress will help to select the correct position of the heart, which will be effective in clinical diagnosis by minimizing the image quality improvement and the patient's exposure to radiation.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Korean Society of Forest Science
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• v.90 no.2
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• pp.197-209
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• 2001

TOPMODEL, semi-distributed hydrological model, is frequently applied to predict the amount of discharge, main flow pathways and water quality in a forested catchment, especially in a spatial dimension. TOPMODEL is a kind of conceptual model, not physical one. The main concept of TOPMODEL is constituted by the topographic index and soil transmissivity. Two components can be used for predicting the surface and subsurface contributing area. This study is conducted for the validation of applicability of TOPMODEL at small forested catchments in Korea. The experimental area is located at Gwangneung forest operated by Korea Forest Research Institute, Gyeonggi-do near Seoul metropolitan. Two study catchments in this area have been working since 1979 ; one is the natural mature deciduous forest(22.0 ha) about 80 years old and the other is the planted young coniferous forest(13.6 ha) about 22 years old. The data collected during the two events in July 1995 and June 2000 at the mature deciduous forest and the three events in July 1995 and 1999, August 2000 at the young coniferous forest were used as the observed data set, respectively. The topographic index was calculated using $10m{\times}10m$ resolution raster digital elevation map(DEM). The distribution of the topographic index ranged from 2.6 to 11.1 at the deciduous and 2.7 to 16.0 at the coniferous catchment. The result of the optimization using the forecasting efficiency as the objective function showed that the model parameter, m and the mean catchment value of surface saturated transmissivity, $lnT_0$ had a high sensitivity. The values of the optimized parameters for m and InT_0 were 0.034 and 0.038; 8.672 and 9.475 at the deciduous and 0.031, 0.032 and 0.033; 5.969, 7.129 and 7.575 at the coniferous catchment, respectively. The forecasting efficiencies resulted from the simulation using the optimized parameter were comparatively high ; 0.958 and 0.909 at the deciduous and 0.825, 0.922 and 0.961 at the coniferous catchment. The observed and simulated hyeto-hydrograph shoed that the time of lag to peak coincided well. Though the total runoff and peakflow of some events showed a discrepancy between the observed and simulated output, TOPMODEL could overall predict a hydrologic output at the estimation error less than 10 %. Therefore, TOPMODEL is useful tool for the prediction of runoff at an ungaged forested catchment in Korea.