• Journal of the Korean Institute of Gas
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• v.19 no.4
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• pp.22-28
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• 2015

In order to keep the competitiveness of LPG fuel for transportation fuel, the difference in fuel consumption with gasoline and cost for an aftertreatment system should be reduced with continuous development of technology for LPG engine. In the present study, spray-guided type direct injection combustion system, whose configuration is composed of direct injector in the vicinity of spark plug, was employed to realize stable lean combustion. A certain level of nitrogen oxides($NO_x$) emits due to a locally rich mixture regions in the stratified mixture. With the application of EGR system for the reduction of $NO_x$, 15% of $NO_x$ reduction was achieved whereas fuel consumption and hydrocarbon emission increased. By the application of EGR, the combustion speed reduced especially appeared at initial flame development period and peak heat release rates and increasing rates for heat release rate decreased as EGR rate increased due to the dilution effect of intake air.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.1
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• pp.97-104
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• 2015

This study is a part of the high pressure injection system development on the Turbo GDI engine in order to reduce smoke emission in case of using the low volatile(high DI) fuel which is used as normal gasoline fuel in the US market. Firstly, theoretical approach was done regarding gasoline fuel property, performance, definition of particle matters and its creation as well as problems of the high DI fuel. In this experimental study, 2L Turbo GDI engine was selected and optimized system parameter was inspected by changing fuel, fuel injection mode (single/multiple), fuel pressure, distance between injector tip and combustion chamber, start of injection, intake valve timing in engine dyno at all engine speed range with full load. In case of normal gasoline fuel, opacity was contained within 2% in all conditions. On the other hands, in case of low volatile fuel (high DI fuel), it was confirmed that the opacity was rapidly increased above 5,000 rpm at 14.5 ~ 20 MPa of fuel pressure and there were almost no differences on the opacity(smoke) between 17 MPa and 20 MPa fuel pressure. According to the SOI retard, smoke decrease tendency was observed but intake valve close timing change has almost no impact on the smoke level in this area. Consequently, smoke decrease was observed and 16% at 6000rpm respectively with injector washer ring installed. By removing injector washer to make injector tip closer to the combustion chamber, smoke decrease was observed by 46% at 5,500 rpm, 42% at 6,000 rpm. It is assumed that the fuel injection interaction with cylinder head, piston head, intake and exhaust valve is reduced so that impingement is reduced in local area.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.16 no.2
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• pp.195-202
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• 2018

As a rule, geological disposal is considered a safe method for final disposal of high-level radioactive waste. However, some long-lived fission products like $^{99}Tc$ and $^{129}I$ contained in spent nuclear fuel are highly mobile as less sorbing anionic species in the subsurface environment and can mainly cause exposure dose to the ecosystem by emission of beta rays in the hundreds of keV range. Therefore, if these two nuclides can be separated and converted with high efficiency into radioactively unharmful nuclides, this would have a positive effect on disposal safety. One candidate method is to transmute these two nuclides in nuclear reactors into short-lived nuclides or into stable nuclides. For this purpose, it is necessary to evaluate which reactor type is more efficient in burning these two nuclides. In this study, the simulation results of nuclear transmutation of $^{99}Tc$ and $^{129}I$ in light water reactor (PWR), heavy water reactor (CANDU) and fast neutron reactor (SFR, MET-1000) are compared and discussed.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.10
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• pp.839-843
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• 2011

ZnO nanostructures were developed on a Si (100) substrate from powder mixture of ZnO and 5 mol% Pd (ZP-5) as reactants by ${\times}$ sccm oxygen pressures(x= 0, 10, 20, 40). DTA (differential thermal analysis) result shows the Pd(5 mol%)+ZnO mixtured powder(PZ-5) is easily evaporated than pure ZnO powder. The PZ-5 mixtured powder was characterized by DTA to determine the thermal decomposition which was found to be at $800^{\circ}C$, $1,100^{\circ}C$. Weight loss(%) and ICP (inductively coupled plasma) analysis reveal that Zn vaporization is decreased by increased oxygen pressures from the PZ-5 at $1,100^{\circ}C$ for 30 mins. Needle-like ZnO nanostructures array developed from 10 sccm oxygen pressure, was well aligned vertically on the Si substrate at $1,100^{\circ}C$ for 30 mins. The lengths of the Needle-like ZnO nanostructures is about 2 ${\mu}m$ with diameters of about 65 nm. The developed ZnO nanostructures exhibited growth direction along [001] with defect-free high crystallinity. It is considered that Zn vaporization is responsible for the growth of Needle-like ZnO nanostructures by controlling the oxygen pressures. The photoluminescence spectra of ZnO nanostructures exhibited stronger 376.7 nm NBE (near band-edge emission) peak and 529.3 nm DLE (deep level energy) peak.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1053-1065
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• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.

• Korean Journal of Materials Research
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• v.27 no.12
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• pp.705-709
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• 2017

The electronic and optical characteristics of molybdenum disulphide ($MoS_2$) film significantly vary with its thickness, and thus a rapid and accurate estimation of the number of $MoS_2$ layers is critical in practical applications as well as in basic researches. Various existing methods are currently available for the thickness measurement, but each has drawbacks. Transmission electron microscopy allows actual counting of the $MoS_2$ layers, but is very complicated and requires destructive processing of the sample to the point where it will no longer be useable after characterization. Atomic force microscopy, particularly when operated in the tapping mode, is likewise time-consuming and suffers from certain anomalies caused by an improperly chosen set point, that is, free amplitude in air for the cantilever. Raman spectroscopy is a quick characterization method for identifying one to a few layers, but the laser irradiation causes structural degradation of the $MoS_2$. Optical microscopy works only when $MoS_2$ is on a silicon substrate covered with $SiO_2$ of 100~300 nm thickness. The last two optical methods are commonly limited in resolution to the micrometer range due to the diffraction limits of light. We report here a method of measuring the distribution of the number of $MoS_2$ layers using a low voltage field emission electron microscope with acceleration voltages no greater than 1 kV. We found a linear relationship between the FESEM contrast and the number of $MoS_2$ layers. This method can be used to characterize $MoS_2$ samples at nanometer-level spatial resolution, which is below the limits of other methods.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.1 no.2
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• pp.123-129
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• 2015

$^{64}Cu$-labeled diacetyl-bis($N^4$-methylthiosemicarbazone) is a promising agent for internal radiation therapy and imaging of hypoxic tissues. In the study, we confirmed hypoxia regions in VX2 tumor implanted rabbits with injection $^{64}Cu$-ATSM and $^{18}F$-FDG using positron emission tomography (PET)/computed tomography (CT). PET images with $^{18}F$-FDG and $^{64}Cu$-ATSM were obtained for 40 min by dynamic scan and additional delayed PET images of $^{64}Cu$-ATSM the acquired up to 48 hours. Correlation between intratumoral $O_2$ level and $^{64}Cu$-ATSM PET image was analyzed. $^{64}Cu$-ATSM and $^{18}F$-FDG were intravenously co-injected and the tumor was dissected and cut into slices for a dual-tracer autoradiographic analysis. In the PET imaging, $^{64}Cu$-ATSM in VX2 tumors displayed a specific uptake in hypoxic region for48 h. The uptake pattern of $^{64}Cu$-ATSM in VX2 tumor at 24 and 48 h did not match to the $^{18}F$-FDG. Through ROI analysis, in the early phase (dynamic scan), $^{18}F$-FDG has positive correlation with $^{64}Cu$-ATSM but late phase (24 and 48 h) of the $^{64}Cu$-ATSM showed negative correlation with $^{18}F$-FDG. High uptake of $^{64}Cu$-ATSM in hypoxic region was responded with significant decrease of oxygen pressure, which confirmed by $^{64}Cu$-ATSM PET imaging and autoradiographic analysis. In conclusion, $^{64}Cu$-ATSM can utilize for specific targeting of hypoxic region in tumor, and discrimination between necrotic- and viable hypoxic tissue.

• Tunnel and Underground Space
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• v.17 no.2 s.67
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• pp.128-138
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• 2007

At low in-situ stress, the continuity and distribution of natural fractures in rock mass predominantly control the failure processes. However at high in-situ stress, the failure process are affected and eventually dominated by stress-induced fractures preferentially growing parallel to the excavation boundary. This fracturing is often observed in brittle type of failure such as slabbing or spatting. Recent studies on the stress- or excavation-induced damage of rock revealed its importance especially in a highly stressed regime. In order to evaluate the brittle failure around a deep underground opening, physical model experiments were carried out. For the experiments a new tue triaxial testing system was made. According to visual observation and acoustic emission detection, brittle failure grades were classified under three categories. The test results indicate that where higher horizontal stress, acting perpendicular $(S_{H2})$ and parallel $(S_{H1})$ to the axis of the tunnel respectively, were applied, the failure grade at a constant vertical stress level (Sy) was lowered. The failure initiation stress was also increased with the increasing $S_{H1}\;and\;S_{H2}$. From the multi-variable regression on failure initiation stress and true triaxial stress conditions, $f(S_v,\;S_{H1},\;S_{H2})$ was proposed.

• Journal of Veterinary Clinics
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• v.31 no.6
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• pp.502-506
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• 2014

Recently nano particles have proven for wide array of bioactive properties. In the present study, antibacterial properties of chitosan silver nano composites (CAgNCs) were investigated against fish pathogenic Edwardsiella tarda. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of CAgNCs against E. tarda were $25{\mu}g/mL$ and $125{\mu}g/mL$, respectively. The field emission scanning electron microscope (FE-SEM) image of CAgNCs treated E. tarda showed the strongly damaged bacteria cells than non-treated bacteria. Furthermore, treatment of CAgNCs induced the level of intracellular reactive oxygen species (ROS) in E. tarda cells in concentration and time dependent manner suggesting that it may generate oxidative stress leading to bacterial cell death. In addition, MTT assay results showed that the lowest cell viability at $100{\mu}g/mL$ of CAgNCs treated E. tarda. Overall results of this study suggest that CAgNCs is a potential antibacterial agent to control pathogenic bacteria.