• Bulletin of the Korean Space Science Society
• /
• 2008.10a
• /
• pp.31.2-31.2
• /
• 2008

We present the results offar ultraviolet (FUV, 1350-1750 ${\AA}$) auroral observations made by the Far-ultraviolet IMaging Spectrograph (FIMS) instrument on the Korean microsatellite STSAT-1. The instrument was capable of resolving spatial structures of a few kilometers with the spectral resolution of 2-3 ${\AA}$. The observations were carried out simultaneously with the measurement of precipitating electrons using an electrostatic analyzer (ESA, 100 eV-20 keV) and a solid state telescope (SST, 170 keV-360 keV) on board the same satellite. With a careful mapping of the field lines, we were able to correlate the particle spectrum to the corresponding FUV spectrum of the footprints of the FIMS image that varied significantly in fine scales. We divided the FIMS spectral band into the LBH long (LBHL, 1640-1715 ${\AA}$) and LBH short (LBHS, 1380-1455 ${\AA}$) bands, and compared the electron energies with the intensities of LBHL and LBHS for the well-defined inverted-V structures. The result shows a strong correlation between the total LBH intensity and the energy flux measured by ESAwhile the peak energy itself does not correlate well with the LBH intensity. On the other hand, it was observed that the ratio of the LBHL intensity to that of LBHS increased significantly as the peak electron energy increased, primarily due to a smaller absorption by O2 at LBHL than at LBHS.

• The Bulletin of The Korean Astronomical Society
• /
• v.43 no.1
• /
• pp.57.2-57.2
• /
• 2018

We present the preliminary result from our KVN single-dish observations of the MALATANG sample. The MALATANG (Mapping the dense molecular gas in the strongest star-forming Galaxies) is one of the JCMT legacy surveys on the nearest 23 IR-brightest galaxies beyond the Local Group. The goal of the MALATANG survey is to map the sample in the dense gas tracers (HCN and HCO+J=4-3), and probe the relationships between the dense molecular gas and star formation activities. As a complementary study, we recently launched a KVN/KaVA program on the same sample, in order to measure their flux densities and parsec-scale jet/outflows in the millimeter regime, which will be greatly useful in understanding the initial conditions of the feedback process. In this work, we present the preliminary result from our pilot KVN single-dish program on a sub-sample, which will be used to select the future VLBI imaging study under plan. We investigate the KVN spectral energy distributions (SED) of the sample as a function of the power source of the luminous IR brightness of each target (starburst? AGN? or hybrid?). We also discuss the technical challenges that we experienced during our KVN observations due to the large size of the sample in the sky.

• Journal of Biomedical Engineering Research
• /
• v.24 no.3
• /
• pp.159-165
• /
• 2003

We developed a 37-channel magnetoencephalogram (MEG) measurement system based on low-noise superconducting quantum interference device (SQUID) magnetometets, and operated the system to measure MEG signals. By using double relaxation oscillation SQUIDs with high flux-4o-voltage transfers, the SQUID outputs could be measured directly by room temperature preamplifiers and compact readout circuits were used for SQUID operation. The average field noise level of the magnetometers is about 3 fT/√Hz in the white region, low enough for MEG measurements when operated inside a magnetically shielded room. The 37 magnetometers were distributed on a hemispherical surface haying a radius of 125 mm. In addition to the 37 sensing channels. 11 reference channels were installed to pickup external noise and to form software gradiometers. A low-noise liquid helium dewar was fabricated with a liquid capacity of 30 L and boil-off rate of 4 L/d. The signal processing software consists of digital filtering, software gradiometer, isofield mapping and source localization. By using the developed system, we measured auditory-evoked fields and localized the current dipoles, demonstrating the effectiveness of the system.

• The Bulletin of The Korean Astronomical Society
• /
• v.46 no.1
• /
• pp.42.1-42.1
• /
• 2021

In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts "Yes" or "No" for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.6
• /
• pp.1474-1480
• /
• 2013

Silicon carbide (SiC)-zirconium diboride ($ZrB_2$) composites were prepared by subjecting a 60:40 vol% mixture of ${\beta}$-SiC powder and $ZrB_2$ matrix to spark plasma sintering (SPS) in 15 $mm{\Phi}$ and 20 $mm{\Phi}$ molds. The 15 $mm{\Phi}$ and 20 $mm{\Phi}$ compacts were sintered for 60 sec at $1500^{\circ}C$ under a uniaxial pressure of 50 MPa and argon atmosphere. Similar composites were simulated using $Flux^{(R)}$ 3D computer simulation software. The current and power densities of the specimen sections of the simulated SiC-$ZrB_2$ composites were higher than those of the mold sections of the 15 $mm{\Phi}$ and 20 $mm{\Phi}$ mold simulated specimens. Toward the centers of the specimen sections, the current densities in the simulated SiC-$ZrB_2$ composites increased. The power density patterns of the specimen sections of the simulated SiC-$ZrB_2$ composites were nearly identical to their current density patterns. The current densities of the 15 $mm{\Phi}$ mold of the simulated SiC-$ZrB_2$ composites were higher than those of the 20 $mm{\Phi}$ mold in the center of the specimen section. The volume electrical resistivity of the simulated SiC-$ZrB_2$ composite was about 7.72 times lower than those of the graphite mold and the punch section. The power density, 1.4604 $GW/m^3$, of the 15 $mm{\Phi}$ mold of the simulated SiC-$ZrB_2$ composite was higher than that of the 20 $mm{\Phi}$ mold, 1.3832 $GW/m^3$. The $ZrB_2$ distributions in the 20 $mm{\Phi}$ mold in the sintered SiC-$ZrB_2$ composites were more uniform than those of the 15 $mm{\Phi}$ mold on the basis of energy-dispersive spectroscopy (EDS) mapping. The volume electrical resistivity of the 20 $mm{\Phi}$ mold of the sintered SiC-$ZrB_2$ composite, $6.17{\times}10^{-4}{\Omega}cm$, was lower than that of the 15 $mm{\Phi}$ mold, $9.37{\times}10^{-4}{\Omega}{\cdot}cm$, at room temperature.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.6
• /
• pp.1-16
• /
• 2021

Global warming due to climate change is expected to significantly affect the hydrological cycle of agriculture. Therefore, in order to predict the magnitude of climate impact on agricultural water resources in the future, it is necessary to estimate the water demand for irrigation as the climate change. This study aimed at evaluating the future changes in water demand for irrigation under two Shared Socioeconomic Pathways (SSPs) (SSP2-4.5 and SSP5-8.5) scenarios for paddy rice in Gimje, South Korea. The APEX-Paddy model developed for the simulation of paddy environment was used. The model was calibrated and validated using the H₂O flux observation data by the eddy covariance system installed at the field. Sixteen General Circulation Models (GCMs) collected from the Climate Model Intercomparison Project phase 6 (CMIP6) and downscaled using Simple Quantile Mapping (SQM) were used. The future climate data obtained were subjected to APEX-Paddy model simulation to evaluate the future water demand for irrigation at the paddy field. Changes in water demand for irrigation were evaluated for Near-future-NF (2011-2040), Mid-future-MF (2041-2070), and Far-future-FF (2071-2100) by comparing with historical data (1981-2010). The result revealed that, water demand for irrigation would increase by 2.3%, 4.8%, and 7.5% for NF, MF and FF respectively under SSP2-4.5 as compared to the historical demand. Under SSP5-8.5, the water demand for irrigation will worsen by 1.6%, 5.7%, 9.7%, for NF, MF and FF respectively. The increasing water demand for irrigating paddy field into the future is due to increasing evapotranspiration resulting from rising daily mean temperatures and solar radiation under the changing climate.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.21 no.1
• /
• pp.1-10
• /
• 2016

Currently available surface seawater partial pressure carbon dioxide ($pCO_2$) data sets in the East Sea are not enough to quantify statistically the carbon dioxide flux through the air-sea interface. To complement the scarcity of the $pCO_2$ measurements, we construct a neural network (NN) model based on satellite data to map $pCO_2$ for the areas, which were not observed. The NN model is constructed for the Ulleung Basin, where $pCO_2$ data are best available, to map and estimate the variability of $pCO_2$ based on in situ $pCO_2$ for the years from 2003 to 2012, and the sea surface temperature (SST) and chlorophyll data from the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor of the Aqua satellite along with geographic information. The NN model was trained to achieve higher than 95% of a correlation between in situ and predicted $pCO_2$ values. The RMSE (root mean square error) of the NN model output was $19.2{\mu}atm$ and much less than the variability of in situ $pCO_2$. The variability of $pCO_2$ with respect to SST and chlorophyll shows a strong negative correlation with SST than chlorophyll. As SST decreases the variability of $pCO_2$ increases. When SST is lower than $15^{\circ}C$, $pCO_2$ variability is clearly affected by both SST and chlorophyll. In contrast when SST is higher than $15^{\circ}C$, the variability of $pCO_2$ is less sensitive to changes in SST and chlorophyll. The mean rate of the annual $pCO_2$ increase estimated by the NN model output in the Ulleung Basin is $0.8{\mu}atm\;yr^{-1}$ from 2003 to 2014. As NN model can successfully map $pCO_2$ data for the whole study area with a higher resolution and less RMSE compared to the previous studies, the NN model can be a potentially useful tool for the understanding of the carbon cycle in the East Sea, where accessibility is limited by the international affairs.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.13 no.2
• /
• pp.69-78
• /
• 2011

Rice is one of the world's staple foods. Paddy rice fields have unique biophysical characteristics that the rice is grown on flooded soils unlike other crops. Information on the spatial distribution of paddy fields and the timing of irrigation are of importance to determine hydrological balance and efficiency of water resource management. In this paper, we detected the timing of irrigation and spatial distribution of paddy fields using the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Aqua satellite. The timing of irrigation was detected by the combined use of MODIS-based vegetation index and Land Surface Water Index (LSWI). The detected timing of irrigation showed good agreement with field observations from two flux sites in Korea and Japan. Based on the irrigation detection, a land cover map of paddy fields was generated with subsidiary information on seasonal patterns of MODIS enhanced vegetation index (EVI). When the MODISbased paddy field map was compared with a land cover map from the Ministry of Environment, Korea, it overestimated the regions with large paddies but underestimated those with small and fragmented paddies. Potential reasons for such spatial discrepancies may be attributed to coarse pixel resolution (500 m) of MODIS images, uncertainty in parameterization of threshold values for discarding forest and water pixels, and the application of LSWI threshold value developed for paddy fields in China. Nevertheless, this study showed that an improved utilization of seasonal patterns of MODIS vegetation and water-related indices could be applied in water resource management and enhanced estimation of evapotranspiration from paddy fields.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.1
• /
• pp.88-100
• /
• 2018

Methane emissions from rice paddies are the largest source of greenhouse gases in the agricultural sector, but there are significant regional differences depending on the surrounding conditions and cultivation practices. To visualize these differences and to analyze their causes and characteristics, the methane emissions from each administrative district in South Korea were calculated according to the IPCC guidelines using the data from the 2010 Agriculture, Forestry and Fisheries Census, and then the results were mapped by using the ArcGIS. The nationwide average of methane emissions per unit area was $380{\pm}74kg\;CH_4\;ha^{-1}\;yr^{-1}$. The western region showed a trend toward higher values than the eastern region. One of the major causes resulting in such regional differences was the $SF_o$ (scaling factor associated with the application of organic matter), where the number of cultivation days played an important role to either offset or deepen the differences. Comparison of our results against the actual methane emissions data observed by eddy covariance flux measurement in the three KoFlux rice paddy sites in Gimje, Haenam and Cheorwon showed some differences but encouraging results with a difference of 10 % or less depending on the sites and years. Using the updated GWP (global warming potential) value of 28, the national total methane emission in 2010 was estimated to be $8,742,000tons\;CO_2eq$ - 13% lower than that of the National Greenhouse Gas Inventory Report (i.e., $10,048,000tons\;CO_2eq$). The administrative districts-based map of methane emissions developed in this study can help identify the regional differences, and the analysis of their key controlling factors will provide important scientific basis for the practical policy makings for methane mitigation.