• The Bulletin of The Korean Astronomical Society
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• v.32 no.1
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• pp.34.2-34.2
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• 2007

• Publications of The Korean Astronomical Society
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• v.18 no.1
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• pp.21-24
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• 2003

The problem of the collapse of a self gravitating disk is here considered. We show self-similar solutions for the above problem under a modified viscous parameter. Surface density depends on r$^m$ in the inner region, where m is -1.6. Therefore growing central mass goes on without mass inflow to the system.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.6
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• pp.571-576
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• 2015

Temperature characteristics of supply oil in an ultra-precision hydrostatic table are largely influenced by parameter setting in an oil cooler such as the location of reference sensor and cooling temperature. In this paper, influences of the parameter setting on the temperature variation in the hydrostatic table are experimentally analyzed to suggest the guidelines for practical application. In case of using temperature of inlet oil as a reference sensor in the oil cooler, temperature rise of the supply oil is smaller and thermal settling time is faster than that of using temperature of outlet oil as a reference sensor. The experimental results also show that temperatures of table, rail and return oil can be made almost same, and thermal settling time can be decreased by setting cooling temperature in the oil cooler to be lower than atmospheric temperature.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.195.1-195.1
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• 2016

Currently, as Plasma application is expanded to the industrial and medical industrial, low temperature plasma applications became important. Especially in medical and biology, many researchers have studied about generated radical species in atmospheric pressure low temperature plasma directly adapted to human body. Therefore, so measurement their plasma parameter is very important work and is widely studied all around world. One of the plasma parameters is electron density and it is closely relative to radical production through the plasma source. some kinds of method to measuring the electron density are Thomson scattering spectroscopy and Millimeter-wave transmission measurement. But most methods have very expensive cost and complex configuration to composed of experiment system. We selected Michelson interferometer system which is very cheap and simple to setting up, so we tried to measuring electron density by laser interferometer with laser beam chopping module for measurement of temporal phase difference in plasma jet. To measuring electron density at atmospheric pressure Ar plasma jet, we obtained the temporal phase shift signal of interferometer. Phase difference of interferometer can occur because of change by refractive index of electron density in plasma jet. The electron density was able to estimate with this phase difference values by using physical formula about refractive index change of external electromagnetic wave in plasma. Our guiding laser used Helium-Neon laser of the centered wavelength of 632 nm. We installed chopper module which can make a 4kHz pulse laser signal at the laser front side. In this experiment, we obtained more exact synchronized phase difference between with and without plasma jet than reported data at last year. Especially, we found the phase difference between time range of discharge current. Electron density is changed from Townsend discharge's electron bombardment, so we observed the phase difference phenomenon and calculated the temporal electron density by using phase shift. In our result, we suggest that the electron density have approximately range between 1014~ 1015 cm-3 in atmospheric pressure Ar plasma jet.

• Atmosphere
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• v.25 no.1
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• pp.19-30
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• 2015

The Ka-band cloud radar (KCR) has been operated by the National Institute of Meteorological Research (NIMR) of Korea Meteorological Administration (KMA) at Boseong National Center for Intensive Observation of severe weather since 2013. Evaluation of data quality is an essential process to further analyze cloud information. In this study, we estimate the measurement error and the sampling uncertainty to evaluate data quality. By using vertically pointing data, the statistical uncertainty is obtained by calculating the standard deviation of each radar parameter. The statistical uncertainties decrease as functions of sampling number. The statistical uncertainties of horizontal and vertical reflectivities are identical (0.28 dB). On the other hand, the statistical uncertainties of Doppler velocity (spectrum width) are 2.2 times (1.6 times) larger at the vertical channel. The reflectivity calibration of KCR is also performed using X-band vertically pointing radar (VertiX) and 2-dimensional video disdrometer (2DVD). Since the monitoring of calibration values is useful to evaluate radar condition, the variation of calibration is monitored for five rain events. The average of calibration bias is 10.77 dBZ and standard deviation is 3.69 dB. Finally, the statistical characteristics of cloud properties have been investigated during two months in autumn using calibrated reflectivity. The percentage of clouds is about 26% and 16% on September to October. However, further analyses are required to derive general characteristics of autumn cloud in Korea.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.E2
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• pp.63-73
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• 2008

In this study, neural network models (NNMs) were examined as alternatives to dispersion models in predicting the short-term $SO_2$ concentrations in a coastal area because the performances of dispersion models in coastal areas have been found to be unsatisfactory. The NNMs were constructed for various combinations of averaging time and prediction time in advance by using the historical data of meteorological parameters and $SO_2$ concentrations in 2002 in the coastal area of Boryeung, Korea. The NNMs were able to make much more accurate predictions of 1 hr $SO_2$ concentrations at ground level in the morning in coastal area than the atmospheric dispersion models such as fumigation models, ADMS3 and ISCST3 for identical conditions of atmospheric stability, area, and weather. Even when predictions of 24-h $SO_2$ concentrations were made 24 hours in advance, the predictions and measurements were in good accordance(correlation coefficient=0.65 for n=216). This accordance level could be improved by appropriate expansion of training parameters. Thus it may be concluded that the NNMs can be successfully used to predict short-term ground level concentrations averaged over time less than 24 hours even in complex terrain. The prediction performance of ANN models tends to improve as the prediction lagging time approaches the concentration averaging time, but to become worse as the lagging time departs from the averaging time.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.293-296
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• 2008

Land surface Temperature (LST) is a very useful surface parameter for the wide range of applications, such as agriculture, numerical and climate modelling community. Whereas operational observation of LST is far from the needs of application community in the spatial Itemporal resolution and accuracy. So, we developed split-window type LST retrieval algorithm to estimate the LST from MTSAT-IR data. The coefficients of split-window algorithm were obtained by means of a statistical regression analysis from the radiative transfer simulations using MODTRAN 4 for wide range of atmospheric profiles, satellite zenith angle and lapse rate conditions including the surface inversions. The sensitivity analysis showed that the LST algorithm reproduces the LST with a reasonable quality. However, the LST algorithm overestimates and underestimates for the strong surface inversion and superadiabatic conditions especially for the warm temperature, respectively. And the performance of LST algorithms is superior when satellite zenith angle is small. The accuracy of the retrieved LST has been evaluated with the Moderate Resolution Imaging Spectroradiometer (MODIS) LST data. The validation results showed that the correlation coefficients and RMSE are about 0.83${\sim}$0.98 and 1.38${\sim}$4.06, respectively. And the quality of LST is significantly better during night and winter time than during day and summer. The validation results showed that the LST retrieval algorithm could be used for the operational retrieval of LST from MTSAT-IR and COMS(Communication, Ocean and Meteorological Satellite) data with some modifications.

• Journal of Radiation Protection and Research
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• v.38 no.2
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• pp.60-67
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• 2013

A diffusion coefficient is an important parameter in the prediction of atmospheric dispersion using a Gaussian plume model, and its modelling approach varies. In this study, dispersion coefficients recommended by the U. S. Nuclear Regulatory Commission's (U. S. NRC's) regulatory guide and the Canadian Nuclear Safety Commission's (CNSC's) regulatory guide, and used in probabilistic accident consequence analysis codes MACCS and MACCS2 have been investigated. Based on the atmospheric dispersion model for a hypothetical accidental release recommended by the U. S. NRC, its influence to atmospheric dispersion factor was discussed. It was found that diffusion coefficients are basically predicted from a Pasquill- Gifford curve, but various curve fitting equations are recommended or used. A lateral dispersion coefficient is corrected with consideration for the additional spread due to plume meandering in all models, however its modelling approach showed a distinctive difference. Moreover, a vertical dispersion coefficient is corrected with consideration for the additional plume spread due to surface roughness in all models, except for the U. S. NRC's recommendation. For a specified surface roughness, the atmospheric dispersion factors showed differences up to approximately 4 times depending on the modelling approach of a dispersion coefficient. For the same model, the atmospheric dispersion factors showed differences by 2 to 3 times depending on surface roughness.

• Asian Journal of Atmospheric Environment
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• v.9 no.4
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• pp.288-297
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• 2015

In this study, wind conditions and its energy potential have been assessed by conducting a Weibull analysis of the wind speed data (over the period of 2002-2011) measured from a port city (Mongla) and an isolated island (Sandwip) in Bangladesh. The monthly mean wind speed at Mongla ranged from 1.60 m/s (December) to 2.47 m/s (April). The monthly values of Weibull shape parameter (k) were from 1.27 to 2.53. In addition, the values of the scale parameter (c) and the monthly wind power density ranged from 1.76 to 2.79 m/s and 3.95 to $17.45W/m^2$, respectively. The seasonal mean wind speed data varied from 1.72 (fall) to 2.29 m/s (spring) with the wind power density from 5.33 (fall) to $14.26W/m^2$ (spring). In the case of Sandwip, the results were comparable to those of Mongla, but moderate reductions in all the comparable variables were observed. The wind data results of these two areas have been compared with those of eight other locations in the world with respect to wind power generation scale. According to this comparison, the wind power generation scale for Mongla and Sandwip was adequate for stand-alone small/micro-scale applications such as local household consumption, solar-wind hybrid irrigation pumps, and battery charging.

• Atmosphere
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• v.23 no.1
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• pp.93-102
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• 2013

Atmospheric transport pathway of an air mass is an important constraint controlling the chemical properties of the air mass observed at a designated location. Such information could be utilized for understanding observed temporal variabilities in atmospheric concentrations of long-lived chemical compounds, of which sinks and/or sources are related particularly with natural and/or anthropogenic processes in the surface, and as well as for performing inversions to constrain the fluxes of such compounds. The Lagrangian particle dispersion model FLEXPART provides a useful tool for estimating detailed particle dispersion during atmospheric transport, a significant improvement over traditional "single-line" trajectory models that have been widely used. However, those without a modeling background seeking to create simple back-trajectory maps may find it challenging to optimize FLEXPART for their needs. In this study, we explain how to set up, operate, and optimize FLEXPART for back-trajectory analysis, and also provide automatization programs based on the open-source R language. Discussions include setting up an "AVAILABLE" file (directory of input meteorological fields stored on the computer), creating C-shell scripts for initiating FLEXPART runs and storing the output in directories designated by date, as wells as processing the FLEXPART output to create figures for a back-trajectory "footprint" (potential emission sensitivity within the boundary layer). Step by step instructions are explained for an example case of calculating back trajectories derived for Anmyeon-do, Korea for January 2011. One application is also demonstrated in interpreting observed variabilities in atmospheric $CO_2$ concentration at Anmyeon-do during this period. Back-trajectory modeling information introduced in this study should facilitate the creation and automation of most common back-trajectory calculation needs in atmospheric research.