• Korean Journal of Remote Sensing
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• v.30 no.5
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• pp.677-686
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• 2014

Sea ice covers wide area in Terra Nova Bay in East Antarctica where the Jangbogo Antarctic Research Station was built in 2014, which affects greatly on the sailing of an icebreaker research vessel. In this study, we analyzed the optimum sea route and sailable period of the icebreaker to visit the Jangbogo Antarctic Research Station by using sea ice concentration data observed by passive microwave sensors such as Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) for the last decade, and by using sea route of the Araon, an icebreaker of Republic of Korea, from 2010 to 2012. It is found that Araon sailed in the route of sea ice concentration up to 78%. Sailing speed of the Araon decreased due to increasing sea ice concentration. However, Araon maintained the speed close to the average speed for the entire sailing period (~11 kn) in the route of sea ice concentration up to 70%. Therefore, we confirm that the Araon can sail typically in the route which shows sea ice concentration below 70%. We derived annually available sailing period in recent 10 years for the sea route of the Araon in 2010, 2011 and 2012, which is defined as the period showing sea ice concentration below 70% through the route. Maximum sailable period was analyzed to be 61 and 62 days for the route of the Araon in 2010 and 2011, respectively. However, the typical sailing in the routes was unavailable in some years because sea ice concentration was higher than 70% through the routes. Meanwhile, the sailable period for the routes of the Araon in 2012 was observed in every year, which was a minimum of 15 days and is a maximum of 89 days. Therefore, we could suggest that optimum route of icebreaker to visit the Jangbogo Antarctic Research Station is the route of the Araon in 2012. High resolution images from SAR or optical sensors are necessary to investigate sea ice condition near shoreline of Jangbogo research station due to several kilometers of low resolution of sea ice concentration.

• Journal of the Korean Applied Science and Technology
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• v.34 no.1
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• pp.50-57
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• 2017

Zinc oxide is, one of metal oxide semiconductor, harmless to human and environment-friendly. It has excellent chemical and thermal stability properties. Wurtzite-zinc oxide is a large band gap energy of 3.37 eV and high exciton binding energy of 60 meV. It can be applied to various fields, such as solar cells, degradation of the dye waste, the gas sensor. The photocatalytic activity of zinc oxide is varied according to the particle shape and change of crystallinity. Therefore, It is very important to specify the additives and the experimental variables. In this study, the zinc oxide were synthesized by using a microwave assisted hydrothermal synthesis. The precursor was used as the zinc nitrate, the pH value was controlled as 11 by NaOH. Surfactants are the ethanolamine, cetyltrimethylammonium bromide, sodium dodecyl sulfate, sorbitan monooleate was added by changing the concentration. The composite particles had the shape of a star-like, curcular cone, seed shape, flake-sphere. Physical and chemical properties of the obtained zinc oxide was characterized using x-ray diffractometer, field emission scanning electron microscopy, thermogravimetric analysis and optical properties was characterized using UV-visible spectroscopy, photoluminescence and raman spectroscopy.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.19-28
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• 2014

The radiative transfer for TIROS operational vertical sounder (RTTOV) and the community radiative transfer model (CRTM) are two fast radiative transfer models (RTM) that are used as observation operators in numerical weather prediction (NWP) systems. This study compares the basic structure and input data of the two models. With data from Advanced Microwave Sounding Unit-A (AMSU-A), which has channels of various frequencies, observed brightness temperature ($T_B$) and simulated $T_B$s from the two models are compared over the ocean surface in two cases-one where cloud information is included and the other without it. Regarding AMSU-A sounding channels (5-14), the two models produce no large significant differences in their calculated $T_B$, but RTTOV produces smaller first guess (FG) departures (i.e., better results) in window and near-surface sounding channels than does CRTM. When adding cloud water and ice particles from Unified Model (UM), the $T_B$ bias between observations and simulations are reduced in both models and the bias at 31.4 and 89 GHz is substantially decreased in CRTM compared to those of RTTOV.

• Journal of Venture Innovation
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• v.5 no.2
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• pp.85-99
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• 2022

Dryers becoming commercially available for experimental and industrial use are classified to general drying oven, hot-air dryer, vacuum dryer, freezing dryer, etc. and kinds of them are various from the function, size and volume, etc. But the moisture measurement is not applied although it is important factor for the quality control and the performance improvement of products, and then now is very passive because the weight is weighed arbitrarily after dry-end. Generally the method for measuring moisture is divided by a direct measurement method and a indirect measurement method, and the former such as the change of weight or volume on the front and rear of separation of moisture, etc. is mainly used. Relatively a indirect measurement is very limited to apply due to utilize measurement apparatuses using temperature conductivity and micro-wave etc. In this research, we easily designed the moisture measurement system using the open-source based Arduino, and monitored moisture fluctuations and weight profiles in the real-time without the effect of external environment. Concretely the temperature-humidity and load cell sensors were packaged into a drying oven and the various change values were measured, and their sensors capable to operate 60℃ and 80℃ were selected to suitable for the moisture sensitive materials and the food dry. And also the performance safety using the organic samples of banana, pear, sawdust could be secured because the changes of evaporation rate as the dry time and temperature, and the measurement values of load cell appeared stable response characteristics through repeated experiments. Hereafter we judge that the reliability can be improved increasingly through the expansion of temperature-humidity range and the comparative analysis with CFD(Computational Fluid Dynamics) program.

• Korean Journal of Remote Sensing
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• v.23 no.6
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• pp.507-520
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• 2007

Satellite passive microwave(PM) sensors have been observing polar sea ice concentration(SIC), ice temperature, and snow depth since 1970s. Among them SIC is playing an important role in the various studies as it is considered the first factor for the monitoring of global climate and environment changes. Verification and correction of PM SIC is essential for this purpose. In this study, we calculated SIC from KOMPSAT-1 EOC images obtained from Arctic sea ice edges from July to August 2005 and compared with SSM/I SIC calculated from NASA Team(NT) algorithm. When we have no consideration of sea ice types, EOC and SSM/I NT SIC showed low correlation coefficient of 0.574. This is because there are differences in spatial resolution and observing time between two sensors, and the temporal and spatial variation of sea ice was high in summer Arctic ice edge. For the verification of SSM/I NT SIC according to sea ice types, we divided sea ice into land-fast ice, pack ice, and drift ice from EOC images, and compared them with SSM/I NT SIC corresponding to each ice type. The concentration of land-fast ice between EOC and SSM/I SIC were calculated very similarly to each other with the mean difference of 0.38%. This is because the temporal and spatial variation of land-fast ice is small, and the snow condition on the ice surface is relatively dry. In case of pack ice, there were lots of ice ridge and new ice that are known to be underestimated by NT algorithm. SSM/I NT SIC were lower than EOC SIC by 19.63% in average. In drift ice, SSM/I NT SIC showed 20.17% higher than EOC SIC in average. The sea ice with high concentration could be included inside the wide IFOV of SSM/I because the drift ice was located near the edge of pack ice. It is also suggested that SSM/I NT SIC overestimated the drift ice covered by wet snow.

• Journal of the Korean earth science society
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• v.40 no.5
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• pp.447-463
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• 2019

Sea surface wind is a fundamental element for understanding the oceanic phenomena and for analyzing changes of the Earth environment caused by global warming. Global research institutes have developed and operated scatterometers to accurately and continuously observe the sea surface wind, with the accuracy of approximately ${\pm}20^{\circ}$ for wind direction and ${\pm}2m\;s^{-1}$ for wind speed. Given that the spatial resolution of the scatterometer is 12.5-25.0 km, the applicability of the data to the coastal area is limited due to complicated coastal lines and many islands around the Korean Peninsula. In contrast, Synthetic Aperture Radar (SAR), one of microwave sensors, is an all-weather instrument, which enables us to retrieve sea surface wind with high resolution (<1 km) and compensate the sparse resolution of the scatterometer. In this study, we investigated the Geophysical Model Functions (GMF), which are the algorithms for retrieval of sea surface wind speed from the SAR data depending on each band such as C-, L-, or X-band radar. We reviewed in the simulation of the backscattering coefficients for relative wind direction, incidence angle, and wind speed by applying LMOD, CMOD, and XMOD model functions, and analyzed the characteristics of each GMF. We investigated previous studies about the validation of wind speed from the SAR data using these GMFs. The accuracy of sea surface wind from SAR data changed with respect to observation mode, GMF type, reference data for validation, preprocessing method, and the method for calculation of relative wind direction. It is expected that this study contributes to the potential users of SAR images who retrieve wind speeds from SAR data at the coastal region around the Korean Peninsula.