• Korean Journal of Food Science and Technology
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• v.30 no.4
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• pp.781-786
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• 1998

To assess effect of surface area on the oxidative stability in thermooxidative system, soybean oil and lard with different surface area $(0.04{\;}cm^2/g,{\;}0.08{\;}cm^2/g,{\;}0.12{\;}cm^2/g{\;}and{\;}0.16{\;}cm^2/g)$ were heated 8 hrs/day at $185^{\circ}C$. The iodine values of soybean oil heated for 64 hrs with surface area of $0.12{\;}cm^2/g$ and $0.16{\;}cm^2/g$ were 113.1 and 116.9, while those of the oil heated for the same length time with surface area of $0.04{\;}cm^2/g$ and $0.08{\;}cm^2/g$ were 126.4 and 125.9, respectively. The same trend was observed in lard, but less markedly than in soybean oil. The polar lipid content, dielectric constant and refractive index of soybean oil and lard heated with surface area above $0.12{\;}cm^2/g$ significantly increased as the heating time increased, while those of the oil heated with surface area below $0.08{\;}cm^2/g$ slowly increased as the heating time increased. On the other hand, the conjugated diene content of soybean oil heated with surface area above $0.12{\;}cm^2/g$ oil increased as the heating time increased while ratio of linoleic acid to palmitic acid decreased as the heating time increased. However, the conjugated diene content and ratio of linoleic acid to palmitic acid of lard were not significantly different depending on the surface area. The results obtained from this investigation indicated that the thermooxidative stability of oil heated with surface area below $0.08\;}cm^2/g$ was better than that of oil heated with surface area above $0.12{\;}cm^2/g$ (P<0.05).

• Journal of the Korean Applied Science and Technology
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• v.34 no.4
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• pp.827-838
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• 2017

Twenty male soccer players volunteered as the experimental subjects. The subjects were randomly assigned to the "Winter training with Cornus officinalis extract intake group"(WCI; n=10) and "Winter training group"(WT; n=10). The variables of blood fatigue substance, muscle damage and liver function where measured in all the subjects before the start of the Cornus officinalis intake after at the end of the 4 weeks winter training. Winter training program was proceeded with 6 days a week for 4 weeks. Exercise intensity was measured using the HRR(70~95%). Changes in the heart rate were also measured with the Polar. WCI group were ingested 80 mL of Cornus officinalis extract after every breakfast and dinner for 4 weeks. The test data were analyzed by paired t-test, and independent t-test(%diff), and the alpha level of p<.05 was set for all tests of significance. Lactate in WCI group had significantly decreased, and the changes between the groups were significantly lower in the WCI group than in the WT group. Ammonia in WCI group had significantly increased. CK in WCI group had significantly decreased. LDH in both group had significantly decreased. AST in WCI group had significantly decreased, and the changes between the groups were significantly lower in the WCI group than in the WT group. ALT in WCI group had significantly decreased. In conclusion, the results of this study show that Cornus officinalis extract seem to have a positive influence on blood fatigue substance, muscle damage and liver function. Therefore Cornus officinalis extract is considered to be able to contribute an ergogenic aids.

• 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.

• Korean Journal of Environmental Agriculture
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• v.40 no.4
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• pp.353-358
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• 2021

BACKGROUND: Prochloraz has been widely used as an imidazole fungicide on fruits and vegetables in Korea. Analytical approaches to evaluate prochloraz residues in herbal medicine are required for their safety management. In this study, we developed a GC-ECD method for quantitative determination of prochloraz in Platycodi Radix. The metabolite 2,4,6-trichlorophenol (2,4,6-T) was used as a target compound to evaluate total prochloraz residues as it is categorized to a representative residue definition of prochloraz. All residues containing 2,4,6-T were converted to 2,4,6-T and subjected to GC-ECD. METHODS AND RESULTS: In order to verify the applicability, the method was optimized for determining prochloraz and it metabolite 2,4,6-T in Platycodi Radix. Prochloraz and its metabolite 2,4,6-T residuals were extracted using acetone. The extract was diluted with and partitioned directly into dichloromethane to remove polar co-extractives in the aqueous phase. The extract was decomposed to 2,4,6-T, and then the partitioned ion-associate was finally purified by optimized aminopropyl solid-phase extraction (SPE). The limits of quantitation of the method (MLOQs) were 0.04 mg/kg and 0.02 mg/kg, respectively for prochloraz and 2,4,6-T, considering the maximum residue level (MRL) of prochloraz as 0.05 mg/kg in Platycodi Radix. Recovery tests were carried out at two levels of concentration (MLOQ, 10 MLOQ) and resulted in good recoveries (82.1-89.7%). Good reproducibilities were obtained (coefficient of variation < 2.8%), and the linearities of calibration curves were reasonable (r² > 0.9986) in the range of 0.005-0.5 ㎍/mL. CONCLUSION(S): The method developed in this study was successfully validated to meet the guidelines required for quantitative determination of pesticides in herbal medicine. Thus, the method could be useful to monitor prochloraz institutionally in herbal medicine.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1177-1186
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• 2021

As the Arctic melt ponds play an important role in determining the interannual variation of the sea ice extent and changes in the Arctic environment, it is crucial to monitor the Arctic melt ponds with high accuracy. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), which is the NASA's latest altimeter satellite based on the green laser (532 nm), observes the global surface elevation. When compared to the CryoSat-2 altimetry satellite whose along-track resolution is 250 m, ICESat-2 is highly expected to provide much more detailed information about Arctic melt ponds thanks to its high along-track resolution of 70 cm. The basic products of ICESat-2 are the surface height and the number of reflected photons. To aggregate the neighboring information of a specific ICESat-2 photon, the segments of photons with 10 m length were used. The standard deviation of the height and the total number of photons were calculated for each segment. As the melt ponds have the smoother surface than the sea ice, the lower variation of the height over melt ponds can make the melt ponds distinguished from the sea ice. When the melt ponds were extracted, the number of photons per segment was used to classify the melt ponds covered with open-water and specular ice. As photons are much more absorbed in the water-covered melt pondsthan the melt ponds with the specular ice, the number of photons persegment can distinguish the water- and ice-covered ponds. As a result, the suggested melt pond detection method was able to classify the sea ice, water-covered melt ponds, and ice-covered melt ponds. A qualitative analysis was conducted using the Sentinel-2 optical imagery. The suggested method successfully classified the water- and ice-covered ponds which were difficult to distinguish with Sentinel-2 optical images. Lastly, the pros and cons of the melt pond detection using satellite altimetry and optical images were discussed.

• Journal of the Korean earth science society
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• v.42 no.5
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• pp.479-495
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• 2021

This study investigates the synoptic (patterns of southern highs, northern lows, and lows rapidly developed by tropopause folding), thermodynamic, and kinematic characteristics of a strong wind that occurred in the Yeongdong region of South Korea on March 18-20, 2020. To do so, we analyzed data from an automatic weather station (AWS), weather charts, the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis, rawinsonde, and windprofiler radars. The daily maximum instantaneous wind speed, exceeding 20 m s^-1, was observed at five weather stations during the analysis period. The strongest instantaneous wind speed (27.7 m s^-1) appeared in the Daegwallyeong area. According to the analysis of weather charts, along with the arrangement of the north-south low-pressure line, the isobars were moved to the Yeongdong area. It showed a sine wave shape, and a strong wind developed owing to the strong pressure gradient. On March 19, in the northern part of the Korean Peninsula, with a drop in atmospheric pressure of 19 hPa or more within one day, a continuous strong wind was developed by the synoptic structure of the developing polar low. In the adiabatic chart observed in Bukgangneung, the altitude of the inversion layer was located at an altitude of approximately 1-3 km above the mountaintop, along with the maximum wind speed. We confirmed that this is consistent with the results of the vertical wind field analysis of the rawinsonde and windprofiler data. In particular, based on the thermodynamic and kinematic vertical analyses, we suggest that strong winds due to the vertical gradient of potential temperature in the lower layer and the development of potential vorticity due to tropopause folding play a significant role in the occurrence of strong winds in the Yeongdong region.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1829-1836
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• 2021

Cloud detection means determining the presence or absence of clouds in a pixel in a satellite image, and acts as an important factor affecting the utility and accuracy of the satellite image. In this study, among the satellites of various advanced organizations that provide cloud detection data, we intend to perform quantitative and qualitative comparative analysis on the difference between the cloud detection data of GK-2A/AMI, Terra/MODIS, and Suomi-NPP/VIIRS. As a result of quantitative comparison, the Proportion Correct (PC) index values in January were 74.16% for GK-2A & MODIS, 75.39% for GK-2A & VIIRS, and 87.35% for GK-2A & MODIS in April, and GK-2A & VIIRS showed that 87.71% of clouds were detected in April compared to January without much difference by satellite. As for the qualitative comparison results, when compared with RGB images, it was confirmed that the results corresponding to April rather than January detected clouds better than the previous quantitative results. However, if thin clouds or snow cover exist, each satellite were some differences in the cloud detection results.

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.247-263
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• 2021

Climate change has been accelerating in coastal waters recently; therefore, the importance of coastal environmental monitoring is also increasing. Chlorophyll-a concentration, an important marine variable, in the surface layer of the global ocean has been retrieved for decades through various ocean color satellites and utilized in various research fields. However, the commonly used chlorophyll-a concentration algorithm is only suitable for application in clear water and cannot be applied to turbid waters because significant errors are caused by differences in their distinct components and optical properties. In addition, designing a standard algorithm for coastal waters is difficult because of differences in various optical characteristics depending on the coastal area. To overcome this problem, various algorithms have been developed and used considering the components and the variations in the optical properties of coastal waters with high turbidity. Chlorophyll-a concentration retrieval algorithms can be categorized into empirical algorithms, semi-analytic algorithms, and machine learning algorithms. These algorithms mainly use the blue-green band ratio based on the reflective spectrum of sea water as the basic form. In constrast, algorithms developed for turbid water utilizes the green-red band ratio, the red-near-infrared band ratio, and the inherent optical properties to compensate for the effect of dissolved organisms and suspended sediments in coastal area. Reliable retrieval of satellite chlorophyll-a concentration from turbid waters is essential for monitoring the coastal environment and understanding changes in the marine ecosystem. Therefore, this study summarizes the pre-existing algorithms that have been utilized for monitoring turbid Case 2 water and presents the problems associated with the mornitoring and study of seas around the Korean Peninsula. We also summarize the prospective for future ocean color satellites, which can yield more accurate and diverse results regarding the ecological environment with the development of multi-spectral and hyperspectral sensors.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1299-1310
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• 2018

In this study, we analyzed distribution and movement trends using in-situ observations and particle tracking methods to understand the movement of the drift ice in the Arctic Ocean. The in-situ movement data of the drift ice in the Arctic Ocean used ITP (Ice-Tethered Profiler) provided by NOAA (National Oceanic and Atmospheric Administration) from 2009 to 2018, which was analyzed with the location and speed for each year. Particle tracking simulates the movement of the drift ice using daily current and wind data provided by HYCOM (Hybrid Coordinate Ocean Model) and ECMWF (European Centre for Medium-Range Weather Forecasts, 2009-2017). In order to simulate the movement of the drift ice throughout the Arctic Ocean, ITP data, a field observation data, were used as input to calculate the relationship between the current and wind and follow up the Lagrangian particle tracking. Particle tracking simulations were conducted with two experiments taking into account the effects of current and the combined effects of current and wind, most of which were reproduced in the same way as in-situ observations, given the effects of currents and winds. The movement of the drift ice in the Arctic Ocean was reproduced using a wind-imposed equation, which analyzed the movement of the drift ice in a particular year. In 2010, the Arctic Ocean Index (AOI) was a negative year, with particles clearly moving along the Beaufort Gyre, resulting in relatively large movements in Beaufort Sea. On the other hand, in 2017 AOI was a positive year, with most particles not affected by Gyre, resulting in relatively low speed and distance. Around the pole, the speed of the drift ice is lower in 2017 than 2010. From seasonal characteristics in 2010 and 2017, the movement of the drift ice increase in winter 2010 (0.22 m/s) and decrease to spring 2010 (0.16 m/s). In the case of 2017, the movement is increased in summer (0.22 m/s) and decreased to spring time (0.13 m/s). As a result, the particle tracking method will be appropriate to understand long-term drift ice movement trends by linking them with satellite data in place of limited field observations.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1125-1139
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• 2022

In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.