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신대근,양한술,민병록,Carlos Narciso-Gaytan,Marcos X. Sanchez-Plata,Ciro A. Ruiz-Feria 한국축산식품학회 2011 한국축산식품학회지 Vol.31 No.5
To evaluate the antioxidant effects of vitamin C, vitamin E and sorghum bran, alone or in combination on chicken sausages,9 kg of chicken thigh meat was prepared. All thigh meat was divided into seven different batches as follows; no antioxidant (CON); vitamin C (VTC), vitamin E (VTE) or sorghum bran (SOR) at 0.02%; or three different combination ratios of vitamin C, vitamin E and sorghum bran at 0.02% (VT2, 2:1:1; VT4, 4:1:1; VT6, 6:1:1). All cooked sausages were stored at 4oC, and six sausages per treatment were used for chemical analysis on five different storage days. As the addition of vitamin E was increased, sausages stored for 10 d had decreased redness; thereby, VTE showed the lowest CIE a^* (p<0.05). Sausages mixed with vitamins and sorghum bran combinations had lower peroxide and free fatty acid values (p<0.05) when compared to sausages without antioxidants. The TBARS were the lowest in sausages containing vitamin C, vitamin E and sorghum bran at 6:1:1 ratio, and they significantly differed to CON, VTC and SOR treatments (p<0.05). Therefore, our results suggest that meat mixed with vitamins and sorghum bran had more antioxidant activity than the meat mixed with only antioxidant vitamins or without antioxidants.
정지궤도 위성 대류권 오존 관측 자료를 이용한 대류권 이동벡터 산출 가능성 연구
신대근,김소명,박주선,백강현,홍성재,김재환,Shin, Daegeun,Kim, Somyoung,Bak, Juseon,Baek, Kanghyun,Hong, Sungjae,Kim, Jaehwan 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
The tropospheric ozone is a pollutant that causes a great deal of damage to humans and ecosystems worldwide. In the event that ozone moves downwind from its source, a localized problem becomes a regional and global problem. To enhance ozone monitoring efficiency, geostationary satellites with continuous diurnal observations have been developed. The objective of this study is to derive the Tropospheric Ozone Movement Vector (TOMV) by employing continuous observations of tropospheric ozone from geostationary satellites for the first time in the world. In the absence of Geostationary Environmental Monitoring Satellite (GEMS) tropospheric ozone observation data, the GEOS-Chem model calculated values were used as synthetic data. Comparing TOMV with GEOS-Chem, the TOMV algorithm overestimated wind speed, but it correctly calculated wind direction represented by pollution movement. The ozone influx can also be calculated using the calculated ozone movement speed and direction multiplied by the observed ozone concentration. As an alternative to a backward trajectory method, this approach will provide better forecasting and analysis by monitoring tropospheric ozone inflow characteristics on a continuous basis. However, if the boundary of the ozone distribution is unclear, motion detection may not be accurate. In spite of this, the TOMV method may prove useful for monitoring and forecasting pollution based on geostationary environmental satellites in the future.
A New Application of Unsupervised Learning to Nighttime Sea Fog Detection
신대근,김재환 한국기상학회 2018 Asia-Pacific Journal of Atmospheric Sciences Vol.54 No.4
This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the 3.7 μm and 10.8 μm channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation–maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.