• Food Engineering Progress
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• v.15 no.1
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• pp.75-79
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• 2011

Analytical methods for food antioxidants including ascorbic acid, erythorbic acid, ascorbyl palmitate (AP), and ascorbyl stearate (AS), were validated using high performance liquid chromatography. Validation parameters such as linearity, limit of detection (LOD), limit of quantification (LOQ), and recovery were tested using lard and cider as food model systems. Linearity of ascorbic acid and erythorbic acid were both higher than ($R^2$> 0.99), LOD of these compounds were 0.46 and 0.48 ${\mu}g/mL$, respectively and LOQ were 1.39 and 1.45 ${\mu}g/mL$, respectively. The recovery rates of these compounds were 86.35-94.78% and 84.76-95.02%, respectively. However, the concentration of AP and AS decreased in methanol stock solution. Four other solvents including ethanol, acetonitrile, mixture of methanol and acetonitrile, and mixture of ethanol and acetonitrile were tested to increase the stability of AP and AS under room temperature and refrigerated temperature. Ethanol provided better stability of AP and AS under both room and refrigerated temperature. This study can help to accurately analyze the content of ascorbic acid and its derivatives in processed foods.

• Journal of The Korean Society of Grassland and Forage Science
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• v.44 no.2
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• pp.71-82
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• 2024

It would be advantageous to grow legume forage crops in order to increase the productivity and sustainability of sloped croplands in Hamkyongbukdo. In particular, the identification of potential cultivation areas for alfalfa in the given region could aid decision-making on policies and management related to forage crop production in the future. This study aimed to analyze the climate suitability of alfalfa in Hamkyongbukdo under current and future climate conditions using the Fuzzy Union model. The climate suitability predicted by the Fuzzy Union model was compared with the actual alfalfa cultivation area in the northern United States. Climate data obtained from 11 global climate models were used as input data for calculation of climate suitability in the study region to examine the uncertainty of projections under future climate conditions. The area where the climate suitability index was greater than a threshold value (22.6) explained about 44% of the variation in actual alfalfa cultivation areas by state in the northern United States. The climatic suitability of alfalfa was projected to decrease in most areas of Hamkyongbukdo under future climate scenarios. The climatic suitability in Onseong and Gyeongwon County was analyzed to be over 88 in the current climate conditions. However, it was projected to decrease by about 66% in the given areas by the 2090s. Our study illustrated that the impact of climate change on suitable cultivation areas was highly variable when different climate data were used as inputs to the Fuzzy Union model. Still, the ensemble of the climate suitability projections for alfalfa was projected to decrease considerably due to summer depression in Hamkyongbukdo. It would be advantageous to predict suitable cultivation areas by adding soil conditions or to predict the climate suitability of other leguminous crops such as hairy vetch, which merits further studies.

• Horticultural Science & Technology
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• v.33 no.2
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• pp.202-209
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• 2015

In this study, we investigated the cumulative effect of low temperature on bud dormancy release and bud break characteristics in 'Campbell Early' grapevine (Vitis labruscana B.) cuttings grown in water culture. Additionally, we observed the development of buds while exposed to low temperatures in an attempt to improve our understanding of dormancy and bud break. The shoots were collected 120 days after full bloom (DAFB; leaf abscission period), and the accumulated chill unit (CU) value was calculated by reducing the temperature to $7.2^{\circ}C$ at 125 DAFB. The rate of bud break was 100% in shoots collected at 150 DAFB, The period until the first bud break was two times longer than in the shoots collected 165 DAFB, and bud break speed was significantly reduced. These results indicate that buds are released from endodormancy after 165 DAFB, because at this point the bud break was complete (bud break rate 100%) and it occurred in a very short time period. During this period, when the low-temperature accumulated value was 321h and 442CU according to the CH and Utah models, respectively. Furthermore, the survival rate of main buds decreased rapidly after 165 DAFB, and survival rate of accessory buds was maintained at more than 90% without seasonal differences. The rate of flower bud formation of main buds was much higher than in accessory buds (1:0.23) before the release from endodormancy at 150 DAFB. The final ratio of accessory buds to main buds was high, 1:1.54, at 255 DAFB. Correlation analysis of each investigated factor revealed that bud survival rate and bud formation rate were related only for the main buds, and there was a close relationship between the survival rate of main bud and time. In addition, the survival rate of main buds was positively correlated to the rate of flower bud formation.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.2
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• pp.165-174
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• 2010

The numerical modeling of a coal gasification reaction occurring in an entrained flow coal gasifier is presented in this study. The purposes of this study are to develop a reliable evaluation method of coal gasifier not only for the basic design but also further system operation optimization using a CFD(Computational Fluid Dynamics) method. The coal gasification reaction consists of a series of reaction processes such as water evaporation, coal devolatilization, heterogeneous char reactions, and coal-off gaseous reaction in two-phase, turbulent and radiation participating media. Both numerical and experimental studies are made for the 1.0 ton/day entrained flow coal gasifier installed in the Korea Institute of Energy Research (KIER). The comprehensive computer program in this study is made basically using commercial CFD program by implementing several subroutines necessary for gasification process, which include Eddy-Breakup model together with the harmonic mean approach for turbulent reaction. Further Lagrangian approach in particle trajectory is adopted with the consideration of turbulent effect caused by the non-linearity of drag force, etc. The program developed is successfully evaluated against experimental data such as profiles of temperature and gaseous species concentration together with the cold gas efficiency. Further intensive investigation has been made in terms of the size distribution of pulverized coal particle, the slurry concentration, and the design parameters of gasifier. These parameters considered in this study are compared and evaluated each other through the calculated syngas production rate and cold gas efficiency, appearing to directly affect gasification performance. Considering the complexity of entrained coal gasification, even if the results of this study looks physically reasonable and consistent in parametric study, more efforts of elaborating modeling together with the systematic evaluation against experimental data are necessary for the development of an reliable design tool using CFD method.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Journal of Life Science
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• v.33 no.12
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• pp.1002-1014
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• 2023

The root of Cirsium japonicum var. maackii (Maxim.) has long been used in traditional medicine to prevent the onset and progression of various diseases and has been reported to exert a wide range of physiological effects, including antioxidant activity. However, research on its effects on hepatocytes remains scarce. This study used the human hepatocellular carcinoma HepG2 cell line to investigate the antioxidant activity of ethanol extract of C. japonicum root (EECJ) on hepatocytes. Hydrogen peroxide (H₂O₂) was used to mimic oxidative stress. The results showed that EECJ significantly reverted the decrease in cell viability and suppressed the release of lactate dehydrogenase in HepG2 cells treated with H₂O₂. Moreover, an analysis of changes in cell morphology, flow cytometry, and microtubule-associated protein light chain 3 (LC3) expression showed that EECJ significantly inhibited HepG2 cell autophagy induced by H₂O₂. Furthermore, it attenuated H₂O₂-induced apoptosis and cell cycle disruption by blocking intracellular reactive oxygen species and mitochondrial superoxide production, indicating strong antioxidant activity. EECJ also restored the decreased levels of intracellular glutathione (GSH) and enhanced the expression and activity of superoxide dismutase and GSH peroxidase in H₂O₂-treated HepG2 cells. Although an analysis of the components contained in EECJ and in vivo validation using animal models are needed, these findings indicate that EECJ is a promising candidate for the prevention and treatment of oxidative stress-induced liver cell damage.

• Journal of the Korean Chemical Society
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• v.35 no.4
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• pp.316-323
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• 1991

We calculated a difference between the YBa$_2Cu _3O_{7-x}$ superconductor (123 system) of critical temperature, 95 K and the YBa$_2Cu_4 O_8$ superconductors (124 system) of critical temperature, 80 K in Y-system superconductors using Extended Huckel Theory (EHT). The valence electron population (VEP), reduced overlap population (ROP) and net charge for the charged cluster models relating to the layer and the chain in 123 and 124 systems were compared. The VEPs of Cu atom in the layer of 123 and 124 systems populated d$_{z^2}$ orbital more than d$_{x^2-y^2}$ orbital, and in the chain of 123 and 124 systems populated d$_{y^2-z^2}$ orbital more than d$_{z^2}$ orbital. The ROP of the Cu(1)-O(1) in the layer of 123 system was larger than the value of the Cu(1)-O(2), but the ROP of the Cu(1)-O(2) in the layer of 124 system was larger than the value of the Cu(1)-O(1). The ROP of Cu(2)-O(4) in the chain of 123 and 124 systems were larger than the value of the Cu(2)-O(3). In 123 system the net charge values of the Cu in the layer was larger than the value of the Cu in the chain. However, in 124 system the net charge value of the Cu in the chain was larger than the value in the layer.

• Cartoon and Animation Studies
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• s.40
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• pp.367-395
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• 2015

Mobile games have taken 80% of the market sales in smart device application industry that is highly regarded as one of the fast growing pool of cultural content after the distribution of smart devices. One of the most successful mobile games after the smart device's appearance is . created by Gung-ho Online entertainment under Softbank Japan, has gained the sales revenue of one trillion dollars after its release in 2012, just after one year of its exposure to the market. The game also has been the top rank by Worldwide Mobile Game Revenues for 2years achieving 40 million downloads worldwide in 2015. However, there is no place for a Korean game in world mobile game sales ranks yet. Even though the mobile game industry has been expanding every year, Korean games are losing its places in the market. Therefore, the analysis of a successful game such as is vital for diagnosing Korea's game content and its lack of direction. This study utilizes K. Masanao's Matrix for Creating Profit System for analyzing 's factors for its success. First, the game has incorporated puzzle and RPG contents for creating a new genre, which led various age groups to play the game. Second, the developers have applied 'limited time' in-game festivals and collaborations between the game and famous contents such as God Festival and Character Draw system to increase the profit revenue. Third, the company communicated with on and off line players to seek their needs for developing the game's better development. Consequently, the three success factors of deduced from this study not only reflect the related researches and academic values, but also contribute for the search in finding better ways to developing game contents for Korean mobile game industry.

• Atmosphere
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• v.31 no.2
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• pp.229-240
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• 2021

The Global Ocean Data Assimilation and Prediction System (GODAPS) in operation at the KMA (Korea Meteorological Administration) is introduced. GODAPS consists of ocean model, ice model, and 3-d variational ocean data assimilation system. GODAPS assimilates conventional and satellite observations for sea surface temperature and height, observations of sea-ice concentration, as well as temperature and salinity profiles for the ocean using a 24-hour data assimilation window. It finally produces ocean analysis fields with a resolution of 0.25 ORCA (tripolar) grid and 75-layer in depth. This analysis is used for providing a boundary condition for the atmospheric model of the KMA Global Seasonal Forecasting System version 5 (GloSea5) in addition to monitoring on the global ocean and ice. For the purpose of evaluating the quality of ocean analysis produced by GODAPS, a one-year data assimilation experiment was performed. Assimilation of global observing system in GODAPS results in producing improved analysis and forecast fields with reduced error in terms of RMSE of innovation and analysis increment. In addition, comparison with an unassimilated experiment shows a mostly positive impact, especially over the region with large oceanic variability.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.