• Convergence Security Journal
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• v.23 no.3
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• pp.57-64
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• 2023

Due to the COVID-19 pandemic, the broadcasting industry has been greatly affected, to the extent that the footprint of travel programs has disappeared. Although travel programs have been back on the air since 2022, there remains a task of recovering the stagnant desire for travel. Based on a study that travel programs have a positive impact on viewers' travel intentions, this study examined a data service that provides preferred additional information on travel programs, considering the broadcasting environment of satellite broadcasters that transmit multiple travel programs through various channels. Specifically, preferred additional information was investigated for travel programs of various genres and formats, and a feature model based on FODA was designed to be used when the satellite broadcaster decides the data service configuration. In addition, the necessary information for operating the data service was defined based on the feature model, and a method of transmitting it using the DVB-S SI, a domestic satellite broadcasting standard, was devised. The feasibility of this study was also confirmed using a DVB-MHP based data service prototype.

• Smart Media Journal
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• v.13 no.8
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• pp.58-66
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• 2024

Nitrogen oxides(NOx) in coal-fired power plants are significant contributors to air pollution, influencing the formation of ozone and fine particulate matter, thereby adversely affecting health. Therefore, accurate prediction of NOx emissions is essential. Existing researches have mainly performed based on off-line learning methods, leading to poor prediction performance with the limited training dataset. This paper proposes the online learning model of online support vector regression to predict NOx emissions from coal-fired power plants. Online learning model, which updates a model whenever new observations come out, demonstrates high prediction accuracy even when initial data is scarce. The experimental results showed that the performance of online learning prediction was better than existing off-line learning methods. The results indicated online learning method is a valuable tool for predicting NOx emissions, especially in situations where initial data is limited and data is continuously updated in real-time.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.283-292
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• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.1-18
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• 2014

The purpose of this study was to analyze the sensitivity of meteorological fields and the variation of concentration of particulate matters (PMs) due to aerosol schemes and dust options within the WRF-Chem model to estimate Asian dusts affected on 29 May 2008 in the Korean peninsula. The anthropogenic emissions within the model were adopted by the $0.5^{\circ}{\pm}0.5^{\circ}$ RETRO of the global emissions, and the photolysis option was by Fast-J photolysis. Also, three scenarios such as the RADM2 chemical mechanism and MADE/SORGAM aerosol, the MOSAIC 8 section aerosol, and the GOCART dust erosion were simulated for calculating Asian dust emissions. As a result, the scenario of the RADM2 chemical mechanism & MADE/SORGAM aerosol depicted higher concentration than the others' in both Asian dusts and the background concentration of PMs. By comparing of the daily mean of PM10 measured at each air quality monitoring site in Seoul with the scenario results, the correlation coefficient was 0.67, and the root mean square error was $44{\mu}gm^{-3}$. In addition, the air temperature, the wind speed, the planetary boundary layer height, and the outgoing long-wave radiation were simulated under conditions of no chemical option with these three scenarios within the WRF or WRF-Chem model. Both the spatial distributions of the PBL height and the wind speed of u component among the meteorological factors were similar to those of the Asia dusts in range of 1,800-3,000 m and $2-16ms^{-1}$, respectively. And, it was shown that both scenarios of the RADM2 chemical mechanism and MADE/SORGAM aerosol and the GOCART dust erosion were interacted on-line between meteorological factors and Asian dusts or aerosols within the model because the outgoing long-wave radiation was changed to lower than the others.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of Bio-Environment Control
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• v.28 no.2
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• pp.95-103
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• 2019

Temperature and relative humidity are important factors in crop cultivation and should be properly controlled for improving crop yield and quality. In order to control the environment accurately, we need to predict how the environment will change in the future. The objective of this study was to predict air temperature and relative humidity at a future time by using a multilayer perceptron (MLP). The data required to train MLP was collected every 10 min from Oct. 1, 2016 to Feb. 28, 2018 in an eight-span greenhouse ($1,032m^2$) cultivating mango (Mangifera indica cv. Irwin). The inputs for the MLP were greenhouse inside and outside environment data, and set-up and operating values of environment control devices. By using these data, the MLP was trained to predict the air temperature and relative humidity at a future time of 10 to 120 min. Considering typical four seasons in Korea, three-day data of the each season were compared as test data. The MLP was optimized with four hidden layers and 128 nodes for air temperature ($R^2=0.988$) and with four hidden layers and 64 nodes for relative humidity ($R^2=0.990$). Due to the characteristics of MLP, the accuracy decreased as the prediction time became longer. However, air temperature and relative humidity were properly predicted regardless of the environmental changes varied from season to season. For specific data such as spray irrigation, however, the numbers of trained data were too small, resulting in poor predictive accuracy. In this study, air temperature and relative humidity were appropriately predicted through optimization of MLP, but were limited to the experimental greenhouse. Therefore, it is necessary to collect more data from greenhouses at various places and modify the structure of neural network for generalization.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.6
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• pp.1-14
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• 2022

To alleviate the urban heat island phenomenon, this study aims to quantitatively analyze the effects of neighborhood green spaces on the improvement of the thermal environment based on detailed scenarios of five types of green spaces, including parks, pocket parks, parking lot greening, roadside planting, and rooftop-wall greening. The ENVI-met 4.4.6v model, a microclimate simulation program, was used to analyze the effects of green spaces. As a result, it was found that the air temperature decreased as the planting density of the park increased, but the thermal comfort index PET, which is the degree of heat sensation felt by humans, was not directly proportional to temperature. The establishment of a pocket park reduced air temperature up to a radius of 56m, while the range of temperature reduction increased by about 12.5% when three additional pocket parks were established at 250m intervals. Unlike the air temperature, PET was only affected in the vicinity of the planted area, so there was no significant difference in the thermal comfort of the surrounding environment due to the construction of pocket parks. Changing the surface pavement from asphalt to lawn blocks and implementing rooftop or wall greening did not directly act as solar shading but positively affected air temperature reduction; PET showed no significant difference. Roadside planting showed a higher air temperature reduction effect as the planting interval was narrower, but PET was not directly proportional to tree density. In the case of shrub planting under trees, it did not significantly affect the air temperature reduction but positively affected the improvement of thermal comfort. This study can outline strategies for constructing neighborhood green spaces to solve the urban heat island phenomena and establish detailed strategies for efficient thermal environment improvements.

• Journal of Bio-Environment Control
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• v.8 no.1
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• pp.9-18
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• 1999

Natural ventilation in a four and one-half span, double polyethylene commercial greenhouse was investigated with actual data collected at Quailcrest Farm near Wooster, Ohio. Moreover, a computational fluid dynamics (CFD) numerical technique, FLUENT V4.3, was used to predict natural ventilation rates, thermal conditions, and airflow distributions in the greenhouse. The collected climate data showed that the multi-span greenhouse was well ventilated by the natural ventilation system during the typical summer weather conditions. The maximum recorded air temperature difference between inside and outside the greenhouse was 3.5$^{\circ}C$ during the hottest (34.7$^{\circ}C$) recorded sunny day; the air temperatures in the greenhouse were very uniform with the maximum temperature difference between six widely dispersed locations being only 1.7$^{\circ}C$. The CFD models predicted that air exchange rates were as high as 0.9 volume per minute (A.C. .min$^{-1}$ ) with 2.5m.s$^{-1}$ winds from the west as designed.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.5 no.1
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• pp.65-78
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• 2007

The pollutants (Rn, CH, CO, HS, radioactive gas from radiolysis) were generated from the process of construction and operation of underground repository, and after disposal of low-intermediate radioactive waste inside there must be controlled by a ventilation system to distribute them in area where enough air is supported. Therefore, a suitable technical approach is needed especially at an underground repository that is equipped with many entry tunnels, storage tunnels, exhaust-blowing tunnels, and vertical shafts in complicated network form. For the technical approach of such a ventilation system, WIPP (Waste Isolation Pilot Plant) in U. S and SFR (Slutforvar for Reaktorafall) low-intermediate radioactive waste repository in Sweden were selected as the models, for calculating the required air quantity, organizing a ventilation network considering cross section, length, surface roughness of the air passage, and describing a calculation of resistance of each circuit. Based on these procedures, a best suited ventilation system was completed with designing proper capacity of fans and operating plan of vertical shafts. As a result of comparing the two repositories based on the geometry dimensions and ventilation facility equipment operation, more parallel circuit as in WIPP, brought decrease in resistance for entire system leading to reduce of operating costs, and the larger cross-sectional area of the SFR, the greater the percentage of disposal capacity. Accordingly, the mixture of parallel circuit of WIPP repository for reducing resistance and SFR repository formation for enlargement of disposal capacity would be the most rational and efficient ventilation system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.1-7
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• 2017

The atomization of a liquid into multiple droplets has many important industrial applications, including the atomization of fuels in combustion processes and coating of surfaces and particles. Ultrasonic atomizing nozzle has a transducer that receives electrical input in the form of a high frequency signal from a power generator and converts that into mechanical energy at the same frequency. Liquid is atomized into a fine mist spray using high frequency sound vibrations. In coating applications, the unpressurized, low-velocity spray reduces the amount of overspray significantly because the droplets tend to settle on the substrate, rather than bouncing off it. The spray can be controlled and shaped precisely by entraining the slow-moving spray in an ancillary air stream using specialized types of spray-shaping equipment. The desired patterns of spray can be obtained using an air stream. To simulate the water mist behavior of an ultrasonic atomizing nozzle using an air stream, the Lagrangian dispersed phase model was employed using the commercial code FLUENT. The effects of the nozzle contraction shape, water droplet size and the pneumatic pressure drop on the spray characteristics were investigated to obtain the optimal condition for coating applications.