• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.4707-4712
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• 2014

Considerable fuel in cars is consumed by air resistance. The flow resistance against the air stream was analyzed by flow analysis near the passenger car body. In this study, the models were used were cars available on the real market. Two velocities entered into inlet plane of flow were 80 km/h and 110 km/h using the flow analysis of CFX. As the study method, the velocity of air flow near the car and the pressure on the rear part of car body were investigated at the driving of car. The shapes of the study models were models 1 and 2, and the flow streams were four cases of 1, 2, 3, and 4. In case 1 among the four cases, the maximum pressure ($1.017{\times}10^5Pa$) on the rear part was highest and the maximum velocity (43.81m/s) of air flow near car body was fastest. The air drag force in the case of high speed (110km/h) driving a passenger car was higher than that of a normal driving speed (80km/h). The drag force at wide section area of the car body becomes higher than the narrow section area. The shape of the car body can be effectively designed to reduce the air resistance using the study results of this analysis.

• Korean Journal of Food Science and Technology
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• v.21 no.5
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• pp.676-685
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• 1989

Optimal operating conditions of on-farm red pepper dryer were searched by using the simulation-optimization algorithm combining the drying and quality deterioration models of red pepper with Box's complex method. Determination of control variables such as air temperature, air recycle ratio and air flow rate was based on a criterion of minimizing energy consumption under the constrainst conditions that satisfied the specified color retention of carotenoids. As quality constraint was stricter, energy consumption increased and total drying time decreased with lower recycle ratio and higher air flow rate Product mixing during drying was found to be able to improve the energy efficiency and product quality. Currently used air flow rate was assessed to be increased for the optimal operation. Two stage drying at the fixed optimal air flow rate was proven to be useful means for further saying of energy consumption. In the optimal bistaged drying, the second stage began at about one third of the total drying time and low air temperature in the first stage Increased to a high value and air recycle ratio increased slightly in the second stage. Optimal control variable scheme could be explained by the dryer performance and the carotenoids destruction kinetics in red pepper drying.

• Korean Journal of Remote Sensing
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• v.34 no.6_2
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• pp.1261-1272
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• 2018

It is important to measure the Arctic surface air temperature because it plays a key-role in the exchange of energy between the ocean, sea ice, and the atmosphere. Although in-situ observations provide accurate measurements of air temperature, they are spatially limited to show the distribution of Arctic surface air temperature. In this study, we proposed machine learning-based models to estimate the Arctic surface air temperature in summer based on buoy data and Advanced Microwave Scanning Radiometer 2 (AMSR2)satellite data. Two machine learning approaches-random forest (RF) and support vector machine (SVM)-were used to estimate the air temperature twice a day according to AMSR2 observation time. Both RF and SVM showed $R^2$ of 0.84-0.88 and RMSE of $1.31-1.53^{\circ}C$. The results were compared to the surface air temperature and spatial distribution of the ERA-Interim reanalysis data from the European Center for Medium-Range Weather Forecasts (ECMWF). They tended to underestimate the Barents Sea, the Kara Sea, and the Baffin Bay region where no IABP buoy observations exist. This study showed both possibility and limitations of the empirical estimation of Arctic surface temperature using AMSR2 data.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.451-463
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• 2018

The empirical/statistical models to estimate the ground Particulate Matter ($PM_{2.5}$) concentration from Geostationary Ocean Color Imager (GOCI) Aerosol Optical Depth (AOD) product were developed and analyzed for the period of 2015 in Seoul, South Korea. In the model construction of AOD-$PM_{2.5}$, two vertical correction methods using the planetary boundary layer height and the vertical ratio of aerosol, and humidity correction method using the hygroscopic growth factor were applied to respective models. The vertical correction for AOD and humidity correction for $PM_{2.5}$ concentration played an important role in improving accuracy of overall estimation. The multiple linear regression (MLR) models with additional meteorological factors (wind speed, visibility, and air temperature) affecting AOD and $PM_{2.5}$ relationships were constructed for the whole year and each season. As a result, determination coefficients of MLR models were significantly increased, compared to those of empirical models. In this study, we analyzed the seasonal, monthly and diurnal characteristics of AOD-$PM_{2.5}$model. when the MLR model is seasonally constructed, underestimation tendency in high $PM_{2.5}$ cases for the whole year were improved. The monthly and diurnal patterns of observed $PM_{2.5}$ and estimated $PM_{2.5}$ were similar. The results of this study, which estimates surface $PM_{2.5}$ concentration using geostationary satellite AOD, are expected to be applicable to the future GK-2A and GK-2B.

• Journal of Distribution Research
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• v.12 no.4
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• pp.1-25
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• 2007

The purpose of this study is to provide the strategic implication of the Korean open air market by examining the factors affecting customer loyalty for various market segments as their competitive environment becomes more turbulent. We have undertaken empirical research that uses the methodology of a mixture regression modeling, as a way to ascertain the determinants of customer loyalty toward the Korean open air market, which should form the base of strategy for each segment. We construct a mixture regression model which uses perceived the Korean open air market value dimensions as explanatory variables, an income as a covariate variable, and a customer loyalty as a dependent variable. The analysis of results show that customers are statistically divided into four segments: 'Accessibility'(33.7%), 'Price'(29.7%), 'Shopping environment,'(22.0%), and 'Merchandising,'(14.5%) groups. The findings also showed that parameter estimates are different for each group, which indicates that the sensitivity to changes in the Korean traditional market perceived value and the income variable affecting customer loyalty vary among segments.

• The Journal of the Korea Contents Association
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• v.20 no.7
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• pp.674-682
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• 2020

Recently, the problem of air pollution has become a global concern due to industrialization and overcrowding. Air pollution can cause various adverse effects on human health, among which respiratory diseases such as asthma, which have been of interest in this study, can be directly affected. Previous studies have used clinical data to identify how air pollutant affect diseases such as asthma based on relatively small samples. This is high likely to result in inconsistent results for each collection samples, and has significant limitations in that research is difficult for anyone other than the medical profession. In this study, the main focus was on predicting the actual asthmatic occurrence, based on data on the atmospheric environment data released by the government and the frequency of asthma outbreaks. First of all, this study verified the significant effects of each air pollutant with a time lag on the outbreak of asthma through the time-lag Pearson Correlation Coefficient. Second, train data built on the basis of verification results are utilized in Deep Learning algorithms, and models optimized for predicting the asthmatic occurrence are designed. The average error rate of the model was about 11.86%, indicating superior performance compared to other machine learning-based algorithms. The proposed model can be used for efficiency in the national insurance system and health budget management, and can also provide efficiency in the deployment and supply of medical personnel in hospitals. And it can also contribute to the promotion of national health through early warning of the risk of outbreak by atmospheric environment for chronic asthma patients.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.1-8
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• 2020

Air ducts installed for ventilation inside buildings accumulate contaminants during their service life. Robots are installed to clean the air duct at low cost, but they are still not fully automated and depend on manpower. In this study, an intersection detection algorithm for autonomous driving was applied to an air duct cleaning robot. Autonomous driving of the robot was achieved by calculating the distance and angle between the extracted point and the center point through the intersection detection algorithm from the camera image mounted on the robot. The training data consisted of CAD images of the duct interior as well as the cross-point coordinates and angles between the two boundary lines. The deep learning-based CNN model was applied as a detection algorithm. For training, the cross-point coordinates were obtained from CAD images. The accuracy was determined based on the differences in the actual and predicted areas and distances. A cleaning robot prototype was designed, consisting of a frame, a Raspberry Pi computer, a control unit and a drive unit. The algorithm was validated by video imagery of the robot in operation. The algorithm can be applied to vehicles operating in similar environments.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.2
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• pp.171-179
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• 2012

Cooling of hot sections, especially the turbine nozzle and rotor blades, has a significant impact on gas turbine performance. In this study, the influence of precooling of the cooling air on the performance of gas turbines and their combined cycle plants was investigated. A state-of-the-art F-class gas turbine was selected, and its design performance was deliberately simulated using detailed component models including turbine blade cooling. Off-design analysis was used to simulate changes in the operating conditions and performance of the gas turbines due to precooling of the cooling air. Thermodynamic and aerodynamic models were used to simulate the performance of the cooled nozzle and rotor blade. In the combined cycle plant, the heat rejected from the cooling air was recovered at the bottoming steam cycle to optimize the overall plant performance. With a 200K decrease of all cooling air stream, an almost 1.78% power upgrade due to increase in main gas flow and a 0.70 percent point efficiency decrease due to the fuel flow increase to maintain design turbine inlet temperature were predicted.

• Journal of Radiation Protection and Research
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• v.28 no.4
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• pp.299-309
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• 2003

In an accidental release of radioactive materials to the environment the contaminative influence of animal products due to the inhalation of air and the ingestion of soil of livestock, both of which are dealt with as minor contaminative pathways in most radioecological models but may not be neglected, was investigated with the improvement of the Korean dynamic food chain model DYNACON Although mathematical models for both contaminative pathways have been established for considering all animal products and incorporated into the model, investigation was limited to milk. As a result, it was found that both pathways are influential in the contamination of milk in the case of an accidental release during the non-grazing period of dairy cows. In the case of an accidental release during the non-grazing period, the inhalation of air was more influential than the ingestion of soil in the early days following an accidental release. While, it was the opposite with the lapse of time. If precipitation is encountered during an accidental release, contaminative influence due to the ingestion of soil was greater compared with the cases of no precipitation, in general, because of a stealer deposition of radionuclides onto the ground. Precipitation during an accidental release was a less influential factor in $^{131}I$ (elemental iodine) contamination compared with the $^{137}Cs\;and\;^{90}Sr$ contaminations. In the case of an accidental release during the grazing period of dairy cows, the contaminative influence due to the inhalation of air was negligible.

• Journal of Bio-Environment Control
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• v.28 no.3
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• pp.225-233
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• 2019

In the summer season, natural ventilation is commonly used to reduce the inside air temperature of greenhouse when it rises above the optimal level. The greenhouse shape, vent design, and position play a critical role in the effectiveness of natural ventilation. In this study, computational fluid dynamics (CFD) was employed to investigate the effect of different roof vent designs along with side vents on the buoyancy-driven natural ventilation. The boussinesq hypothesis was used to simulate the buoyancy effect to the whole computational domain. RNG K-epsilon turbulence model was utilized, and a discrete originates (DO) radiation model was used with solar ray tracing to simulate the effect of solar radiation. The CFD model was validated using the experimentally obtained greenhouse internal temperature, and the experimental and computed results agreed well. Furthermore, this model was adopted to compare the internal greenhouse air temperature and ventilation rate for seven different roof vent designs. The results revealed that the inside-to-outside air temperature differences of the greenhouse varied from 3.2 to $9.6^{\circ}C$ depending on the different studied roof vent types. Moreover, the ventilation rate was within the range from 0.33 to $0.49min^{-1}$. Our findings show that the conical type roof ventilation has minimum inside-to-outside air temperature difference of $3.2^{\circ}C$ and a maximum ventilation rate of $0.49min^{-1}$.