• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.47-56
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• 2023

Hydrogen energy is an eco-friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances such as greenhouse gases and fine dust. In particular, smart hydrogen energy is an economical, sustainable, and safe future smart hydrogen energy service, which means a service that stably operates based on 'data' by digitally integrating hydrogen energy infrastructure. In this paper, in order to implement a data-based hydrogen charging station demand forecasting model, three hydrogen charging stations (Chuncheon, Sokcho, Pyeongchang) installed in Gangwon-do were selected, supply and demand data of hydrogen charging stations were secured, and 7 machine learning and deep learning algorithms were used. was selected to learn a model with a total of 27 types of input data (weather data + demand for hydrogen charging stations), and the model was evaluated with root mean square error (RMSE). Through this, this paper proposes a machine learning-based hydrogen charging station energy demand prediction model for optimal hydrogen energy supply and demand.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1111-1116
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• 2011

Heat exchangers have been used for the automotive, HVAC systems, and other various industrial facilities, so the market is very wide. In general, high-efficiency heat exchangers with louver fins are used in the dust-free environment while heat exchangers with wavy fins are used for dusty environment such as construction site, etc. In this study, numerical analysis has been performed for typical heat exchangers, used as oil coolers or fuel coolers, with dual cell model that can handle different grids for the air-side and oil-side of heat exchangers. First wind tunnel tests were conducted to obtain one-dimensional thermal performance data of heat exchangers. Then, heat release rates with varying air flows were numerically predicted using the three-dimensional dual-cell model. The model can greatly enhance the accuracy of thermal design since it includes the effects of nonuniformity of air flows across heat exchangers.

• Safety and Health at Work
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• v.11 no.3
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• pp.322-334
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• 2020

Background: Spontaneous combustion of coal is one of the factors which causes direct or indirect gas and dust explosion, mine fire, the release of toxic gases, loss of reserve, and loss of miners' life. To avoid these incidents, the prediction of spontaneous combustion is essential. The safety of miner's in the mining field can be assured if the prediction of a coal fire is carried out at an early stage. Method: Adularya Underground Coal Mine which is fully mechanized with longwall mining method was selected as a case study area. The data collected for 2017, by sensors from ten gas monitoring stations were used for the simulation and prediction of a coal fire. In this study, the fuzzy logic model is used because of the uncertainties, nonlinearity, and imprecise variables in the data. For coal fire prediction, CO, O₂, N₂, and temperature were used as input variables whereas fire intensity was considered as the output variable.The simulation of the model is carried out using the Mamdani inference system and run by the Fuzzy Logic Toolbox in MATLAB. Results: The results showed that the fuzzy logic system is more reliable in predicting fire intensity with respect to uncertainties and nonlinearities of the data. It also indicates that the 1409 and 610/2B gas station points have a greater chance of causing spontaneous combustion and therefore require a precautional measure. Conclusion: The fuzzy logic model shows higher probability in predicting fire intensity with the simultaneous application of many variables compared with Graham's index.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.3
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• pp.329-341
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• 2005

The purpose of this study is to study the $PM_{2.5}$ source characteristics affecting the Seoul area using a chemical mass balance (CMB) receptor model. This study was also to evaluate the $PM_{2.5}$ source profiles, which were directly measured and developed. Asian Dust Storm usually occurred in the spring, and very high $PM_{2.5}$ concentrations were observed in the fall among the sampling periods. So the ambient data collected in the spring and fall were evaluated. The CMB model results as well as the $PM_{2.5}$ source profiles were validated using the diagnostic categories, such as: source contribution estimate, t-statistic, R-square, Chi-square, and percent of total mass explained. In the spring months, the magnitude of $PM_{2.5}$ mass contributors was in the following order: Chinese aerosol $(31.7\%)>$ secondary aerosols ($22.3\%$: ammonium sulfate $13.4\%$ and ammonium nitrate $8.9\%)>$ vehicles ($16.1\%$: gasoline vehicle $1.4\%$ and diesel vehicles $14.7\%)>$biomass burning $(15.5\%)>$ geological material $(10.5\%)$. In the fall months, the general trend of the $PM_{2.5}$ mass contributors was the following: biomass burning $(31.1\%)>$ vehicles ($26.9\%$: gasoline vehicle $5.1\%$ and diesel vehicles $21.8\%)>$ secondary aerosols ($23.0\%$: ammonium sulfate $9.1\%$ and ammonium nitrate $13.9\%)>$ Chinese aerosol $(10.7\%)$. The results show that the $PM_{2.5}$ mass in the Seoul area was mainly affected by the Chinese area.

• Atmosphere
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• v.28 no.2
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• pp.211-222
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• 2018

Cloud droplet activation process is well described by $K{\ddot{o}}hler$ theory and several parameterizations based on $K{\ddot{o}}hler$ theory are used in a wide range of models to represent this process. Here, we test the two different method of calculating the solute effect in the $K{\ddot{o}}hler$ equation, i.e., osmotic coefficient method (OSM) and ${\kappa}-K{\ddot{o}}hler$ method (KK). To do that, each method is implemented in the cloud droplet activation parameterization module of WRF-CHEM (Weather Research and Forecasting model coupled with Chemistry) model. It is assumed that aerosols are composed of five major components (i.e., sulfate, organic matter, black carbon, mineral dust, and sea salt). Both methods calculate similar representative hygroscopicity parameter values of 0.2~0.3 over the land, and 0.6~0.7 over the ocean, which are close to estimated values in previous studies. Simulated precipitation, and meteorological variables (i.e., specific heat and temperature) show good agreement with reanalysis. Spatial patterns of precipitation and liquid water path from model results and satellite data show similarity in general, but on regional scale spatial patterns and intensity show some discrepancy. However, meteorological variables, precipitation, and liquid water path do not show significant differences between OSM and KK simulations. So we suggest that the relatively simple KK method can be a good alternative to the OSM method that requires various information of density, molecular weight and dissociation number of each individual species in calculating the solute effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1737-1743
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• 2008

A weather radar extracts the weather information from the return echoes which consist of scattered electromagnetic wave signals from rain, cloud and dust particles, etc. The characteristics of Doppler weather signal and ground clutter should be analyzed to extract the accurate weather information. However, the conventional symmetric weather Doppler model is somewhat inappropriate in representing various weather situations. Therefore, the improved model is suggested to describe the skewness in the Doppler spectrum model. Using the suggested model, many various weather signals can be simulated efficiently in time and spectral domain according to weather situations, operation environment and system characteristics. This simulation method may be very helpful in verifying the accuracy of the weather information extraction algorithms and developing the new system for further performance improvement.

• IMMUNE NETWORK
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• v.13 no.6
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• pp.295-300
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• 2013

Der f 2 is the group 2 major allergen of a house dust mite (Dermatophagoides farinae) and its function has been recently suggested. To determine the optimal condition of sensitization to recombinant Der f 2 (rDer f 2) in murine model of asthma, we compared the effectiveness with different adjuvants in BALB/c and C57BL/6 mice. Mice from both strains sensitized with rDer f 2 by intraperitoneal injection or subcutaneous injection on days 1 and 14. The dosage was $20{\mu}g$. Freund's adjuvants with pertussis toxin (FP) or alum alone were used as adjuvants. On days 28, 29, and 30, mice were challenged intranasally with 0.1% rDer f 2. We evaluated airway hyperresponsivenss, eosinophil proportion in lung lavage, airway inflammation, and serum allergen specific antibody responses. Naive mice were used as controls. Airway hyperresponsiveness was increased in C57BL/6 with FP, and BALB/c with alum (PC200: $13.5{\pm}6.3$, $13.2{\pm}6.7$ vs. >50 mg/ml, p<0.05). The eosinophil proportion was increased in all groups; C57BL/6 with FP, BALB/c with FP, C57BL/6 with alum, BALB/c with alum ($24.8{\pm}3.6$, $20.3{\pm}10.3$, $11.0{\pm}6.9$, $5.7{\pm}2.8$, vs. $0.0{\pm}0.0$%, p<0.05). The serum allergen specific IgE levels were increased in C57BL/6 with FP or alum (OD: $0.8{\pm}1.4$, $1.1{\pm}0.8$, vs. $0.0{\pm}0.0$). C57BL/6 mice were better responders to rDer f 2 and as for adjuvants, Freund's adjuvant with pertussis toxin was better.

• Resources Recycling
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• v.15 no.4 s.72
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• pp.44-51
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• 2006

Most electric arc furnace dust (EAFD) treatment processes to recover zinc from EAFD employ carbon as a reducing agent for the zinc oxide in the EAFD. In the present work, the reduction reaction of zinc oxide with carbon in the present of iron oxide was kinetically studied. The experiments were carried out at temperatures between 1173 K and 1373 K under nitrogen atmosphere using a weight-loss technique. From the experimental results, it was concluded that adding the proper amount of iron oxide to the reactant accelerates the reaction rate of zinc oxide with carbon. This is because iron oxide in the reduction reaction of zinc oxide with carbon promotes the carbon gasification reaction. The spherical shrinking core model for a surface chemical reaction control was found to be useful in describing kinetics of the reaction over the entire temperature range. The reaction has an activation energy of 53 kcal/mol (224 kJ/mol) for ZnO-C reaction system, an activation energy of 42 kcal/mol (175 kJ/mol) for $ZnO-Fe_{2}O_{3}-C$ reaction system, and an activation energy of 44 kcal/mol (184 kJ/mol) for ZnO-mill scale-C reaction system.

• The Journal of Internal Korean Medicine
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• v.38 no.6
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• pp.891-901
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• 2017

Objectives: The aim of the study was to evaluate the anti-asthmatic effect of alismatis rhizoma and alisol acetate B combination therapy in a murine asthma model. Methods: C57BL/6 mice were sensitized to and challenged with a mixture of ragweed, dust mite, and aspergillus to induce an asthma animal model. Alismatis rhizoma extract and alisol acetate B combination therapy was co-administered only in the experimental group. To evaluate the anti-asthmatic effect of the combination therapy, inflammatory cell counts in bronchoalveolar lavage (BAL) fluid were determined, and tissue was examined histologically with hematoxylin and eosin (H & E) and periodic acid-Schiff (PAS) stains, by enzyme-linked immunosorbent assay (ELISA) of IgE, IL-4, and IL-5, and with reverse transcription polymerase chain reaction (RT-PCR) of IL-5, IL-33, MUC5AC. Results: Alismatis rhizoma and alisol acetate B combination therapy reduced the number of inflammatory cells, alleviated histologic features, and down-regulated all the investigated asthma mediators, IgE, IL-4, IL-5, IL-33, and MUC5AC. Conclusions: According to the above results, alismatis rhizoma and alisol acetate B combination therapy may have therapeutic potential for asthma.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.267-275
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• 2022

This paper proposed a Deep Neural Network Model system utilizing Keras for predicting air pollution risk of the cardiovascular disease through the effect of each component of air on the harmful virus using past air information, with analyzing 18,000 data sets of the Seoul Open Data Plaza. By experiments, the model performed tasks with higher accuracy when using methods of sigmoid, binary_crossentropy, adam, and accuracy through 3 hidden layers with each 8 nodes, resulting in 88.92% accuracy. It is meaningful in that any respiratory disease can utilize the risk prediction system if there are data on the effects of each component of air pollution and fine dust on oil-borne diseases. It can be further developed to provide useful information to companies that produce masks and air purification products.