• Fire Science and Engineering
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• v.23 no.6
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• pp.82-90
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• 2009

Natural smoke ventilator is one of domestic prescriptive methods to be used to exhaust smoke in case of fire in a high-rise buildings. The goal of this study is to evaluate the stack effect and the smoke exhaust performance in high-rise buildings with the opening of natural smoke ventilators using computer modeling technology, thus to estimate its effectiveness as a tool of smoke exhaust. For this purpose, the pressure differential in a domestic high-rise building with natural smoke ventilators was experimentally measured to analyze the stack effect with the closure or the opening of natural smoke ventilators and to calculate compensated air leakage of the building. Computer modeling based on experimentally measured data was carried out to estimate effectiveness of natural smoke ventilators in high-rise buildings using CONTAMW network program.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.04a
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• pp.318-325
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• 2011

In order to understand the operation characteristics and major design parameters of a dual combustion ramjet engine, a fundamental analysis model based on gasdynamics and thermodynamic theories was established. The preliminary performance analysis was accomplished and the results clearly describe the intimate relationship between air inlets, gas generator, and supersonic combustor. The methodology presented provides a means for quantitatively determining the geometries of the gas generator and supersonic combustor and assessing the effects on performance of each of the engine components. Also the design results for a basic configuration were provided.

• Solar Energy
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• v.14 no.2
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• pp.29-37
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• 1994

Radiative charaacteristics of glass windows and porous absorbing media which can be used for a solar air heater are determined through the measurements of spectral transmittances. Those in the visible range are measured by the UV-IR spectrometer. Refractive index of glass are obtained by the comparison of the measured transmittances and the correlations derived from the electromagnetic theory and are compared to the theoretical ones calculated from the classical dispersion theory. Absorption and back-scattering coefficients of 15-mesh stainless wire screens are calcuated by the comparison of the measured transmittances and the correlations derived from the two flux model.

• Journal of the Korean Magnetics Society
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• v.21 no.5
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• pp.174-179
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• 2011

Magnetic force calculation methods such as Maxwell stress, virtual work principle, equivalent magnetic charge, and equivalent magnetizing current are widely used until now. The force density is still controversial issue even though it is common sense that all of these methods have legitimate results. The surface force densities of each method are quite different with each other in the point of numerical result and final expression. In this paper, it is shown that a unified expression of body force density is derived using virtual air-gap scheme for an analytic model in which cylindrical magnetic material with a vertical current runs through its center.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• Journal of the Korean Society of Mineral and Energy Resources Engineers
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• v.55 no.6
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• pp.649-659
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• 2018

In this study, after operational data for a landfill gas power plant were collected, the methane gas concentration was predicted using a deep learning method. Concentrations of methane gas, carbon dioxide, hydrogen sulfide, oxygen concentration, as well as data related to the valve opening degree, air temperature and humidity were collected from 23 pipeline bases for 88 matches from January to November 2017. After the deep learning model learned the collected data, methane gas concentration was estimated by applying other data. Our study yielded extremely accurate estimation results for all of the 23 pipeline bases.

• Journal of the Regional Association of Architectural Institute of Korea
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• v.20 no.6
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• pp.17-23
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• 2018

The purpose of this study is to suggest 100MPa class ultra high strength concrete mix design model applying neural network theory, in order to minimize an effort wasted by trials and errors method until now. Mix design model was applied to each of the 70 data using binary binder, ternary binder and quaternary binder. Then being repeatedly applied to back-propagation algorithm in neural network model, optimized connection weight was gained. The completed mix design model was proved, by analyzing and comparing to value predicted from mix design model and value measured from actual compressive strength test. According to the results of this study, more accurate value could be gained through the mix design model, if error rate decreases with the test condition and environment. Also if content of water and binder, slump flow, and air content of concrete apply to mix design model, more accurate and resonable mix design could be gained.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.4
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• pp.1-12
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• 2018

Baggage Handling System(BHS) is an important element of ground operation services and the importance is further emphasized as air demand increases and passengers change. If there is a problem with departure baggage handling, the aircraft's gate occupancy time will be longer than the initial plan, resulting in congestion of the anchorage leading to final passenger terminal and mooring and road congestion and arrival or It is delayed until the processing of the baggage of the connecting flight and it can cause an economic loss such as confusion in the operation of the airport. The purpose of this study is to investigate the effect of the perception of service quality of BHS users on public Institutions performance through user satisfaction, user performance, and user loyalty. Analysis results using Structural Equation Model was suggested and its implication was discussed in the conclusion.

• Journal of the Korean Society of Propulsion Engineers
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• v.23 no.4
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• pp.10-20
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• 2019

Recently, fluidic thrust vector control has become a core technique to control multifarious air vehicles, such as supersonic aircraft and modern rockets. Fluidic thrust vector control using the shock vector concept has many advantages for achieving great vectoring performance, such as fast vectoring response, simple structure, and low weight. In this paper, computational fluid dynamics methods are used to study a three-dimensional rectangular supersonic nozzle with a slot injector. To evaluate the reliability and stability of computational methodology, the numerical results were validated with experimental data. The pressure distributions along the upper and lower nozzle walls in the symmetry plane showed an excellent match with the test results. Several numerical simulations were performed based on the shear stress transport(SST) $k-{\omega}$ turbulence model. The effect of the momentum flux ratio was investigated thoroughly, and the performance variations have been clearly illustrated.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.540-542
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• 2019

With many announcements on air pollution and human effects from particulate matters, particulate matter forecasts are attracting a lot of public attention. As a result, various efforts have been made to increase the accuracy of particulate matter forecasting by using statistical modeling and machine learning technique. In this paper, the particulate matter AQI index prediction is performed using the multilayer perceptron neural network for particulate matter prediction. For this purpose, a prediction model is designed by using the meteorological factors and particulate matter concentration values commonly used in a number of studies, and the accuracy of the particulate matter AQI prediction is compared.