• Tribology and Lubricants
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• v.20 no.3
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• pp.163-167
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• 2004

Reducing friction torque of the oil hydraulic vane pump used as the power source of power steering system should consider friction torque including viscous and mechanical friction torque according to the changes of rpm and pressure. This paper analyzes the forces acting on the vane to reduce the friction torque of the vane of the hydraulic vane pump used for Hydraulic Power Steering(HPS) system, and futhermore, the forces according to the shapes of cam profiles are analyzed.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2004.05a
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• pp.269-271
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• 2004

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2006.04a
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• pp.339-341
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• 2006

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2004.11a
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• pp.375-376
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• 2004

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2006.10a
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• pp.131-133
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• 2006

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.5
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• pp.345-353
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• 1997

The purpose of the study was to stastically classify individual PM-10 measured by SEM/EDX (scanning electron microscopy/energy dispersive x-ray analyzer). The SEM/EDX provided various physical parameters like optical diameter, as well as major 18 chemical information (Mg, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, Br, Pb) for a particle-by-particle basis. The total of 1,419 particles were analyzed for the study. Thus density and mass of each particle can be estimated based on its chemical composition. Further the study developed 4 semisource profiles including highway, oil boiler, incinerator, and soil emissions, where each sample was collected near the source in the ambient air The profiles developed were consisted of mass fractions and their uncertainties based on a particle class concept. To obtain mass fraction of each particle class, an agglomerative hierarchical cluster analysis was initially applied to create particle classes for each sample. Then uncertainties were calculated for each class based on the jacknife method. The 1,258 particles out of 1,419 (88.7%) were assorted in newly generated particle classes. The study provides opportunities to identify particle's source quantitatively and to develope various receptor models.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.2
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• pp.169-178
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• 2011

The dispersion of a pollutant in a turbulent boundary layer has been described in this study by using a two-dimensional lattice Boltzmann method (LBM) and the Smagorinsky sub-grid-scale (SGS) model. The scalar transport equation corresponding to the pollutant concentration is adopted; the pollutant is considered to be in a continuous phase. The pollutant source is classified as ground-level source (GLS) and elevated-point source (ES). Air velocity and particle concentration profile for the pollutant are compared with the respective results and profiles obtained in the experiments of Fackrell and Robins (1982) and Raupach and Legg (1983). The numerical results obtained in this study, i.e., the simulation and the experimental data for the mean flow velocity profiles and the pollutant concentration profiles, are in good agreement with each other.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.46.3-47
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• 2021

Recently a multilayer spectral inversion (MLSI) model has been proposed to infer the physical parameters of plasmas in the solar chromosphere. The inversion solves a three-layer radiative transfer model using the strong absorption line profiles, H alpha and Ca II 8542 Å, taken by the Fast Imaging Solar Spectrograph (FISS). The model successfully provides the physical plasma parameters, such as source functions, Doppler velocities, and Doppler widths in the layers of the photosphere to the chromosphere. However, it is quite expensive to apply the MLSI to a huge number of line profiles. For example, the calculating time is an hour to several hours depending on the size of the scan raster. We apply deep neural network (DNN) to the inversion code to reduce the cost of calculating the physical parameters. We train the models using pairs of absorption line profiles from FISS and their 13 physical parameters (source functions, Doppler velocities, Doppler widths in the chromosphere, and the pre-determined parameters for the photosphere) calculated from the spectral inversion code for 49 scan rasters (~2,000,000 dataset) including quiet and active regions. We use fully connected dense layers for training the model. In addition, we utilize a skip connection to avoid a problem of vanishing gradients. We evaluate the model by comparing the pairs of absorption line profiles and their inverted physical parameters from other quiet and active regions. Our result shows that the deep learning model successfully reproduces physical parameter maps of a scan raster observation per second within 15% of mean absolute percentage error and the mean squared error of 0.3 to 0.003 depending on the parameters. Taking this advantage of high performance of the deep learning model, we plan to provide the physical parameter maps from the FISS observations to understand the chromospheric plasma conditions in various solar features.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.4
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• pp.285-296
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• 1997

The suspended particulate matters had been collected on quartz fiber fiters by a cascade impactor having 9 size stages for 4 years (Sep. 1991 to Dec. 1995) in Kyung Hee University-Suwon Campus. Membrane filters were used to collected the particulate matters on each stage. The weight concentration on each stage was obtained by a microbalance and further chemical element levels were determined by an x-ray fluorescence system. Based on these chemical information, our study focused on applying the target transformation factor analysis (TTFA), a receptor model, to identify aerosol sources and to apportion quantitatively their mass contribution. There are total of 63 ambient data sets. Each data set consists of the 8 size-ranged subdata sets characterized by 16 elemental variables. By the results, four to five sources were extracted from each size range and some sources reappeared in other size ranges. Then total of 8 source profiles were statistically generated from all the ranges, such as oil burning source, soil source, field burning source, gasoline related source, coal burning source, marine source, glass related source, and unknown sources. Apportioning aerosol mass to each source was intensively examined by investigating emission inventories near the study area. The results showed that soil particle source was the most significant contributor. However, coal and oil burning sources were the major anthropogenic ones. The study finally proposed some air quality control strategies to achieve the clean air quality in Suwon area.

• 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.