• Journal of Environmental Science International
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• v.20 no.1
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• pp.25-34
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• 2011

The new empirical static model was constructed on the basis of dimension analysis to predict the pressure drop according to the operating conditions. The empirical static model consists of the initial pressure drop term (${\Delta}P_{initial}$) and the dust mass number term($N_{dust}=\frac{{\omega}_0{\nu}_f}{P_{pulse}t}$), and two parameters (dust deposit resistance and exponent of dust mass number) have been estimated from experimental data. The optimum injection distance was identified in the 64 experimental data at the fixed filtration velocity and pulse pressure. The dust deposit resistance ($K_d$), one of the empirical static model parameters got the minimum value at d=0.11m, at which the total pressure drop was minimized. The exponent of dust mass number was interpreted as the elasticity of pressure drop to the dust mass number. The elasticity of the unimodal behavior had also a maximum value at d=0.11m, at which the pressure drop increased most rapidly with the dust mass number. Additionally, the correlation coefficient for the new empirical static model was 0.914.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Korean Journal of Computational Design and Engineering
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• v.10 no.5
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• pp.375-379
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• 2005

Durability of rubber dust cover in the ball joint for automotive suspension parts is analyzed by FEM and compared with experimental data. Upper open area of ball joint is sealed by dust cover for preventing outflow of the lubricating oil and intrusion of send, dust, water, etc. This rubber cover undergoes repeated loadings such as tension and compression while the car is running. Analysis about rubber material needs to consider every kinds of nonlinearities arise in finite element analysis, which are geometric nonlinearity due to large displacement and small strain, materially nonlinearity and nonlinear boundary condition such as contact. The deformation behavior of dust cover is analysed by using the commercial finite element program MARC. In the study, this program could solve these kinds of nonlinear analysis accurately. Finite element model of dust cover is considered as 3-dimensional half model based on 2-dimensional axisymmetric model. Material property of rubber is modeled by Ogden model and input data for calculation takes form uniaxial tension test of rubber specimen. The final object of the study is obtaining the design specification of dust covers and the result of analysis should be a useful data to design of rubber cover.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.89-94
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• 1999

The paper first proposes an empirical algorithm for detecting dust-loaded airmass observed by Sea Wide Field-of-view Scanner (SeaWiFS). The proposed dust index formula is based on the curvature of the spectral reflectance estimated from the SeaWiFS band 4 (510 nm band) through band 8 (865 nm band) data, assuming "clear ocean water" reflectance. Evaluation of the algorithm is made over several typical Asian dust images including the ones over the Pacific in April, 1998, when a major dust event was reported. The study analyzes the performance and the characteristics of the algorithm by comparing the satellite-derived dust index images with contemporaneous columnar concentration of dust particles predicted by a numerical dust transport model. The comparison reveals several small-scale differences although their dust distribution patterns show good agreement generally.

• Publications of The Korean Astronomical Society
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• v.24 no.1
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• pp.1-8
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• 2009

A plane-parallel model of the diffuse Galactic light (DGL) is calculated assuming exponential disks of interstellar dust and OB stars, by solving exactly the radiative transfer equation using an iterative method. We perform a radiative transfer calculation for a model with generally accepted scale heights of stellar and dust distribution and compare the results with those of van de Hulst & de Jong for a constant slab model. We also find that the intensity extrapolated to zero dust optical depth has a negative value, against to the usual expectation.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.738-740
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• 2003

A method for identifying the released region and time of Asian dust using the long-range inverse transport model that traces the wind field in the backward direction from positions where Asian dust was observed is described. Initial conditions for the inverse transport simulation were obtained from the time variation of the density distribution of the suspended particulate matter (SPM) in the air measured at various places in Japan. Based on a concentration of trajectories of the air mass computed by the inverse transport model, the source region of Asian dust clouds observed at meteorological stations in Japan on March 17 to 18, 2002 was estimated. As a result, it was found that dust particles were released at about 6h on March 15 in the neighborhood of Inner Mongolian Autonomous Region.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.5
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• pp.821-827
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• 2008

This paper presents the prediction model of attenuation characteristics of satellite communication signals operating in the range from 1 to 20GHz, associated with the effects of the Asian Dust. And this paper analyze the effects of the Asian Dust in theory that dust particles size and density, OPC, signal levels, exponentail distribution and the permittivity. The prediction model of the dust attenuation was got, combining the formula of the complex dielectric constant of Asian dust. Expressions for specific attenuation and attenuation are derived in terms of the height, visibility. Therefore it make an investigate to the prediction model of attenuation characteristics continuously.

• Journal of The Korean Astronomical Society
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• v.38 no.2
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• pp.267-270
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• 2005

We investigate the spectral energy distributions (SEDs) of low mass-loss rate O-rich asymptotic giant branch (AGB) stars using the infrared observational data including the Infrared Space Observatory (ISO) data. Comparing the results of detailed radiative transfer model calculations with observations, we find that the dust formation temperature is much lower than 1000 K for standard dust shell models. We find that the superwind model with a density-enhanced region can be a possible alternative dust shell model for LMOA stars.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.778-781
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• 2002

Durability of rubber dust cover in the ball joint for automotive suspension parts were analyzed by FEM and compared with experimental data. Upper open area of ball joint is sealed by dust cover for preventing outflow of the lubricating oil and intrusion of send, dust, water, etc. This rubber cover undergoes repeated loadings such as tension and compression while the car is running. Analysis about rubber material needs to consider every kinds of nonlinearities arise in finite element analysis, which are geometric nonlinearity due to large displacement and small strain, materially nonlinearity and nonlinear boundary condition such as contact. So in the study, the deformation behavior of dust cover was analysed by using the commercial finite element program MARC. This program could solve these kinds of nonlinear analysis accurately. Finite element model of dust cover is considered as 3-dimensional half model based on 2-dimensional axisymmetric model. Material property of rubber was modeled by Ogden model and input data for calculation takes form uniaxial tension test of rubber specimen, The final object of the study is obtaining the design specification of dust covers and the result of analysis should be a useful data to design of rubber

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.510-512
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• 2022

Various studies are being conducted to improve the accuracy of fine dust, but there is a problem that deep learning models are not well learned due to various characteristics according to the concentration of fine dust. This paper proposes an EEMD-based fine dust concentration prediction model to decompose the characteristics of fine dust concentration and reflect the characteristics. After decomposing the fine dust concentration through EEMD, the final fine dust concentration value is derived by ensemble of the prediction results according to the characteristics derived from each. As a result of the model's performance evaluation, 91.7% of the fine dust concentration prediction accuracy was confirmed.