• The Bulletin of The Korean Astronomical Society
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• v.22
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• pp.13.2-13.2
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• 1997

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.2
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• pp.85-95
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• 2019

The internal environment in pig house is closely related to the animal productivity. In addition, it is important to consider a working environment inside the pig house due to high gas and dust concentrations. The poor working environment inside the pig house can cause health problems including respiratory diseases. To analyze the working environment, it is important to evaluate the ventilation efficiency to effectively remove harmful gases and dust. The purpose of this study is to develop a 3D CFD model to analyze the working environment in the pig house. CFD model was validated by comparing air temperature distributions between CFD computed and field measured data. The average air flow rate at the pig height was 40.1 % lower than the working height when incoming air was concentrated on upper layer by the installed ventilation system on the experimental pig house. Using the validated CFD model, the regional ventilation efficiency was computed by the TGD(tracer gas decay) method at the pig and working heights. There was a difference of ventilation efficiency on 14 % between the air stagnated section and the rest sections. Stagnated gas concentration can be effected by animal and human health.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.531-536
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• 2022

This paper compared the performance of the parametric method and the nonparametric method when estimating the distribution for the tail of the distribution with heavy tails. For the parametric method, the generalized extreme value distribution and the generalized Pareto distribution were used, and for the nonparametric method, the kernel density estimation method was applied. For comparison of the two approaches, the results of function estimation by applying the block maximum value model and the threshold excess model using daily fine dust public data for each observatory in Seoul from 2014 to 2018 are shown together. In addition, the area where high concentrations of fine dust will occur was predicted through the return level.

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.109-111
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• 2017

We detected bright mid- to far-infrared emission from the helium nova V445 Puppis in the AKARI all-sky survey data taken in 2006. Assuming an optically thin condition, we decomposed the spectral energy distribution (SED) of V445 Puppis in October 2006 by model fitting and found that the SED can be explained by a combination of cold amorphous carbon (125 K and the mass of $4.5^{+6.6}_{-2.7}{\times}10^{-4}M_{\odot}$) and warm amorphous carbon (250 K and the mass of $1.8^{+1.0}_{-0.5}{\times}10^{-5}M_{\odot}$). Assuming that the former is pre-existing dust formed in the past nova outbursts and the latter is newly formed dust in December 2000's nova wind, this result suggests that the amount of dust formed around V445 Puppis in a single outburst is larger than $10^{-5}M_{\odot}$, which is larger than those in any other classical novae ever reported.

• Journal of the Korean Institute of Gas
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• v.16 no.4
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• pp.32-37
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• 2012

The aim of this study is to evaluate the characteristics of explosion and flame velocity that can be utilized to factories where Mg-Al alloy metal powders are handled in the form of raw materials, products or by-product for similar dust explosion prevention and mitigation. Because the strength of the blast pressure is the result due to flame propagation, flame velocity in dust explosion can be utilized as a valuable information for damage prediction. An experimental investigation was carried out on the influences of content ratio of Mg-Al alloy (mean particle size distribution of 151 to 161 ${\mu}m$). And a model of flame propagation velocity based on the time to peak pressure and flame arrival time in dust explosion pressure, assuming the constant burning velocity, leads to a representation of flame velocity during dust explosion. As the results, the maximum flame velocity of Mg-Al(60:40 wt%), Mg-Al(50:50 wt%) and Mg-Al(40:60 wt%) was estimated 15.5, 18 and 15.2 m/s respectively, and also tend to change with content ratio of Mg-Al.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.512-517
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• 2014

This paper describes the development of new sweeping machine based on Cyclone Technology, which maintains constant suction power and uses it in a industrial applications as a method for dust removed from grinding work. The performance of a cyclone separator is determined by the turbulence characteristics and particle-particle interaction. To achieve this goal, we design cyclone technology based dust separator for sweeping machine has been proposed as a system which is suitable to work utilizing dust suction alternative to conventional manual system. and Numerical analysis with computational fluid dynamics(CFD) was carried out to investigate the working fluid that flow into cyclone dust separator in order to design optimal structure of the sweeping machine. The validation of cyclone model with CFD is carried out by comparing with experimental results.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.241-249
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• 2014

The continuous observations for atmospheric aerosol were carried out during 3 years (2009-2011) by using a multi-wavelength Raman lidar at the Gwangju Institute of Science and Technology (GIST), Korea ($35.11^{\circ}N$, $126.54^{\circ}E$). The particle depolarization ratios were retrieved from the observations in order to distinguish the Asian dust layer. The vertical information of Asian dust layers were used as input parameter for the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model for analysis of its backward trajectories. The source regions and transport pathways of the Asian dust layer were identified. The most frequent source region of Asian dust in Korea was Gobi desert during observation period in this study. The statistical analysis on the particle depolarization ratio of Asian dust was conducted according to their transport route in order to retrieve the variation of optical properties of Asian dust during long-range transport. The transport routes were classified into the Asian dust which was transported to observation site directly from the source regions, and the Asian dust which was passed over pollution regions of China. The particle depolarization ratios of Asian dust which were transported via industrial regions of China was ranged 0.07-0.1, whereas, the particle depolarization ratio of Asian dust which was transported directly from the source regions to observation site were comparably higher and ranged 0.11-0.15. It is considered that the pure Asian dust particle from source regions were mixed with pollution particles, which is likely to spherical particle, during transportation so that the values of particle depolarization of Asian dust mixed with pollution was decreased.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2003.11a
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• pp.51-54
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• 2003

We analyzed radiative properties of aerosols, $CO^{2}$ and clouds using Optical Properties of Aerosols and Clouds(OPAC) and the Column Radiation Model (CRM). From OPAC, if the soot component is disregarded, dust-like components depict the highest extinction values in the solar spectral range and the lowest. single scattering albedoes, which are attributable to the presence of large particles. In the dust aerosol, the high absorptivity in the infrared may induce a warming of the lower atmospheric layer in the nighttime. The radiative properties of aerosols, clouds and double $CO^{2}$ using the CRM model at Seoul (37N, 127.4 E) on 3 April 2003 were calculated. The solar zenith angle is 65˚ and the surface albedo is 0.1836 during the clear day. The aerosol optical depth change 0.14 to 1.7, which is derived during Asian dust days in Korea. At this time, abedo by aerosols is considered as 0.3. In cloudy condition, the short wave cloud forcing on both the TOA and the surface is -193.89 $Wm^{-2}$ and -195.03 $Wm^{-2}$, respectively, and the long wave cloud forcing is 19.58 $Wm^{-2}$ and 62.08 $Wm^{-2}$, respectively. As a result, the net radiative cloud forcing is -174.31 $Wm^{-2}$ and -132.95 $Wm^{-2}$, respectively. We calculate also radiative heating rates by double $CO^{2}$ during the clear day. The $CO^{2}$ volumn mixing ratio is 3.55E-4.

• Atmosphere
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• v.32 no.4
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• pp.341-351
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• 2022

Recently, interest in the possibility of a washout effect using artificial rain enhancement technology to reduce high-concentration fine dust is growing. Therefore, in this study, the reduction rate of PM₁₀ concentration according to the amount of artificial rain enhancement was calculated during Asian Dust event which occurred over the Korean Peninsula on March 29, 2021 using air quality model [i.e., Community Multiscale Air Quality (CMAQ)] combined with the mesoscale model for artificial rain enhancement (i.e., WRF-MMS). According to WRF-MMS, the washout effect lasted 5 hours, and the maximum precipitation rate was calculated to be 1.5 mm hr^-1. According the CMAQ results, the PM₁₀ reduction rate was up to 22%, and the affected area was calculated to be 6.4 times greater than that of the artificial rain enhancement area. Even if the maximum amount of precipitation per hour is lowered to 0.8 mm hr^-1 (about 50% level), the PM₁₀ reduction rate appears to be up to 16%. In other words, it is believed that this technique can be used as a direct method for reducing high-concentration fine dust even when the artificial rain enhancement effect is weak.

• The Journal of Bigdata
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• v.6 no.2
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• pp.39-49
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• 2021

Recently, large and diverse weather data are being collected by sensors from various sources. Efforts to predict the concentration of fine dust through machine learning are being made everywhere, and this study intends to compare PM10 and PM2.5 prediction models using data from 840 outdoor air meters installed throughout the city. Information can be provided in real time by predicting the concentration of fine dust after 5 minutes, and can be the basis for model development after 10 minutes, 30 minutes, and 1 hour. Data preprocessing was performed, such as noise removal and missing value replacement, and a derived variable that considers temporal and spatial variables was created. The parameters of the model were selected through the response surface method. XGBoost, Random Forest, and Deep Learning (Multilayer Perceptron) are used as predictive models to check the difference between fine dust concentration and predicted values, and to compare the performance between models.