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79 results on '"Kwon, Soon-Bark"'

74. Design of an intelligent duct cleaning robot with force compliant brush.

Author

Jeon, Seung Woo, Jeong, Wootae, Park, Duckshin, and Kwon, Soon-Bark

Abstract

Conserving clean air and removing pollutant matters accumulated in the air duct ventilation system of the underground facilities are key issue for protecting from respiratory ailment of people and saving the energy for heating and air conditioning. In fact, indoor air can be contaminated by re-floating accumulated various particular matters in the duct when HVAC works. This study investigates status of dust contaminants inside of subway HVAC duct including various duct cleaning technologies. Particularly, effective cleaning duct method with an autonomous robot is suggested by using simple compliant device to control the force for the brush pushing the inner surface of the duct. Control method of moving brush with compliant device has also been analyzed to blow off the dusts well and to prevent the brush from overloading. The proposed design of automated device is expected to save the time, give a good quality of cleaning the duct for every operator and sustain clean air environment of subway spaces. [ABSTRACT FROM PUBLISHER]

Published
2012

76. Study on CO2 Adsorption on LiOH-Modified Al2O3

Author

Cho, Young Min, Yang, Young Min, Park, Duck Shin, Kwon, Soon Bark, Jung, Woo Sung, and Lee, Ju Yeol

Abstract

LiOH is known to be one of the most efficient CO2 adsorbent because it reacts with CO2 to form Li2CO3. However, LiOH still suffers from lack of enough hardness for practical use. In this study, various substrates, were modified with LiOH. Their X-ray diffraction patterns were investigated, and LiOH peak was observed from all prepared samples. CO2 adsorption capacity of each prepared sample was measured by monitoring CO2 concentration change during the adsorption process under constant CO2 gas inflow condition. LiOH-modified Al2O3 and zeolite 5A showed good CO2 adsorption performance, while LiOH-modified AC and SiO2 showed relatively poor CO2 adsorption. Al2O3 and zeolite 5A contains many basic functional groups of Al3+, which promote the neutralization reaction with acidic CO2. The effect of carrier gas, carrier gas flow rate, initial CO2 concentration, and amount of LiOH-modified Al2O3 loading was investigated. CO2 adsorption performance was better when the carrier gas was N2, because O2 competes with CO2 on LiOH. CO2 adsorption performance was better with lower carrier gas flow rate, lower initial CO2 concentration, and less loading of adsorbent due to the increase of contact time and contact points.

Published
2013
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77. Inhalation cancer risk from PM10in the metropolitan subway stations in Korea

Author

Roy, Debananda, Seo, Yong-Chil, Namgung, Hyeong-Gyu, and Kwon, Soon-Bark

Abstract

Although subways have many advantages as a transport system, passengers may be exposed to indoor air pollution. In such cases, the dominant risk factors are associated with particulate matter (PM) bound metals and polycyclic aromatic hydrocarbons (PAHs). In this study, inhalation cancer risks (ICR) were assessed at Seoul metro subway stations (SMS), South Korea, in 2014–2015.

Published
2019
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78. Development of CO2 gas cluster cleaning method and its characterization

Author

Choi, Hoomi, Kim, Hojoong, Yoon, Deokjoo, Lee, Jong W., Kang, Bong-Kyun, Kim, Min-Su, Park, Jin-Goo, Kwon, Soon-Bark, and Kim, Taesung

Subjects
*CARBON dioxide, *GAS cleaning, *DRY cleaning, *SILICON wafers, *SEMICONDUCTOR wafers, *NANOSILICON
Abstract

Abstract: As devices become smaller to the extent of nano-scale, dry damage-free cleaning process has become an important area of study. Gas cluster cleaning technology has a potential to remove contaminant particles from the surface of semiconductor wafers without damage because a gas cluster is smaller than 100nm in diameter. In this paper, a new dry cleaning process using a CO2 gas cluster beam is developed and a characterization method for gas cluster generation was studied experimentally. First, a CO2 gas cluster cleaning system was built to evaluate the feasibility of cleaning by use of a gas cluster beam, which is generated by expansion of gas through a converging–diverging nozzle and nozzle chilling. Cleaning tests were performed several times. Twenty-five to three hundred nanometers silica particles were cleaned off from the surface of a bare Si wafer and of a wafer with 60nm poly-silicon structure by using this cleaning system. Preliminary result showed that the contaminants were successfully removed without damage to the wafer surface. To characterize the gas cluster, we used the particle beam mass spectrometer (PBMS) for the measurement of the size distribution of the gas clusters. The size distribution of clusters is measured by varying flow rate and nozzle coolant temperature. From the measurement results, the size of a gas cluster was less than 40nm, which is expected to cause no damage on a surface based on the measurement of pattern collapse force. By using the PBMS, CO2 gas cluster cleaning system can be optimized to provide the maximum cleaning efficiency for nano-scale contaminant particles without damage to substrate surfaces. [Copyright &y& Elsevier]

Published
2013
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79. Predicting PM 10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN).

Author

Park S, Kim M, Kim M, Namgung HG, Kim KT, Cho KH, and Kwon SB

Abstract

The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Published
2018
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