• Journal of Environmental Impact Assessment
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• v.27 no.4
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• pp.378-393
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• 2018

As atmospheric fine dust problems in Korea become more serious, there are growing needs to find the concentration of fine particles in indoor and outdoor areas and there are increasing demands for sensor-based portable monitoring devices capable of measuring fine dust concentrations instantly. The low-cost portable monitoring devices have been widely manufactured and used without the prescribed certification standards which would cause unnecessary confusion to the concerned public. To evaluate the reliability those devices and to improve their usability, following studies were conducted in this work; 1) The comparisons between sensor-based devices and comparison with more accurate devices were performed. 2) Several experiments were conducted to understand usefulness of the portable monitoring devices. As results, the absolute concentration levels need to be adjusted due to insensitivity of the tiny light scattering sensors in the portable devices, but their linearity and reproducibility seem to be acceptable. By using those monitoring devices, users are expected to have benefits of recognizing the changes of concentration more quickly and could help preventing themselves from the adverse health impacts.

• Journal of Korean Society of Transportation
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• v.28 no.5
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• pp.55-64
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• 2010

In order to prepare an efficient solution for PM10 reduction in underground stations, the authors measured PM10 concentration levels every 30 minutes in the concourse, platform, and tunnel of Gaehwasan Station of Seoul's subway line 5. Through a correlation analysis of each changing pattern of PM10 concentration, the direction and velocity of diffusion in underground stations were estimated. The PM10 concentration levels were highest in the tunnel, followed by the platform and concourse. PM10 concentrations in the tunnel, platform, and concourse showed a pattern of increasing in the rush hours and decreasing in the non-rush hours. According to the statistical analysis of PM10 concentrations and changing patterns in each location, the higher PM10 concentration in the tunnel expanded to the platform, and some from the platform expanded to the concourse. Therefore, to efficiently reduce PM10 concentrations, it is essential to detect the centralized generation, diffusion factor, expanding route, expanding measure, and other variables and to remove or reduce the diffusion factor and level. Through operating the ventilation system in the right time frame while the PM10 concentration level increases, the power consumption and peak power consumption can be reduced.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.285-292
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• 2023

Recently, the impact of fine dust on health has become a major topic. Fine dust is dangerous because it can penetrate the body and affect the respiratory system, without being filtered out by the mucous membrane in the nose. Since fine dust is directly related to the industry, it is practically impossible to completely remove it. Therefore, if the concentration of fine dust can be predicted in advance, pre-emptive measures can be taken to minimize its impact on the human body. Fine dust can travel over 600km in a day, so it not only affects neighboring areas, but also distant regions. In this paper, wind direction and speed data and a time series prediction model were used to predict the concentration of fine dust in Seoul, and the correlation between the concentration of fine dust in Seoul and the concentration in each region was confirmed. In addition, predictions were made using the concentration of fine dust in each region and in Seoul. The lowest MAE (mean absolute error) in the prediction results was 12.13, which was about 15.17% better than the MAE of 14.3 presented in previous studies.

• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.45-55
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• 2020

Recently, as the awareness of fine dust has increased in Korea, various countermeasures have been suggested. This study examines the current status of fine dust free zones at home and abroad in order to analyze changes in guest space according to the occurrence of fine dust and to find activity patterns. I would like to predict and find implications. The purpose of this study is to forecast demand centering on domestic and foreign countermeasures for dust and domestic industry. In order to secure competitiveness for the smart city in the era of the 4th Industrial Revolution, the research is aimed at proposing a strategic plan to cope with the fine dust that is a threat to urban space. The research method is described in the following order.

• Proceedings of the KAIS Fall Conference
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• 2011.05a
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• pp.300-302
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• 2011

본 논문에서는 울산지역의 79개 고등학교 238개 교실을 대상으로 측정한 미세먼지($PM_{10}$)의 농도를 학교, 교실, 지역별로 평가하였다. 울산지역 고등학교 미세먼지($PM_{10}$)의 평균농도는 $63.8 \;{\mu}g/m^3$이었고 일반계가 $64.9 \;{\mu}g/m^3$으로 전문계 고등학교 미세먼지($PM_{10}$)의 평균농도에 비해 높게 나타났으며, 사립고등학교가 공립 고등학교 미세먼지($PM_{10}$)의 평균농도 보다 높았다. 또한, 남녀공학 교실의 미세먼지($PM_{10}$) 평균농도가 남고와 여고에 비해 높았으나 통계적인 유의성은 없었다. 학생들의 활동이 많은 일반교실의 평균 미세먼지($PM_{10}$) 농도가 특별실 보다 통계적으로 유의하게 높게 나타났고 유지기준 초과율도 특별실에 비해 약 2배 이상 높았다. 학년별로는 1학년 교실의 미세먼지($PM_{10}$) 농도가 2학년에 비해 통계적으로 유의하게 높았다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.623-625
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• 2018

As the issue of particulate matter spreads, services for providing particulate matter information in real time are increasing. However, when a sensor node for collecting particulate matter is defective, a corresponding service may not be provided. To solve these problems, it is necessary to predict and deduce particulate matter. In this paper, a particulate matter prediction model is designed using artificial neural network algorithm based on past particulate matter and meteorological data to predict particulate matter. Also, the prediction results are compared by learning the input data of the model in the design stage.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.397-400
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• 2019

급격히 증가하는 자동차 수, 발전량 증가 등으로 인하여 미세먼지로 인한 환경오염이 심각한 사회문제로 대두되고 있는 실정이다. 50개가 넘는 국가들이 권고치 이상의 미세먼지로 인해 피해를 받고 있으며 각 피해국들은 미세먼지 저감 대책 및 발생을 최소화하기 위한 방안을 연구하고 있다. 하지만 현재 고정형 미세먼지 취득 드론으로는 다양한 포인트의 미세먼지 데이터를 수집하기 힘든 상황이며, 기존 드론을 활용한 방법에서 도 회전 날개의 영향으로 인해 정확한 데이터를 수집하기 힘든 실정이다. 본 논문에서는 드론과 특정 구조물을 활용한 미세먼지 수집 방법을 제안하고 이의 효율성을 보여주고자 한다.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.345-347
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• 2021

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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.53-56
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• 2019

본 논문에서는 화력을 이용하는 대형 파워 플랜트 설비의 미세먼지 발생량을 저감시키고 능동적으로 제어 할 수 있는 효율적인 시스템을 제안한다. 이 시스템은 기존의 고정형으로 설계된 집진기 방식의 고정부하량 한계점과 극복하고 초미세먼지 PM2.5, 미세먼지 PM10의 발생량에 따라 IoT센서 감지에 의해 지능형 알고리즘으로 효율적으로 저감 제어 처리량을 극대화하고, 미세먼지 발생량을 최소화한다. 또한 이 시스템의 차별성은 기존의 집진기에서 잡혀지지 않는 초미세먼지를 새로운 형태의 물질인 FAA(Fine-dust Adsorption Agent)를 통해 연료 연소 시 발생되는 초미세먼지 미세입자 자체를 크게 만들어 기존 설비 집진기 필터에 포집되게 하는 혁신적인 방식이다. 이번 연구를 통해 350도~1000도 열원에서 작용할 수 있는 화학물질 FAA 용액(Agent)을 개발 하였으며 지능형 AI 분사장치를 통해 연료에 첨가되어 연소 시 미세먼지를 20배~50배까지 볼륨을 확대시켜 기존 집진필터에 포집될 수 있게 동작된다. 이때, 기존 설계된 집진기의 한계(부하)용량에 상관없이 미세먼지 발생량을 상황인식 반응형 알고리즘(AI제어) 통해 분사량을 능동적으로 조절하여 미세먼지 발생량을 저감하는 진보적 혁신성을 지닌다.