• Journal of Korean Society of Environmental Engineers
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• v.32 no.11
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• pp.1001-1010
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• 2010

The purpose of this study is to investigate the concentration levels of particle materials ($PM_{10}$, asbestos), gas materials ($CO_2$, CO, $NO_2$, HCHO, Rn, VOCs) and total suspended colony (TSC), and the correlations among these materials in indoor air quality of 54 multiple-use facilities and 15 public-use facilities of Gwangju. The highest mean concentration of $PM_{10}$ was $69.2\;{\mu}g/m^3$ at indoor parking place, followed by childcare facilities, large commercial building and subway station building. The highest mean concentration of CO was 2.7 ppm at indoor parking place and that of $CO_2$ was 604.1 ppm at medical service facilities. The highest mean concentration of $NO_2$ was 0.036 ppm at indoor parking place. The geomean concentration of HCHO was $3.6\;{\mu}g/m^3$ in all facilities and the highest was $631.8\;{\mu}g/m^3$ at art gallery. The geomean concentration of VOCs (5 species) was $24.14\;{\mu}g/m^3$ in all facilities and toluene was the highest material of $15.30\;{\mu}g/m^3$, followed by xylene, ethylbenzene, benzene and styrene. The highest mean concentration of TSC was $625.3\;CFU/m^3$ at jjimjilbang, followed by childcare facilities, medical service facilities and large commercial building. The highest of asbestos was 0.0072 each/cc at childcare facilities and that of radon was 1.41 pCi/L at art gallery. PM10 showed positive correlations to TSC with $R^2\0.5332$ by lognormal equation at childcare facilities. CO2 showed positive correlations to CO at childcare facilities and indoor parking place. Lognormal equation fitted to the VOCs data more than normal equation in all facilities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.536-539
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• 2018

In this paper, we propose a system that analyzes environment information by using deep learning and then provides a suitable environment for users by predicting environmental information change. As the level of living improves, interest in improving the quality of life is increasing. In particular, as the air quality deteriorated due to the recent occurrence of dust, smog, fine dust, and ultrafine dust, the indoor air quality as well as the outdoor air became a serious problem. The increase of indoor pollution due to the lack of ventilation and the use of chemicals is a serious problem for modern people who have a lot of indoor living. In order to solve this indoor air pollution, a system has been proposed that measures the state of air quality through sensors and maintains proper temperature and humidity. However, existing system has a difficulty to apply most of the atmospheric environment information to various users depending on sensors only. The system proposed in this paper predicts the indoor environment by analyzing the indoor pollution information collected through the sensor using the deep learning. Then, the predicted indoor environment is modeled and learned in this system, and the environment suitable for the user is suggested. Afterwards, the system receives feedback from the user and repeats the process of re-learning the proposed environment so that it can create the optimal environment for the user.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1204-1210
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• 2014

New control methods are proposed for indoor air quality by removing fine airborne dust-particles. As suspended fine dust-particles contain inorganic dust as well as fine organic bacteria, studies for simultaneous control of these contaminants are required. In this study, photocatalytic disinfection of indoor suspended microorganisms such as E. coli and Bacillus subtilis is performed by three types of photocatalysts with UVA irradiation. The UVA irradiation strength was controlled to the minimum $3{\mu}W/cm^2$, and ZnO, $TiO_2$, and ZnO/Laponite ball were used as the catalysts. The results indicate that E. coli was removed over 80 % after about 2 hours of reaction with UVA and all three types of photocatalysts, whereas only with UVA, around 50 % E. coli removal was obtained. Among the catalysts, ZnO/Laponite composite ball was found to have similar sterilizing capacity to $TiO_2$. However, in case of B. subtilis, which has thick cell wall in its spore state, disinfection was not effective under the low UVA irradiation condition, even with the catalysts. Further studies need to figure out the optimal UVA irradiation ranges as well as photocatalysts doses to control airborne dust, to provide healthy clean air environment.

• Journal of Korean Society of Environmental Engineers
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• v.38 no.8
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• pp.459-467
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• 2016

In order to assess the pollution status and distribution characteristics of PM and PM-bound species, PM10 samples were collected using mini-volume air sampler at the subway cabin in Daejeon city. Measurements of about 24 elements including toxic metals (e.g., As, Cr, Mn, V, Zn) in PM10 were made by instrumental neutron activation analysis and X-ray fluorescence. The average PM10 concentration was $59.3{\pm}14.5{\mu}g/m^3$ in the subway cabin with a range of 42.2 to $97.4{\mu}g/m^3$, while the associated elemental concentrations were varied in the range of $10^{-3}$ to $10^5ng/m^3$. It was found that the concentration of Fe ($12.5{\mu}g/m^3$) was substantially higher than any other element. The Fe concentration was apportioned by about 20% of the PM10 concentration. The results of factor analysis indicate that there are no more than six sources in the cabin (e.g., brake-nonferrous metal particle, resuspended rail dust, fuel combustion, vehicle exhaust, black carbon, Cr-related).

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.13-15
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• 2016

에너지 소비가 큰 건물은 내부 온/습도, 이산화탄소 농도, 미세먼지 농도 등의 일정 공기 질을 유지하면서 에너지 비용을 최소화할 수 있는 제어계획을 수립하는 것이 필요하다. 기존 건물에서 실내 환경의 운영은 설정된 실내 환경 값을 기준을 벗어나면 설비 기기를 제어하는 방식으로 이루어진다. 이는 단 시간에 고에너지를 투입하여 장비를 가동시키므로 에너지 소모가 크며 peak 전력이 높아 에너지 비용이 크다는 문제가 있다. 따라서 온도를 포함한 환경이 변해가는 상황을 예측하고 사전에 에너지 사용 계획을 수립하여 관리 제어를 수행함으로써 예열부하 등의 불필요한 에너지 손실을 절감하려 한다. 이를 위해 실내 환경이 변화하는 것을 예측하고 후보 제어계획으로 제어를 수행할 때 소요되는 에너지가 어느 정도인지 시뮬레이션하여 제어계획의 적합도를 평가한다. 기존 EnergyPlus와 같은 시뮬레이션 도구는 모델이 복잡하여 시뮬레이션에 많은 시간이 필요하기 때문에 환경 변화를 반영하기 위해 주기적으로 재수립되는 수많은 제어계획 데이터를 단시간에 시뮬레이션하기에 부적합하다. 본 논문에서는 빠른 시뮬레이션을 위해 실제 운영 데이터와 에뮬레이션을 통해 획득한 운영 데이터를 기반으로 학습 알고리즘을 이용하여 제어계획 적용 시의 미래 상황을 예측한다.

• 건강소식
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• v.30 no.12 s.337
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• pp.22-23
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• 2006

기온이 내려가기 시작하면 사람들은 몸과 마음도 움츠러들어 바깥 생활을 줄이고 행동이 둔해질 뿐만 아니라 일상생활에 제약을 받기 마련이다. 그렇다 보니 자연스럽게 추위를 피해 많은 사람이 따뜻한 실내에서만 생활을 하게 되는데, 밀폐된 실내는 미세한 먼지나 오염 물질로 차게 되고, 감기 바이러스를 가진 사람들이 그 공간으로 들어올 경우 쉽게 감염이 된다. 따뜻한 실내에서만 지내던 사람들이 추운 외부에 노출되면서 둔한 활동으로 말미암아 그렇지 않아도 미끄러운 길에서 넘어져 다치는 사고가 잦아지고, 고혈압이나 당뇨가 있는 사람들은 뇌졸중 등의 사고를 당하기도 한다. 또 추위와 이에 따른 밀폐된 생활, 그리고 건조한 환경 속에서 독감, 감기, 천식 등을 포함한 호흡기 질환에 쉽게 걸리기도 한다. 그렇지만 우리 몸은 환경의 변화에 못지않은 적응력을 가지고 있기 때문에 아무리 추운 겨울이라도 평소 규칙적인 생활 습관을 통하여 활기차고 건강한 생활을 할 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1687-1692
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• 2019

In the era of the fourth industrial revolution, advanced technologies such as the Internet of Things(IoT) and big data are emerging. However, the level of application of IoT to indoor environment is very weak. Therefore, it is necessary to develop a system for analyzing air pollutants or indoor air quality through real-time monitoring using the IoT. This paper implements a system that measures indoor environmental values using Arduino and various sensors, and stores the information obtained from various sensors into a database of server. The information stored in the server was built as a database and utilized in the ventilation system or air cleaner installed in the home or company's room. In the proposed system, it is possible to check the immediate indoor environmental condition through the LED status display of the monitoring sensor module while reducing the cost of the sensor used to implement IoT technology.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.77-82
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• 2022

The effect of indoor air pollution on the human body is greater and more dangerous than outdoor air pollution. In general, a person stays indoors for a long time, and in a closed room, pollutants are continuously accumulated and the polluted air is better delivered to the lungs. Especially in the case of young children, it is very sensitive to indoor air and it is fatal. In addition, methods to reduce indoor air pollution, which cannot be ventilated with more frequent indoor activities and continuously increasing external fine dust due to Covid 19, are becoming more important. In order to improve the problems of the existing autonomous driving air purifying robot, this paper divided the map and Upper Confidence bounds applied to Trees(UCT) based algorithm to solve the problem of the autonomous driving robot not sterilizing a specific area or staying in one space continuously, and the problem of children who are vulnerable to indoor air pollution. We propose a space-adaptive autonomous driving air purifying robot for a green smart school that can be improved.

• Journal of the Korean Society of Visualization
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• v.22 no.1
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• pp.34-39
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• 2024

With the fine particulate matter (PM) poses a serious threat to public health and the environment. The ultrafine PM in particular can cause serious problems. This study investigates the effectiveness of a submicron dry fog system in removing fine PM. Two methods are used to create fine dust particles: burning incense and utilizing an aerosol generator. Results indicate that the dry fog system effectively removes fine dust particles, with a removal efficiency of up to 81.9% for PM₁₀ and 61.9% for PM_2.5 after 30 minutes of operation. The dry fog, characterized by a mean size of approximately 1.5 ㎛, exhibits superior performance in comparison to traditional water spraying methods, attributed to reduced water consumption and increased contact probability between water droplets and dust particles. Furthermore, experiments with uniform-sized particles which sizes are 1 ㎛ and 2 ㎛ demonstrate the system's capability in removing ultrafine PM. The proposed submicron dry fog system shows promise for mitigating fine dust pollution in various industrial settings, offering advantages such as energy consumption and enhanced safety for workers and equipment.

• Journal of Internet of Things and Convergence
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• v.3 no.1
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• pp.25-31
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• 2017

In the future a future type new city and the latest apartment will be built, and the IoT technology will be constructed and combined in large numbers indoors of ordinary households. An air cleaner that makes people in it comfortable is built in, and the suction port that sucks in the air of the air purifier and the discharge port that discharges it are set up around the room and come out. However, this is impossible to adjust because there is only a central switch on / off. Also, consuming a lot of electricity so that many electricity charges are caused by huge electricity consumption will eventually be a waste of resources. This would like to be adjusted and driven through the fine dust detection device to the room built-in air purifier and that has provided a solution to save electricity. In addition, since it exists in and around the room, you can grasp the position of the person and connect to the home network to enhance the convenience. Also, through analysis of the air, we provide a health-check solution that checks the user's health and adjusts it appropriately.