• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.182-183
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• 2008

환기시설이 열악한 도시 지하철역 내의 대기환경은 지상보다 열악할 수밖에 없다. 현재 지하철역을 주로 사용하는 시민들의 안전을 보호 하고 지하철의 대기환경을 개선하기 위해 스크린 도어, 자동 제어 환기시설, 종합 영상 감지시스템 등 다양한 노력을 기울이고 있다. 하지만 일부 지하철역에 설치되어 있는 공기질 모니터링 시스템은 수입품에 의존하고 고가의 장비이므로 초기설치 비용뿐만 아니라 유지보수의 어려움을 겪고 있다. 본 논문에서는 이러한 문제점을 해결하기 위해 무선 센서 네트워크 기술을 적용하여 저가형 대기환경 모니터링 시스템을 개발했다. 이 시스템의 구성은 센서노드(ZED : ZigBee End Deice), 네트워크 코디네이터(ZCM : ZigBee Coordinator Modem), 수신서버로 구성된다. 지하철역 내부의 미세먼지, CO2, CO, 온습도, VOCs 데이터를 센싱할 수 있는 확장 센서보드를 설계한 후, 지하공간에서의 열악한 통신환경에서 QoS를 보장할 수 있도록 ZigBee 라우팅 기술을 이용한 센서노드(ZED)를 인터페이스하여 하나의 통합된 대기환경 센서 노드(ZED)를 개발했다. 또한 수신서버에 USB방식으로 연결되어 각각의 ZED로부터 데이터를 수신하는 센서노드(ZCM)과 전송된 데이터를 저장 및 처리하여 언제 어디서나 누구든지 인터넷을 통해 확인 가능하도록 지하철 대기환경 모니터링을 위한 수신서버를 개발했다.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.899-904
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• 2022

As the world becomes an aging society due to a decrease in the birth rate and an increase in life expectancy, a system for health management of the elderly population is needed. Among them, various studies on occupancy and activity types are being conducted for smart home care services for indoor health management. In this paper, we propose a random forest model that classifies activity type as well as occupancy status through indoor temperature and humidity, CO2, fine dust values and UWB radar positioning for smart home care service. The experiment measures indoor environment and occupant positioning data at 2-second intervals using three sensors that measure indoor temperature and humidity, CO2, and fine dust and two UWB radars. The measured data is divided into 80% training set data and 20% test set data after correcting outliers and missing values, and the random forest model is applied to evaluate the list of important variables, accuracy, sensitivity, and specificity.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.301-307
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• 2023

Deep learning requires building a deep neural network, collecting a large amount of training data, and then training the built neural network for a long time. If training does not proceed properly or overfitting occurs, training will fail. When using deep learning tools that have been developed so far, it takes a lot of time to collect training data and learn. However, due to the rapid advent of the mobile environment and the increase in sensor data, the demand for real-time deep learning technology that can dramatically reduce the time required for neural network learning is rapidly increasing. In this study, a real-time deep learning system was implemented using an Arduino system equipped with a fine dust sensor. In the implemented system, fine dust data is measured every 30 seconds, and when up to 120 are accumulated, learning is performed using the previously accumulated data and the newly accumulated data as a dataset. The neural network for learning was composed of one input layer, one hidden layer, and one output. To evaluate the performance of the implemented system, learning time and root mean square error (RMSE) were measured. As a result of the experiment, the average learning error was 0.04053796, and the average learning time of one epoch was about 3,447 seconds.

• 한국지구과학회:학술대회논문집
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• 2010.04a
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• pp.37-37
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• 2010

2008년 동아시아 대륙에서 발생기원이 다른 황사와 인위적 오염입자의 광역적 이동 사례를 NOAA위성 RGB 합성영상과 지상 TSP, PM10, PM2.5 질량농도 관측으로 구별하였다. 또한 Terra/Aqua 위성MODIS (MODerate Imaging Spectroradiometer) 센서의AOD (Aerosol Optical Depth)와 FW (Fine aerosol Weighting)를 통해 동아시아 지역에서 발생기원이 다른 대기 에어로졸의 분포와 입자 크기 특성을 분석하였다. 중국 북부와 몽골, 그리고 중국 황토고원에서 모래폭풍이 발생하여 광역적으로 이동하여 청원에 먼지입자(황사)로 영향을 주는 6 사례를 분석했다. 질량농도 TSP중 PM10 은 70%, PM2.5 는 16% 로 조대입자 (> $2.5{\mu}m$)의 비율이 큰 것은 사막과 반사막의 자연적 발생원에서 생성되었기 때문이다. 그러나, 모래 폭풍이 이동 과정에서 중국 동부의 산업 지역을 거쳐 유입 되는 사례에서는 TSP 중 PM2.5 가 23% 까지 증가하기도 했다. 중국 동부로부터 황해를 거쳐 한반도로 유입하고 있는 다른5사례는 TSP 중 PM10, PM2.5가 각각 82%, 65% 로 PM2.5 의 비율이 높았는데 인위적 오염입자의 영향 때문이다. 동아시아 지역에서 인위적 오염입자의 광역적 이동 사례에 대한 평균 AOD는 $0.42{\pm}0.17$로 황사에 의한 AOD ($0.36{\pm}0.13$)와 비교하여 대기 에어로졸에 대한 비율이 높게 나타났다. 특히, 중국 동부에서 황해, 한반도, 동해에 이르는 광역적 지역에 높은 AOD값이 분포했다. 인위적 오염입자의 사례는 FW가 평균 $0.63{\pm}0.16$로 모래폭풍의 이동 사례의 $0.52{\pm}0.13$ 보다 높은 값을 보이고 있어, 대기 에어로졸에 대한 인위적 미세 오염입자의 기여가 크게 나타나고 있었다.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.95-104
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• 2020

In this paper, we propose the real time indoor air quality monitoring and controlling platform on cloud using IoT sensor data such as PM10, PM2.5, CO2, VOCs, temperature, and humidity which has direct or indirect impact to indoor air quality. The system is connected to air ventilator to manage and optimize the indoor air quality. The proposed system has three main parts; First, IoT data collection service to measure, and collect indoor air quality in real time from IoT sensor network, Second, Big data processing pipeline to process and store the collected data on cloud platform and Finally, Big data analysis and visualization service to give real time insight of indoor air quality on mobile and web application. For the implication of the proposed system, IoT sensor kits are installed on three different public day care center where the indoor pollution can cause serious impact to the health and education of growing kids. Analyzed results are visualized on mobile and web application. The impact of ventilation system to indoor air quality is tested statistically and the result shows the proper optimization of indoor air quality.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.5
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• pp.80-88
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• 2020

With the issuance of one-week fine dust emergency reduction measures in March 2019, the public's anxiety about fine dust is increasingly growing. In order to assess the application of air purifying plant-based bio-filters to public facilities, this study presented a method for measuring pollutant reduction effects by creating an indoor environment for continuous discharge of particle pollutants and conducted basic studies to verify whether indoor air quality has improved through the system. In this study conducted in a lecture room in spring, the background concentration was created by using mosquito repellent incense as a pollutant one hour before monitoring. Then, according to the schedule, the fine dust reduction capacity was monitored by irrigating for two hours and venting air for one hour. PM₁₀, PM_2.5, and temperature & humidity sensors were installed two meters front of the bio-filters, and velocity probes were installed at the center of the three air vents to conduct time-series monitoring. The average face velocity of three air vents set up in the bio-filter was 0.38±0.16 m/s. Total air-conditioning air volume was calculated at 776.89±320.16㎥/h by applying an air vent area of 0.29m×0.65m after deducing damper area. With the system in operation, average temperature and average relative humidity were maintained at 21.5-22.3℃, and 63.79-73.6%, respectively, which indicates that it satisfies temperature and humidity range of various conditions of preceding studies. When the effects of raising relatively humidity rapidly by operating system's air-conditioning function are used efficiently, it would be possible to reduce indoor fine dust and maintain appropriate relative humidity seasonally. Concentration of fine dust increased the same in all cycles before operating the bio-filter system. After operating the system, in cycle 1 blast section (C-1, β=-3.83, β=-2.45), particulate matters (PM₁₀) were lowered by up to 28.8% or 560.3㎍/㎥ and fine particulate matters (PM_2.5) were reduced by up to 28.0% or 350.0㎍/㎥. Then, the concentration of find dust (PM₁₀, PM_2.5) was reduced by up to 32.6% or 647.0㎍/㎥ and 32.4% or 401.3㎍/㎥ respectively through reduction in cycle 2 blast section (C-2, β=-5.50, β=-3.30) and up to 30.8% or 732.7㎍/㎥ and 31.0% or 459.3㎍/㎥ respectively through reduction in cycle 3 blast section (C-3, β=5.48, β=-3.51). By referring to standards and regulations related to the installation of vegetation bio-filters in public facilities, this study provided plans on how to set up objective performance evaluation environment. By doing so, it was possible to create monitoring infrastructure more objective than a regular lecture room environment and secure relatively reliable data.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.92-99
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• 2021

Light-scattering fine particulate matter monitors can measure particulate matter (PM) concentrations in every second and can be designed in a portable size. They can measure the concentrations of various PM sizes (PM_1.0, PM_2.5, PM_4.0 and PM₁₀) with a single sensor. They measure the number and size of particulate matters and convert them to weight per volume (concentration). These devices show a large error for asian dust. This paper proposes a scheme that compensates the PM_2.5 concenstration error for asian dust by multiple linear regression machine learning in light-scattering PM monitors. This scheme can be effective with only two or three types of PM sizes. The experimental results compare a beta-ray PM monitor of national institute of environmental research and a light-scattering PM monitor during a month. The correlation coefficient (R²) of theses two devices was 0.927 without asian dust, but it was 0.763 due to asian dust during the entire experimental period and improved to 0.944 by the proposed machine learning.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.265-266
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• 2020

본 논문에서는 IoT 기술을 이용한 재난 안전 모니터링 시스템을 제안한다. 기술의 발전으로 개인 통신 기기에도 IoT가 범용적으로 사용되고 있으나 재난 안전 모니터링 시스템과의 접목은 쉽지 않다. 본 논문에서는 IoT 기술 기반 재난 안전 모니터링 시스템을 개인 통신 기기에 접목 시키기 위해 카카오톡 플랫폼을 이용한다. 재난 안전 모니터링 시스템은 평시에 IoT 센서로 온도, 강우량, 진동 및 미세먼지를 모니터링하여 정보를 제공한다. 만약 화재, 폭우, 지진 등의 자연 재난 등이 발생하면 메신저 플랫폼인 카카오톡을 통하여 재난정보를 재난 초기에 제공함으로써 피해를 최소화 하는 것을 목표로 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.571-573
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• 2018

The loss of data collected from the sensor node in the wireless sensor network environment is caused by the hidden node of the sensor node and power shortage. In order to solve these problems, researches have been actively carried out to maintain the network effectively, but there is no study on the situation where the maintenance of the network is impossible. Therefore, research is needed to infer lost data in situations where network maintenance is impossible. In this paper, use particulate matter data of specific cities to deduce lost data. Analyze the accumulated data through machine learning and identify the possibility of inferring lost data.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1231-1234
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• 2017

이 논문은 실내 환경을 개선하기 위한 창문 자동제어 시스템을 제안한다. 현대인들은 하루에 약 70% 이상을 실내에서 생활하고 있다. 이로 인해 실내 환경의 질이 매우 중요한 요소로 부각되면서 사람들의 관심이 크게 증가하고 있다. 이 논문에서는 아두이노, 라즈베리 파이 및 다양한 센서를 이용하여 실내 환경을 적정수준으로 유지하고 개선할 수 있는 창문 자동제어 시스템을 제안한다. 제안 시스템 구현을 위해 온습도 센서, 미세먼지 센서, 공기 질 센서, 모터 등을 이용한다. 또한 3D프린팅을 이용하여 제작한 프로토타입을 보인다.