• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1629-1639
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• 2009

This paper describes the environmental monitoring system for supporting comfortable subways based on USN. Our development system includes the sensor field based on integrated sensor, monitoring system for supporting the local and remote monitoring and middle-ware performs the collecting, analyzing, and storing the data. In this paper, we installed the temperature, humidity, micro-dust sensor and water-level sensor for supporting the rail-roads and make up the integrated sensor enables to reuse the analog device from 4~20mA output with connection of wireless sensor device. Middleware includes the modules of collecting, analysis, and storing the data and monitoring system supports the local for administrator and remote monitoring for citizen services based on web. The middleware and monitoring in this paper is comprised of some components can reuse and support the change of application and sensors. Our development system supports the mobility of sensor devices and distributes system. Data collection and management function supported by middleware will use assessment.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1209-1211
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• 2017

창문을 열고 닫음에 있어서 사람의 신체에 따라, 창문의 위치에 따라 어려움이 발생한다. 또한 최근 대기 오염도가 심해지고 지구 온난화로 인한 한반도 기후 변화로 국지성 집중호우의 빈도가 증가하고 있다. 이로 인해 사용자가 부재중일 때 창이 열려있다면 집 안으로 먼지가 들어올 것이고, 국지성 호우에 의해 비가 들어오는 경우가 생길 수 있다. 본 논문에서는 이 문제점들에 주목하여, 어플과 연계를 통한 원격 창문 개폐 장치를 고안하였다. 본 장치를 이용한다면 신체적인 한계가 있는 어린이, 노약자, 장애인과 같은 이들이 쉽게 창문을 개폐할 수 있을 것이다. 그리고 환기가 필요한 공장, 격납고 등의 높은 위치에 있는 창문을 쉽게 개폐할 수 있어 분진 폭발로 인한 안전사고 역시 예방하는 효과가 있을 것이다. 또한 사용자가 외출할 때 창문을 닫고 나오지 못한 경우 어플을 이용하여 원격으로 닫을 수 있으며, 이를 통해 밖에 황사, 미세먼지가 들어오는 것과 비가 들어오는 것을 차단할 수 있다. 마지막으로 열린 창을 닫는다는 것에서 방범의 효과 역시 가져올 수 있다.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.77-84
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• 2022

In this paper, we propose a system for monitoring air pollutants such as fine dust using an unmanned aerial vehicle capable of autonomous navigation. The existing air quality management system used a method of collecting information through a fixed sensor box or through a measurement sensor of a drone using a control device. This has disadvantages in that additional procedures for data collection and transmission must be performed in a limited space and for monitoring. In this paper, to overcome this problem, a GPS module for location information and a PMS7003 module for fine dust measurement are embedded in an unmanned aerial vehicle capable of autonomous navigation through flight information designation, and the collected information is stored in the SD module, and after the flight is completed, press the transmit button. It configures a system of one-stop structure that is stored in a remote database through a smartphone app connected via Bluetooth. In addition, an HTML5-based web monitoring page for real-time monitoring is configured and provided to interested users. The results of this study can be utilized in an environmental monitoring system through an unmanned aerial vehicle, and in the future, various pollutants measuring sensors such as sulfur dioxide and carbon dioxide will be added to develop it into a total environmental control system.