• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.2
• /
• pp.271-278
• /
• 2021

This paper is a study on the implementation of smart filter system that recognizes the external environment and automatically removes pollutants according to pollution level. Recently, the occurrence of various pollutants in indoor and outdoor space has adversely affected the human body. Especially, various fine dust generated in the atmosphere becomes worse in closed residential space or office space. Although air pollution can be temporary lowered through ventilation, it is difficult to respond to fine dust changes in real time, and such problems become serious in the space where many people reside, such as at home or industry. Therefore, it is necessary to measure the pollution level of fine dust inside the residential space in real time and to reduce the pollution of indoor ventilation through automatic ventilation with the outside. To improve these problems, this paper proposes the implementation of smart filter system for external environment recognition. The structure of smart filter system that automatically measures air quality inside and outside, removes pollutants, implements the function, and confirms the operability by manufacturing prototypes. Finally, the effectiveness of the smart filter system for solving fine dust problems was examined.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.4
• /
• pp.829-835
• /
• 2017

Recently, the health issues triggered by fine dust matters occurred in higher frequency. Having adverse effects on health, particulate matters affect the human body indoors as well as outdoors. There is thus a need for a system to measure the concentration of particulate matters and control harmful particulate matters for human health in the indoor spaces where people live. The present study applied Internet of Things(IoT) technologies in order to increase the efficiency of the conventional fine dust measurement system. Especially, for the bidirectional communication environment, directly construct a separate server and applied to the system instead of a free cloud server also we used it directly in the school lab and home. When the proposed system is used in schools and homes, it can recognize the indoor environment quickly and it is expected that this will gradually contribute to the health of the individual. Users can also check the server data outside and deal with the current indoor situations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.344-346
• /
• 2018

Air pollution has become a social issue. Particularly, interest in fine dust is increasing. Various kinds of sensors are being used to measure fine dust. The most commonly used infrared detection dust sensors operate by sensing the diffraction of light through an infrared receiver and sensing the light reflected by the dust in the air. However, this method has a drawback in which accurate data analysis is difficult due to deviation caused by the noise during measurement. In order to overcome such drawbacks, in this thesis, a low pass filter algorithm of FIR(Finite Impulse Response) filter was designed and implemented.

• Fire Science and Engineering
• /
• v.32 no.3
• /
• pp.134-139
• /
• 2018

Extremely fine particles seriously affect people and are becoming a social problem. Conventional methods using the type of beta ray absorption are difficult to have real-time measurements and miniaturization for the acquisition of fine dust. In this paper, a light scattering method was used. The sensors were configured internally with semiconductor laser diodes for miniaturization, low cost and lightweight. The use of the FFT method makes it easier to separate fine dust according to size compared to conventional light scattering sensors. Bluetooth communication also allows the connection, monitoring and control of devices using smart phones.

• Korean Journal of Artificial Intelligence
• /
• v.8 no.2
• /
• pp.1-5
• /
• 2020

Children's home accidents are less common than in the past. However, safety accidents continue to occur due to carelessness of the parents. To solve the problem, there are fall prevention screens that can withstand the weight of children, and safety railings that can be adjusted directly to solve the problem. However, these have disadvantages such as stability, convenience, and damage to the landscape. In this paper, we developed an automatic safety accident prevention fence system that can be installed on a window using Arduino, eliminating the disadvantages of previous safety accident prevention products. This system measures the height of a person standing in front of the fence and the distance between the person and the fence with two infrared sensors and moves automatically using a motor. In addition, in accordance with the U-Healthcare society, users can check the temperature, humidity, and fine dust concentration of the external environment through mobile. Each information can be obtained through DHT 11 sensor, fine dust concentration sensor, and Bluetooth connected toArduino. These can help the user's health care.

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

• The Journal of Bigdata
• /
• v.6 no.2
• /
• pp.39-49
• /
• 2021

Recently, large and diverse weather data are being collected by sensors from various sources. Efforts to predict the concentration of fine dust through machine learning are being made everywhere, and this study intends to compare PM10 and PM2.5 prediction models using data from 840 outdoor air meters installed throughout the city. Information can be provided in real time by predicting the concentration of fine dust after 5 minutes, and can be the basis for model development after 10 minutes, 30 minutes, and 1 hour. Data preprocessing was performed, such as noise removal and missing value replacement, and a derived variable that considers temporal and spatial variables was created. The parameters of the model were selected through the response surface method. XGBoost, Random Forest, and Deep Learning (Multilayer Perceptron) are used as predictive models to check the difference between fine dust concentration and predicted values, and to compare the performance between models.

• Journal of IKEEE
• /
• v.23 no.3
• /
• pp.1088-1091
• /
• 2019

In this paper, a real-time monitoring system for environmental safety by living area is proposed. The proposed system is designed to measure radiation, fine dust and basic living information (temperature) using fixed and mobile measuring equipment, and constitutes a web database that stores data received from the equipment. It also develops web programs for displaying received data on PCs and mobile phones. The results of testing the performance of the system by an authorized testing agency showed that the radiation measurement range was measured in the range of $10{\mu}Sv/h$ to 10mSv/h, which is comparable to the world's highest level, and that the accuracy was measured between ${\pm}6.7$ and ${\pm}8.7$ percent of the measurement uncertainty was measured and normal operation at or below the international standard of ${\pm}15$ percent. In addition, the temperature test was conducted on a section of $-20^{\circ}C$ to $50^{\circ}C$ and normal operation was confirmed in response to the temperature change. Stability of radiated electromagnetic waves was ensured by a suitable judgment. The product's testing in general and high and low temperature environments for about four months after the prototype was made confirmed to be more than five years of durability. The measurement range and accuracy of fine dust sensors are compared with those of companies that measure the air environment, and the performance level is similar through the air quality measurement register.

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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.5-8
• /
• 2019

Recently, we have been demanding the comfort of the indoor environment due to environmental problems such as fine dust and yellow dust. In addition it is pursuing the most pleasant indoor environment for humans. In this study, we designed and implemented monitoring systems and mobile applications to enable information such as room temperature and humidity. Open source software and open source hardware are required to design and implement the systems proposed in this study. In this study, the systems implemented used a bluetooth module, a light sensor, and a temperature and humidity sensor. In the future, we expect to be a basic study of systems that can operate dehumidifiers, warm-air fans, fans and humidifiers using motors and optimize indoor environment.