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

• Asian Journal of Atmospheric Environment
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• v.5 no.4
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• pp.247-262
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• 2011

This study assessed the health risks of childhood exposure to PBDEs via different possible pathways in children's facilities and indoor playgrounds. When PBDE contamination was measured, it was determined through multiple routes, including inhalation of indoor dust, dermal contact with product surfaces and children's hands, and incidental dust ingestion. Samples were collected from various children's facilities (playrooms, daycare centers, kindergartens, and indoor playgrounds) during summer (Jul-Sep, 2007) and winter (Jan-Feb, 2008). The hazard index (HI) was estimated for non-carcinogens, and PBDEs, such as TeBDE, PeBDE, HxBDE, and DeBDE, were examined. The sensitivity to the compounds did not exceed 1.0 (HI) for any of the subjects in any facility. However, current data about toxicity does not reflect effects that were fully sensitive in children, so there is uncertainty in the dose-response data. The contribution rates of PBDEs were 71.4 to 96.1% and 3.7 to 28.2% for intake and inhalation exposure, respectively, indicating that intake of floor dust and inhalation are the primary routes.

• Journal of Sensor Science and Technology
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• v.14 no.2
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• pp.125-130
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• 2005

Six modules composed of individual sensors and circuit systems for oxygen, temperature, humidity, volatile organic compounds, dust, carbon dioxide, in the indoor air quality control were examined to see their individual performances. Experimental results showed that all the six modules were in good linearity with individual concentration variations

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.217-218
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• 2021

With the prolonged Covid-19 epidemic, movement restrictions such as social distancing are prolonged, and as people stay indoors for a longer time, interest in indoor air pollution is increasing. Indoor air quality is not easily purified unlike outdoors. Among indoor building materials, paints and flooring contain formaldehyde that causes sick house syndrome and VOCs that contain carcinogenicity and harmfulness. For modern people who spend a lot of time living indoors for more than an hour, the occurrence of these harmful substances can be said to be fatal. In response to these risks, in July 2019, the government reinforced the standards for indoor air quality to protect the public's health by raising the detection standards for fine dust, ultrafine dust, and formaldehyde in indoor multi-use facilities. People use machines such as air purifiers to improve indoor air quality, or make efforts such as periodic ventilation. In order to reduce or support these other ancillary efforts more effectively, to reduce the generation of pollutants in the building itself, or to adsorb or purify pollutants in the air, use carbon black as an admixture to make a cement hardened body, and to grasp basic physical properties and adsorption capacity. And the result is as follows. As a result of the experiment to determine the appropriate amount of carbon black, it was confirmed that the more the amount of carbon black was added, the better it was in the formaldehyde emission test, but the tendency was not clear when measuring the flexural strength, so a further experiment to improve this is needed.

• Journal of the Korean Geotechnical Society
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• v.35 no.10
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• pp.67-78
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• 2019

The efficiency of suppressing fine dust was evaluated by conducting indoor and field experiments for the ground treated with EICP solution, which is an eco-friendly ground improvement method. In laboratory experiments, the EICP solution was prepared with inexpensive materials for the field applicability, and the optimal mixing ratio and optimal spraying volume of EICP solution were calculated. The optimum amount of calcium carbonate was shown when the ratio of urea/calcium chloride and white powder were 1.5 and 15 g/L, respectively. The optimum spraying amount of the EICP solution was $7L/m^2$ determined by fine dust suppression and cone tip resistance experiments. The spraying of water and EICP solution was conducted at the test-bed where dump trucks pass for the effect of suppressing fine dust of each method. The effective fine dust suppression method can be chosen depending on the situation of the site.

• 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 Korean Society of Environmental Engineers
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• v.34 no.3
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• pp.168-176
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• 2012

A total of 48 dust samples were collected from large shopping mall parking lots in Daegu metropolitan city in March 2011. Samples were sieved through a 100 ${\mu}m$ mesh and the concentration of 14 elements have been determined using by ICP after acid extraction. Results showed that Ca, Fe, K, Mg, Mn, Na and V were affected by natural sources while Cd, Cr, Cu, Ni, Pb and Zn were affected by anthropogenic sources. The measured values were remarkably higher in components from natural sources than in components from anthropogenic sources. Anthropogenic trace element concentrations of ground roof dust were higher than those of ground and underground indoor dust. A large percentage of trace elements came from natural sources rather than anthropogenic sources. The percentage composition of chemicals of ground roof dust were higher than those of ground and underground indoor dust. This study showed that investigated parking lots were rarely contaminated with hazardous heavy metals. The heavy metal pollution of ground roof were higher than those of ground and underground indoors. The correlation analysis among trace elements suggest that components in ground roof were more highly correlated than those in ground and underground indoor. Also anthropogenic trace element levels were well correlated with parking lot age and parking density.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.95-103
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• 2019

According to the Statistics Korea in 2017, the 10 leading causes of death contain a cardiac disorder disease, self-injury. In terms of these diseases, urgent assistance is highly required when people do not move for certain period of time. We propose an unusual event detection algorithm to identify abnormal user behaviors using dust, vision and activity sensors in their houses. Vision sensors can detect personalized activity behaviors within the CCTV range in the house in their lives. The pattern algorithm using the dust sensors classifies user movements or dust-generated daily behaviors in indoor areas. The accelerometer sensor in the smartphone is suitable to identify activity behaviors of the mobile users. We evaluated the proposed pattern algorithms and the fusion method in the scenarios.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.183-190
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• 2021

This study is a study on the implementation and operation of smart air cleaning system to improve indoor air quality. Recently, the problem of indoor air quality is getting serious due to various environmental factors. In this study, to improve the problems of indoor air quality, we implement an air cleaning system using IoT sensor. In particular, we proposed a system that can measure air pollution in real time and change different air flow paths according to pollution level. Through this, we examined efficient air quality improvement, extension of filter life, and system energy reduction. In addition, the main functions of the indoor air quality improvement system were constructed and prototypes were manufactured to confirm the operability. Finally, the utility of fine dust resolution through the implementation of the indoor air quality improvement system was examined.

• 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.