• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.234-235
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• 2018

본 논문에서는 R.Box와 연결되어 미세먼지정보를 무선으로 전송하는 아두이노 기반의 시스템을 제안한다. R.Box는 라즈베리파이를 기반으로 다양한 센서로 구성된 일반목적의 IoT 허브이다. 아두이노와 미세먼지센서를 이용해 미세먼지농도를 측정하고 데이터를 WiFi를 통해 R.Box로 전송한다. 아두이노와 R.Box는 TCP 소켓 방식으로 통신하며 이 시스템을 통하여 원하는 위치의 미세먼지정보를 확인할 수 있다.

• 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 Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.224-226
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• 2021

Recently, in order to increase the efficiency of the product production process, the automation of facilities and devices in the factory is in progress, and a smart factory is being built using ICT and IoT technologies. In order to organically solve many problems occurring in the smart factory, a system for monitoring the wireless communication function between facilities and devices and the manufacturing process environment of the smart factory is required. In this paper, we propose a monitoring system using a Bluetooth module, a temperature/humidity sensor and a fine dust sensor to remotely monitor the process environment of a smart factory. The proposed monitoring system collect Arduino sensor values wirelessly through Bluetooth communication.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.397-400
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• 2019

급격히 증가하는 자동차 수, 발전량 증가 등으로 인하여 미세먼지로 인한 환경오염이 심각한 사회문제로 대두되고 있는 실정이다. 50개가 넘는 국가들이 권고치 이상의 미세먼지로 인해 피해를 받고 있으며 각 피해국들은 미세먼지 저감 대책 및 발생을 최소화하기 위한 방안을 연구하고 있다. 하지만 현재 고정형 미세먼지 취득 드론으로는 다양한 포인트의 미세먼지 데이터를 수집하기 힘든 상황이며, 기존 드론을 활용한 방법에서 도 회전 날개의 영향으로 인해 정확한 데이터를 수집하기 힘든 실정이다. 본 논문에서는 드론과 특정 구조물을 활용한 미세먼지 수집 방법을 제안하고 이의 효율성을 보여주고자 한다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.17-25
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• 2020

In this paper, we deal with a system that provides temperature, humidity and fine dust data around infants to parents through Arduino and Raspberry Pi. It controls the operation of the mobiles remotely through applications. Android applications can perform the following functions. First, the infant's surrounding temperature, humidity and fine dust data are received. Second, mobile controls the smart mobile remotely. Third, recording and playing the voices of parents and enhancing the convenience of parenting. Through the experiment of measuring the operating time of the remote control module, it was confirmed that the application can quickly access the system. Existing products on the market do not provide environmental information around the infants and application that has various functions. The system covered in this paper is expected to improve child-rearing convenience by providing parents environmental information around infants, remotely controllable function and convenient functions of the application.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.83-95
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• 2019

Recently, a variety of IoT data is collected by attaching geosensors to many vehicles that are on the road. IoT data basically has time and space information and is composed of various data such as temperature, humidity, fine dust, Co2, etc. Although a certain sensor data can be retrieved using time, latitude and longitude, which are keys to the IoT data, advanced search engines for IoT data to handle high-level user queries are still limited. There is also a problem with searching large amounts of IoT data without generating indexes, which wastes a great deal of time through sequential scans. In this paper, we propose a unified spatial index model that handles both grid and trajectory queries using a cell-based space-filling curve method. also it presents a visualization method that helps user grasp intuitively. The Trajectory query is to aggregate the traffic of the trajectory cells passed by taxi on the road searched by the user. The grid query is to find the cells on the road searched by the user and to aggregate the fine dust. Based on the generated spatial index, the user interface quickly summarizes the trajectory and grid queries for specific road and all roads, and proposes a Web-based prototype system that can be analyzed intuitively through road and heat map visualization.

• 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.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1697-1707
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• 2021

On 19 February 2020, the 2nd Geostationary Ocean Color Imager (GOCI-II), a maritime sensor of GEO-KOMPSAT-2B, was launched. The GOCI-II instrument expands the scope of aerosol retrieval research with its improved performance compared to the former instrument (GOCI). In particular, the newly included UV band at 380 nm plays a significant role in improving the sensitivity of GOCI-II observations to the absorbing aerosols. In this study, we calculated the aerosol index and detected absorbing aerosols from January to June 2021 using GOCI-II 380 and 412 nm channels. Compared to the TROPOMI aerosol index, the GOCI-II aerosol index showed a positive bias, but the dust pixels still could be clearly distinguished from the cloud and clear pixels. The high GOCI-II aerosol index coincided with ground-based observations indicating dust aerosols were detected. We found that 70.5% of dust and 80% of moderately-absorbing fine aerosols detected from the ground had GOCI-II aerosol indices larger than the 75th percentile through the whole study period.

• 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.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.142-149
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• 2011

The basic of installing smart air cleaner in railway system is improvement of continuous real time sensing technologies. And building smart air cleaner in railway system with IT is needed. When smart air cleaner in railway system is developed, the installation of sensor which is for measuring air quality in the passenger room and setting revolution cycle of filter which is for removing fine dust is very important. In order to install it in a train which is now running, after making of standard test certification and verification of product's stability with enough self-test, application test will be performed.