• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1263-1270
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• 2021

Recently, in the food culture life, the trend of consumers cooking is changing, and the use rate of induction cookware is increasing. Therefore, in this study, we propose the development of a functional auxiliary device to improve the safety of induction cookware to improve the convenience of cooking according to the increase in the cooking population. The proposed device is linked with IoT through the app. Through the app, the device can control the induction heating power adjustment and time reservation. In addition, an ultrasonic sensor is used to prevent the container from overflowing during cooking, and the user can safely use induction through the fine dust sensor. The implemented device conducts research assuming the actual cooking situation. Finally, it was confirmed that the user's fatigue was reduced during cooking through the device and the user's safety was improved in emergency situations such as overcooking or overflowing of water.

• 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.

• KEPCO Journal on Electric Power and Energy
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• v.5 no.1
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• pp.33-38
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• 2019

Thermal power generation accounts for the highest percentage of domestic power generation, among which coal-fired boiler generation accounts for the highest percentage. Coal boilers generate harmful substances and fine dust during coal combustion and have a serious effect on air pollution. So, fluidized-bed boilers have been introduced as eco-friendly coal boilers. It uses a fluid medium which affect the combustion temperature of coal. Because of it fluidized-bed boilers emit less pollutants than original one. Water-wall tubes play an important role in this fluidized bed boiler. Due to the fluid medium, the wall damage is more severe than the existing boiler. However, there is no quantitative maintenance technique in Korea yet. Remote field eddy current testing is a non-destructive evaluation technique that is often used for inspection of inner and outer wall of tube. it can inspect with non-contact and high speed. However, it is an inspection that proceeds from inside the pipe, and the water-wall tube is not able to enter the interior. In this study, we designed and simulated an external remote field eddy current sensor suitable for water-wall tube of a fluidized - bed boiler using simulations. By obtaining a signal similar to the existing remote field eddy current test, the criteria for the external remote field eddy current sensor design can be presented.

• 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 Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.157-163
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• 2020

Recently, fine dust has been known to cause cardiovascular diseases here, raising interest in ways to reduce emissions by efficiently using fuel from cars that cause air pollution. Accordingly, a driving assistance system was developed to save fuel by eco-driving and improve the driver's bad driving habits. The system was developed using raspberry pi, arduino and Android. Using RPM, speed, fuel injection information obtained from OBD-II, and gyro-sensor values, Fuel-Cut is induced to create an optimal inertial driving environment. It also provides various information system such as weather, driving environment, and preventing drowsy driving through GUI and voice recognition functions. It is possible to check driving records and vehicle fault information using Android application and has low overhead for message transmission using MQTT protocol optimized for IoT environment.

• Smart Media Journal
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• v.7 no.4
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• pp.44-51
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• 2018

The purpose of this research is to collect atmospheric environmental information at specific altitudes in a range of 0 to 1 km above the surface and to monitor it using drones. The corresponding temperature and humidity were measured with the meteorological factors, and the amounts of fine dust and $CO_2$ were observed by the environmental factors so that they could receive the normal values. Monitoring the status of atmospheric gas emission in specific enterprises, industrial complexes and regions through the measurement is meant to help establish policies to reduce pollution factors. In conventional means previously practiced, exhaust gas detection accompanies a great deal of risks in terms of safety because the surveyor is directly exposed to the source of contamination such as the holes installed in the chimney. However, in our proposed method, the drone can collect information in a wide range under safe circumstances, which can be utilized through wide industrial areas.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.294-305
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• 2022

Recently, drones have been widely used to solve environmental problems such as environmental protection and natural disaster monitoring. This study focuses on the problem of the search altitude of drones when using drones to search for air pollution in order to maintain the urban air environment. In particular, when exploring air pollution in cities using drones, various experiments are conducted to determine the appropriate search altitude for each air pollution source and each communication module. Through the experiment, the maximum measurable altitude for the most common air pollutants, such as CO (carbon monoxide), NO₂ (nitrogen dioxide), O₃ (ozone), and P10, P2.5 (fine dust), was identified, and the effective search altitude for each air pollution source was determined. As a result of the experiment, three types of drone search altitudes including legally measurable altitudes were suggested. The communication module measurable altitude was 60m to 120m depending on the communication module, and the effective measurable altitude was analyzed from 10m to 100m.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.21-27
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• 2022

As society develops and becomes more complex, new and diverse types of disasters such as fine dust and infectious diseases are occurring. However, in the past, there was no PA(Public Address) system that provided accurate information to prepare for such a disaster. In this paper, we propose a public address system that automatically broadcasts an alarm by analyzing polluted air quality data collected from public data and IoT sensors. The warning level varies depending on the air quality, and the information provided by public data may show a significantly different result from the guide area due to various factors such as the distance from the measuring station or the wind direction. To compensate for this, we are going to propose a method for broadcasting by comparing and analyzing data obtained from public data and data from on-site IoT sensors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.546-549
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• 2022

In recent years, to prevent the increase in accidents at industrial sites, various innovative technologies from the 4th industrial era have been incorporated into the construction administration to promote the advancement of safety management. As a result, smart safety management systems using intelligent monitoring that prevent and manage risks in industrial sites in real time are attracting attention. Smart safety management systems provide users with real-time, remote monitoring of factors such as noise, gas concentration fine dust concentration, building material quality, building tilt, and RFID-based worker access through sensors located everywhere. This paper presents a method for collecting and monitoring various data for smart safety management systems via Zigbee communication using Raspberry Pi.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.397-399
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• 2021

The increase in single-person households and the development of Home IoT technology make it important to improve the convenience of the residential environment. In addition, the increase in indoor activities caused by COVID-19 calls for the development of products to make life more convenient for single-person households. This trend of increased indoor activity has made it easier to interact with the current residential environment than before, and as a result, the need to develop technology for Home IoT is emerging. Therefore, the Home IoT system will be developed to monitor the information needed to maintain an ideal indoor environment such as temperature, humidity, and fine dust. The system will also interact with users, and propose a system that improves safety in indoor activities by equipping the home with IoT sensors for preventing safety accidents such as gas leakage and fire.