• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.4
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• pp.412-417
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• 2022

Capacitive-type humidity sensors with a high sensitivity and fast response/recovery times have attracted a great attention in non-contact respiration biological signal monitoring applications. However, complicated fabrication processes involving high-temperature heat treatment for the hygroscopic film is essential in the conventional ceramic-based humidity sensors. In this study, a non-toxic ceramic/metal halide (BaTiO₃(BT)/NaCl) humidity sensor was prepared at room temperature using a solvent-free aerosol deposition process (AD) without any additional process. Currently prepared BT/NaCl humidity sensor shows an excellent sensitivity (245 pF/RH%) and superior response/recovery times (3s/4s) due to the NaCl ionization effect resulting in an immense interfacial polarization. Furthermore, the non-contact respiration signal variation using the BT/NaCl sensor was determined to be over 700% by maintaining the distance of 20 cm between the individual and the sensor. Through the AD-fabricated sensor in this study, we expect to develop a non-contact biological signal monitoring system that can be applied to various fields such as respiratory disease detection and management, infant respiratory signal observation, and touchless skin moisture sensing button.

• Journal of Platform Technology
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• v.9 no.4
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• pp.42-50
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• 2021

Recently, the need to introduce smart farms is increasing in order to solve the problems of intensifying competition such as a decrease in rural population due to aging, a decrease in production, and the inflow of foreign agricultural products, and accordingly, the need for education is increasing. This paper is a study on the implementation of an Android-based IoT smart farm for education so that it can be used in a real environment by reducing the farm's smart farm system. To confirm that Android-based education can be applied in a real environment using the IoT smart farm for education, experiments were performed in automatic mode and manual mode using Bluetooth, Wi-Fi, and server/client communication methods. In the automatic mode, the current status can be checked in real time by receiving all data, and in the manual mode, commands are transmitted in real time using the received sensor data and remote control is performed. As a result of the experiment, it was possible to understand the characteristics of each communication method, and it was confirmed that remote monitoring and remote control of the smart farm using the Android App was possible.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.5
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• pp.459-465
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• 2022

This paper reports a method to use a wireless sensor network deployed in the field to real-time monitor soil moisture, warning when the moisture level reaches a specific value, and wirelessly controlling an additional device (LED or water supply system, etc.). In addition, we report all processes related to wireless irrigation system, including field deployment of sensors, real-time monitoring using a smartphone, data calibration, and control of additional devices deployed in the field by smartphone. A commercially available open-source Internet of Things (IoT) platform, NodeMCU, was used, which was combined with a 9V battery, LED and soil humidity sensor to be integrated into a portable prototype. The IoT-based soil humidity sensor prototype deployed in the field was installed next to a tree for on-site demonstration for the measurement of soil humidity in real-time for about 30 hours, and the measured data was successfully transmitted to a smartphone via Wifi. The measurement data were automatically transmitted via e-mail in the form of a text file, stored on the web, followed by analyses and calibrations. The user can check the humidity of the soil real-time through a personal smartphone. When the humidity of a soil reached a specific value, an additional device, an LED device, placed in the field was successfully controlled through the smartphone. This LED can be easily replaced by other electronic devices such as water supplies, which can also be controlled by smartphones. These results show that farmers can not only monitor the condition of the field real-time through a sensor monitoring system manufactured simply at a low cost but also control additional devices such as irrigation facilities from a distance, thereby reducing unnecessary energy consumption and helping improve agricultural productivity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1469-1476
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• 2022

Recently, many researches have been conducted to estimate blood pressure using ECG(Electrocardiogram) and PPG(Photoplentysmography) signals. In this paper, we designed and implemented a mobile system to monitor blood pressure in real time by using 1-D convolutional neural networks. The proposed model consists of deep 11 layers which can learn to extract various features of ECG and PPG signals. The simulation results show that the more the number of convolutional kernels the learned neural network has, the more detailed characteristics of ECG and PPG signals resulted in better performance with reduced mean square error compared to linear regression model. With receiving measurement signals from wearable ECG and PPG sensor devices attached to the body, the developed system receives measurement data transmitted through Bluetooth communication from the devices, estimates systolic and diastolic blood pressure values using a learned model and displays its graph in real time.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.19-27
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• 2022

In this study, we propose a language and platform to describe and manage the MLOps(Machine Learning Operations) workflow for time series data anomaly detection. Time series data is collected in many fields, such as IoT sensors, system performance indicators, and user access. In addition, it is used in many applications such as system monitoring and anomaly detection. In order to perform prediction and anomaly detection of time series data, the MLOps platform that can quickly and flexibly apply the analyzed model to the production environment is required. Thus, we developed Python-based AI/ML Modeling Language (AMML) to easily configure and execute MLOps workflows. Python is widely used in data analysis. The proposed MLOps platform can extract and preprocess time series data from various data sources (R-DB, NoSql DB, Log File, etc.) using AMML and predict it through a deep learning model. To verify the applicability of AMML, the workflow for generating a transformer oil temperature prediction deep learning model was configured with AMML and it was confirmed that the training was performed normally.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.1-8
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• 2022

In Korea, drones, which are at the core of the 4th industrial revolution, are used to promote Korean New Deal policies to digitalize the SOC. Overseas, the use of convergence sensors, such as thermal imaging cameras, on drones is increasing in various industrial fields. In this research, to improve pavement quality in highway bridge pavement construction, a thermal imaging camera was mounted on a drone to measure and verify the temperature of the pavement work section. Using a laser thermometer allows the partial measurement of pavement temperature. It was confirmed that the proposed method allows not only real-time temperature monitoring of the whole pavement work section but also uniformity verification by checking temperature distribution. The proposed method has the potential to control highway pavement quality and enable quick decision-making on traffic opening times by reducing the possibility of misjudging road opening times(pavement surface temperature ≦ 40℃).

• Fashion & Textile Research Journal
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• v.24 no.6
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• pp.694-706
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• 2022

This study focusses on the development of a motion-sensing smart leggings prototype with the help of a module that monitors motion using a fiber-type stretch sensor. Additionally, it acquires data on Electrocardiogram (ECG), respiration, and body temperature signals, for the development of smart clothing used in online exercise coaching and customized healthcare systems. The research process was conducted in the following order: 1) Fabrication of a fiber-type elastic strain sensor for motion monitoring, 2) Positioning and attaching the sensor, 3) Pattern development and three-dimensional (3D) design, 4) Prototyping 5) Wearability test, and 6) Expert evaluation. The 3D design method was used to develop an aesthetic design, and for sensing accurate signal acquisition functions, wearability tests, and expert evaluation. As a result, first, the selection or manufacturing of an appropriate sensor for the function is of utmost importance. Second, the selection and attachment method of a location that can maximize the function of the sensor without interfering with any activity should be studied. Third, the signal line selection and connection method should be considered, and fourth, the aesthetic design should be reflected along with functional verification. In addition, the selection of an appropriate material is important, and tests for washability and durability must be made. This study presented a manufacturing method to improve the functionality and design of smart clothing, through the process of developing a prototype of motion-sensing smart leggings.

• Smart Media Journal
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• v.12 no.7
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• pp.27-42
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• 2023

The development of cloud services and IoT technology has radically changed the cloud environment, and has evolved into a new concept called fog computing and F2C (fog-to-cloud). However, as heterogeneous cloud/fog layers are integrated, problems of access control and security management for end users and edge devices may occur. In this paper, an F2C-based IoT smart health monitoring system architecture was designed to operate a medical information service that can quickly respond to medical emergencies. In addition, a role-based service access control technology was proposed to enhance the security of user's personal health information and sensor information during service interoperability. Through simulation, it was shown that role-based access control is achieved by sharing role registration and user role token issuance information through blockchain. End users can receive services from the device with the fastest response time, and by performing service access control according to roles, direct access to data can be minimized and security for personal information can be enhanced.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.320-320
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• 2022

기후변화와 도시화로 인한 영향으로 도시의 폭우, 폭염을 비롯한 현상이 빈번하게 발생하고 있다. 특히 시가화 지역의 높은 불투수율은 도시홍수를 야기한다. 이러한 현상을 완화하기 위해 도시지역에서는 그린인프라와 저영향개발이 도입되고 있다. 그 중 지속가능한 발전을 위한 필수요소로 자리매김한 옥상녹화는 도시에 위치한 건물의 상층부와 내부의 온도를 낮춤으로써 에너지절감효과와 강우 시 빗물을 저류함으로써 우수유출 저감효과, 그리고 도시 공간 내 녹지도입으로 인한 이산화탄소 배출 저감 효과 등을 기대할 수 있다. 본 연구에서는 열섬현상완화와 유출 저감의 방안 중 하나인 옥상녹화(Green roof)의 효과를 정량적으로 평가하기 위해 콘크리트로 이루어진 동일한 제원의 실험동을 구축하고, 실험동 내외부의 온도, 습도, 강우, 풍속, 일사량 등의 기상자료를 측정할 수 있는 센서를 설치하였다. 각 실험동에서 측정된 기상요소를 Flux Profile Method를 적용하여 공간 내에 있는 열에너지의 총량인 순복사열을 구성하는 현열속, 잠열속, 토양열속(H, LE, G)을 산정하였다. 또한 에너지 평형에 따라 산정된 각 실험동의 열속과 지표면 복사량 관측자료을 정량적으로 비교하여 옥상녹화의 적용성을 평가하였다. 2021년 7월 21일부터 2021년 7월 28일 까지 모니터링한 결과 옥상녹화의 열 효율은 일반 콘크리트 지붕에 비해 실내온도가 최대 6.83℃ 낮게 나타나며 이는 여름철 건물 내부의 온도조절에 필요한 에너지 저감효과를 볼 수 있다. Flux Profile Method를 적용한 결과 옥상녹화의 전체 열속 중 현열속 28.5% 잠열속 70.7%, 토양열속 0.5% 의 복사량 분포를 보였고, 일반 콘크리트 지붕은 현열속 45.3%, 잠열속 38.6%, 토양열속 16.2% 으로 나타났다. 이와 같은 방법은 옥상녹화의 정량적 평가를 가능하게 함으로서 향후 기후변화 대응방안 및 전략 수립 시 옥상녹화의 온도저감효과 분석에 적극 활용할 수 있을 것으로 판단된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.168-170
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• 2022

The government enacted and promulgated the 'Severe Accident Punishment Act' in January 2021, and is enforcing the law for workplaces with 50 or more full-time workers. However, the number of industrial accident accidents in 2021 increased by 10.7% compared to the same period of the previous year, and chemical gas Safety accidents due to leaks and explosions also occur frequently. Therefore, in high-risk industrial sites, comprehensive Safety measures are urgently needed. In this study, BLE Mesh networking in industrial sites with poor communication environment apply technology. The complex sensor AIoT device recognizes a dangerous situation as a gas sensing value, voice, and motion value, and transmits it to the server. The server monitors the risk situation in real time through information value analysis and judgment through artificial intelligence LSTM algorithm and CNN algorithm for AIoT transmission information. Through this study, through the development of AIoT devices capable of gas sensing, voice and motion recognition, and AI-applied safety management systems, It will contribute to the expansion of the social safety net by expanding its application.