• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.160-169
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• 2014

Along the era of "Internet of Things (IoT)" in which the physical objects can be connected to the Internet, a smart power management system is proposed in this paper. This system consists of two modules, an electrical outlet device embedding the Wi-Fi capabilities and a web server supporting the management as well as monitoring of power consumptions. Also, with the proposed system, a customer can easily check the status of power consumption and turn on/off the sockets through the developed mobile website. The proposed system is designed to avoid firewall or PC setting problems involving a user's inconvenience.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.159-165
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• 2017

As increasing the number of the elderly people, various IT-based home systems for the elderly are actively studied. However, while most systems for the elderly focus on systems detecting emergency cases, systems considering energy reduction in the elderly housing are rarely found. In fact, the systems for the elderly housing consumes rather more electric power for their functions. In this paper, a emergency monitoring system including the functions to reduce energy useage is studied and it is implemented. Though the system, we can achieve both of future-oriented functions for the aged and green society, which are emergency monitoring and energy reduction.

• Journal of the Korea Society of Computer and Information
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• v.23 no.2
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• pp.63-70
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• 2018

Safety management agent manages the risk behavior of the worker with the naked eye, but there is a real difficulty for one the agent to manage all the workers. In this paper, IoT device is attached to a harness safety belt that a worker wears to solve this problem, and behavior data is upload to the cloud in real time. We analyze the upload data through the deep learning and analyze the risk behavior of the worker. When the analysis result is judged to be dangerous behavior, we designed and implemented a system that informs the manager through monitoring application. In order to confirm that the risk behavior analysis through the deep learning is normally performed, the data values of 4 behaviors (walking, running, standing and sitting) were collected from IMU sensor for 60 minutes and learned through Tensorflow, Inception model. In order to verify the accuracy of the proposed system, we conducted inference experiments five times for each of the four behaviors, and confirmed the accuracy of the inference result to be 96.0%.

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

• Journal of Environmental Health Sciences
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• v.48 no.3
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• pp.131-137
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• 2022

Public interest has been increasing the focus on the management of exposure to pollutants and the related health effects. This study reviewed exposure assessment methodologies and addressed future directions. Exposure can be assessed by direct (exposure monitoring) or indirect approaches (exposure modelling). Exposure modelling is a cost-effective tool to assess exposure among individuals, but direct personal monitoring provides more accurate exposure data. There are several population exposure models: stochastic human exposure and dose simulation (SHEDS), air pollutants exposure (APEX), and air pollution exposure distributions within adult urban population in Europe (EXPOLIS). A South Korean population exposure model is needed since the resolution of ambient concentrations and time-activity patterns are country specific. Population exposure models could be useful to find the association between exposure to pollutants and adverse health effects in epidemiologic studies. With the advancement of sensor technology and the internet of things (IoT), exposure assessment could be applied in a real-time surveillance system. In the future, environmental health services will be useful to protect and promote human health from exposure to pollutants.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.178-180
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• 2021

In this paper, we design a system to monitor environmental data in fish farms in real-time and provide machine learning-based prediction services to prevent damage on fish farms caused by changes in the sea environment. The proposed system will install an IoT device module consisting of sensors that can measure hydrogen concentration, salinity, dissolved oxygen, and water temperature, which can be transferred to Cloud DB using LTE or LoRa communication technology and then monitor the real-time condition through a web or mobile application. In addition, it has a function to prepare for changes within the environment of fish farms by applying machine learning-based prediction technology using collected data.

• ETRI Journal
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• v.39 no.2
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• pp.202-212
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• 2017

With the advent of the Internet-of-Things paradigm, the amount of data production has grown exponentially and the user demand for responsive consumption of data has increased significantly. Herein, we present DART, a fast and lightweight stream processing framework for the IoT environment. Because the DART framework targets a geospatially distributed environment of heterogeneous devices, the framework provides (1) an end-user tool for device registration and application authoring, (2) automatic worker node monitoring and task allocations, and (3) runtime management of user applications with fault tolerance. To maximize performance, the DART framework adopts an actor model in which applications are segmented into microtasks and assigned to an actor following a single responsibility. To prove the feasibility of the proposed framework, we implemented the DART system. We also conducted experiments to show that the system can significantly reduce computing burdens and alleviate network load by utilizing the idle resources of intermediate edge devices.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.5
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• pp.287-298
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• 2016

This paper proposes an effective smart plug system capable of monitoring power, cutting off standby power and overload current. The key design concept is focused on measurement accuracy, self power consumption and controlling via smart phone application. The system is composed of several plugs and a hub, and adopts a star-topology-styled TDMA wireless protocol for communication between plug and hub. The test result shows that the implemented smart plug system meets K10026 regulation and is worth in electrical safety, energy saving, easy living.

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.517-524
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• 2020

Recently, many researches have been conducted to automatically recognize license plates of vehicles and use the analyzed information to manage stolen vehicles and track the vehicle. However, such a system must eventually be investigated by people through direct monitoring. Therefore, in this paper, the system of tracking a vehicle is implemented by sharing the information analyzed by the vehicle image among cameras registered in the IoT environment to minimize the human intervention. The distance between cameras is indicated by the node and the weight value of the weighted-graph, and the eigenvector centrality is used to select the camera to search. It demonstrates efficiency by comparing the time between analyzing data using weighted graph searching algorithm and analyzing all data stored in databse. Finally, the path of the vehicle is indicated on the map using parsed json data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.1060-1061
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• 2015

국내 공작기계 시장은 일본 화낙사 컨트롤러를 사용하는 비중이 80[%] 전후이며 그 비중이 높다고 할 수 있다. 최근, 기계가공 분야에서도 IoT(Internet of Thins) 환경을 도입하여 공장의 스마트화가 이루어지고 있다. 대부분의 공작기계를 소유하고 있는 산업현장에선 공작기계에 관한 모니터링 시스템에 대한 필요성을 인식하고 있으며, 공작기계의 가동률 체크 뿐만 아니라 가공패턴 및 가공시간 등을 견적서 자료로 활용하려하고 하고 있다. 이러한 요구조건을 충족시키기 위하여 가공기계 분야에도 ERP(Enterprise Resource Planning)시스템을 도입하여 생산 공정관리의 기초 데이터로 활용하고 있다. 이러한 생산 현장을 감안하여 공작기계에 대한 공정 모니터링 기능 및 스마트화가 무엇보 다 중요하다고 할 수 있다. 본 논문에서는 화낙(fanuc)에서 제공하는 FOCAS 라이브러리를 활용하여 공작기계 가동률에 대한 체크가 가능하도록 Wi-Fi 기반의 데이터 전송 모듈을 개발하여 공작기계 모니터링에 필요한 소형의 통신모듈을 제시하고자 한다.