• Title/Summary/Keyword: Push 메시지

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A UPnP Proxy System for the Remote Control of Home Appliances (댁내 장치의 원격 제어를 위한 UPnP 프록시 시스템)

  • 김동희;임경식;이화영;안준철;조충래;박광로
    • Journal of KIISE:Computing Practices and Letters
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    • v.10 no.4
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    • pp.337-350
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    • 2004
  • Because of a security problem and not enough IPv4 address space, the home network has been made up of private network, and it has been separated from Internet. This fact prevents people in Internet from controlling and monitoring home appliances. So, this paper designs and Implements the UPnP Proxy System which offers functions for users to control and monitor home appliances. When users are in the outside of the home network, they do not know which devices were connected in the home network because the advertisement messages of UPnP devices would not be delivered to the outside of the home network. Also, users cannot access devices directly, and their control messages are not delivered into the home network. So, this paper designs and implements the UPnP Proxy System to solve these problems. The merit of the system is that users can control and monitor home appliances in realtime using presentation web documents with the HTTP push technology.

Learning efficiency checking system by measuring human motion detection (사람의 움직임 감지를 측정한 학습 능률 확인 시스템)

  • Kim, Sukhyun;Lee, Jinsung;Yu, Eunsang;Park, Seon-u;Kim, Eung-Tae
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • fall
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    • pp.290-293
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    • 2021
  • In this paper, we implement a learning efficiency verification system to inspire learning motivation and help improve concentration by detecting the situation of the user studying. To this aim, data on learning attitude and concentration are measured by extracting the movement of the user's face or body through a real-time camera. The Jetson board was used to implement the real-time embedded system, and a convolutional neural network (CNN) was implemented for image recognition. After detecting the feature part of the object using a CNN, motion detection is performed. The captured image is shown in a GUI written in PYQT5, and data is collected by sending push messages when each of the actions is obstructed. In addition, each function can be executed on the main screen made with the GUI, and functions such as a statistical graph that calculates the collected data, To do list, and white noise are performed. Through learning efficiency checking system, various functions including data collection and analysis of targets were provided to users.

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