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      Implementation of Intelligent Home Network and u-Healthcare System based on Smart-Grid

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      https://www.riss.kr/link?id=A104953920

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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      In this paper, we established ZIGBEE home network and combined smart-grid and u-Healthcare system. We assisted for amount of electricity management of household by interlocking home devices of wireless sensor, PLC modem, DCU and realized smart grid and u-Healthcare at the same time by verifying body heat, pulse, blood pressure change and proceeded living body signal by using SVM algorithm and variety of ZIGBEE network channel and enabled it to check real-time through IHD which is developed by user interface. In addition, we minimized the rate of energy consumption of each sensor node when living body signal is processed and realized Query Processor which is able to optimize accuracy and speed of query. We were able to check the result that is accuracy of classification 0.848 which is less accounting for average 17.9% of storage more than the real input data by using Mjoin, multiple query process and SVM algorithm.
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      In this paper, we established ZIGBEE home network and combined smart-grid and u-Healthcare system. We assisted for amount of electricity management of household by interlocking home devices of wireless sensor, PLC modem, DCU and realized smart grid an...

      In this paper, we established ZIGBEE home network and combined smart-grid and u-Healthcare system. We assisted for amount of electricity management of household by interlocking home devices of wireless sensor, PLC modem, DCU and realized smart grid and u-Healthcare at the same time by verifying body heat, pulse, blood pressure change and proceeded living body signal by using SVM algorithm and variety of ZIGBEE network channel and enabled it to check real-time through IHD which is developed by user interface. In addition, we minimized the rate of energy consumption of each sensor node when living body signal is processed and realized Query Processor which is able to optimize accuracy and speed of query. We were able to check the result that is accuracy of classification 0.848 which is less accounting for average 17.9% of storage more than the real input data by using Mjoin, multiple query process and SVM algorithm.

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      참고문헌 (Reference)

      1 황유진, "스마트 그리드를 위한확장 홈 네트워크 기반의 AMI 시스템 설계" 대한전자공학회 49 (49): 56-64, 2012

      2 김낙현, "ZigBee 기반의 u-Health 환경에서 안전한 의료 정보 전송을 위한 프로토콜 설계" 숭실대학교 대학원 2012

      3 S. Lee, "The design of webservices framwork support ontology based dynamic service composition" LNCS 3689 : 721-726, 2005

      4 Y.-W. Heo, "Study for smart grid convergence information technology of an apartment house remote metering system" Chungnam National University 2011

      5 K. R. Farrell, "Speaker recognition using neural networks and conventional classifiers" 2 : 194-205, 1994

      6 Simon Haykin, "Netral network(acompre hensive foundation)" Prentice Hall International 161-172, 1999

      7 J. W. Go, "Multigradient: a new neural network learning algorithm for pattern classification" 39 : 986-993, 2001

      8 Y.Kajitani, "Forecasting nonlinear time series with feed-forward netral networks: a case study of canadian lynx data" 24 : 105-117, 2005

      9 Y. -T. Ko, "A design and implementation of AMI(advanced metering infrastructure)system based on home network for smartgrid" Changwon National University 2010

      1 황유진, "스마트 그리드를 위한확장 홈 네트워크 기반의 AMI 시스템 설계" 대한전자공학회 49 (49): 56-64, 2012

      2 김낙현, "ZigBee 기반의 u-Health 환경에서 안전한 의료 정보 전송을 위한 프로토콜 설계" 숭실대학교 대학원 2012

      3 S. Lee, "The design of webservices framwork support ontology based dynamic service composition" LNCS 3689 : 721-726, 2005

      4 Y.-W. Heo, "Study for smart grid convergence information technology of an apartment house remote metering system" Chungnam National University 2011

      5 K. R. Farrell, "Speaker recognition using neural networks and conventional classifiers" 2 : 194-205, 1994

      6 Simon Haykin, "Netral network(acompre hensive foundation)" Prentice Hall International 161-172, 1999

      7 J. W. Go, "Multigradient: a new neural network learning algorithm for pattern classification" 39 : 986-993, 2001

      8 Y.Kajitani, "Forecasting nonlinear time series with feed-forward netral networks: a case study of canadian lynx data" 24 : 105-117, 2005

      9 Y. -T. Ko, "A design and implementation of AMI(advanced metering infrastructure)system based on home network for smartgrid" Changwon National University 2010

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2022 평가예정 신규평가 신청대상 (신규평가)
      2021-12-01 평가 등재후보 탈락 (계속평가)
      2020-12-01 평가 등재후보로 하락 (재인증) KCI등재후보
      2017-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2013-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2012-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2010-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.45 0.45 0.35
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.28 0.25 0.24 0.05
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