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    Autonomous Decentralized System Identification by Markov Parameter Estimation Using Distributed Smart Wireless Sensor Networks

    Source: Journal of Engineering Mechanics:;2012:;Volume ( 138 ):;issue: 005
    Author:
    Junhee Kim
    ,
    Jerome P. Lynch
    DOI: 10.1061/(ASCE)EM.1943-7889.0000359
    Publisher: American Society of Civil Engineers
    Abstract: Decentralized data processing has the benefit of improving wireless monitoring system scalability, reducing the amount of wireless communications, and reducing overall power consumption. In this study, a system identification strategy for single-input multi-output (SIMO) subspace system identification is proposed based on Markov parameters. The method is specifically customized for embedment within the decentralized computational framework of a wireless sensor network. By using the computational resources of wireless sensors, individual sensor nodes perform local data processing to identify the Markov parameters of a structural system. The data storage and wireless communication requirements of Markov parameters are significantly less than that required by the original raw data, resulting in the preservation of scarce system resources such as communication bandwidth and battery power. Then, the estimated Markov parameters are wirelessly communicated to a wireless sensor network base station where the global structural properties are assembled by execution of the eigensystem realization algorithm, an indirect subspace system identification method. The proposed strategy is evaluated using input-output and output-only data recorded during dynamic testing of a cantilevered balcony in a historic building (Hill Auditorium, Ann Arbor, MI).
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      Autonomous Decentralized System Identification by Markov Parameter Estimation Using Distributed Smart Wireless Sensor Networks

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    http://yetl.yabesh.ir/yetl1/handle/yetl/60831
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    • Journal of Engineering Mechanics

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    contributor authorJunhee Kim
    contributor authorJerome P. Lynch
    date accessioned2017-05-08T21:43:45Z
    date available2017-05-08T21:43:45Z
    date copyrightMay 2012
    date issued2012
    identifier other%28asce%29em%2E1943-7889%2E0000368.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/60831
    description abstractDecentralized data processing has the benefit of improving wireless monitoring system scalability, reducing the amount of wireless communications, and reducing overall power consumption. In this study, a system identification strategy for single-input multi-output (SIMO) subspace system identification is proposed based on Markov parameters. The method is specifically customized for embedment within the decentralized computational framework of a wireless sensor network. By using the computational resources of wireless sensors, individual sensor nodes perform local data processing to identify the Markov parameters of a structural system. The data storage and wireless communication requirements of Markov parameters are significantly less than that required by the original raw data, resulting in the preservation of scarce system resources such as communication bandwidth and battery power. Then, the estimated Markov parameters are wirelessly communicated to a wireless sensor network base station where the global structural properties are assembled by execution of the eigensystem realization algorithm, an indirect subspace system identification method. The proposed strategy is evaluated using input-output and output-only data recorded during dynamic testing of a cantilevered balcony in a historic building (Hill Auditorium, Ann Arbor, MI).
    publisherAmerican Society of Civil Engineers
    titleAutonomous Decentralized System Identification by Markov Parameter Estimation Using Distributed Smart Wireless Sensor Networks
    typeJournal Paper
    journal volume138
    journal issue5
    journal titleJournal of Engineering Mechanics
    identifier doi10.1061/(ASCE)EM.1943-7889.0000359
    treeJournal of Engineering Mechanics:;2012:;Volume ( 138 ):;issue: 005
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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