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Artificial intelligence in ISES measureserver® for remote experiment control

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dc.title Artificial intelligence in ISES measureserver® for remote experiment control en
dc.contributor.author Gerža, Michal
dc.contributor.author Schauer, František
dc.contributor.author Zelinka, Ivan
dc.relation.ispartof Advances in Intelligent Systems and Computing
dc.identifier.issn 2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 9783319074009
dc.date.issued 2014
utb.relation.volume 289
dc.citation.spage 411
dc.citation.epage 420
dc.event.title International conference on prediction, modeling and analysis of complex systems, NOSTRADAMUS 2014
dc.event.location Ostrava
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2012-06-23
dc.event.edate 2012-06-25
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer-Verlag
dc.identifier.doi 10.1007/978-3-319-07401-6_41
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-07401-6_41
dc.subject Change detection test en
dc.subject Fault diagnosis system en
dc.subject Hidden markov model en
dc.subject ISES en
dc.subject Measureserver® en
dc.subject Remote experiment en
dc.subject Sensor network en
dc.description.abstract The paper deals with the area of Internet School Experimental System (ISES) remote experiments in general and its core module called ISES Measureserver ®. In particular ISES Measureserver® is, in fact, a finite state machine, serving for the measured data accumulation, processing and providing communication in the server-client system. Recently, we replenished ISES Measureserver ® by a new functionality, namely diagnostics of the connected to the RE physical hardware, using the artificial intelligence solutions. In the introduction, the state of the art of ISES remote experiments is described. In the next chapter a consideration for the applying of proper artificial intelligence method to improve the Measureserver® reliability is made. We focused on the cognitive Fault Diagnosis System (FDS) intended for distributed sensor networks. FDS makes advantage of spatial and temporal relationship among sensors connected to RE physical hardware to give the information for reduction of the influence of failures, ill effecting the Measureserver® functioning. The lower layer uses Change Detection Test (CDT) based on Hidden Markov models (HMM) configured to detect variations in the relationships among couples of sensors. Changes in the HMM are detected by inspecting the corresponding likelihood. The output information provided by the CDT lower layer is then passed to the cognitive higher layer collecting information to discriminate among faults, changes in the environment and false positive. The intended improvement is the increase of the reliability, monitoring of the state and the fast remedy of the functioning of remote experiments in case of malfunction. Proposed diagnostics solution will contribute to improvement to remote experiments reliability and to a wider acceptance of this new ICT technology. © Springer International Publishing Switzerland 2014 en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1004571
utb.identifier.obdid 43872448
utb.identifier.scopus 2-s2.0-84927654304
utb.source d-scopus
dc.date.accessioned 2015-05-28T11:39:24Z
dc.date.available 2015-05-28T11:39:24Z
utb.contributor.internalauthor Gerža, Michal
utb.contributor.internalauthor Schauer, František
utb.contributor.internalauthor Zelinka, Ivan
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