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  • The target of this contribution is to present an integrated overview of algorithms which could be used for change point detection of any physical dynamical system, e.g. in acoustic system or for the particle detection in accelerated physics. Furthermore we want to show a possible improvement of the localization of the source of acoustic emission while using these algorithms instead of the well-known threshold level method. Among these algorithms we study the methods based on hypothesis testing by probability ratio, which are described in [1], the algorithm based on the Schwarz Information Criteria (SIC) [2], the method using Singular Spectral Analysis (SSA) [4], and finally the algorithms based on the estimation of autoregressive stochastic processes (time series) and the consecutive chi^2 test [3]. As an example, applying these algorithms, we want to show some possible improvements of numerical iterative localization techniques for detecting the source of the acoustic emission. We consider the linear source localization based on the signals emitted and we develop the mechanisms for the algorithmic testing concerning more complex physical experimental setup such as the particle detection in accelerated physics.
  • The target of this contribution is to present an integrated overview of algorithms which could be used for change point detection of any physical dynamical system, e.g. in acoustic system or for the particle detection in accelerated physics. Furthermore we want to show a possible improvement of the localization of the source of acoustic emission while using these algorithms instead of the well-known threshold level method. Among these algorithms we study the methods based on hypothesis testing by probability ratio, which are described in [1], the algorithm based on the Schwarz Information Criteria (SIC) [2], the method using Singular Spectral Analysis (SSA) [4], and finally the algorithms based on the estimation of autoregressive stochastic processes (time series) and the consecutive chi^2 test [3]. As an example, applying these algorithms, we want to show some possible improvements of numerical iterative localization techniques for detecting the source of the acoustic emission. We consider the linear source localization based on the signals emitted and we develop the mechanisms for the algorithmic testing concerning more complex physical experimental setup such as the particle detection in accelerated physics. (en)
Title
  • Change point detection algorithms for complex physical dynamical systems
  • Change point detection algorithms for complex physical dynamical systems (en)
skos:prefLabel
  • Change point detection algorithms for complex physical dynamical systems
  • Change point detection algorithms for complex physical dynamical systems (en)
skos:notation
  • RIV/68407700:21340/12:00199587!RIV13-MSM-21340___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
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  • S, Z(MSM6840770039)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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http://linked.open...iv/duvernostUdaju
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  • 126663
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  • RIV/68407700:21340/12:00199587
http://linked.open...riv/jazykVysledku
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  • Change point detection; Signal analysis; Acoustic emission (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [F1DA92FE7786]
http://linked.open...v/mistoKonaniAkce
  • Zlenice
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  • Praha
http://linked.open...i/riv/nazevZdroje
  • SPMS 2012 Proceedings
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
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http://linked.open...UplatneniVysledku
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  • Kůs, Václav
  • Máca, Jan
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
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  • České vysoké učení technické v Praze
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  • 978-80-01-05130-6
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  • 21340
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