Sequential Window Diagnoser for Discrete-Event Systems Under Unreliable Observations

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This paper addresses the issue of counting the occurrence of special events in the framework of partiallyobserved discrete-event dynamical systems (DEDS). Developed diagnosers referred to as sequential window diagnosers (SWDs) utilize the stochastic diagnoser probability transition matrices developed in [9] along with a resetting mechanism that allows on-line monitoring of special event occurrences. To illustrate their performance, the SWDs are applied to detect and count the occurrence of special events in a particular DEDS. Results show that SWDs are able to accurately track the number of times special events occur.

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Lin, Wen-Chiao; Garcia, Humberto E.; Thorsley, David & Yoo, Tae-Sic September 1, 2009.

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This paper addresses the issue of counting the occurrence of special events in the framework of partiallyobserved discrete-event dynamical systems (DEDS). Developed diagnosers referred to as sequential window diagnosers (SWDs) utilize the stochastic diagnoser probability transition matrices developed in [9] along with a resetting mechanism that allows on-line monitoring of special event occurrences. To illustrate their performance, the SWDs are applied to detect and count the occurrence of special events in a particular DEDS. Results show that SWDs are able to accurately track the number of times special events occur.

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  • Allerton Conference 2009,Allerton Retreat Center, Monticello, Illinois,09/30/2009,10/02/2009

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  • Report No.: INL/CON-09-16976
  • Grant Number: DE-AC07-05ID14517
  • Office of Scientific & Technical Information Report Number: 968672
  • Archival Resource Key: ark:/67531/metadc934936

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  • September 1, 2009

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  • Nov. 13, 2016, 7:26 p.m.

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  • Nov. 21, 2016, 6:28 p.m.

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Lin, Wen-Chiao; Garcia, Humberto E.; Thorsley, David & Yoo, Tae-Sic. Sequential Window Diagnoser for Discrete-Event Systems Under Unreliable Observations, article, September 1, 2009; [Idaho]. (digital.library.unt.edu/ark:/67531/metadc934936/: accessed September 24, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.