Adaptive Sampling using Support Vector Machines

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Reliability/safety analysis of stochastic dynamic systems (e.g., nuclear power plants, airplanes, chemical plants) is currently performed through a combination of Event-Tress and Fault-Trees. However, these conventional methods suffer from certain drawbacks: • Timing of events is not explicitly modeled • Ordering of events is preset by the analyst • The modeling of complex accident scenarios is driven by expert-judgment For these reasons, there is currently an increasing interest into the development of dynamic PRA methodologies since they can be used to address the deficiencies of conventional methods listed above.

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Mandelli, D. & Smith, C. November 1, 2012.

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Description

Reliability/safety analysis of stochastic dynamic systems (e.g., nuclear power plants, airplanes, chemical plants) is currently performed through a combination of Event-Tress and Fault-Trees. However, these conventional methods suffer from certain drawbacks: • Timing of events is not explicitly modeled • Ordering of events is preset by the analyst • The modeling of complex accident scenarios is driven by expert-judgment For these reasons, there is currently an increasing interest into the development of dynamic PRA methodologies since they can be used to address the deficiencies of conventional methods listed above.

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  • 2012 ANS Winter Meeting,San Diego, CA,11/11/2012,11/15/2012

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  • Report No.: INL/CON-12-26354
  • Grant Number: DE-AC07-05ID14517
  • Office of Scientific & Technical Information Report Number: 1060969
  • Archival Resource Key: ark:/67531/metadc828575

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  • November 1, 2012

Added to The UNT Digital Library

  • May 19, 2016, 9:45 a.m.

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  • June 20, 2016, 1:36 p.m.

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Mandelli, D. & Smith, C. Adaptive Sampling using Support Vector Machines, article, November 1, 2012; Idaho Falls, Idaho. (digital.library.unt.edu/ark:/67531/metadc828575/: accessed September 21, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.