An Anticipatory Model of Cavitation

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The Anticipatory System (AS) formalism developed by Robert Rosen provides some insight into the problem of embedding intelligent behavior in machines. AS emulates the anticipatory behavior of biological systems. AS bases its behavior on its expectations about the near future and those expectations are modified as the system gains experience. The expectation is based on an internal model that is drawn from an appeal to physical reality. To be adaptive, the model must be able to update itself. To be practical, the model must run faster than real-time. The need for a physical model and the requirement that the model ... continued below

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2 p.

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Allgood, G.O.; Dress, W.B., Jr.; Hylton, J.O. & Kercel, S.W. April 5, 1999.

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Description

The Anticipatory System (AS) formalism developed by Robert Rosen provides some insight into the problem of embedding intelligent behavior in machines. AS emulates the anticipatory behavior of biological systems. AS bases its behavior on its expectations about the near future and those expectations are modified as the system gains experience. The expectation is based on an internal model that is drawn from an appeal to physical reality. To be adaptive, the model must be able to update itself. To be practical, the model must run faster than real-time. The need for a physical model and the requirement that the model execute at extreme speeds, has held back the application of AS to practical problems. Two recent advances make it possible to consider the use of AS for practical intelligent sensors. First, advances in transducer technology make it possible to obtain previously unavailable data from which a model can be derived. For example, acoustic emissions (AE) can be fed into a Bayesian system identifier that enables the separation of a weak characterizing signal, such as the signature of pump cavitation precursors, from a strong masking signal, such as a pump vibration feature. The second advance is the development of extremely fast, but inexpensive, digital signal processing hardware on which it is possible to run an adaptive Bayesian-derived model faster than real-time. This paper reports the investigation of an AS using a model of cavitation based on hydrodynamic principles and Bayesian analysis of data from high-performance AE sensors.

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2 p.

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OSTI as DE00003441

Medium: P; Size: 2 pages

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  • SPIE AeroSense '99 Conference: Applications and Science of Computational Intelligence, Orlando, FL (US), 04/05/1999--04/09/1999

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  • Report No.: ORNL/CP-101022
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 3441
  • Archival Resource Key: ark:/67531/metadc688378

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • April 5, 1999

Added to The UNT Digital Library

  • July 25, 2015, 2:20 a.m.

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  • April 10, 2017, 2:57 p.m.

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Allgood, G.O.; Dress, W.B., Jr.; Hylton, J.O. & Kercel, S.W. An Anticipatory Model of Cavitation, article, April 5, 1999; Tennessee. (digital.library.unt.edu/ark:/67531/metadc688378/: accessed December 13, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.