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Sökning: WFRF:(Murano S)

  • Resultat 1-6 av 6
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1.
  • Battistoni, G, et al. (författare)
  • FLUKA Monte Carlo calculations for hadrontherapy application
  • 2013
  • Ingår i: CERN-Proceedings-2012-002. ; , s. 461-467
  • Konferensbidrag (refereegranskat)abstract
    • Monte Carlo (MC) codes are increasingly spreading in the hadrontherapy community due to their detailed description of radiation transport and interaction with matter. The suitability of a MC code for application to hadrontherapy demands accurate and reliable physical models for the description of the transport and the interaction of all components of the expected radiation field (ions, hadrons, electrons, positrons and photons). This contribution will address the specific case of the general-purpose particle and interaction code FLUKA. In this work, an application of FLUKA will be presented, i.e. establishing CT (computed tomography)-based calculations of physical and RBE (relative biological effectiveness)-weighted dose distributions in scanned carbon ion beam therapy.
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5.
  • Ljung, Lennart, 1946-, et al. (författare)
  • Robust High-Gain DNN Observer for Nonlinear Stochastic Continuous Time Systems
  • 2001
  • Ingår i: Proceedings of the 40th IEEE Conference on Decision and Control. - 0780370619 ; , s. 3546-3551 vol.4
  • Konferensbidrag (refereegranskat)abstract
    • A class of nonlinear stochastic processes satysfying a "Lipschitz-type strip condition" and supplied by a linear output equation, is considered. Robust asymptotic (high-gain) state estimation for nonlinear stochastic processes via differential neural networks is discussed. A new type learning law for the weight dynamics is suggested. By a stochastic Lyapunov-like analysis (with Ito formula implementation), the stability conditions for the state estimation error as well as for the neural network weights are established. The upper bound for this error is derived. The numerical example, dealing with "module"-type nonlinearities, illustrates the effectiveness of the suggested approach.
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6.
  • Murano, Daishi A., et al. (författare)
  • Robust High-Gain DNN Observer for Nonlinear Stochastic Continuous Time Systems
  • 2007
  • Rapport (övrigt vetenskapligt/konstnärligt)abstract
    • A class of nonlinear stochastic processes satysfying a "Lipschitz-type strip condition" and supplied by a linear output equation, is considered. Robust asymptotic (high-gain) state estimation for nonlinear stochastic processes via differential neural networks is discussed. A new type learning law for the weight dynamics is suggested. By a stochastic Lyapunov-like analysis (with Ito formula implementation), the stability conditions for the state estimation error as well as for the neural network weights are established. The upper bound for this error is derived. The numerical example, dealing with "module"-type nonlinearities, illustrates the effectiveness of the suggested approach.
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  • Resultat 1-6 av 6

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