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The design method proposed uses in synergy the formal verification (FV) and the Discrete Controller Synthesis (DCS) techniques.
The method proposed uses a cascaded arrangement of two algorithms – the classic meta-heuristic, referred to as "jDE-differential evolution" and an algorithm called Covariance Matrix Adaptation Evolution Strategy.
The method proposed uses the decomposition feature of generalized benders decomposition (GBD) to evolve a two-level nested optimization problem (Primal and Master), one involving time-variant decision (manipulated) variables and the other involving time-invariant decision (design) variables.
The USD method proposed uses constant εavg as the scale-up basis under a micromixing controlled regime.
The new method proposed uses colonoscopy as the denominator and its application demonstrates PCCRC rates are falling with time in the English NHS.
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We demonstrate the applicability of the method proposed using a synthetic test case.
With this observation, this method proposes using transfer learning to exploit such text data to improve image classification.
The method proposes using the Error Correcting Output Codes [34] (ECOC) scheme to generate the binary classifiers.
This method proposes using unlabeled co-occurrence data to serve as a bridge between the two domains.
To find these pivots, the method proposes using the leaf nodes of random forest [42] models created for the source and target.
The first method is proposed using the resynchronization technique, which is useful when SNRs are high.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com