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The present article introduces a new analytical solution to this problem, and demonstrates its performance using time domain milling simulations.
This paper derives the properties of this combined state estimation scheme and demonstrates its performance with a realistic simulation case study of animal cell cultures.
Our research investigates how to apply context awareness based on Machine Learning in Process Mining for logistic processes and demonstrates its performance in a logistic scenario.
The paper shows the mathematical derivation of the new algorithm, discusses practical considerations, and demonstrates its performance using simulations and real data.
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The next step is to combine the pieces in an operational device and demonstrate its performance.
Numerical results are presented that validate the convergence properties of the method and demonstrate its performance on more realistic problems.
The designed MPC controller was implemented in the Irving Paper mill, in order to test and demonstrate its performance and stability.
We find an efficient numerical implementation of our grid-free MC approach and demonstrate its performance and usefulness in examples from hierarchical electric load management.
The team initially designed and optimized the concept digitally, then fabricated a small test instrument as a proof-of-principle and demonstrated its performance using a neutron beam facility at MIT's Nuclear Reactor Laboratory.
For definiteness, we construct an explicit second-order accurate algorithm for affine tetrahedral elements in two and three dimensions, and demonstrate its performance with numerical examples.
We also present a pilot C++ library implementation for clusters of multicore machines and demonstrate its performance for irregular block-sparse matrix matrix multiplication.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com