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The normalized input neural networks are considered for reliable computation of the adaptive input.
Handling all together large displacements, motion details and occlusions remains an open issue for reliable computation of optical flow in a video sequence.
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Significantly, it allows for reliable computations of nonlinear hydrodynamics useful in ship design and marine applications, which are forbidden with the traditional methods on the presently available computing platforms.
Interval computations are an essential tool for reliable computing.
The results of this study can be readily applied for the reliable computation of all beam characteristic function state values, with no upper bound limit on the mode shape number.
The oversampling factor for PAPR calculation is U = 8, and hence IDFT size of each CC is N × U = 2, 048 × 8 = 16, 384 for the reliable computation of PAPR.
We show that direct methods for periodic optimal control allow for quick and reliable computation of optimal cycle times for a given set of parameters.
By varying applied load ratios, biaxial tests can offer different datasets which could be particularly useful for the purpose of developing advanced constitutive law, allowing for a more reliable computation of material parameter under a multiaxial stress state.
We present a model and variance reduction method for the fast and reliable computation of statistical outputs of stochastic elliptic partial differential equations.
The objective of this research is to provide a layer of network computational services for reliable remote computations and data transfers between an engineering workstation and a computation server on a high-speed computer.
For regional-scale applications, this study may be used to define the data quality level (uncertainty, frequency and/or number) compatible with reliable computation of river sediment fluxes.
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