A-Priori Estimation of Computation Times in Fog Networked Robotics

Author(s):  
Ajay Kattepur ◽  
Hemant Kumar Rath ◽  
Anantha Simha
Author(s):  
Alexander Dmitrievich Baev ◽  
◽  
Dmitry Alexandrovich Chechin ◽  
Sergey Alexandrovich Shabrov ◽  
Natalya Ivanovna Rabotinskaya ◽  
...  

1996 ◽  
Author(s):  
Scott A. Sallberg ◽  
Byron M. Welsh ◽  
Michael C. Roggemann

2018 ◽  
Vol 132 (1) ◽  
pp. 81-96 ◽  
Author(s):  
Simon Rio ◽  
Tristan Mary-Huard ◽  
Laurence Moreau ◽  
Alain Charcosset

2020 ◽  
Vol 10 (23) ◽  
pp. 13382-13394 ◽  
Author(s):  
Toni Monleon‐Getino ◽  
Jorge Frias‐Lopez
Keyword(s):  
A Priori ◽  

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ruyun Ma ◽  
Zhongzi Zhao

We study the local and global bifurcation of nonnegative nonconstant solutions of a discrete general Brusselator model. We generalize the linear u in the standard Brusselator model to the nonlinear fu. Assume that f∈C0,∞,0,∞ is a strictly increasing function, and f′f−1a∈0,∞. Taking b as the bifurcation parameter, we obtain that the solution set of the problem constitutes a constant solution curve and a nonconstant solution curve in a small neighborhood of the bifurcation point b0j,f−1a,ab/f−1a2. Moreover, via the Rabinowitz bifurcation theorem, we obtain the global structure of the set of nonconstant solutions under the condition that fs/s2 is nonincreasing in 0,∞. In this process, we also make a priori estimation for the nonnegative nonconstant solutions of the problem.


Author(s):  
Ayoub Gouasmi ◽  
Eric J. Parish ◽  
Karthik Duraisamy

Reduced models of nonlinear dynamical systems require closure, or the modelling of the unresolved modes. The Mori–Zwanzig procedure can be used to derive formally closed evolution equations for the resolved physics. In these equations, the unclosed terms are recast as a memory integral involving the time history of the resolved variables. While this procedure does not reduce the complexity of the original system, these equations can serve as a mathematically consistent basis to develop closures based on memory approximations. In this scenario, knowledge of the memory kernel is paramount in assessing the validity of a memory approximation. Unravelling the memory kernel requires solving the orthogonal dynamics, which is a high-dimensional partial differential equation that is intractable, in general. A method to estimate the memory kernel a priori , using full-order solution snapshots, is proposed. The key idea is to solve a pseudo orthogonal dynamics equation, which has a convenient Liouville form, instead. This ersatz arises from the assumption that the semi-group of the orthogonal dynamics is a composition operator for one observable. The method is exact for linear systems. Numerical results on the Burgers and Kuramoto–Sivashinsky equations demonstrate that the proposed technique can provide valuable information about the memory kernel.


Author(s):  
Aleksey V. Golubkov ◽  

The article deals with the solution to the problem of determining the motion mode of an object along a complex trajectory. A hybrid stochastic model is used to describe a complex trajectory. The solution of the problem is based on the application of a sequential decision rule about the choice at an unknown time of the hypothesis about the current mode of motion, with a limited size of the bank of competing Kalman filters. An algorithm is constructed for calculating the average size of the Kalman filter bank in the case of M-possible motion modes. The algorithm is developed in a general form, therefore, it can be used not only for the four types of object motion models considered in this paper, but also for any linear discrete-time models with Gaussian noise presented by equations in the state space. The algorithm for a priori estimation of the average size of a bank of competing Kalman filters for M-possible modes of motion is implemented in MATLAB, the results of computer simulation are presented.


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