General Algorithm for Computing the Theoretical Centering Precision of the Gripping Devices

2016 ◽  
pp. 15-21
Author(s):  
E.-C. Lovasz ◽  
V. Mesaroş-Anghel ◽  
C. M. Gruescu ◽  
C. E. Moldovan ◽  
M. Ceccarelli
Keyword(s):  
General Algorithm

scholarly journals Two-Dimensional Sampling-Recovery Algorithm of a Realization of Gaussian Processes on the Input and Output of Linear Systems

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1079
Author(s):  
Vladimir Kazakov ◽  
Mauro A. Enciso ◽  
Francisco Mendoza
Keyword(s):  
Gaussian Processes ◽  
Research Method ◽  
General Algorithm ◽  
Two Dimensional ◽  
Recovery Algorithm ◽  
Conditional Mean ◽  
Quality Of Recovery ◽  
Input And Output ◽  
Statistical Relationships

Based on the application of the conditional mean rule, a sampling-recovery algorithm is studied for a Gaussian two-dimensional process. The components of such a process are the input and output processes of an arbitrary linear system, which are characterized by their statistical relationships. Realizations are sampled in both processes, and the number and location of samples in the general case are arbitrary for each component. As a result, general expressions are found that determine the optimal structure of the recovery devices, as well as evaluate the quality of recovery of each component of the two-dimensional process. The main feature of the obtained algorithm is that the realizations of both components or one of them is recovered based on two sets of samples related to the input and output processes. This means that the recovery involves not only its own samples of the restored realization, but also the samples of the realization of another component, statistically related to the first one. This type of general algorithm is characterized by a significantly improved recovery quality, as evidenced by the results of six non-trivial examples with different versions of the algorithms. The research method used and the proposed general algorithm for the reconstruction of multidimensional Gaussian processes have not been discussed in the literature.

General algorithm for calculating batches in the ceramics industry

1992 ◽  
Vol 49 (3) ◽  
pp. 131-136
Author(s):  
V. P. Plyutto ◽  
Do Tkhi Loan ◽  
Fam Kuang Bag ◽  
V. V. Babaeva
Keyword(s):  
General Algorithm ◽  
Ceramics Industry

General Algorithm for the Cluster-Variation Method

1983 ◽  
Vol 28 (5) ◽  
pp. 565-568 ◽  
Author(s):  
J S Desjardins ◽  
O Steinsvoll
Keyword(s):  
Cluster Variation Method ◽  
General Algorithm ◽  
Variation Method ◽  
Cluster Variation

A general algorithm for oblique image reconstruction

1990 ◽  
Vol 10 (2) ◽  
pp. 62-65 ◽  
Author(s):  
D.M. Kramer ◽  
L. Kaufman ◽  
R.J. Guzman ◽  
C. Hawryszko
Keyword(s):  
Image Reconstruction ◽  
General Algorithm

General algorithm for calculating the aggregated time series forecast

2021 ◽  
Author(s):  
Vadim Moshkin ◽  
Dmitry Yashin ◽  
Anton Zarubin ◽  
Albina Koval
Keyword(s):  
Time Series ◽  
General Algorithm ◽  
Time Series Forecast

scholarly journals Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2A′ states of LiFH

2019 ◽  
Vol 21 (26) ◽  
pp. 14205-14213 ◽  
Author(s):  
Yafu Guan ◽  
Dong H. Zhang ◽  
Hua Guo ◽  
David R. Yarkony
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Adiabatic Potential ◽  
General Algorithm ◽  
Energy Surfaces

A general algorithm for determining diabatic representations from adiabatic energies, energy gradients and derivative couplings using neural networks is introduced.

scholarly journals An algorithm for LC1 optimization

2005 ◽  
Vol 15 (2) ◽  
pp. 301-306 ◽  
Author(s):  
Nada Djuranovic-Milicic
Keyword(s):  
Rate Of Convergence ◽  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Directional Derivative ◽  
Second Order ◽  
General Algorithm ◽  
Unconstrained Optimization Problems ◽  
Convergence Proof

In this paper an algorithm for LC1 unconstrained optimization problems, which uses the second order Dini upper directional derivative is considered. The purpose of the paper is to establish general algorithm hypotheses under which convergence occurs to optimal points. A convergence proof is given, as well as an estimate of the rate of convergence.

Sign in / Sign up

Export Citation Format

Share Document