Computational implementation

2022 ◽  
Author(s):  
Mehmet Barlo ◽  
Nuh Aygün Dalkıran
Keyword(s):  
Computational Implementation

scholarly journals Material and geometric nonlinear analysis of reinforced concrete frames

2014 ◽  
Vol 7 (5) ◽  
pp. 879-904 ◽  
Author(s):  
E. Parente Jr ◽  
G. V. Nogueira ◽  
M. Meireles Neto ◽  
L. S. Moreira
Keyword(s):  
Reinforced Concrete ◽  
Nonlinear Analysis ◽  
Constitutive Relations ◽  
Concrete Structures ◽  
Reinforced Concrete Structures ◽  
Computationally Efficient ◽  
Concrete Frames ◽  
Frame Element ◽  
Computational Implementation ◽  
Plane Frame

The analysis of reinforced concrete structures until failure requires the consideration of geometric and material nonlinearities. However, nonlinear analysis is much more complex and costly than linear analysis. In order to obtain a computationally efficient approach to nonlinear analysis of reinforced concrete structures, this work presents the formulation of a nonlinear plane frame element. Geometric nonlinearity is considered using the co-rotational approach and material nonlinearity is included using appropriate constitutive relations for concrete and steel. The integration of stress resultants and tangent constitutive matrix is carried out by the automatic subdivision of the cross-section and the application of the Gauss quadrature in each subdivision. The formulation and computational implementation are validated using experimental results available in the literature. Excellent results were obtained.

scholarly journals Slash Truncation Positive Normal Distribution and Its Estimation Based on the EM Algorithm

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2164
Author(s):  
Héctor J. Gómez ◽  
Diego I. Gallardo ◽  
Karol I. Santoro
Keyword(s):  
Expectation Maximization Algorithm ◽  
Likelihood Estimation ◽  
Real Data ◽  
Simulation Studies ◽  
R Software ◽  
Data Application ◽  
The Em Algorithm ◽  
Kurtosis Parameter ◽  
Computational Implementation ◽  
Moments Method

In this paper, we present an extension of the truncated positive normal (TPN) distribution to model positive data with a high kurtosis. The new model is defined as the quotient between two random variables: the TPN distribution (numerator) and the power of a standard uniform distribution (denominator). The resulting model has greater kurtosis than the TPN distribution. We studied some properties of the distribution, such as moments, asymmetry, and kurtosis. Parameter estimation is based on the moments method, and maximum likelihood estimation uses the expectation-maximization algorithm. We performed some simulation studies to assess the recovery parameters and illustrate the model with a real data application related to body weight. The computational implementation of this work was included in the tpn package of the R software.

Computational implementation

2018 ◽  
pp. 192-204
Author(s):  
J. Brendan Ritchie ◽  
Gualtiero Piccinini
Keyword(s):  
Computational Implementation

A Cognitive Framework for Core Language Understanding and its Computational Implementation

2012 ◽  
Vol 6 (1) ◽  
pp. 1-20 ◽  
Author(s):  
L. Massey
Keyword(s):  
Short Term Memory ◽  
Human Cognition ◽  
Long Term Memory ◽  
Neural Activation ◽  
Language Understanding ◽  
Term Memory ◽  
Text Document ◽  
Cognitive Framework ◽  
Computational Implementation ◽  
Core Language

The author argues that the cognitive processes underlying language understanding may not be logico-deductive or inductive, at least not for basic forms of understanding such as the ability to determine the topics of a text document. To demonstrate this point, they present a human cognition inspired framework for core language understanding and its computational implementation. The framework exploits word related knowledge stored in Long Term Memory (LTM) as well as Short Term Memory (STM) limited capacity, neuromorphic spreading activation and neural activation decay to derive the topics of text. The computational model implementing the framework shows the potential of the approach by establishing that the topics generated by the model are as good as those generated by humans.

scholarly journals Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation

AIChE Journal ◽  
2019 ◽  
Vol 65 (11) ◽  
Author(s):  
Zhe Wu ◽  
Anh Tran ◽  
David Rincon ◽  
Panagiotis D. Christofides
Keyword(s):  
Machine Learning ◽  
Predictive Control ◽  
Nonlinear Processes ◽  
Computational Implementation
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