A NEW REDUCTION TECHNIQUE FOR NON LINEAR THERMAL MODELS WITH CONDUCTIVE AND RADIATIVE COUPLINGS

1994 ◽  
Author(s):  
Denis Lemonnier ◽  
Hamou Sadat ◽  
Jean-Bernard Saulnier
Keyword(s):  
Reduction Technique ◽  
Thermal Models ◽  
Non Linear

scholarly journals Towards a framework for non‐linear thermal models in shell domains

2013 ◽  
Vol 23 (1) ◽  
pp. 55-73 ◽  
Author(s):  
Francisco Chinesta ◽  
Adrien Leygue ◽  
Marianne Beringhier ◽  
Linh Tuan Nguyen ◽  
Jean‐Claude Grandidier ◽  
...  
Keyword(s):  
Thermal Models ◽  
Non Linear

A New Reduction Technique for Thermal Models with Fluid Networks

2011 ◽  
Vol 34 (7) ◽  
pp. 667-686 ◽  
Author(s):  
Stefano Zucca ◽  
Daniele Botto ◽  
Muzio Gola
Keyword(s):  
Reduction Technique ◽  
Thermal Models ◽  
Fluid Networks

An adaptive non-linear PDE-based speckle reduction technique for ultrasound images

2011 ◽  
Vol 6 (3) ◽  
pp. 304 ◽  
Author(s):  
Rajeev Srivastava ◽  
J.R.P. Gupta ◽  
Harish Parthasarathy ◽  
Subodh Srivastava
Keyword(s):  
Reduction Technique ◽  
Speckle Reduction ◽  
Ultrasound Images ◽  
Linear Pde ◽  
Non Linear

Strength Reduction Technique for Non-Linear Failure Criterion

2012 ◽  
Vol 446-449 ◽  
pp. 1568-1572
Author(s):  
Li Ren ◽  
Ting Ai ◽  
Zhe Ming Zhu ◽  
Ling Zhi Xie ◽  
Ru Zhang
Keyword(s):  
Failure Criterion ◽  
Strength Reduction ◽  
Reduction Technique ◽  
Strength Reduction Technique ◽  
Non Linear

scholarly journals Semi-Supervised Non-Linear Dimensionality Reduction Technique for Sentiment Analysis Classification

2019 ◽  
Vol 8 (9) ◽  
pp. 1721-1725
Keyword(s):  
Dimensionality Reduction ◽  
Sentiment Analysis ◽  
Reduction Technique ◽  
Sentiment Classification ◽  
Random Subspace ◽  
Wide Scope ◽  
Dimensionality Reduction Technique ◽  
Non Linear ◽  
Linear Dimensionality Reduction ◽  
The Web

With the quick development in data advances, client created substance, for example, reviews, ratings, recommendations can be advantageously posted on the web, which have powered enthusiasm for sentiment classification. The quantity of records accessible on both online and offline is expanding drastically. Sentiment Classification has a wide scope of utilizations in review related sites. In this paper, we present our investigations about some exploration paper in this field and exhibited our plan to distinguish the sentiment extremity of a given content as positive or negative by lessening the documents dimension, through utilizing semi-supervised non-linear dimensionality decrease technique. For Sentiment Classification, Random Subspace strategy is utilized. For exploratory assessment, openly accessible sentiment datasets can be utilized to check the adequacy of the proposed technique.

Model reduction technique for mechanical behaviour modelling: Efficiency criteria and validity domain assessment

2008 ◽  
Vol 222 (3) ◽  
pp. 493-505 ◽  
Author(s):  
K Ordaz-Hernandez ◽  
X Fischer ◽  
F Bennis
Keyword(s):  
Neural Network ◽  
Model Reduction ◽  
Linear Model ◽  
Cantilever Beam ◽  
Reduction Technique ◽  
Reduced Model ◽  
Large Displacements ◽  
Large Deflections ◽  
Non Linear ◽  
Model Reduction Technique

The current paper presents the study of a neural network-based technique used to create fast, reduced, non-linear behavioural models. The studied approach is the use of artificial neural networks (ANNs) as a model reduction technique to create more efficient models, mostly in terms of computational speed. The test case is the deformation of a cantilever beam under large deflections (geometrical non-linearity). A reduced model is created by means of a multi-layer feed-forward neural network, a type of ANN reported as ‘universal approximator’ in the literature. Then it is compared with two finite-element models: linear (inaccurate for large deflections but fast) and non-linear (accurate but slow). Under large displacements, the reduced model approximates well the non-linear model while having similar speed to the linear model. Unfortunately, the resulting model presents a shortening of its validity domain, as being incapable of approximating the deformed configuration of the cantilever beam under small displacements. In other words, the ANN-based model provides a very good compromise between accuracy and speed within its validity domain, despite the low fidelity presented: accurate for large displacements but inaccurate for small displacements.

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