3,538 research outputs found
Rule Based System for Diagnosing Wireless Connection Problems Using SL5 Object
There is an increase in the use of in-door wireless networking solutions via Wi-Fi and this increase infiltrated and utilized Wi-Fi enable devices, as well as smart mobiles, games consoles, security systems, tablet PCs and smart TVs. Thus the demand on Wi-Fi connections increased rapidly. Rule Based System is an essential method in helping using the human expertise in many challenging fields. In this paper, a Rule Based System was designed and developed for diagnosing the wireless connection problems and attain a precise decision about the cause of the problem. SL5 Object expert system language was used in developing the rule based system. An Evaluation of the rule based system was carried out to test its accuracy and the results were promising
Correlation of Preston-tube data with laminar skin friction (Log No. J12984)
Preston tube data within laminar boundary layers obtained on a sharp ten-degree cone in the NASA Ames eleven-foot transonic wind tunnel are correlated with the corresponding values of theoretical skin friction. Data were obtained over a Mach number range of 0.30 to 0.95 and unit Reynolds numbers of 9.84, 13.1, and 16.4 million per meter. The rms scatter of skin friction coefficient about the correlation is of the order of one percent, which is comparable to the reported accuracy for calibrations of Preston tubes in incompressible pipe flows. In contrast to previous works on Preston tube/skin friction correlations, which are based on the physical height of the probe's face, this satisfactory correlation for compressible boundary layer flows is achieved by accounting for the effects of a variable "effective" height of the probe. The coefficients, which appear in the correlation, are dependent on the particular tunnel environment. The general procedure can be used to define correlations for other wind tunnels
I-FENN for thermoelasticity based on physics-informed temporal convolutional network (PI-TCN)
We propose an integrated finite element neural network (I-FENN) framework to
expedite the solution of coupled multiphysics problems. A physics-informed
temporal convolutional network (PI-TCN) is embedded within the finite element
framework to leverage the fast inference of neural networks (NNs). The PI-TCN
model captures some of the fields in the multiphysics problem, and their
derivatives are calculated via automatic differentiation available in most
machine learning platforms. The other fields of interest are computed using the
finite element method. We introduce I-FENN for the solution of transient
thermoelasticity, where the thermo-mechanical fields are fully coupled. We
establish a framework that computationally decouples the energy equation from
the linear momentum equation. We first develop a PI-TCN model to predict the
temperature field based on the energy equation and available strain data. The
PI-TCN model is integrated into the finite element framework, where the PI-TCN
output (temperature) is used to introduce the temperature effect to the linear
momentum equation. The finite element problem is solved using the implicit
Euler time discretization scheme, resulting in a computational cost comparable
to that of a weakly-coupled thermoelasticity problem but with the ability to
solve fully-coupled problems. Finally, we demonstrate the computational
efficiency and generalization capability of I-FENN in thermoelasticity through
several numerical examples
Classification of Message Spreading in a Heterogeneous Social Network
Nowadays, social networks such as Twitter, Facebook and LinkedIn become
increasingly popular. In fact, they introduced new habits, new ways of
communication and they collect every day several information that have
different sources. Most existing research works fo-cus on the analysis of
homogeneous social networks, i.e. we have a single type of node and link in the
network. However, in the real world, social networks offer several types of
nodes and links. Hence, with a view to preserve as much information as
possible, it is important to consider so-cial networks as heterogeneous and
uncertain. The goal of our paper is to classify the social message based on its
spreading in the network and the theory of belief functions. The proposed
classifier interprets the spread of messages on the network, crossed paths and
types of links. We tested our classifier on a real word network that we
collected from Twitter, and our experiments show the performance of our belief
classifier
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