Abstract: The paper introduces a method to efficiently simulate nonlinear changing electrostatic fields occurring in micro-electromechanical systems (MEMS). Large deflections of the capacitor electrodes usually introduce nonlinear electromechanical forces on the mechanical system. Traditional finite element methods require a time-consuming remeshing process to capture exact results for this physical domain interaction. In order to accelerate the simulation process and eliminate the remeshing process, a formulation of a strongly coupled electromechanical transducer element will be introduced which uses a combination of finite-element with an advanced mesh morphing technique using radial basis functions (RBF). The RBF allows large geometrical changes of the electric field domain while retain high element quality of the deformed mesh. Coupling effects between mechanical and electrical domains are directly included within the element formulation. Fringing field effects are described accurate by using traditional arbitrary shape functions.
Abstract: Predicting earthquakes is an important issue in the
study of geography. Accurate prediction of earthquakes can help
people to take effective measures to minimize the loss of personal
and economic damage, such as large casualties, destruction of
buildings and broken of traffic, occurred within a few seconds.
United States Geological Survey (USGS) science organization
provides reliable scientific information about Earthquake Existed
throughout history & the Preliminary database from the National
Center Earthquake Information (NEIC) show some useful factors to
predict an earthquake in a seismic area like Aleutian Arc in the U.S.
state of Alaska. The main advantage of this prediction method that it
does not require any assumption, it makes prediction according to the
future evolution of the object's time series. The article compares
between simulation data result from trained BP and RBF neural
network versus actual output result from the system calculations.
Therefore, this article focuses on analysis of data relating to real
earthquakes. Evaluation results show better accuracy and higher
speed by using radial basis functions (RBF) neural network.
Abstract: The handwriting is a physical demonstration of a
complex cognitive process learnt by man since his childhood. People
with disabilities or suffering from various neurological diseases are
facing so many difficulties resulting from problems located at the
muscle stimuli (EMG) or signals from the brain (EEG) and which
arise at the stage of writing. The handwriting velocity of the same
writer or different writers varies according to different criteria: age,
attitude, mood, writing surface, etc. Therefore, it is interesting to
reconstruct an experimental basis records taking, as primary
reference, the writing speed for different writers which would allow
studying the global system during handwriting process. This paper
deals with a new approach of the handwriting system modeling based
on the velocity criterion through the concepts of artificial neural
networks, precisely the Radial Basis Functions (RBF) neural
networks. The obtained simulation results show a satisfactory
agreement between responses of the developed neural model and the
experimental data for various letters and forms then the efficiency of
the proposed approaches.
Abstract: The concept of adaptive shape parameters (ASP) has been presented for solution of incompressible Navier Strokes equations using mesh-free local Radial Basis Functions (RBF). The aim is to avoid ill-conditioning of coefficient matrices of RBF weights and inaccuracies in RBF interpolation resulting from non-optimized shape of basis functions for the cases where data points (or nodes) are not distributed uniformly throughout the domain. Unlike conventional approaches which assume globally similar values of RBF shape parameters, the presented ASP technique suggests that shape parameter be calculated exclusively for each data point (or node) based on the distribution of data points within its own influence domain. This will ensure interpolation accuracy while still maintaining well conditioned system of equations for RBF weights. Performance and accuracy of ASP technique has been tested by evaluating derivatives and laplacian of a known function using RBF in Finite difference mode (RBFFD), with and without the use of adaptivity in shape parameters. Application of adaptive shape parameters (ASP) for solution of incompressible Navier Strokes equations has been presented by solving lid driven cavity flow problem on mesh-free domain using RBF-FD. The results have been compared for fixed and adaptive shape parameters. Improved accuracy has been achieved with the use of ASP in RBF-FD especially at regions where larger gradients of field variables exist.
Abstract: This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.