I. Elshafiey

Solution of inverse problems in electromagnetics using Hopfield neural networks

Inverse problems are encountered in many fields of science and engineering. In electromagnetics, for example, inverse problems may involve the reconstruction of the source or scatterer on the basis of information contained in electromagnetic measurements. In general, the measurements can be related to the scatterer via integral equations. This paper presents a neural network approach for solving the inverse problem associated with such equations. Results of implementing the method on an application problem are presented. >

Application of neural networks to inverse problems in electromagnetics

The paper presents a neural network approach for solving inverse problems in electromagnetics. In general, the measurements of the scattered electromagnetic fields can be related to the properties of the scatterer through integral equations. The energy minimization property of Hopfield neural networks is exploited to solve these integral equations. Results of implementing the method on an application problem involving reconstruction of material properties of multilayered media are presented. >

Finite Element Modeling of Eddy Current Probes for Edge Effect Reduction

Eddy current methods are a widely used technique in the nondestructive inspection of aircraft structures and parts. The method consists of inducing eddy-currents in the material being tested using a probe coil. The magnetic field produced by these eddy-currents opposes that of the probe coils (Lenz’s law) and the net effect is a reduced magnetic flux linking the coil. The presence of defects in the material under test disturbs the distribution of eddy-currents which in turn disturbs the net field. This change in the field is detected as a change in the impedance of the coil. The changes in coil impedance measured as the probe scans the specimen contitutes an eddy current defect signal.

A Neural Network Approach for Solving Inverse Problems in NDE

Solution to inverse problems is of interest in many fields of science and engineering. In nondestructive evaluation [1], for example, inverse techniques are used to obtain quantitative estimates of the size, shape and nature of defects in materials. Inv.:rse scattering problems in electromagnetics deal with estimation of scatterer information from knowledge of incident and scattered fields. Inverse problems are frequently described by Fredholm integral equations in the form n n

Scattering from two-dimensional objects embedded in multilayered media

smallint _a^bk(x,y)z(y)dy = u(x) (c leqslant x leqslant d)

Development of a Hopfield network for solving integral equations

n n(1) n nwhere u(x) represents the measured data, z(y) represents the source function or the system states or parameters, and k(x,y) represents the kernel of the transformation. The objective of inverse problem is then to solve for the source or state function from known measurements. This problem is sensitive to the system parameters z, to the shape of the kernel k, and to the accuracy of the measurements u.

A neural network approach for solving integral equations

Application of Digital Signal Processing (DSP) techniques in x-ray radiography is a field that is gaining a rapidly growing interest. Dealing with digital x-ray images and enhancing these images using DSP techniques allow the automation of x-ray inspection, which offers several advantages over the traditional film-based inspection. These advantages include reducing the inspection time and cost requirements, obtaing a consistent decision regarding the integrity of the object under test, and allowing the use of real-time inspection [1]. Typically processing of x-ray images to detect and size flaws involves edge detection. In this paper, we primarily focus on an image processing algorithm that is based on a new Gaussian weighted image moment vector edge operator. Application of this operator enhances image edges and suppresses the noise, which results in a significant improvement in the probability of detection of flaws in x-ray images.

Probe design for edge-effect reduction in eddy current inspection

The paper presents a hybrid numerical model for simulating electromagnetic scattering from a two dimensional object embedded in multilayered media. The domain is first divided into interior and exterior regions using a cylindrical surface. Finite element formulation is used to solve for the field in the internal region, and the solution of the field is used to find the induced current in the elements that discretize the 2D object. The scatterer is represented as a summation of line sources embedded in multilayered media, and the scattered field is found by summing the contributions from these sources. Simulation results are presented. >