Experimental Characterization of Ultrasonic Phenomena by a Neural-Like Learning System

This paper describes a novel approach for analyzing ultrasonic signals to permit an experimental determination of the relations between elastic wave phenomena and the properties of a source of sound in a material. It is demonstrated that an adaptive learning system comprising an associative memory can be used to map source and waveform data and vice versa with the auto- and cross-correlation portions of the associative memory. Experiments are described which utilize such an adaptive system, running on a laboratory minicomputer, to process the data from a transient ultrasonic pulse in a plate specimen. In the learning procedure, the system learns from experimental pattern vectors, which are formed from the ultrasonic waveforms and, in this paper, encoded information about the source. The source characteristics are recovered by the recall procedure from detected ultrasonic signals and vice versa. Furthermore, from the discrepancy between the presented and the learned signals, the changes in the wave phenomenon, corresponding, for example, to changes in the boundary conditions of a specimen, can be determined

Novel Approaches for the Ultrasonic NDE of Thick and other Composites

This paper summarizes several recent developments which are facilitating new approaches for both active and passive quantitative ultrasonic measurements in composite materials. These include the development of point sources and point receivers, a theory for analyzing the propagation of transient elastic waves through a bounded, dispersive and attenuative medium, and the development and implementation of appropriate signal processing algorithms. An alternative to these deterministic approaches is a processing scheme based on a simulated intelligent system which processes the signals like a neural network. Examples of applications of these ideas to the NDE of composite materials are shown

Extraction of physical laws from joint experimental data

The extraction of a physical law y=y o(x) from joint experimental data about x and y is treated. The joint, the marginal and the conditional probability density functions (PDF) are expressed by given data over an estimator whose kernel is the instrument scattering function. As an optimal estimator of y o(x) the conditional average is proposed. The analysis of its properties is based upon a new definition of prediction quality. The joint experimental information and the redundancy of joint measurements are expressed by the relative entropy. With the number of experiments the redundancy on average increases, while the experimental information converges to a certain limit value. The difference between this limit value and the experimental information at a finite number of data represents the discrepancy between the experimentally determined and the true properties of the phenomenon. The sum of the discrepancy measure and the redundancy is utilized as a cost function. By its minimum a reasonable number of data for the extraction of the law y o(x) is specified. The mutual information is defined by the marginal and the conditional PDFs of the variables. The ratio between mutual information and marginal information is used to indicate which variable is the independent one. The properties of the introduced statistics are demonstrated on deterministically and randomly related variables. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2005