Application of Neural Networks for Classification of Eddy Current NDT Data

The inverse problem in nondestructiye evaluation involves the characterization of flaw parameters given a transducer response signal. In general the governing equations and boundary conditions describing the underlying physical phenomena are complex. Consequently analytical closed form solutions can be obtained only under. strong simplifying assumptions with regard to geometry and linearity of the problem. This precludes their use as direct inverse models for solving realistic NDT problems necessitating the need for using indirect inverse models based on pattern recognition algorithms. These inverse models classify the NDT signal as belonging to one of the classes of defects stored in a data bank as shown in Fig. 1

A New Approach for Practical Two Dimensional Data Fusion Utilizing a Single Eddy Current Probe

Interest in data fusion techniques have been growing in recent years due to the belief that a single NDE measurement may often be inadequate for providing sufficient information about the state of a test specimen. A variety of data fusion approaches have been proposed for combining results obtained by different methods, as well as different sensors, to provide comprehensive information about the material under test [1–4]. Techniques proposed to date range from blind superposition to approaches that involve the use of statistical and AI methods [5–7]

A Discussion of the Inverse Problem in Electromagnetic NDT

The principal components of a nondestructive testing (NDT) system are shown in Figure 1. The specimen to be tested is energized by a transmitting transducer. The response of the energy-specimen interaction is picked up by a receiving transducer. This signal is then processed suitably and analyzed for defect characterization. The most critical step here is the inverse problem which involves the characterization of the specimen parameters given an NDT probe response signal. This paper is mainly concerned with the solution of the inverse problem

Synthetic Aperture Focusing Technique Using the Envelope Function for Ultrasonic Imaging

In traditional ultrasonic imaging systems, a transducer is scanned across the surface of a specimen at constant intervals. Synthetic aperture focusing techniques (SAFT) have been utilized extensively to process the RF data in order to enhance the signal-to-noise ratio of the image [1]. However, the implementation of the algorithm using sampled RF data has the disadvantage of requiring large memory and high-speed devices. These requirements can be reduced by using the envelope of the RF signal which involves processing the baseband signal. The envelope detection can be easily implemented as part of the receiver circuit

Finite Element Modeling of Binary Acoustic Fresnel Lenses

Binary acoustic Fresnel lenses (BAFLs) have recently emerged as possible replacements for spherical lenses for applications in acoustic microscopy. BAFLs are surface relief structures that are relatively easy to manufacture compared to conventional spherical lenses. While the latter requires careful grinding and polishing, the former can be easily fabricated to sub-micron dimension accuracy using existing VLSI etching technology. The term binary arises from the fact that each masking step during the lens production creates two phase levels. Therefore, a total of 2 n phase levels are created in n masking etching steps. A special case is when n = 1 (2 phase levels), which corresponds to the conventional Fresnel lens (zone plate)

The Remote Field Effect and Its Interpretation

The Remote Field Effect (RFE) and the testing method based on it have attracted considerable attention from the research community. The need to explain the apparent discrepancies between the effect and the known electromagnetic field behavior is the reason for this attention

PREDICTION OF ANKLE JOINT TORQUES USING ARTIFICIAL NEURAL NETWORKS

Major ankle sprains in sports are thought to be due to high levels of ankle torsion. The purpose of this study was to develop a method for measuring in vivo ankle torques developed by athletes. Motion capture, force plate, and insole pressure measurements were used to develop generalized regression neural networks to predict maximum ankle torque and rate of ankle torque based on insole pressures. It was found that network prediction accuracy depended on the number of subjects used for training, as well as the method of pressure sensor grouping. Further work will be performed to determine optimal subject and pressure sensor groupings

Indexing multi-dimensional uncertain data with arbitrary probability density functions

Research Session 26: Spatial and Temporal DatabasesIn an "uncertain database", an object o is associated with a multi-dimensional probability density function (pdf), which describes the likelihood that o appears at each position in the data space. A fundamental operation is the "probabilistic range search" which, given a value p q and a rectangular area r q, retrieves the objects that appear in r q with probabilities at least p q. In this paper, we propose the U-tree, an access method designed to optimize both the I/O and CPU time of range retrieval on multi-dimensional imprecise data. The new structure is fully dynamic (i.e., objects can be incrementally inserted/deleted in any order), and does not place any constraints on the data pdfs. We verify the query and update efficiency of U-trees with extensive experiments.postprintThe 31st International Conference on Very Large Data Bases (VLDB 2005), Trondheim, Norway, 30 August-2 September 2005. In Proceedings of 31st VLDB, 2005, v. 3, p. 922-93