John H. Murphy

Neural network recognition of electronic malfunctions

Neural network software can be applied to manufacturing process control as a tool for diagnosing the state of an electronic circuit board. The neural network approach significantly reduces the amount of time required to build a diagnostic system. This time reduction occurs because the ordinary combinatorial explosion in rules for identifying faulted components can be avoided. Neural networks circumvent the combinatorial explosion by taking advantage of the fact that the fault characteristics of multiple simultaneous faults frequently correlate to the fault characteristics of the individual faulted components. This article clearly demonstrates that state-of-the-art neural networks can be used in automatic test equipment for iterative diagnosis of electronic circuit board malfunctions.

Multi-fault diagnosis of electronic circuit boards using neural networks

A report is presented on the feasibility of using artificial neural networks to recognize faults in electronic circuit boards. In particular, the issue of networks trained for single-fault diagnosis being used to recognize multiple simultaneous faults is investigated. The research concentrated on determining the number of physical test points needed. The effect of using more than one test pattern to identify the fault, and the effect on generalization of the number of nodes in the hidden layer. The study has shown that neural networks can be used for automatic knowledge acquisition in the diagnosis of electronic circuit board failures. The results are not always 100% reliable, and the user must be willing to accept this lower reliability. The fault detection rate is highest with neural networks having 96 nodes, and the false alarm rate is lower. As the number of nodes in the input layer decreases, the fault detection rate decreases and the false alarm rate increases. Increasing the fault threshold values decreases the number of false alarms and also decreases the fault detection rate. The results indicate that it is important to use more than one test vector reading. It is shown that as long as the number of nodes in the hidden layers is sufficient to do the classifications. there is little impact on the fault detection rates

Advanced Applications of Superconductors

Superconductors, materials which can carry large electrical currents without significant electrical losses, will soon live up to their name not only in static magnetic environments but in most time varying magnetic environments also. In the past, high ac losses in superconductors have limited their use to relatively low frequency ac applications. However during the past decade, considerable progress in superconductor fabrication has resulted in filament size reduction from 800 µm to 1 µm leading to ac loss reductions of between two and three orders of magnitude.

Tracking and location technologies for the criminal justice system

Electronic monitoring systems are being used by the criminal justice system to effect behavioral modifications of persons in pre-release prgrams, on parole, and on probation. State-of-the-art electronic monitoring systems are merely radio frequency proximity detection systems that operate over limited ranges, on the order of 45 to 70 meters. One major defect with proximity detection systems is that when the clients leave the area being monitored, there is no way to ensure that the clients are behaving properly. As a result, electronic monitoring systems are only applied to a restricted number of cases of low risk criminal offenders. There is a growing need for community-wide tracking and location technologies to increase the safety and security provided by the electronic monitoring systems, and to expand the number of cliets monitored by these systems. In this paper, a review is made of the tracking and location technologies that are currently available or under development. Also presented is a brief overview of Westinghouses program with the National Institute of Justice. This program aims to demonstrate the practicality of one possible tracking and location technology, spread spectrum based time-of-arrival location systems, for intelligently tracking people on probation and parole.

Fault classification by neural networks and fuzzy logic

A neural fuzzy‐based and a backpropagation neural network‐based fault classifier for a three‐phase motor will be described in this paper. In order to acquire knowledge, the neural fuzzy classifier incorporates a learning technique to automatically generate membership functions for fuzzy rules, and the backpropagation algorithm is used to train the neural network model. Therefore, in this paper, the preprocessing of signals, fuzzy and neural models, training methods, implementations for real‐time response and testing results will be discussed in detail. Furthermore, the generalization capabilities of the neural fuzzy‐ and backpropagation‐based classifiers for waveforms with varying magnitudes, frequencies, noises and positions of spikes and chops in a cycle of a sine wave will be investigated, and the computation requirements needed to achieve real‐time response for both fuzzy and neural methods will be compared.