Development of low entropy coding in a recurrent network.

In this paper we present an unsupervised neural network which exhibits competition between units via inhibitory feedback. The operation is such as to minimize reconstruction error, both for individual patterns, and over the entire training set. A key difference from networks which perform principal components analysis, or one of its variants, is the ability to converge to non-orthogonal weight values. We discuss the network's operation in relation to the twin goals of maximizing information transfer and minimizing code entropy, and show how the assignment of prior probabilities to network outputs can help to reduce entropy. We present results from two binary coding problems, and from experiments with image coding.

An investigation into the residual stresses in an aluminium 2024 test weld

This paper uses finite element (FE) analysis to examine the residual stresses generated during the TIG welding of aluminium aerospace alloys. It also looks at whether such an approach could be useful for evaluating the effectiveness of various residual stress control techniques. However, such simulations cannot be founded in a vacuum. They require accurate measurements to refine and validate them. The unique aspect of this work is that two powerful engineering techniques are combined: FE modelling and neutron diffraction. Weld trials were performed and the direct measurement of residual strain made using the ENGIN neutron diffraction strain scanning facility. The predicted results show an excellent agreement with experimental values. Finally this model is used to simulate a weld made using a "Low Stress No Distortion" (LSND) technique. Although the stress reduction predicted is only moderate, the study suggests the approach to be a quick and efficient means of optimising such techniques.