The variation of the stress intensity factor along the front of an edge crack in an elastic bar of circular cross-section is determined. Both straight and circular-arc crack fronts are modelled using a three-dimensional finite element analysis of the tension and bending problems. Results are obtained for crack depth to bar diameter ratios ranging from 0.1 to 0.594, in the case of cracks with straight fronts, and from 0.175 to 0.6, in the case of cracks with curved fronts.
The pRAM (probabilistic RAM) is a nonlinear stochastic device with neuron like behavior. The pRAM is realizable in hardware, and the third-generation VLSI pRAM chip is described. This chip is adaptive since learning algorithms have been incorporated on-chip, using reinforcement training. The pRAM chip is also adaptive with respect to the interconnections between neurons. Results achieved from a small net of pRAM's performing a pattern-recognition task using reinforcement training are presented.
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