Sitka Spruce Quality Estimation using Neural Networks
This paper describes an automated classifier for the 
identification of good wood and knotty wood from 
computer tomography (CT) images of logs. Such a 
system is intended to allow better assessment of saw 
logs before being cut into timber. We describe a new empirical model for the growth of Sitka Spruce (Picea 
Stichensis (Bong, Carr)) whose operation is adapted to 
Irish conditions. The use of Hopfield networks for 2D 
cross-section image reconstruction from CT data 
obtained from the model is investigated. We also used 
a multi-layer feedforward neural network trained with 
fast-backpropagation to identify good wood from 
knotty wood. The Hopfield approach to image 
reconstruction was seen as being unsuitable for 
application with the wood industry. However, the use 
of a feedforward neural network for wood classification produced very promising results when trained on our tree model. It is expected that results from real wood data would be even more accurate.
Image reconstruction by a Hopfield neural network
Abstract The reconstruction of cross-sectional images from projections involves the solution of a large system of simultaneous equations in which the unknowns are attentuation coefficients associated with the cells consti tuting the image. As an alternative to iterative methodssuch as the algebraic reconstruction technique (ART), a modified linear form of the Hopfield neural network with a summation layer to significantly decrease the number of interconnections is proposed. Higher speed and improved SNR, compared to ART, have been obtained on the Shepp and Logan ‘head phantom’ divided into 100 × 100 cells.
Finite Difference Modeling on the Temperature Field of Substrate in Friction Surfacing
Friction surfacing is an advanced process of great potential, especially in the field of repair and reclamation of worn and damaged components. As a kind of thermal processing technology, the temperature profile and changes of the substrate in friction surfacing are important factors to get coatings of high quality. In this paper, the temperature field of the substrate during the process is simulated by finite difference method. The results are consistent with experiments, reappearing the temperature change of the substrate during friction surfacing, allowing further study on figuration and quality of coatings.