Upgrading automation for nuclear fuel in-core management: from the symbolic generation of configurations to the neural adaptation of heuristics

FUELCON is an expert system in nuclear engineering. Its task is optimized refueling-design, which is crucial to keep down operation costs at a plant. FUELCON proposes sets of alternative configurations of fuel-allocation; the fuel is positioned in a grid representing the core of a reactor. The practitioner of in-core fuel management uses FUELCON to generate a reasonably good configuration for the situation at hand. The domain expert, on the other hand, resorts to the system to test heuristics and discover new ones, for the task described above. Expert use involves a manual phase of revising the ruleset, based on performance during previous iterations in the same session. This paper is concerned with a new phase: the design of a neural component to carry out the revision automatically. Such an automated revision considers previous performance of the system and uses it for adaptation and learning better rules. The neural component is based on a particular schema for a symbolic to recurrent-analogue bridge, called NIPPL, and on the reinforcement learning of neural networks for the adaptation.

Podcasting - delivering learning to the iTunes generation

The Guardian newspaper (21st October 2005) informed its readers that: "Stanford University in California is to make its course content available on iTunes...The service, Stanford on iTunes, will provide…downloads of faculty lectures, campus events, performances, book readings, music recorded by Stanford students and even podcasts of Stanford football games". The emergence of Podcasting as means of sending audio data to users has clearly excited educational technologists around the world. This paper will explore the technologies behind Podcasting and how this could be used to develop and deliver new E-Learning material. The paper refers to the work done to create Podcasts of lectures for University of Greenwich students.