Author name cluster

Mike Smith

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

IJCAI Conference 2011 Conference Paper

Adaptive Data Compression for Robot Perception

Mike Smith
Ingmar Posner
Paul Newman

This paper concerns the creation of an efficient, continuous, non-parametric representation of surfaces implicit in 3D laser data as typically recorded by mobile robots. Our approach explicitly leverages the probabilistic nature of Gaussian Process regression to provide for a principled, adaptive subsampling which automatically prunes redundant data. The algorithm places no restriction on the complexity of the underlying surfaces and enables predictions at arbitrary locations and densities. We present results using real and synthetic data and show that our approach attains decimation factors in excess of two orders of magnitude without significant degradation in fidelity of the workspace reconstructions.

PDF Details DOI

NeurIPS Conference 1994 Conference Paper

Real-Time Control of a Tokamak Plasma Using Neural Networks

Chris Bishop
Paul Haynes
Mike Smith
Tom Todd
David Trotman
Colin Windsor

This paper presents results from the first use of neural networks for the real-time feedback control of high temperature plasmas in a tokamak fusion experiment. The tokamak is currently the prin(cid: 173) cipal experimental device for research into the magnetic confine(cid: 173) ment approach to controlled fusion. In the tokamak, hydrogen plasmas, at temperatures of up to 100 Million K, are confined by strong magnetic fields. Accurate control of the position and shape of the plasma boundary requires real-time feedback control of the magnetic field structure on a time-scale of a few tens of mi(cid: 173) croseconds. Software simulations have demonstrated that a neural network approach can give significantly better performance than the linear technique currently used on most tokamak experiments. The practical application of the neural network approach requires high-speed hardware, for which a fully parallel implementation of the multilayer perceptron, using a hybrid of digital and analogue technology, has been developed.

PDF Details