This paper describes a neural ore grade estimation system developed at the AIMS Research Unit of the University of Nottingham. GEMNET II is a modular neural network system designed to receive drillhole information from an orebody and perform ore grade estimation on a block model basis. The aims of the system are to provide a valid alternative to conventional grade estimation techniques while re...

This thesis describes development of a real-time-implementable algorithm for simultaneous estimation of a heavy vehicle’s mass and time-varying road grade and its verification with experimental data. Accurate estimate of a heavy vehicle’s mass is critical in several vehicle control functions such as in transmission and stability control. The goal is to utilize the standard signals on a vehicle ...

An estimator of the Kullback-Leibler information number by using its representation as a functional of the grade density is introduced. Its strong consistency is proved under the mild conditions on the grade density. The same approach is used to study the entropy measure of bivariate dependence (mutual information). Some applications to detection theory are also given.

A method for estimation of the road grade based on standard mounted sensors in a heavy duty vehicle is presented. The method combines information from a barometer and a GPS with velocity and torque measurements of conventional type. The sensor information is adaptively integrated using extended Kalman filtering. This provides a systematic method for dealing with varying uncertainty in the senso...