The University of Sydney

Technological Resources Pty. Limited

Fabio Tozeto Ramos

Hugh Durrant-Whyte

Eric William Nettleton

Arman Melkumyan

Peter James Hatherly

Shrihari Vasudevan

Ross Hennessy

Ali Haydar Göktogan

Arja Jogita Jewbali

Ali Haydar Goektogan

Eric Nettleton

The present disclosure provides a method of characterizing a resource in a block located in an area. The method comprises the step of providing information concerning a property of the resource for a plurality of sample positions in, at or in the environment of the block. The information is obtained from analyzes of samples from the sample positions. The method further comprises modelling a change in the property along a distance at or within the block using the provided information. The method also comprises calculating parameters associated with the modelled change using Bayes' theorem and calculating a property characteristic of the resource for the block using the calculated parameters.

Method of characterising a resource

Methods and systems are described for generating a data representation of a geographical region as an adjunct to conducting autonomous operations within the region. The method comprises receiving information specifying a plurality of localized caused zones having operation-defined geographical boundaries within the region; receiving heterogeneous data descriptive of the region; associating the received data with respective localized zones; fusing the received data associated with the localized zones into data representations of the localized zones; and integrating the data representations of the localized zones into a common data representation of the geographical region.

Integrated automation system with picture compilation system

A system for large scale data modelling is described. The system includes at least one data measurement sensor () for generating measured data, a training processor () to determine optimized hyperparameter values in relation to a Gaussian process covariance function including a sparse covariance function that is smooth and diminishes to zero outside of a characteristic hyperparameter length. An evaluation processor () determines model data from the Gaussian process covariance function with optimised hyperparameter values and measured data. Also described is methods for modelling date, including a method using a Gaussian process including a sparse covariance function that diminishes to zero outside of a characteristic length, wherein the characteristic length is determined from the data to be modelled.

Method and system of data modelling

A method of computerized data analysis and synthesis is described. First and second datasets of a quantity of interest are stored. A Gaussian process model is generated using the first and second datasets to compute optimized kernel and noise hyperparameters. The Gaussian process model is applied using the stored first and second datasets and hyperparameters to perform Gaussian process regression to compute estimates of unknown values of the quantity of interest. The resulting computed estimates of the quantity of interest result from a non-parametric Gaussian process fusion of the first and second measurement datasets. The first and second datasets may be derived from the same or different measurement sensors. Different sensors may have different noise and/or other characteristics.

Method and system for multiple dataset gaussian process modeling

A method for modelling a dataset includes a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the Gaussian process kernel, and an evaluation phase in which the dataset and Gaussian process kernel with optimized hyperparameters are used to generate model data. The evaluation phase includes a nearest neighbor selection step. The method may include generating a model at a selected resolution.

A system () for analyzing and synthesizing a plurality of sources of sample data () by automated learning and regression. The system includes data storage () with a stored multi-task covariance function, and an evaluation processor () in communication with the data storage (). The evaluation processor () performs regression using the stored sample data and multi-task covariance function and synthesizes prediction data for use in graphical display or digital control.

Method and system for data analysis and synthesis

A system and method are described for generating a model of an environment in which a plurality of equipment units are deployed for the extraction of at least one resource from the environment. The system comprises a pre-extraction modeling unit configured to receive data from a first plurality of heterogeneous sensors in the environment and to fuse the data into a pre-extraction model. An equipment modeling unit is configured to receive equipment data relating to the plurality of equipment units and to combine the equipment data into an equipment model. A post-extraction modeling unit is configured to receive data from a second plurality of sensors and to fuse the data into a post-extraction model. Information from the pre-extraction model, the equipment model and/or the post-extraction model is communicable to the equipment units for use in controlling operation of the equipment units in the environment.

Growing community of inventors

Alexandria, Australia

Fabio Tozeto Ramos