Using data mining methods to remove uncertainties in sensor data streams. This project will develop key techniques for removing uncertainties in sensor data streams and thus improve the monitoring quality of sensor networks. The expected outcomes will benefit Australia by enabling improved, lower-cost monitoring of natural resources and management of stock raising.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0775760
Funder
Australian Research Council
Funding Amount
$101,967.00
Summary
Satellite remote sensing and GIS data processing facilities at Charles Darwin University, Darwin. Northern Australia is vast, remote and spreads across diverse and extensive landscapes. There is no centralised remote sensing and GIS facility within 2000 kilometres of the CDU, Darwin. The upgraded infrastructure at CDU will assist in strengthening the research base in this remote part of Australia. This will allow the NT researchers to focus on the environmental applications of remote sensing and ....Satellite remote sensing and GIS data processing facilities at Charles Darwin University, Darwin. Northern Australia is vast, remote and spreads across diverse and extensive landscapes. There is no centralised remote sensing and GIS facility within 2000 kilometres of the CDU, Darwin. The upgraded infrastructure at CDU will assist in strengthening the research base in this remote part of Australia. This will allow the NT researchers to focus on the environmental applications of remote sensing and GIS technologies which will have many community benefits through better management of water resources, land degradation, wetlands, cultural knowledge and sustainable use of Australian biodiversity. The infrastructure will also assist in the training of new researchers within this developing field.
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Managing infectious disease through partial wildlife social networks. This project aims to investigate the dynamics of the spread of infectious disease in wildlife, derived from incomplete information about contact networks. Infectious diseases in wildlife are difficult to track and control, because it is not feasible to monitor each individual in a population and know the contact network for a population. The project will create ways to best utilise incomplete observational data of contact netw ....Managing infectious disease through partial wildlife social networks. This project aims to investigate the dynamics of the spread of infectious disease in wildlife, derived from incomplete information about contact networks. Infectious diseases in wildlife are difficult to track and control, because it is not feasible to monitor each individual in a population and know the contact network for a population. The project will create ways to best utilise incomplete observational data of contact networks to develop robust predictions of disease spread and population fate, and to reliably predict the outcomes of management interventions. These robust prediction methods will provide better insights for conservation of Australian wildlife.Read moreRead less
Machine Assisted, Multi-scale Spatial and Temporal Observation and Modeling of Marine Benthic Habitats. The Integrated Marine Observing System (IMOS) science plans include sampling campaigns reliant on Autonomous Underwater Vehicle (AUV) Facility data and designed to address the issues of marine biodiversity quantification and assurance. The proposed research will directly enhance the effectiveness of these programs by speeding labour-intensive analyses, aggregating the results, and searching f ....Machine Assisted, Multi-scale Spatial and Temporal Observation and Modeling of Marine Benthic Habitats. The Integrated Marine Observing System (IMOS) science plans include sampling campaigns reliant on Autonomous Underwater Vehicle (AUV) Facility data and designed to address the issues of marine biodiversity quantification and assurance. The proposed research will directly enhance the effectiveness of these programs by speeding labour-intensive analyses, aggregating the results, and searching for ecological patterns on a national scale that would be difficult to identify using traditional approaches tuned to process-scale studies. Australian society stands to benefit by virtue of improved large-scale models of ecosystem function and reduced cost for conducting marine ecosystem investigations.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH180100002
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. Thes ....ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. These dynamic systems will help determine what goal to achieve and the most efficient plan to achieve it. This Hub is expected to contribute to higher farming efficiency, lower production costs and fewer disease risks, giving the Australian industry new business opportunities and an international competitive advantage.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100265
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and ....A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.Read moreRead less
A more intelligent knowledge-based system apprentice. Our previous techniques already had an impact on Australian industry, with five Australian companies marketing such technology, and for three of these it is a central technology. We expect an early uptake of the enhancements we propose by these companies, greatly increasing their international competitiveness against other rule technologies. Three of these companies are very recent, so we would expect other company uptake of the new enhance ....A more intelligent knowledge-based system apprentice. Our previous techniques already had an impact on Australian industry, with five Australian companies marketing such technology, and for three of these it is a central technology. We expect an early uptake of the enhancements we propose by these companies, greatly increasing their international competitiveness against other rule technologies. Three of these companies are very recent, so we would expect other company uptake of the new enhanced technology. In turn Australian companies using the technology will improve their competitiveness in an increasingly knowledge-based economy by being able to more rapidly and easily deploy knowledge-based systems. Our previous techniques have already had a significant impact in medical practice.Read moreRead less