Developing novel big-data based models for designing greener turbines. Developing novel big-data based models for designing greener turbines. This project aims to improve the fuel efficiency of gas turbines, the backbone of power generation and aircraft propulsion, for efficient and affordable power generation and air travel. Australia is large, remote and has some of the world’s highest carbon dioxide emissions per capita. Improving fuel efficiency will reduce cost and emissions, but current de ....Developing novel big-data based models for designing greener turbines. Developing novel big-data based models for designing greener turbines. This project aims to improve the fuel efficiency of gas turbines, the backbone of power generation and aircraft propulsion, for efficient and affordable power generation and air travel. Australia is large, remote and has some of the world’s highest carbon dioxide emissions per capita. Improving fuel efficiency will reduce cost and emissions, but current design tools lack the accuracy to advance technology. This project will investigate fluid flow in gas turbines and use big-data analytics to develop more accurate design tools. Gas turbines with reduced fuel usage and carbon dioxide emissions are expected to reduce the cost and environmental impact of power generation and air travel in Australia.Read moreRead less
Multi-Agent Solutions for the Development of Self-Organised and Self-Adapted Distributed Energy Generation Systems. The project aims to develop a self-organised multi-agent framework for modelling Marco-Smart Grid (SMG), dynamic coordination mechanisms between SMGs in distributed energy systems, and self-adaptation approaches for SMGs and restoration strategies to detect and recover an SMG network from faults and outages. The significance of this project lies in its promise to solve the challeng ....Multi-Agent Solutions for the Development of Self-Organised and Self-Adapted Distributed Energy Generation Systems. The project aims to develop a self-organised multi-agent framework for modelling Marco-Smart Grid (SMG), dynamic coordination mechanisms between SMGs in distributed energy systems, and self-adaptation approaches for SMGs and restoration strategies to detect and recover an SMG network from faults and outages. The significance of this project lies in its promise to solve the challenging issues of Smart Grid (SG) in multi-agent research and provide practical solutions to the development of effective and higher-quality distributed energy-generation systems with renewable energy resources. The expected outcomes are a framework, models, mechanisms and approaches in SG research and their practical applications.Read moreRead less
Machine learning techniques for fuel loss detection at service stations. This project aims to develop effective techniques to identify the sources of fuel losses, such as leaks and calibration errors in underground storage tanks at service stations. Monitoring fuel losses at service stations is influenced by many external factors which can be difficult to predict. The project expects to use machine learning to develop the techniques and test them with live data at service stations. The expected ....Machine learning techniques for fuel loss detection at service stations. This project aims to develop effective techniques to identify the sources of fuel losses, such as leaks and calibration errors in underground storage tanks at service stations. Monitoring fuel losses at service stations is influenced by many external factors which can be difficult to predict. The project expects to use machine learning to develop the techniques and test them with live data at service stations. The expected outcomes are a set of tailor-made machine learning techniques for effective fuel loss detection and a software suite that can be easily incorporated into the normal operation of service stations. This should reduce the costs to the petroleum industry from wasteful leaks and the environmental damage caused by these leaks. Read moreRead less
Quantification, optimisation, and application of deep uncertainty. This project aims to develop a framework for deep uncertainty quantification. There is currently a fundamental gap between deep learning research and the methods required to quantify and manage uncertainties. The research will propose a novel distribution-free methodology to generate deep predictive uncertainty estimates to avoid the assumptions of existing methods. The quality of estimates will be enhanced by applying an interva ....Quantification, optimisation, and application of deep uncertainty. This project aims to develop a framework for deep uncertainty quantification. There is currently a fundamental gap between deep learning research and the methods required to quantify and manage uncertainties. The research will propose a novel distribution-free methodology to generate deep predictive uncertainty estimates to avoid the assumptions of existing methods. The quality of estimates will be enhanced by applying an interval-based adversarial training step. The project is expected to help data-driven Australian organisations and industries to better quantify and manage forecasting uncertainties. This project will provide them with significant cost savings through better decision making and more robust planning.Read moreRead less
Robust AI Planning for Hybrid Systems. Automated planning, a core area of Artificial Intelligence, can effectively deal with the automatic synthesis of optimised action strategies for discrete system models. Extending the reach of planning to hybrid discrete/continuous systems, under exogenous uncertainty, is a widely open problem which this project will address. This will enable the proactive, and therefore more effective, management of microgrids and other cyber-physical systems, based on fore ....Robust AI Planning for Hybrid Systems. Automated planning, a core area of Artificial Intelligence, can effectively deal with the automatic synthesis of optimised action strategies for discrete system models. Extending the reach of planning to hybrid discrete/continuous systems, under exogenous uncertainty, is a widely open problem which this project will address. This will enable the proactive, and therefore more effective, management of microgrids and other cyber-physical systems, based on forecast information.Read moreRead less
Dynamic Deep Learning for Electricity Demand Forecasting. This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in ....Dynamic Deep Learning for Electricity Demand Forecasting. This project aims at developing a deep learning technology for high resolution electricity demand forecasting and residential demand response modelling. Electricity consumption data are dynamic and highly uncertain. The deep learning technology expects to provide accurate demand forecasting, and thus enabling optimal use of existing
grid assets and guiding future investments. The expected outcome can support data-driven decision-making in Australia's electricity distribution network planning and operation by considering future challenges such as integrating battery storage and electric vehicles into the grid, and thus providing reliable energy. The project expects to train next generation expert workforce for Australia's future power grid.Read moreRead less
Advanced analytics utilising conjoint mining of data and content with applications in business, bio-medicine and electrical power systems. This project will provide techniques that enable effective analysis of unstructured content and related information from relational databases in a conjoint manner. These techniques will be applied in the business, bio-medicine and electrical power systems domains.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0561231
Funder
Australian Research Council
Funding Amount
$671,715.00
Summary
MRI GRID Computing Facility: Design, Optimisation and Image Processing. The MRI Grid Computing Facility provides the IT infrastructure to achieve effective e-research in the area of magnetic resonance (MR) imaging, a field of neuroscience research that revolutionizes the way brain diseases are identified and treated. The facility consists of a dedicated high performance grid compute engine, distributed visualisation workstations, and distributed data warehouse facilities. Software tools acc ....MRI GRID Computing Facility: Design, Optimisation and Image Processing. The MRI Grid Computing Facility provides the IT infrastructure to achieve effective e-research in the area of magnetic resonance (MR) imaging, a field of neuroscience research that revolutionizes the way brain diseases are identified and treated. The facility consists of a dedicated high performance grid compute engine, distributed visualisation workstations, and distributed data warehouse facilities. Software tools accessible through the Internet will enable researchers to archive, retrieve and exchange data and software; access distributed MR image databases and the latest MR image analysis tools; schedule analysis tasks on the grid compute engine, the outcomes of which will be visualized by the visualization workstations.Read moreRead less
eResearch in the Neurosciences: Building collaborations in Asia. The proposed Australasian collaboration on eResearch in Neuroscience will promote and maintain the good health of Australians by 'improving critical mass through collaboration and information sharing' through increased access to advanced imaging technology in Korea and analysis techniques in Japan. The collaboration will also promote frontier technologies for building and transforming Australian industries by developing a creative ....eResearch in the Neurosciences: Building collaborations in Asia. The proposed Australasian collaboration on eResearch in Neuroscience will promote and maintain the good health of Australians by 'improving critical mass through collaboration and information sharing' through increased access to advanced imaging technology in Korea and analysis techniques in Japan. The collaboration will also promote frontier technologies for building and transforming Australian industries by developing a creative and innovative research environment and enhancing Australian scientists' participation in breakthrough science. Great national benefit can be derived from international research collaboration, due to the contribution frontier technology can make to science and health. Read moreRead less
Uncertainty quantification using type-2 fuzzy systems. This project will develop new interval type-2 fuzzy logic system-based tools for quantifying uncertainties present in complex systems. The outcome of this project will greatly help all Australian industries and organisations that directly or indirectly use model-based estimation for prediction and forecasting purposes.