Data Fusion Techniques for Electro-Mechanical Braking Systems. The main focus of this project is to develop data fusion techniques for clamp force estimation and optimum utilisation of redundant information in a brake-by-wire system. Efficient integration of redundant information in an EMB system is expected to significantly improve the reliability and fault tolerance of such systems. The need for costly and complicated clamp force measurement sensors in electric callipers will also be eliminate ....Data Fusion Techniques for Electro-Mechanical Braking Systems. The main focus of this project is to develop data fusion techniques for clamp force estimation and optimum utilisation of redundant information in a brake-by-wire system. Efficient integration of redundant information in an EMB system is expected to significantly improve the reliability and fault tolerance of such systems. The need for costly and complicated clamp force measurement sensors in electric callipers will also be eliminated by accurate estimation of the clamp force signal, through fusion of more readily available measurements. Development of the proposed data fusion techniques influences the design of future EMBs and enhances the functionality of existing brake-by-wire systems.Read moreRead less
Advanced matrix-analytic methods with applications. Over the last twenty-five years, matrix-analytic methods have proved to be very successful in formulating and analysing certain classes of stochastic models. Motivated by applications, this project will investigate more advanced matrix-analytic methods than have hitherto been studied.
Industrial Transformation Research Hubs - Grant ID: IH130200012
Funder
Australian Research Council
Funding Amount
$2,748,358.00
Summary
ARC Research Hub for Basin GEodyNamics and Evolution of SedImentary Systems (GENESIS). ARC Research Hub for Basin GEodyNamics and Evolution of SedImentary Systems (GENESIS). This Research Hub aims to undertake simultaneous modelling of deep Earth and surface processes, spanning basin scales to individual sediment grains. The Hub will develop and apply cutting-edge basin simulation approaches to transform the seeding and testing of basin exploration models, extending their viability to complex, ....ARC Research Hub for Basin GEodyNamics and Evolution of SedImentary Systems (GENESIS). ARC Research Hub for Basin GEodyNamics and Evolution of SedImentary Systems (GENESIS). This Research Hub aims to undertake simultaneous modelling of deep Earth and surface processes, spanning basin scales to individual sediment grains. The Hub will develop and apply cutting-edge basin simulation approaches to transform the seeding and testing of basin exploration models, extending their viability to complex, inaccessible remote and deep exploration targets. The Hub will fuse multidimensional data into five dimensional basin models (space and time, with uncertainty estimates) by coupling the evolution of mantle flow, crustal deformation, erosion and sedimentary processes, achieving a quantum leap in basin modelling and petroleum systems analysis.Read moreRead less
Human Leukocyte Antigen-A and -B regulation of Natural Killer cell function. The aim of this project is to determine how genetic variation in the genes encoding cell surface receptors expressed by innate lymphocytes and the molecules they recognise diversifies their capacity to sense and respond to infection. This knowledge is critical for understanding why there are intrinsic differences between individuals with respect to their capacity to respond to different types of infection and will ultim ....Human Leukocyte Antigen-A and -B regulation of Natural Killer cell function. The aim of this project is to determine how genetic variation in the genes encoding cell surface receptors expressed by innate lymphocytes and the molecules they recognise diversifies their capacity to sense and respond to infection. This knowledge is critical for understanding why there are intrinsic differences between individuals with respect to their capacity to respond to different types of infection and will ultimately inform our capacity to better deploy personalised medicines.Read moreRead less
Computational methods for population-size-dependent branching processes. Branching processes are the primary mathematical tool used to model populations that evolve randomly in time. Most key results in the theory are derived under the simplifying assumption that individuals reproduce and die independently of each other. However, this assumption fails in most real-life situations, in particular when the environment has limited resources or when the habitat has a restricted capacity. This project ....Computational methods for population-size-dependent branching processes. Branching processes are the primary mathematical tool used to model populations that evolve randomly in time. Most key results in the theory are derived under the simplifying assumption that individuals reproduce and die independently of each other. However, this assumption fails in most real-life situations, in particular when the environment has limited resources or when the habitat has a restricted capacity. This project aims to develop novel and effective algorithmic techniques and statistical methods for a class of branching processes with dependences. We will use these results to study significant problems in the conservation of endangered island bird populations in Oceania, and to help inform their conservation management.Read moreRead less
ARC Financial Integrity Research Network. The integrity of the financial system is constantly under stress because of the development of ever more complex financial instruments, structures and strategies, and the associated research technologies that continues to accelerate worldwide. FIRN's vision is to harness the considerable strengths of Australia's internationally renowned finance, accounting and economics researchers into a research agenda to address issues concerning the integrity of the ....ARC Financial Integrity Research Network. The integrity of the financial system is constantly under stress because of the development of ever more complex financial instruments, structures and strategies, and the associated research technologies that continues to accelerate worldwide. FIRN's vision is to harness the considerable strengths of Australia's internationally renowned finance, accounting and economics researchers into a research agenda to address issues concerning the integrity of the financial system. It will enable Australian research in this area to match the scale and impact of similar research in other major international financial centres, and play an essential role in placing Australia among the world's leaders in financial markets related research.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC180100030
Funder
Australian Research Council
Funding Amount
$3,925,357.00
Summary
ARC Training Centre for Transforming Maintenance through Data Science. The ARC Training Centre for Transforming Maintenance through Data Science aims to equip practising engineers and Australian graduates with the next generation of data science methods for the maintenance sector. The Centre plans to introduce timely and cost-efficient maintenance scheduling by developing data-intensive mathematical and computational algorithms for asset management and fault prediction. The Centre’s overarching ....ARC Training Centre for Transforming Maintenance through Data Science. The ARC Training Centre for Transforming Maintenance through Data Science aims to equip practising engineers and Australian graduates with the next generation of data science methods for the maintenance sector. The Centre plans to introduce timely and cost-efficient maintenance scheduling by developing data-intensive mathematical and computational algorithms for asset management and fault prediction. The Centre’s overarching objectives are to enable development and adoption of new practices to improve productivity and asset reliability for industry and to foster a new maintenance technology service sector for national and international markets.Read moreRead less
Sewer Monitoring and Management in the Digital Era. Overflow, flooding, corrosion, and odorous emissions are persistent issues for utilities managing sewers. Current sewer maintenance is reactive, and focuses on solving problems in local networks, despite that optimal solutions require a system-wide approach. Capitalising on recent development in IoT sensors, wireless transmission, and machine learning, this multidisciplinary project aims to develop digital-twin supported data analytics for proa ....Sewer Monitoring and Management in the Digital Era. Overflow, flooding, corrosion, and odorous emissions are persistent issues for utilities managing sewers. Current sewer maintenance is reactive, and focuses on solving problems in local networks, despite that optimal solutions require a system-wide approach. Capitalising on recent development in IoT sensors, wireless transmission, and machine learning, this multidisciplinary project aims to develop digital-twin supported data analytics for proactive sewer management including network-wide real-time control. The project aims to generate significant social, environmental and economic benefits by enabling utilities to better protect public and environmental health, reduce sewer odour and greenhouse gas emissions, and extend sewer asset life.Read moreRead less
Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provi ....Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provide real-time decisions for plant operators on the required treatment regime for incoming raw water, and advise them on the optimal reservoir offtake depth. This will potentially minimise treatment costs and health risks for consumers. The ultimate goal is to significantly enhance current water supply management practices.Read moreRead less