Design guidelines for safety-critical controllers in high-risk environments. This project aims to generate novel product design guidelines for developing safer controllers for use by potentially stressed individuals in high-risk situations. It will do this by generating specific insights and verifying generalisable solutions from the context of total artificial heart recipients –who must engage with critical controllers constantly. This project expects to generate new knowledge in design by esta ....Design guidelines for safety-critical controllers in high-risk environments. This project aims to generate novel product design guidelines for developing safer controllers for use by potentially stressed individuals in high-risk situations. It will do this by generating specific insights and verifying generalisable solutions from the context of total artificial heart recipients –who must engage with critical controllers constantly. This project expects to generate new knowledge in design by establishing a new research topic around an under-examined user cohort. Expected outcomes of this project include interaction design theory developments and improved controller design techniques. This should provide significant benefits and competitive advantages by lowering stress and improving safety across a range of contexts.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
Optimal control of nonlinear delay systems: theory, algorithms, and applications. Time delays are present in many engineering systems, including robots, irrigation canals, and chemical reactors. This project aims to develop state-of-the-art techniques for controlling systems with time delays in an optimal manner.
Relays and robustness: achieving optimal efficiency in wireless sensor networks. Wireless sensor networks consist of coordinated sensing and relay devices that offer us new ways to observe and interact with the physical world, with direct applications to national security and environmental monitoring. We aim to develop powerful new methods to get the best performance from a planned sensor network through smart relay deployment.
Optimal maintenance planning for critical mining and energy infrastructure. This project aims to develop cutting-edge mathematical algorithms for optimising maintenance activities in the mining and energy sectors. Such maintenance activities are prone to budget and time overruns due to poor planning - the result of outdated, inefficient manual processes. The project is expected to result in new maintenance planning methods, underpinned by rigorous mathematical theory, for reducing manual interve ....Optimal maintenance planning for critical mining and energy infrastructure. This project aims to develop cutting-edge mathematical algorithms for optimising maintenance activities in the mining and energy sectors. Such maintenance activities are prone to budget and time overruns due to poor planning - the result of outdated, inefficient manual processes. The project is expected to result in new maintenance planning methods, underpinned by rigorous mathematical theory, for reducing manual intervention and optimising both short- and long-term maintenance based on real-time sensor data. These new methods will be powerful tools for tackling the complexity of large-scale, time-critical maintenance projects, driving productivity in the resources industry and fostering collaboration between mathematicians and engineers.Read moreRead less
Maximisation of value in underground mine access design. This project represents a major advance in the problem of optimising the mine value associated with the access infrastructure of underground mines and providing powerful planning tools for management. The usefulness to the mining industry of the methods and algorithms the project is pioneering lies in their accuracy, flexibility and generality. Not only can they be used for benchmarking value in the design of specific mines, but they can ....Maximisation of value in underground mine access design. This project represents a major advance in the problem of optimising the mine value associated with the access infrastructure of underground mines and providing powerful planning tools for management. The usefulness to the mining industry of the methods and algorithms the project is pioneering lies in their accuracy, flexibility and generality. Not only can they be used for benchmarking value in the design of specific mines, but they can also determine the profitability or viability of mines under the use of new technologies. This is an important project for ensuring that Australia's mining industry remains efficient and internationally competitive. Given Australia’s economic dependence on mineral resources, it will also benefit the country as a whole.Read moreRead less
Data-Driven Multistage Robust Optimization—the New Frontier in Optimization. Robust optimisation is a powerful technology for decision-making in uncertain environments. Yet, developing numerically certifiable optimisation principles and data-driven methods that can be readily implemented by common computer algorithms remains an elusive goal for multistage robust optimisation. But it is crucial for the practical use of multistage optimisation. This project aims to develop this novel mathematical ....Data-Driven Multistage Robust Optimization—the New Frontier in Optimization. Robust optimisation is a powerful technology for decision-making in uncertain environments. Yet, developing numerically certifiable optimisation principles and data-driven methods that can be readily implemented by common computer algorithms remains an elusive goal for multistage robust optimisation. But it is crucial for the practical use of multistage optimisation. This project aims to develop this novel mathematical theory and methods by extending the investigators' recent award winning advances, including the von Neumann-prizewinning Lasserre-hierarchy approach. Results will provide a foundation and technologies for making superior decisions in the pervasive presence of big data uncertainty, enhancing data-driven innovation in AustraliaRead moreRead less