Discovery Early Career Researcher Award - Grant ID: DE240100042
Funder
Australian Research Council
Funding Amount
$339,237.00
Summary
Hybrid optimisation for coordinating autonomous trucks and drones. This project aims to build analytics for controlling a fleet of autonomous trucks and drones working in tandem to deliver retail goods and disaster relief. This project expects to develop new mathematical and artificial intelligence algorithms for routing and scheduling the vehicles and for directing the multi-modal transfer of goods between vehicles in real-time as traffic conditions change. Expected outcomes of this project inc ....Hybrid optimisation for coordinating autonomous trucks and drones. This project aims to build analytics for controlling a fleet of autonomous trucks and drones working in tandem to deliver retail goods and disaster relief. This project expects to develop new mathematical and artificial intelligence algorithms for routing and scheduling the vehicles and for directing the multi-modal transfer of goods between vehicles in real-time as traffic conditions change. Expected outcomes of this project include new theories and technologies that enable a central computer to remotely control the autonomous fleet for maximum efficiency. Benefits in transport and logistics include improved freight productivity through reducing costs and delivery times.Read moreRead less
Faster, cheaper, better: mathematical advances for improved design and scheduling of robotic instrumentation. This project extends previous research addressing mathematical challenges in the optimal design and scheduling of robotic instrumentation. The Partner Organisation manufactures instruments for cancer diagnostics, and designs instruments that need to produce rapid, high-quality results, at a reasonable cost in a competitive market. It is intended that powerful new scheduling algorithms wi ....Faster, cheaper, better: mathematical advances for improved design and scheduling of robotic instrumentation. This project extends previous research addressing mathematical challenges in the optimal design and scheduling of robotic instrumentation. The Partner Organisation manufactures instruments for cancer diagnostics, and designs instruments that need to produce rapid, high-quality results, at a reasonable cost in a competitive market. It is intended that powerful new scheduling algorithms will be devised to handle their complex problem, which is more challenging than standard problems. The developed methodologies aim to reduce the product development cycle and boost the competitiveness of Australian manufacturers. In addition, new theoretical and algorithmic contributions aim to enable improved scheduling in other application areas.Read moreRead less
Targeting Hypermutable ‘superbugs’ In Chronic Respiratory Infections By Optimised Antibiotic Combination Dosage Regimens
Funder
National Health and Medical Research Council
Funding Amount
$697,731.00
Summary
Many bacterial ‘superbugs’ can increase their mutation rate, i.e. become hypermutable, and thus rapidly become resistant to multiple antibiotics. Chronic lung infections with hypermutable bacteria cause increased ill-health and death in patients and current treatments do not work well. We will develop improved treatments using combinations of available antibiotics. This project will provide guidance to doctors on how to treat infections more effectively and minimise emergence of resistance.
Designing minimum-cost networks that are robust and avoid obstacles. The goal of this project is to construct a mathematical framework for the design of minimum-cost networks that are robust and avoid obstacles. Physical networks such as those required for communication, power and transportation are vital for our society, but are costly from economic and environmental viewpoints. There is a need for mathematical optimisation tools to design minimum-cost networks that take into account practical ....Designing minimum-cost networks that are robust and avoid obstacles. The goal of this project is to construct a mathematical framework for the design of minimum-cost networks that are robust and avoid obstacles. Physical networks such as those required for communication, power and transportation are vital for our society, but are costly from economic and environmental viewpoints. There is a need for mathematical optimisation tools to design minimum-cost networks that take into account practical considerations such as surviving local connectivity failures and avoiding pre-existing obstacles. These are recognised as mathematically challenging problems. Current approaches employ restrictive models that do not capture the flexibility of modern infrastructure networks. This project aims to develop geometric design methods using variable ‘Steiner points’, leading to fast algorithms for optimally solving these problems.Read moreRead less
Distributed Optimisation without Central Coordination. This project will develop the mathematical foundations for discovery and analysis of iterative methods for optimisation problems in distributed computing systems. Most methods in distributed optimisation were not designed for distributed computing, rather they were adapted for purpose post-hoc. By building on recent advances in monotone operator splitting, this project expects to develop a mathematical theory for decentralised optimisation a ....Distributed Optimisation without Central Coordination. This project will develop the mathematical foundations for discovery and analysis of iterative methods for optimisation problems in distributed computing systems. Most methods in distributed optimisation were not designed for distributed computing, rather they were adapted for purpose post-hoc. By building on recent advances in monotone operator splitting, this project expects to develop a mathematical theory for decentralised optimisation algorithms specially designed for distributed systems. The framework is expected to produce a suite of algorithms, each customised to exploit a specific network configuration. The project will provide significant benefits in distributed machine learning applications such as federated learning.Read moreRead less
Derivative free algorithms for large scale nonsmooth and global optimization and their applications. The outcomes expected from this research fall broadly into two categories: 1) the development of a new class of effective readily implementable derivative free techniques for large scale non-smooth and global optimisation and 2) the development of new algorithms based on derivative free optimization techniques for solving data mining, resource allocation problems and some problems in bioinformati ....Derivative free algorithms for large scale nonsmooth and global optimization and their applications. The outcomes expected from this research fall broadly into two categories: 1) the development of a new class of effective readily implementable derivative free techniques for large scale non-smooth and global optimisation and 2) the development of new algorithms based on derivative free optimization techniques for solving data mining, resource allocation problems and some problems in bioinformatics. In particular, the application of these techniques to molecular biology and cluster analysis will be very important for the development of competitive technologies for Australia.
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Approximate bundle methods in nonsmooth optimisation and their applications in some complex systems. Non-smooth and non-convex optimisation has many applications in industry and science. One of the powerful methods in non-smooth optimisation is a bundle method. This project will develop new versions of the bundle method by using continuous approximations to the sub-differential and extend this method for solving non-convex (smooth and non-smooth) optimisation problems by using max-min of linear ....Approximate bundle methods in nonsmooth optimisation and their applications in some complex systems. Non-smooth and non-convex optimisation has many applications in industry and science. One of the powerful methods in non-smooth optimisation is a bundle method. This project will develop new versions of the bundle method by using continuous approximations to the sub-differential and extend this method for solving non-convex (smooth and non-smooth) optimisation problems by using max-min of linear functions for the approximation of the functions involved. The outcome will be a new class of effective readily implementable algorithms for the minimization of non-smooth and non-convex functions, whose usefulness will be demonstrated by applications in cluster analysis, biochemistry and robotics.
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Constrained and Stable Solutions of Nonlinear and Semismooth Equations. In this project, comprehensive models for designing safe power system parameters will be proposed, efficient algorthms for solving these models will be constructed. The new models and algorithms in this project will provide efficient tools to prevent catastrophic events in power systems, which is related with national security. This project will also strengthen collaboration of Australian applied
mathematians with inter ....Constrained and Stable Solutions of Nonlinear and Semismooth Equations. In this project, comprehensive models for designing safe power system parameters will be proposed, efficient algorthms for solving these models will be constructed. The new models and algorithms in this project will provide efficient tools to prevent catastrophic events in power systems, which is related with national security. This project will also strengthen collaboration of Australian applied
mathematians with international researchers and engineering scientists. This is important for the advance of science and technology in
Australia.Read moreRead less
Scalable Robust Video Surveillance over Constrained Networks. Real-time monitoring of large numbers of people is becoming increasingly important for applications such as efficient service delivery and security against both common crime and terrorism. The use of human operators for such tasks is infeasible due to the large amount of data collected. Existing autonomous video surveillance systems are prone to high numbers of false alarms and often require expensive hardware. This proposal seeks ....Scalable Robust Video Surveillance over Constrained Networks. Real-time monitoring of large numbers of people is becoming increasingly important for applications such as efficient service delivery and security against both common crime and terrorism. The use of human operators for such tasks is infeasible due to the large amount of data collected. Existing autonomous video surveillance systems are prone to high numbers of false alarms and often require expensive hardware. This proposal seeks to address both difficulties by using rigorous statistical signal processing methods to optimally fuse information from a network of low-cost cameras.Read moreRead less
Economic Scheduling for Efficient Management of Clusters and their Cooperative Federation. Clusters of commodity computers have emerged as mainstream parallel and distributed platforms for high-performance computing. They are presented together as a single, unified resource to the end users by middleware technologies such as resource management and scheduling (RMS) systems. However, existing cluster RMS systems continue to use system centric models rather than utility models for the management a ....Economic Scheduling for Efficient Management of Clusters and their Cooperative Federation. Clusters of commodity computers have emerged as mainstream parallel and distributed platforms for high-performance computing. They are presented together as a single, unified resource to the end users by middleware technologies such as resource management and scheduling (RMS) systems. However, existing cluster RMS systems continue to use system centric models rather than utility models for the management and allocation of resources. There is also little emphasis on the construction of a cooperative federation of clusters to facilitate transparent sharing of resources. To enhance the value delivered by shared clusters, we propose the use of computational economy metaphor in resource management. This project aims to develop (A) computational economy based scheduling policies for allocation of resources and (B) a software infrastructure for creation of cooperative federation of distributed clusters.Read moreRead less