Damage Detection and Quantification using Infrastructure Digital Twins. Structural health monitoring is vital for infrastructure assets management as early detection of structural conditions is key to both safety and ongoing maintenance. This project combines computer vision, vibration tests, finite element modelling and deep learning technologies to develop an efficient structural health monitoring system. Digital twins created from images taken by cameras or UAVs will be correlated through dee ....Damage Detection and Quantification using Infrastructure Digital Twins. Structural health monitoring is vital for infrastructure assets management as early detection of structural conditions is key to both safety and ongoing maintenance. This project combines computer vision, vibration tests, finite element modelling and deep learning technologies to develop an efficient structural health monitoring system. Digital twins created from images taken by cameras or UAVs will be correlated through deep learning with structural conditions and load-carrying capacities obtained from vibration tests and finite element model analysis for efficient structural damage detection and quantification. The project will lead to effective structural health monitoring and enhance structural safety and reduce maintenance costs. Read moreRead less
Legal and social dynamics of eBook lending in Australia’s public libraries. Legal and social dynamics of eBook lending in Australia’s public libraries. This project aims to develop an evidence base of quantitative and qualitative data about how eBooks are used in libraries. EBooks have tremendous beneficial potential, particularly for Australians in remote areas and those with impaired mobility or vision. However, libraries’ rights to acquire and lend them are more restricted than for physical b ....Legal and social dynamics of eBook lending in Australia’s public libraries. Legal and social dynamics of eBook lending in Australia’s public libraries. This project aims to develop an evidence base of quantitative and qualitative data about how eBooks are used in libraries. EBooks have tremendous beneficial potential, particularly for Australians in remote areas and those with impaired mobility or vision. However, libraries’ rights to acquire and lend them are more restricted than for physical books. Libraries and legal, social and data science researchers will investigate eBook lending practices and understand their social impacts. The project will identify ways of reforming policy, law, and practice to help libraries fulfil their public interest missions. This project is expected to enable libraries to extract more value from existing public investments.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
RAINBOW - RAdIo Networks Based On machine learning for situation aWareness. This project aims to develop software-defined and cognitive radio networks (SDR) to detect adversarial communications and achieve situation awareness on radio frequency (RF) spectrum. The project will generate novel SDR architectures and new attack-resistant detection algorithms through innovative approaches combining machine learning and game theory. Expected outcomes include a strategic alliance between the University ....RAINBOW - RAdIo Networks Based On machine learning for situation aWareness. This project aims to develop software-defined and cognitive radio networks (SDR) to detect adversarial communications and achieve situation awareness on radio frequency (RF) spectrum. The project will generate novel SDR architectures and new attack-resistant detection algorithms through innovative approaches combining machine learning and game theory. Expected outcomes include a strategic alliance between the University of Melbourne and the Northrop Grumman Corporation. Among significant benefits, the project will improve cybersecurity of RF spectrum as a national asset, help protect critical infrastructure relying on wireless networks such as telecommunications and defence, and build skills in cybersecurity and Artificial Intelligence.Read moreRead less
Characterising satellites using un-resolved optical observations . Space situational awareness is a critical priority for Australian national security and the commercial space sector as the economy is investing in space. Space debris is a growing international threat to all major economies that rely on space for communications and defence. This project will understand how defunct satellites degrade over time to produce space debris. To accomplish this the project will collaborate with the Polish ....Characterising satellites using un-resolved optical observations . Space situational awareness is a critical priority for Australian national security and the commercial space sector as the economy is investing in space. Space debris is a growing international threat to all major economies that rely on space for communications and defence. This project will understand how defunct satellites degrade over time to produce space debris. To accomplish this the project will collaborate with the Polish Space Agency to employ low resolution spectroscopy to measure the surface degradation of geostationary satellites. Read moreRead less
Deep mining neurological abnormalities from brain signal data. This project aims to develop a reliable, robust and real-time analysis system for automatic and accurate detection of neurological abnormalities, and the prediction of impending neurological problems from brain signal data. The project expects to design novel algorithms for brain signal processing, data compression, and detection and prediction of neurological abnormalities from massive brain signal data. The project will lead to sig ....Deep mining neurological abnormalities from brain signal data. This project aims to develop a reliable, robust and real-time analysis system for automatic and accurate detection of neurological abnormalities, and the prediction of impending neurological problems from brain signal data. The project expects to design novel algorithms for brain signal processing, data compression, and detection and prediction of neurological abnormalities from massive brain signal data. The project will lead to significant improvement of existing methods in health monitoring applications in Australia and worldwide and hence will save lives, money and resources.Read moreRead less
Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended ....Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended outcomes will be an innovative incident analysis and management framework synergising traffic data analytics and traffic simulation modelling as well as its key enabling techniques and prototype systems. This will significantly help mitigate incident impacts on daily commuters.Read moreRead less
Dialogue-to-Action:Towards A Self-Evolving Enterprise Intelligent Assistant. The project aims to develop a novel Self-Evolving Enterprise Intelligent Assistant (EIA) by leveraging the Chatbot-based dialogue technique to acquire information, infer user intentions, understand languages, and determine subsequent actions to take through Dialogue-to-Action modelling. This new generation EIA is equipped with Artificial Generalised Intelligence, with a broad skill set able to tackle multiple business t ....Dialogue-to-Action:Towards A Self-Evolving Enterprise Intelligent Assistant. The project aims to develop a novel Self-Evolving Enterprise Intelligent Assistant (EIA) by leveraging the Chatbot-based dialogue technique to acquire information, infer user intentions, understand languages, and determine subsequent actions to take through Dialogue-to-Action modelling. This new generation EIA is equipped with Artificial Generalised Intelligence, with a broad skill set able to tackle multiple business tasks and handle fast-changing scenarios in business. The Self-Evolving EIA is a critical step on the path towards the future generation of EIA. Expected outcomes of this project are to develop adaptive EIA for Small and Medium Enterprise to improve their customer service quality.Read moreRead less
A Machine Learning Framework for Concrete Workability Estimation . Concrete is the most used construction material in Australia. The project aims to develop a system to measure the workability of concrete in transit in agitator trucks using advanced machine vision and machine learning, and provide a reliable alternative to the current practice of visually testing concrete workability by certified testers. Concrete that fails to meet workability requirements is one of the most frequent reasons fo ....A Machine Learning Framework for Concrete Workability Estimation . Concrete is the most used construction material in Australia. The project aims to develop a system to measure the workability of concrete in transit in agitator trucks using advanced machine vision and machine learning, and provide a reliable alternative to the current practice of visually testing concrete workability by certified testers. Concrete that fails to meet workability requirements is one of the most frequent reasons for rejection at construction sites, resulting in significant costs, waste, and delays. Multimodal data sources will be used to provide a reliable workability estimate in real time, enabling construction teams to identify and rectify workability issues in transit while continuously monitoring the adjustments effects.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