ARC Centre of Excellence for Carbon Science and Innovation. ARC Centre of Excellence for Carbon Science and Innovation. This Centre aims to develop carbon-based catalysts for clean energy, CO2 capture, and green chemistry to reduce emissions. The Centre expects to use pioneering data-guided atomic-precision synthesis and multiscale analysis to transform fundamental science of carbon materials. Expected outcomes of this Centre will benefit new technologies for energy, environmental, and green che ....ARC Centre of Excellence for Carbon Science and Innovation. ARC Centre of Excellence for Carbon Science and Innovation. This Centre aims to develop carbon-based catalysts for clean energy, CO2 capture, and green chemistry to reduce emissions. The Centre expects to use pioneering data-guided atomic-precision synthesis and multiscale analysis to transform fundamental science of carbon materials. Expected outcomes of this Centre will benefit new technologies for energy, environmental, and green chemical industries by utilising abundant sunlight, seawater, and waste feedstocks. This should provide significant benefits, through industry collaborations, our new world-leading capacity will train a next generation of game changers to empower emerging carbon industries to solve grand socio-economic challenges, ultimately meeting zero-carbon emissions targets.Read moreRead less
Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-s ....Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.Read moreRead less
Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning proc ....Multi-Modal Dictionary Learning for Smart City Operation and Management. This Project aims to provide new digital asset management tools for city councils to improve city services by utilising new sensing and automated learning technologies for recognising, tracking and auditing of assets. Currently, there are no digital tools available to handle these services. This project proposes new multi-modal sensing and mapping of city asset techniques by building new multi-modal dictionary learning procedures. The new framework will recognise different conditions of city assets in real-time to make decisions. Expected outcomes of this Project include integration and easy access of assets with unique digital identities to help city councils, governments, and navigation services for real-time asset monitoring.Read moreRead less
Fine-grained Human Action Recognition with Deep Graph Neural Networks. This project aims to develop novel graph neural network based deep learning algorithms for fine-grained human action recognition. This project expects to bring human action analysis to the next level and to significantly advance the analysis of subtle yet complex human actions. Expected outcomes of this project include theoretical advances on graph representation based deep learning algorithms for spatial-temporal data, and e ....Fine-grained Human Action Recognition with Deep Graph Neural Networks. This project aims to develop novel graph neural network based deep learning algorithms for fine-grained human action recognition. This project expects to bring human action analysis to the next level and to significantly advance the analysis of subtle yet complex human actions. Expected outcomes of this project include theoretical advances on graph representation based deep learning algorithms for spatial-temporal data, and enabling techniques for more objective human action analysis in many domains such as sports and health. This should provide significant benefits to any application domain involving big and complex spatial-temporal data for finer analytics and better knowledge discovery.Read moreRead less
Personalised Online Learning Analytics by Exploring Multilayer Graph Data . Learning analytics is becoming a significant factor in reducing the drop-out rate of students in online learning. The aim of this project is to develop a reliable, robust, real-time analysis system that automatically reveals multilayered relationships, evaluates students' learning performance, and generates a personal study plan through discovery. This project includes the design of novel algorithms for multilayer graph ....Personalised Online Learning Analytics by Exploring Multilayer Graph Data . Learning analytics is becoming a significant factor in reducing the drop-out rate of students in online learning. The aim of this project is to develop a reliable, robust, real-time analysis system that automatically reveals multilayered relationships, evaluates students' learning performance, and generates a personal study plan through discovery. This project includes the design of novel algorithms for multilayer graph processing, pattern recognition in learning activities, learning performance assessment, and personalised study plan recommendations. The success of this project will significantly enhance the success of online education both in Australia and worldwide and; hence, will save time, money and resources for end users.Read moreRead less
Open Standards design for improved road network information exchange. Open Standards design for improved road network information exchange. This project aims to design a draft digital open source performance-based construction contract specification for delivering road construction information to operational network asset management. This will be a common information exchange specification for all road agencies to standardise exchange of their assets data. Road network construction and maintenan ....Open Standards design for improved road network information exchange. Open Standards design for improved road network information exchange. This project aims to design a draft digital open source performance-based construction contract specification for delivering road construction information to operational network asset management. This will be a common information exchange specification for all road agencies to standardise exchange of their assets data. Road network construction and maintenance costs $21 billion annually, but the outcome of this project is expected to save $65 to $130 million annually through data harmonisation. This project is at the leading edge of information management for roads and is expected to change several international standards.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH220100012
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Carbon Utilisation and Recycling. This Research Hub aims to develop technologies to transform carbon dioxide emissions from our energy and manufacturing sectors into valuable products and create pathways to market to drive industry transformation. This hub aims to achieve this by developing novel electro, thermo, and biochemical methods for converting CO2 from sectors that cannot easily avoid emissions and a technological pathway for CO2 recycling. The outcomes of this Hub a ....ARC Research Hub for Carbon Utilisation and Recycling. This Research Hub aims to develop technologies to transform carbon dioxide emissions from our energy and manufacturing sectors into valuable products and create pathways to market to drive industry transformation. This hub aims to achieve this by developing novel electro, thermo, and biochemical methods for converting CO2 from sectors that cannot easily avoid emissions and a technological pathway for CO2 recycling. The outcomes of this Hub are likely to be transformative for industry, the economy, and society in moving the fate of CO2 from pollutant to feedstock. The benefits to Australia are intended to be the stimulation of a new industry, a skilled workforce for this emerging industry and a contribution to meeting CO2 reduction targets.Read moreRead less
Quantifying kelp carbon and nutrient flows for nature-based solutions . This fellowship aims to resolve carbon removal and nutrient mitigation potential of Australia’s kelp forests now and in future. It will create new understanding of the ecosystem services provided by the Great Southern Reef, and the capacity of kelp forests to provide nature-based solutions to reduce emissions and improve coastal water quality. Using a combination of global models and ecological experiments on kelp forests an ....Quantifying kelp carbon and nutrient flows for nature-based solutions . This fellowship aims to resolve carbon removal and nutrient mitigation potential of Australia’s kelp forests now and in future. It will create new understanding of the ecosystem services provided by the Great Southern Reef, and the capacity of kelp forests to provide nature-based solutions to reduce emissions and improve coastal water quality. Using a combination of global models and ecological experiments on kelp forests and their replacement ecosystem states, the fellowship will predict changes in function with warming. This information is critical to determine net ecosystem mitigation potential and will significantly advance our understanding of the potential of kelp forests to generate co-benefits while conserving biodiversity. Read moreRead less