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
Discovery Early Career Researcher Award - Grant ID: DE140101094
Funder
Australian Research Council
Funding Amount
$395,220.00
Summary
Precision Spectroscopy of CO2 Exchange in Hydrates for Clean Energy Production. Carbon dioxide capture and sequestration is a widely considered climate change mitigation strategy. Clathrate hydrates of natural gas, found in deep-water ocean sediments, represent a tremendous opportunity for simultaneous carbon dioxide sequestration and clean energy production. By injecting carbon dioxide into the hydrate reservoir, methane can be displaced and replaced by carbon dioxide. This project will use Ram ....Precision Spectroscopy of CO2 Exchange in Hydrates for Clean Energy Production. Carbon dioxide capture and sequestration is a widely considered climate change mitigation strategy. Clathrate hydrates of natural gas, found in deep-water ocean sediments, represent a tremendous opportunity for simultaneous carbon dioxide sequestration and clean energy production. By injecting carbon dioxide into the hydrate reservoir, methane can be displaced and replaced by carbon dioxide. This project will use Raman spectroscopy and nuclear magnetic resonance imaged core-flood experiments to develop a fundamental understanding of the exchange mechanisms governing the replacement of the methane molecule in the hydrate cage with carbon dioxide. This knowledge will be critical for future development of these resources to safely extract methane from sub-sea hydrates.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
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE120100109
Funder
Australian Research Council
Funding Amount
$370,000.00
Summary
A facility for non-destructive quantification of coal structures, composition and percolation fluid flows in energy and environmental applications. The facility will advance our scientific understanding of 3D micro- and nanostructures of coal under various mechanical and chemical conditions. It will help develop process innovation and breakthrough technologies for energy and environmental applications. It will also enhance the research capabilities of the collaborating institutions.
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
Novel Characterization of Porous Structure and Surface Chemistry of Carbon. The aim of this project is to develop novel characterisation methods that probe the structure and surface chemistry of carbons, ranging from highly graphitised thermal carbon black through ordered mesoporous carbon to disordered porous activated carbon. The project plans to develop a new generic molecular model based on wedge-shaped pores. Conventional parallel sided pore models fail to account for real structures and th ....Novel Characterization of Porous Structure and Surface Chemistry of Carbon. The aim of this project is to develop novel characterisation methods that probe the structure and surface chemistry of carbons, ranging from highly graphitised thermal carbon black through ordered mesoporous carbon to disordered porous activated carbon. The project plans to develop a new generic molecular model based on wedge-shaped pores. Conventional parallel sided pore models fail to account for real structures and therefore for the physics of adsorption in real materials. The project then plans to back the theoretical model with high-resolution experimental measurements. It is expected that the model will unify the structural analysis for all carbons and account for all experimental isotherms within a rational and physically plausible framework.Read moreRead less
Domain-driven information, quality assurance and interoperability for road transport systems. This project addresses the ever increasing need for improved information quality in the area of road transport and urban planning. Public bodies, like State and Federal authorities, require highly accurate sets of personal records as they are central authorities for the identification of individuals. This project will develop techniques for the enhancement of these records.
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE110100205
Funder
Australian Research Council
Funding Amount
$150,000.00
Summary
A novel high-pressure system for multiple gas adsorption. This facility will equip researchers with analytical capabilities for research in the field of multi-gas adsorption. The facility will be of great significance to clean energy research, such as greenhouse gas emission control and hydrogen production and storage.