Discovery Early Career Researcher Award - Grant ID: DE180100266
Funder
Australian Research Council
Funding Amount
$367,446.00
Summary
Granular interfaces for sustainable processing of raw materials. This project aims to develop an innovative interface model and a comprehensive understanding of the interfacial behaviours between granular materials using advanced numerical, experimental and theoretical approaches. This project expects to generate new knowledge of mixing and segregation in particle science and technology and a practical guide to applications. Expected outcomes of this project include the enhanced competitiveness ....Granular interfaces for sustainable processing of raw materials. This project aims to develop an innovative interface model and a comprehensive understanding of the interfacial behaviours between granular materials using advanced numerical, experimental and theoretical approaches. This project expects to generate new knowledge of mixing and segregation in particle science and technology and a practical guide to applications. Expected outcomes of this project include the enhanced competitiveness of Australia and energy efficiency in its important industries such as minerals, metallurgical, chemical, energy and pharmaceutical. These outcomes should provide significant benefits such as mitigated emissions and global warming in a carbon and resource constrained world.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE170100174
Funder
Australian Research Council
Funding Amount
$193,000.00
Summary
Acoustic levitation facility for high pressure multiphase systems research. This project aims to create a specialised acoustic levitation facility that delivers precise control of a suspended particle/droplet/bubble within a high pressure continuous phase, and simultaneous measurement of multiple bulk and interfacial properties. Acoustic levitation enables container-less experiments, offering opportunities for applied engineering and fundamental science. This acoustic levitation system will be i ....Acoustic levitation facility for high pressure multiphase systems research. This project aims to create a specialised acoustic levitation facility that delivers precise control of a suspended particle/droplet/bubble within a high pressure continuous phase, and simultaneous measurement of multiple bulk and interfacial properties. Acoustic levitation enables container-less experiments, offering opportunities for applied engineering and fundamental science. This acoustic levitation system will be integrated with a specialised Raman imaging microscope to study crystallisation, mass transfer and molecular exchange, in application areas including energy transport, carbon capture and storage, and protein nucleation. This project is expected to open new avenues in engineering, chemistry and physics.Read moreRead less
Robust Federated Learning for Imperfect Decentralised Data. This project aims to develop a next-generation robust federated learning framework to tackle the challenging scenarios of imperfect decentralised data in real applications, e.g. mobile phones and the Internet of Things (IoT) devices. The outcomes will bring great benefits to a broad range of industry sectors by providing novel large-scale intelligent applications with privacy preservation. The proposed method will advance the developmen ....Robust Federated Learning for Imperfect Decentralised Data. This project aims to develop a next-generation robust federated learning framework to tackle the challenging scenarios of imperfect decentralised data in real applications, e.g. mobile phones and the Internet of Things (IoT) devices. The outcomes will bring great benefits to a broad range of industry sectors by providing novel large-scale intelligent applications with privacy preservation. The proposed method will advance the development of a cutting-edge technique to develop new intelligent applications in a decentralised and privacy-sensitive scenario. This game-changing research will advance current data mining and artificial intelligence research from centralised intelligence to decentralised intelligence with a collaboration network.Read moreRead less
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
Subband centroids and deep neural networks for robust speech recognition. This project aims to improve the robustness and accuracy of automatic speech and speaker recognition systems. Though these systems work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. To overcome this problem, this project proposes a missing-feature approach for robust speech and speaker recognition. This approach is expected to make th ....Subband centroids and deep neural networks for robust speech recognition. This project aims to improve the robustness and accuracy of automatic speech and speaker recognition systems. Though these systems work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. To overcome this problem, this project proposes a missing-feature approach for robust speech and speaker recognition. This approach is expected to make the speech and speaker recognition systems less sensitive to additive background noise and make them more useful in telecommunications and business.Read moreRead less
Superior silicon carbide nanoscale sensors (SCANS) for harsh environments. This project aims to demonstrate a large increase in sensitivity, ultra-fast response, and super robust characteristics of nanoscale sensors suitable for harsh environment applications. Sensors in mining, power and aerospace industries must function properly in high temperature, aggressive chemical erosion, and high impact environments. Silicon carbide (SiC) sensors formed using a unique growth process of SiC films on lar ....Superior silicon carbide nanoscale sensors (SCANS) for harsh environments. This project aims to demonstrate a large increase in sensitivity, ultra-fast response, and super robust characteristics of nanoscale sensors suitable for harsh environment applications. Sensors in mining, power and aerospace industries must function properly in high temperature, aggressive chemical erosion, and high impact environments. Silicon carbide (SiC) sensors formed using a unique growth process of SiC films on large-diameter silicon wafers can meet these requirements through nanoscale structures. This project expects to bring direct economic benefits to the resource and manufacturing sectors, creating valuable intellectual property and new jobs for Australians.Read moreRead less
Enhancing the Australian theme park experience by harnessing virtual-physical play. This project will deliver methodologies for designing games enriched by virtual-physical play. The project will contribute to furthering Australia's lead in the production of compelling theme park experiences, computer games and interactive training systems. The entertainment, digital media and information and communications technology industries will all benefit as a result.
Discovery Early Career Researcher Award - Grant ID: DE200100964
Funder
Australian Research Council
Funding Amount
$427,068.00
Summary
Early Prediction in Large-scale Time-variant Information Networks. This project aims to develop an early prediction system to predict possible outbreaks of malicious messages in time-variant information networks. The research will primarily leverage deep representations of time-variant subsequence and substructure patterns in large-scale social networks to signal malicious and malevolent messages before it has a chance to propagate. This project will lay the theoretical foundations of this emerg ....Early Prediction in Large-scale Time-variant Information Networks. This project aims to develop an early prediction system to predict possible outbreaks of malicious messages in time-variant information networks. The research will primarily leverage deep representations of time-variant subsequence and substructure patterns in large-scale social networks to signal malicious and malevolent messages before it has a chance to propagate. This project will lay the theoretical foundations of this emerging field to strengthen Australia’s world leadership role in data science. Practically, the novel theories and data analytics technologies developed will help to safeguard Australian business, industry, and society from cyberfraud, online rumour-mongering, and financial loss.Read moreRead less
Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts ....Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge. This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts existing knowledge to new domains, and provide explanations to the graph learning system. The research results should provide benefit to governments and businesses in many critical applications, such as bioassay activity prediction, credit assessment, and drug discovery and vaccine development in response to the pandemic.Read moreRead less
Advanced Computer Vision Techniques for Marine Ecology. Ever expanding human activity coupled with climate change has severely damaged marine ecosystems, which play a key role in our planet's ability to sustain life. Yet automated technology to monitor the health of our oceans still does not exist, with marine scientists still having today to process manually a massive amount of raw underwater imagery. This research aims to address this bottleneck by developing advanced computer vision tools for ....Advanced Computer Vision Techniques for Marine Ecology. Ever expanding human activity coupled with climate change has severely damaged marine ecosystems, which play a key role in our planet's ability to sustain life. Yet automated technology to monitor the health of our oceans still does not exist, with marine scientists still having today to process manually a massive amount of raw underwater imagery. This research aims to address this bottleneck by developing advanced computer vision tools for rapid, large-scale, automatic identification of marine species. Such an automated technology is expected to greatly benefit marine ecological studies in terms of speed, cost, accuracy of the spatial/temporal sampling and thus in better quantifying the level of environmental change marine ecosystems can tolerate.Read moreRead less